Some examples of products related to biological, biotechnology-derived, and biopharmaceutical products.
\\n\\n
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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Finding an appropriate definition or a clear classification for biologically occurring pharmaceutical products is a complicated task because of overlapping borders and consequent misconceptions in this area. Indeed, numerous definitions and classifications for this category of products have been proposed so far, and different points of view for this concept can be found in research literature, business, industry, and even the general public [1, 2].
To obtain a better view of biopharmaceutical concept, first, it is necessary to know the present definitions for the main constituents of the word, that is, pharmaceutical product and biological product.
According to the WHO, a finished pharmaceutical product (FPP) is “A finished dosage form of a pharmaceutical product, which has undergone all stages of manufacture, including packaging in its final container and labeling." [3]
An active pharmaceutical ingredient can be defined as “A substance used in a finished pharmaceutical product (FPP), intended to furnish pharmacological activity or to otherwise have direct effect in the diagnosis, cure, mitigation, treatment or prevention of disease, or to have direct effect in restoring, correcting or modifying physiological functions in human beings." [3] Hence, in brief, it can be said that any material, regardless of its origin or structure, with treatment, diagnosis, or prevention applications and passing regulatory requirements, is a pharmaceutical product.
On the other hand, the FDA definition for biological products is as follows: “Biological products or biologics are medical products made from a variety of natural sources (human, animal or microorganism). Like drugs, some biologics are intended to treat diseases and medical conditions or to prevent or diagnose diseases.” [4] Consequently, any product of biological origin with treatment, diagnosis, or prevention applications is a biological product.
A biotechnology-derived product is another concept that should be taken into consideration. It is defined by Walsh as “any pharmaceutical product used for a therapeutic or in vivo diagnostic purpose, which is produced in full or in part by either traditional or modern biotechnological means.” [5]
On comparing the definitions for a biotechnology-derived product and a biological product, both of which should be of biological origin, it is obvious that the key element in the former definition is the application of biotechnological means for production.
In practice, the regulatory requirements needed for a biotechnology-derived product and a biological product are methodologically different from the pharmaceutical product due to their biological essence. For example, the determination of adventitious agents such as viruses, transmitting spongiform encephalopathy (TSE), and mycoplasma are included in most of the related guidelines and pharmacopeias for a biotechnology-derived product and a biological product.
Finally, a biopharmaceutical is defined by Walsh as “A protein or nucleic acid based pharmaceutical substance used for therapeutic or in vivo diagnostic purposes, which is produced by means other than direct extraction from a native (non-engineered) biological source.” This definition that will be used in the present chapter for biopharmaceuticals includes all pharmaceutical products produced by modern biotechnology techniques as well as nucleic acid (DNA or RNA) based pharmaceutical products for gene therapy. Hence, the overlapping area between biotechnology-derived products and biopharmaceuticals is the application of modern biotechnological means in their production. However, the differentiating area can be the application of traditional biotechnological means for the production of biotechnology-derived products. In addition, nucleic acid-based pharmaceutical products that are categorized as biopharmaceuticals are not biotechnology-derived products [6]. Figure 1 illustrates these overlapping and differentiating areas. Moreover, some examples of products in these categories are shown in Table1.
Blood and blood products | Therapeutic proteins from natural sources | Recombinant proteins | Recombinant proteins |
Human cells and tissues | Antibiotics fully or partially from microorganisms | Monoclonal antibody produced by hybridoma technology | Nucleic acid-based pharmaceutical products for gene therapy |
Some examples of products related to biological, biotechnology-derived, and biopharmaceutical products.
The schematic representation for biotechnology-derived products and biopharmaceutical categories and the overlapping areas between them.
The main category of biopharmaceuticals is manufactured via recombinant DNA technology. Indeed, recombinant DNA technologies are enabling techniques that manipulate and engineer different gene fragments and which have been introduced less than 50 years ago by the revolutionary invention of Polymerase Chain Reaction (PCR) by Kary Mullis [7].
DNA and RNA extraction from different cell types, cutting DNA fragments using restriction endonucleases, joining DNA fragments by DNA ligases, PCR to amplify gene fragments, cloning of the gene fragments into different vectors, introduction of recombinant constructs into proper hosts, protein expression, extraction, and purification are some of the most widely used means in recombinant protein production.
Figure 2 schematically represents the summarized process of production of a recombinant protein. As can be seen from the chart, first, the gene of interest should be isolated and amplified from the original cell. According to the type of the cell, it can be done through direct total DNA extraction followed by a PCR using proper primers to obtain the gene in prokaryotes. On the other hand, in eukaryotes, due to the existence of introns and some modifications that occur in the transcribed mRNA, the process is considerably complicated. Introns are non-coding sequences which are removed after transcription versus coding sequences that are called
Schematic representation of recombinant protein production process
Based on their sizes, the obtained genes can now be introduced into a proper vector for maintenance, replication, or expression purposes. Plasmids, cosmids, and bacteriophages are the most important cloning vectors that are classified according to the size of the DNA fragment that can be inserted into them. Ultimately, the new recombinant construct should be introduced into an expression system for production. Different classes of expression systems with their certain merits and disadvantages are available and range from cell systems such as bacterial, animal, fungal, and yeast cells to transgenic systems such as transgenic plants and animals [7, 8].
Each specific antibody is secreted by a specific B cell and could recognize a specific region on the antigen that is called
In general, for the successful fusion of hybridomas, the cells are grown in HAT selection medium. The selection medium is called
Normal cells can synthesize the required nucleotides in two pathways: (1) the main one or de novo biosynthetic pathway, and (2) the alternative one or the salvage pathway (when the main pathway is blocked).
Aminopterin (a folic acid analog that inhibits dihydrofolate reductase) blocks the activation of tetrahydrofolate, which is required for the synthesis of nucleotides via the de novo synthetic pathway, and, therefore, the main pathway is blocked. Thus, in aminopterin-treated cells (HGPRT+ and TK+), the synthesis of nucleotides shifts to the salvage pathway only if hypoxanthine and thymidine are supplied in the medium. HGPRT and TK, the two enzymes, are required for the salvage pathway, and they catalyze the synthesis of purine and thymidylate from hypoxanthine and thymidine substrates, respectively.
Since unfused myeloma cells lack HGPRT or TK, they cannot use the salvage pathway. Thus, the unfused myeloma cells get killed in the HAT medium, as both biosynthetic pathways are blocked. Normal unfused B cells die in the HAT medium, as they are not immortalized and cannot grow for a long time. Nevertheless, the fusion of normal B cells with the HGPRT-or TK-myeloma cells allows the hybridoma cells to grow in HAT medium, as the B cells provide the necessary enzymes for growth of the hybridoma cells.
Hence, the HAT selection medium offers an ideal environment for the isolation of fused myeloma and B cells (hybridoma cells) from unfused myeloma cells and unfused B cells, as this medium allows only the hybridoma cells to survive in the culture.
The production of monoclonal antibodies could be accomplished by ascites (ascitic fluid) production (in vivo) or by cell culture (in vitro) methods. In the in vivo method, hybridoma cells are injected intraperitoneally into mice. The peritoneum serves as a growth chamber for the injected cells. These cells could secrete a high-titered solution of desired antibodies as they grow in the cavity. Finally, the produced antibodies are extracted from the ascitic fluid accumulated in the peritoneal cavity [6]. The antibody concentrations typically range between 1 and 15 mg/ml. The in vivo method offers a very high concentration of monoclonal antibody that often does not need more concentration procedures. Nevertheless, monoclonal antibodies produced by this technique may be contaminated by considerable levels of mouse proteins and other contaminants that might require more complicated, subsequent downstream purifications. The other disadvantage of the ascites production is related to animal welfare issues, as these could cause distress in mice.
Currently, more than 90% of monoclonal antibodies are produced by in vitro techniques [12] that use large-scale manufacturing plants containing several 10,000-L or larger culture bioreactors [13]. The in vitro method of monoclonal antibody production decreases the use of mice and also avoids the need for experienced personnel for animal handling. Regardless of the privileges and importance of the in vitro methods of antibody production, there are some situations in which this method is not applicable; for instance:
Hybridoma cells do not adapt well to in vitro conditions.
Downstream purification methods cause protein denaturation or decreased antibody activity.
The cell line cannot maintain the production of monoclonal antibodies.
When hybridoma cells are contaminated with infectious agents (such as yeasts or fungi), the cells must often be passed through mice. Since removal of the organisms cannot be accomplished by current antimicrobial drugs, thus the in vivo method may save a valuable hybridoma.
When in vitro methods result in monoclonal antibodies that are glycosylated at positions different from those harvested from mouse ascites, they affect antigen-binding capacity as well as biological functions [12].
Taken together, the cell culture technique is a method of choice for large-scale monoclonal antibody production due to the simplicity of the cell culture and financial considerations without ethical concerns that are related to animal use.
The diagram of the monoclonal antibody production via classical hybridoma technology
In 1986, about 10 years after the conception of monoclonal antibody technology, Orthoclone OKT3 was approved by the FDA for use in patients with acute rejection of a transplanted kidney [14]. Unfortunately, early clinical applications of murine monoclonal antibodies were disappointing. This was due to the fact that monoclonal antibodies produced via the classical method are of murine origin and are, therefore, immunogenic to human subjects. In general, patients receiving an antibody exhibit HAMA responses (human anti-mouse antibodies) within two weeks. Multiple infusions of murine monoclonal antibodies significantly enhance the HAMA reactions [6]. In addition, the immune system eliminates the murine monoclonal antibody molecule. Thus, murine monoclonal antibodies demonstrate short serum half lives after administration to humans. Furthermore, the other main difficulty related to murine monoclonal antibodies is the poor recognition of the Fc region by human effector systems of complement and Fc receptors.
Thus, new strategies that are used for producing humanized mouse antibodies that are less immunogenic have been discovered. The first strategy includes the production of functional specific recombinant IgG molecules consisting of mouse variable regions and human constant regions; these are known as chimeric antibodies. Taken together, in the chimeric antibody, 8 out of 12 domains are of human origin (constant regions of the heavy and light chains) (CH and CL) [15]. Chimeric antibodies exhibit reduced HAMA responses compared with mouse antibodies, but the affinity and the selectivity are the same. Furthermore, since the Fc region contains human sequences, the activation of Fc-mediated immune effector functions is allowed.
To further minimize the antigenicity of murine antibodies, humanized antibodies were developed. For their generation, hyper-variable complementarity-determining regions (CDRs) of the specific murine antibody are transferred to a fully human framework. In comparison with the mouse antibodies, humanized antibodies suggest a lower occurrence of HAMA responses.
Further efforts have been invested in the development of technologies that generate fully human monoclonal antibodies. One of the approaches entails the development of transgenic mice, in which a repertoire of human immunoglobulin germline gene segments is inserted into the mouse genome. After the immunization of these mice, they produce fully human antibodies, which can subsequently be separated with the classical hybridoma technology [15].
Figure 4 illustrates the schematic structures of mouse, chimeric, humanized, and human antibodies.
The structure of mouse (shown in red color), chimeric, humanized (shown in black color), and human antibodies. Chimeric antibodies comprise mouse variable regions and human constant regions. Humanized antibodies consist of murine hyper-variable complementarity-determining regions (CDRs) that are grafted to fully human framework.
Mass production of recombinant products can be achieved in a process that is divided into two main sections called
Mass production of a recombinant product.
The first step, the upstream processing step, is the mass production of a recombinant protein via the fermentation process. The original recombinant system that is used for the expression of the recombinant protein (i.e. in the form of a recombinant plasmid maintained in a suitable host cell) forms the cell deposit in a cell banking system. There are two levels of cell deposits in the cell banking system: The first line is called
The second line that is referred to as the
On the other hand,
The second stage entails (1) several chromatographic steps that complete the purification of the product; (2) the potency test; (3) the addition of suitable excipients; (4) sterilization; (5) filling of the product in its final form (liquid or solid) into the final container before sealing; and labeling [6].
In line with conventional pharmaceutical products, the main sources of biological contamination in biopharmaceuticals can be related to raw materials and the production environment. Indeed, the biological contaminant content of any pharmaceutical product is a representative of their starting materials and the production environment flora.
Animal origin materials, such as cell culture media, sera, and supplements that are extensively used in biopharmaceutical production, are of high contamination risk. These materials can be considered the main source for the contamination of biopharmaceuticals with adventitious agents such as TSEs, viruses, and mycoplasmas. Therefore, they should be supplied from reliable resources, and special attention should be paid to their quality control procedure. It should be ensured that all raw materials, especially those of high risk, gain quality specifications for current good manufacturing practice.
Standard methods for sterilization of cell culture media, sera, and supplements should be established according to the properties of the materials. Due to the heat-labile nature of the majority of materials used in biopharmaceutical production, autoclaving is usually replaced with alternative strategies such as filter-sterilization or less frequently high-temperature, short-time treatment strategies. In spite of the routine filter-sterilization procedure that uses 0.22 μm, it is usually performed with 0.1-μm membrane filters due to the risk of contamination with adventitious agents.
Furthermore, high-temperature, short-time treatment strategies are sometimes employed for the elimination of biological contaminants from small solutes such as vitamins and amino acids [16].
Another important raw material that is used in the production of any pharmaceutical product, including biopharmaceuticals, is water, which can be considered an important source for contamination, with water-borne bacteria such as Pseudomonas spp., Alcaligenes spp., Flavobacterium spp., Chromobacter spp., and Serratia spp. Water for pharmaceutical purposes is discussed in detail in the USP [17]. Due to the fact that the intended administration of biopharmaceuticals in the majority of cases is via injection, Water for Injection (WFI) which is sterile and apyrogen is routinely used in this area.
Pharmaceutical products’ contamination may occur from the transformation of microorganisms from the production environment to the product. The production environment includes air, surfaces, instruments, equipments, and personnel.
The main groups of microorganisms that are isolated from air are the spore-forming bacteria (Bacillus spp. and Clostridium spp., the non-sporing bacteria Staphylococcus spp., Streptococcus spp., and Corynebacterium spp.), the molds (Penicillium spp., Cladosporium spp., Aspergillus spp., and Mucor spp.), and the yeast (Rhodotorula spp.). These contaminants may be air borne or can be initiated from process equipment or personnel [16]. Consequently, environmental monitoring programs in a production environment are essential actions. Furthermore, the critical operations in biopharmaceutical production should be performed in controlled environments or clean rooms. A clean room is a place with high control of the entrance of particles via the establishment of some air filters called
According to the
A | 3500 | <1 |
B | 3500 | 10 |
C | 350000 | 100 |
D | 3500000 | 200 |
Clean room grades according to the number of viable microorganisms and the number of particulates
Critical operations such as inoculum preparation and aseptic filling are generally performed in the highest air grade (A); however, less critical operations can be performed in lower grades or even non-classified air.
In addition to the establishment of suitable filters in the clean rooms, special attention should be paid to the position, type, and texture of surfaces, floors, and fixtures. They should be made from smooth and chemically stable materials. In addition, a distinct transfer lock area should exist before entry to the clean room for sanitization of materials and personnel or garment changing. Furthermore, all doors should be interlocking [6].
Similar to other pharmaceuticals, biological contamination of biopharmaceuticals may perhaps cause product spoilage. It may result in product metabolization by microorganisms, and, therefore, lead to a decrease in biopharmaceutical potency. The product spoilage may also provide a potential health hazard to patients and lead to outbreaks of infections that may cause additional complications. In addition, microbial-derived agents secreted in products such as endotoxins can be hazardous to a patient\'s health.
Bacteria and fungi can be considered important contamination sources for all kinds ofpharmaceutical products, including biopharmaceuticals; hence, the control of them is of critical importance. The control of both bacteria and fungi is considered to be worthy of mandatory tests for nearly all kinds of pharmaceuticals in pharmacopoeias. All the related tests and procedures are covered in detail in the major pharmacopoeias such as USP and EP [17, 18].
Since almost all the biopharmaceuticals are administered intravenously, general sterility testing must be carried out for these products. Basically, sterility testing can be defined as "a test that evaluates whether a sterilized pharmaceutical product is free of contaminating microorganisms." The European Pharmacopoeia (2002) proposes two media for sterility testing:
(1) fluid mercaptoacetate medium (also known as
Two main methods are used for sterility tests: (1) direct inoculation of the test samples in the media mentioned earlier; or (2) filtration of the test material through a sterile membrane filter with a pore size of 0.45 μm; then, the filter containing any microorganism present in the fluids is divided aseptically, and portions are transferred to the media.
The eradication of bacteria and fungi from the products is generally carried out via inactivation and sterile filtration.
Since most of the biopharmaceuticals are administered intravenously, finished-product biopharmaceuticals must be sterile and free from pyrogenic substances. The endotoxin limit for the intravenous administration of pharmaceutical and biological products is 5 endotoxin units (EU)/kg of body weight/hour by all pharmacopoeias [19]. Hence, the detection and removal of pyrogenic substances, especially endotoxins (lipopolysaccharides in the cell wall of gram-negative bacteria), are necessary to ensure safety of biopharmaceutical products. Currently available methods for endotoxin detection include the U.S. Pharmacopeia rabbit test and the Limulus amebocyte lysate (LAL) test [7].
The rabbit pyrogen test entails measurements of the rise in body temperature of rabbits after an intravenous injection of a test substance. The presence of pyrogens of all kinds can be tested using this method. However, this method suffers from a number of disadvantages and limitations: (1) Endotoxin tolerance occurs after repeated use of rabbits; (2) variations in the response depending on sex, age, and species; (3) differences between the responses of rabbits and humans to various pyrogen types; and (4) the rabbit pyrogen test is inadequate for sera, radiopharmaceuticals, chemotherapeutics, analgesics, cytokines, immunosuppressive agents, and others [20].
Accordingly, the use of the rabbit pyrogen test has been reduced. Nowadays, the most widely used endotoxin detection systems are based on the highly sensitive LAL test. It is based on the coagulation cascade of the blood of a horseshoe crab, Limulus polyphemus, which is induced by lipopolysaccharide. The currently known methods for lipopolysaccharide detection entail (1) gel-clot assay, (2) turbidimetric LAL technique, and (3) the chromogenic LAL technique.
The gel-clot assay is a limit test that provides simple positive or negative results. The LAL reagent is introduced to a sample, and the test material is considered endotoxin positive if a gel is formed via a clotting reaction.
The turbidimetric and the chromogenic LAL techniques are quantitative tests. The former is based on the fact that turbidity increases as a result of the precipitation of the clottable protein that is related to endotoxin concentration in the sample. The optical density is read by a spectrophotometer at either a fixed time (for the end-point method) or progressively (for the kinetic assay) as turbidity develops.
The chromogenic LAL technique makes use of a synthetic substrate which contains an amino acid sequence similar to that of the clottable protein, coagulogen, in order to detect endotoxin. The enzyme cleaves a yellow-colored substance from the chromogenic substrate, and the color intensity produced is proportional to the amount of endotoxin present in the sample.
Endotoxins are temperature and pH stable, and, therefore, their removal is one of the most challenging issues. Numerous techniques are used to reduce endotoxin contamination of biopharmaceuticals, including ion-exchange chromatography, sucrose gradient centrifugation, gel filtration chromatography [19], affinity adsorption [21], charged membrane/depth filtration, and ultrafiltration [22].
Owing to the risks of transmission of adventitious agents to patients, the different cell levels should be studied for the absence of these agents. Among the adventitious agents, special attention should be paid to viruses that are capable of contaminating the original species. Generally, the virological safety of biopharmaceuticals includes several levels of control at various manufacturing stages, including 1 - rigorous screening of cell banks (both master cell bank and working cell bank) for viruses; 2 - screening of each cell culture harvest for adventitious agents; and 3 - a demonstration that the purification process can clear potential adventitious agents [15].
The detection of viruses in cell lines can be carried out via various techniques. The commonly used methods of detecting viral infections include
co-cultivation assays (specific in vitro tests),
in vivo assays,
antibody production in animals (MAPs, RAPs, or HAPs),
immunoassays for viral specific proteins,
Transmission Electron Microscopy (TEM),
Polymerase Chain Reaction (PCR).
For the co-cultivation assays (specific in vitro tests), the cells used for production, or culture supernatant, or the final product are incubated with the detector cells. The detector cell lines are susceptible to different viruses and are used to detect desired viruses via monitoring subsequent cytopathic effects, hemadsorption, morphological changes, or other signs of viral infection. The detector cells usually contain humans, primates, and cells from the same species.
The in vivo assay can be performed by the inoculation of cells or cell lysates into animals, including newborn and adult mice, guinea pigs, rabbits, or embryonated chicken eggs to detect viruses. The animals are consequently monitored for any abnormality.
Species-specific viruses potentially present in rodent cell lines can be examined using assays for antibody production in the animals. The MAP, RAP, and HAP (mouse, rat, and hamster antibody production assays, respectively) tests involve an injection of the test article into the animals. The inoculated animals are bled after four weeks, and the sera are tested for the presence of the antibodies against the specific viral antigens. For instance, Hantaan virus, Lactic Dehydrogenase virus, and Sendai virus have been screened using MAP.
An immunoassay for viral-specific proteins can be undertaken through production of the relevant antibodies after an injection of a virus of interest into animals. Currently commercially available immunoassays are able to detect various viruses.
Another method that is used for virus detection is TEM (Transmission Electron Microscopy). TEM is a quantitative assay that is based on the visualization and morphological identification of virus particles in samples [23].
Nevertheless, more sensitive methods, such as the PCR identification methods, can be employed for the detection of sequences of the viruses [16].
Since the biopharmaceuticals can be originated from mammalian cell lines with a high risk of endogenous retroviruses, on one hand, and these products may be infected with adventitious viruses through processing, on the other hand, virus inactivation and removal steps in the purification process are required [24]. These entail gamma irradiation, low pH treatment, or virus filtration.
Indeed, ensuring the absence of virus contamination in biopharmaceuticals is challenging. For instance, a limited number of commercial poultry vaccines were contaminated by avian leukosis virus even after routine quality assurance procedures. In addition, reovirus was found as a contaminant in urokinase. On the whole, sourcing and testing alone cannot guarantee the virological safety of biopharmaceuticals owing to some limitations: the limit of sensitivity for cell culture and PCR tests and also due to the fact that cell culture or in vivo tests are not able to detect all known kinds of potential contaminants [25]. Thus, practical methods are required for the virological safety of biopharmaceuticals, which involve the inclusion of risk assessment as well as management policies.
Mycoplasmas are the smallest free-living and self-replicating organisms in nature that are sized between 50 and 500nm. They lack a rigid cell wall and, consequently, are highly pleomorphic from round to filamentous. They are filterable and penicillin-resistant forms. Furthermore, their membrane contains sterol and due to this, mycoplasmas require the addition of serum or cholesterol to the growth medium. They grow on special media in aerobic or anaerobic conditions with optimum growth at 37°C and pH 7.0 and form with a “fried egg” morphology on agar media (Figure 6).
Mycoplasma contamination of cell culture systems for the production of mycoplasmas is a critical problem due to its effect on various parameters within the cell culture system. Mycoplasma contaminates cell cultures approximately without any sign, and it persists for a long time. Indeed, mycoplasma-positive cell cultures can be considered the major source of biopharmaceutical infection, and they should be discarded or effectively decontaminated. Taken together, mycoplasma-positive cell cultures pose a serious problem and should be effectively detected and eradicated [26].
Mycoplasma colonies with fried egg morphology on mycoplasma agar medium. Picture was taken at Mycoplasma Reference laboratory, Razi vaccine and serum research institute, Iran
Different methods are used in international pharmacopoeias and guidance for detecting mycoplasma in biological test samples, mainly categorizing them as direct assay by microbiological culture, indirect assay by indicator mammalian cell culture, and PCR.
Direct assay by microbiological culture: The principle of detection is based on the growth of mycoplasma on supporting agar and liquid media (broth). First, the test sample is introduced into a special broth culture, is incubated for an appropriate time, and, consequently, it is sub-passaged to plate agar. After the required incubation period, the presence of mycoplasma colonies is observed microscopically in the agar plates (Figure 6).
Indirect assay by indicator mammalian cell culture: The indirect method requires the co-cultivation of the test sample with an indicator cell line for two to three days. Typically, VERO cells with a large cytoplasm area around the nucleus were used. Consequently, the cells were stained using a DNA binding stain (such as Hoechst stain) that binds specifically to DNA and is observed via fluorescent microscopy. Due to the affinity of mycoplasmas for the mammalian cell membrane, mycoplasmas appear as granules surrounding the nucleus.
Mycoplasma PCR: In this method, detection is carried out using specific oligonucleotide primers for the amplification of mycoplasma DNA. This method is specially recommended for detecting contamination with the non-cultivable strains of
On the whole, it is advisable to use two different methods in the detection of mycoplasmas in order to allow for the differentiation between false-positive and false-negative results.
The importance of DNA contamination detection in biopharmaceuticals is related to the fact that the DNA from some sources such as hybridoma cell lines in monoclonal antibody production may act as active oncogenes. These kinds of DNA contaminants can be introduced and expressed in human cells and result in the initiation of cancer cells. According to guidelines, the acceptable level of residual DNA in recombinant products is 10 pg per therapeutic dose. DNA hybridization studies that use radiolabeled DNA probes with a specific nucleic acid sequence constitute one of the most widely used methods for the detection of DNA contaminants in the product to a nanogram (ng) range [6]. The important steps involved in DNA hybridization are shown in Figure 7.
The main steps of the DNA hybridization procedure
Cell cultures may be infected with other cell types due to the use of contaminated items or operator mistakes. Also fail of the sterilization process can be another reason. The detection of cross-contamination is very challenging due to the fact that macroscopic and microscopic properties of the original and contaminant cells are commonly the same. Cross-contamination in the production of biopharmaceuticals would prove to be disastrous and terrible.
Various tests for detecting cross-contaminations can be applied; however, a product-specific identity test will be the best choice [16].
Microbial quality control plays a prominent role in the manufacture of safe and effective biopharmaceuticals. The main sources of microbial contamination can be related to raw materials and the production environment. The main categories of raw materials that are involved in the manufacturing of biopharmaceuticals with a high risk of contamination are those of animal origin such as cell culture media, sera, and supplements. The production environment includes air, surfaces, instruments, equipments, and personnel. All these can be considered the main source for the contamination of biopharmaceuticals with adventitious agents such as viruses, bacteria, fungi, transmitting spongiform encephalopathy, and mycoplasma. The use of contaminated biopharmaceuticals causes product spoilage, which may lead to (i) metabolization of the therapeutic agents by microorganisms, thus bringing about a decrease in the potency of the therapeutic agent; (ii) a potential health hazard to patients as a result of either infectious diseases or microbial-derived agents such as endotoxins that are secreted into products.
Various methods are used for detecting and eliminating different biological contaminants that are used in the manufacturing of biopharmaceuticals. Generally, bacteria and fungi can be detected by standard sterility testing or macroscopic and microscopic characteristics, as well as biochemical tests. In addition, viruses can be detected via a number of methods such as co-cultivation assays (specific in vitro tests), in vivo assays, antibody production in animals (MAPs, RAPs, or HAPs), immunoassays for viral specific proteins, TEM, or PCR. The detection of endotoxin can be carried out using the pharmacopeial rabbit test or LAL test. The available approaches for the detection of mycoplasma include direct assay using special culture media, indirect assay by mammalian cells, and DNA staining, as well as PCR. Furthermore, DNA hybridization is a widely used approach for the detection of DNA contaminants in biopharmaceuticals. The best method for cross-contamination detection includes a product-specific identity test.
With regard to the collection of tests for biological quality control of biopharmaceuticals summarized in this chapter, it is obvious that various sets of methods are available in different guidelines and pharmacopeias which are complicated and problematic. The development and compilation of harmonized guidelines for biological quality control of biopharmaceuticals is a critical necessity that can facilitate the control of the safety of these ever-increasing products.
All living beings require healthcare and monitoring, and the requirement increases with age. According to the Department of Economic and Social Affairs of the United Nations Secretariat, the elderly population (persons of age 60 years and over) in the world in 2020 was 1049 million and is projected to be 1,198 million in 2025, or 15% of world population [1]. Healthcare is expensive and the treatment and its management require a lot of data collection. Occurrence of pandemics amplifies healthcare requirement for living beings of all ages, and more so for geriatric subjects, pressurizing the healthcare systems. Medical cost trends are increasing all over the world for multiple reasons and are expected to maintain an upward trend in the future, irrespective of the healthcare models used by the different countries in the world. According to a study by PricewaterhouseCoopers Health Research Institute, there will be a 7% medical cost trend in 2021, a percent above the trend in 2020 [2]. A study on healthcare spending by Peterson foundation reported that during 2019, the spending was close to $3.8 trillion, or $11,582 per person in the U.S. These costs are expected to climb to $6.2 trillion—roughly $18,000 per person by 2028 [3].
In most healthcare systems, rocketing expenses, insufficient staffing, medical inaccuracies, and the incapability of the patient to get to a hospital in time are adding to the workload of the already overloaded existing healthcare provisions. Vital parameters for living subjects often require monitoring that needs appropriate sensors. Use of wires for sensor data transfer requires the patient to be either stationary or that sensors, electronics, wires, and human-machine interface (HMI) unit, all move with the subject. A wired monitoring system impacts the mobility of the subjects. It is also a major inconvenience to patients if they must visit hospitals every time for getting the readings of vital parameters taken. Such monitoring done only during hospital visits is not continuous, gives the healthcare professionals merely a snapshot of the patient’s health parameters for a short time window, and is hence neither efficient nor perfectly reliable. Mobility of geriatric patients using such a wired system could be even more difficult. Quite a few times, the subject does not need to be confined to a bed and the health parameters still need to be monitored. The traditional healthcare monitoring sensor system designed using wired connections is cumbersome and impracticable for such applications.
There is a strong need for ubiquitous and pervasive monitoring of physiological, biochemical, and physical parameters in any environment without activity constraint and behavior alteration for managing patients with chronic ailments and geriatric care. Other important use cases could include general monitoring of well-being of any subject, performance evaluation of sportspersons and deployed soldiers and other applications involving travel and distant patients.
With recent advances in wireless technologies, it is possible to get rid of the wires and relay the data from the sensors to the HMI unit over wireless links, often via multiple hops across wireless transceivers built into the IoT sensors, thus creating an Internet of Things - Healthcare Sensor Network (IoT-HSN) that can exist in or around the subject’s body.
To address the design requirements of an IoT-HSN, the technical issues that need to be focused on include the necessity for wearable or implantable devices with better sensor design, power source miniaturization with possible energy harvesting, biocompatibility, Micro Electro-mechanical Systems (MEMS) integration, low power wireless communication, secure data transmission and seamless incorporation with smart therapeutic schemes. The design would also benefit from redundancy and complementary sources of data to boost the information content and lessen systematic and random errors in sensor data. What is even more important is that such a system must do this inexpensively.
Non-intrusive, ambulatory, continuous, yet economical health monitoring systems using IoT-HSNs are now being developed to achieve a better and complete picture of health diagnosis and reduce the cost of healthcare. In this approach, multiple miniature, battery-powered, networked wireless sensor devices can be attached to or implanted inside the subject’s body. These devices sense and collect data on subject’s vital signs and transmit the data wirelessly to a central device implemented in a personal digital assistant (PDA) or a smartphone that collects and sends the data to a base station over an external network making them available to healthcare personnel for further assessment and analysis. The system obviates the need for wires that restrict the subject’s movement and confine the subject, thus making ubiquitous but unobtrusive monitoring possible.
While IoT-HSNs are extremely useful and the need of the day, human tissue can be harmed by the heat produced by the electronic circuitry for the sensor node and antenna. This paper tries to address this issue related to IoT-HSNs in a novel way at the physical and data-link layer level.
For prevention and complex intermediation, clinical practice relies heavily on early, truthful, and thorough diagnosis supported by tight scrutinizing of the results. To obtain qualitative and quantitative data for physiological parameters for living beings, a variety of sensors have traditionally been in use. These sensors need to convey their data to an HMI unit that can collect, analyze, and display the data in a variety of formats for use by healthcare personnel and store the data for future use. Traditionally, such data is relayed over wires to the HMI unit. The complexity of such a system increases with the number of physiological parameters being monitored. However, for the most part, this practice depends on a sequence of snapshots of physiological, bio-mechanical, and biochemical data which might not capture transient abnormalities reliably. An objective determination of a patient’s recovery after diagnosis can be tricky due to the episodic and subjective nature of outpatient clinic assessment.
Vital signs monitoring systems for hospital ward-based patients have a propensity to be intensive on labor as they involve manual measurement and documentation, which also makes them prone to human error. Such systems restrict patient movement which might be redundant in several cases and can be benefited immensely by using wireless sensors. Automation of this process using wireless sensors with the capacity to pervasively observe patients wherever they are, not just on a hospital bed, is suitable to the patient as well as the healthcare provider.
Acute as well as chronic disease management through clinical medicine, health monitoring and healthcare delivery need to involve home and community settings and require radical changes in system design. Close monitoring of some patients needs to be made possible with safe early discharge without hospitalization being necessary, also reducing the cost for the patient and improving hospital bed availability. The pandemic has already proved that availability of hospital beds and their management can be extraordinarily challenging and critical at times.
There are rapid changes happening in the social and economic structure of our society connected to demographic variations associated with increase in vulnerable aging population living alone, a sizable part of which constitutes the high-risk group that would benefit immensely by regular and non-intrusive healthcare monitoring. The volume of this group is set to expand, along with its prospective need upon healthcare resources because people in industrialized countries are living longer than ever before and average life expectancy has improved to more than 65 years [4].
The incapacity of the elderly residents to get medical assistance early enough for simple and treatable conditions may lead to substantial morbidity. Inclement and extreme weather conditions and the fact that they live alone could be two major factors responsible for delayed medical intervention that could make things worse. It is an additional consideration if they live in rural areas. There is an acute need for unobtrusive monitoring of such patients in their home environment in any weather for earlier detection of any worsening in their condition, so that they can be promptly treated, thus reducing the necessity for hospital admission, related morbidity and even chances of mortality.
In recent times, the focus of healthcare also altered towards the general health and wellbeing of the populace rather than just the supervision of disease advancement or the effectiveness of therapeutic processes. Several healthy people actively monitor their health parameters because of increasing awareness towards healthy living these days. This is required for patients as well. Certain critical health-related events might not occur in the time window when the patient is in front of healthcare professionals. Such events could be missed, make a difference to the diagnosis and treatment, and thus create room for error. Therefore, several patients require health monitoring although they do not have to live in a hospital for this purpose.
Health is defined as “a state of comprehensive physical, mental and social well-being and not simply the non-existence of illness of infirmity” by the World Health Organization (WHO) [5]. Blocking disease through campaign of healthy lifestyle choice is a prospective cost-effective methodology to address contemporary healthcare risks [6]. The healthcare approach is shifting towards watching lifestyle behaviors and intervening when essential.
Selections such as smoking and alcohol, diet, sleep, physical activity, have all been linked with numerous medical conditions. The cardiovascular disease is one of the most documented illnesses related to lifestyle choices today [7]. Undesirable lifestyles that lead to chronic conditions need to be advocated against, in favor of promotion of healthy living with prevention and early intervention of ailments. There is plenty of evidence to link inactivity with poor physical condition which is why physical activity monitors are commonly available today and are still evolving for better efficiency [8].
The user-friendly software that comes with these activity monitor sensors is true value addition because it permits customized activity targets to be established, and progress towards those targets to be presented at any time or archived and examined later. The software can help with weight monitoring and management as well as diet tracking. Such monitors have demonstrated that they enhance quality of life as much as expensive, overseen workout programs [9].
Anomalies of heart rhythm (arrhythmias) are frequently confronted in clinical practice, affecting almost 4% of the populace beyond the age of 60, rising with age to roughly 9% in people above 80 [10]. Heart failure affects up to 10% of patients who have attained an age of 65 years [11]. Early symptoms of atrial fibrillation arrhythmias include fatigue and palpitations, and often lead to the patient seeking medical advice. Averting the longer-term issues of tachycardia (rapid heart rate induced) involving cardiomyopathy (expansion of the heart causing pump failure) and stroke in such patients becomes crucial. Prospective bleeding problems caused by anticoagulant medication affect an escalation in mortality in this geriatric patient cluster, in addition to other risk factors [12]. Continuous and pervasive monitoring of heart rate is desirable for several patients and the elderly.
One of the principal vital signs, the systemic arterial pressure (ART) outcomes from the pressure exerted by the circulating blood in the large arteries and is then measured within large arteries in the systemic circulation in mmHg units. The parameter is dependent upon cardiac output and total peripheral resistance and its value varies with each heartbeat in accordance with the pumping action of the heart. All levels of ART exert some systematic stress on the arterial walls. Arterial pressure directly relates to cardiac output, arterial elasticity, and peripheral vascular resistance [13]. It is vital for the subject’s body to be capable of adjusting to acute changes in arterial pressure and for the subject to obtain medical therapy or lifestyle modifications for chronic variations. Arterial pressure regulation is required to sustain a sufficiently high pressure that permits appropriate perfusion of body organs and tissue; but not high enough to cause harm. The connected medical condition is known as essential hypertension and is seen in roughly 95% of patients with hypertension [14, 15]. Treating hypertension is crucial because it can cause cerebral, cardiac, and renal problems. As it is a key parameter connected to the cardiac condition of the subject, the author decided to choose the analysis of this parameter as a representative of vital signs for the present work while the author dealt with data for several other equally important parameters.
Atrial fibrillation is known to have several associated complications such as hypertension or high blood pressure. High blood pressure is known to affect nearly one billion persons globally [16] and can relate to cardiac problems. Early identification of hypertension is vital, but its monitoring can be labor-intensive and might involve several clinic visits.
The technology for new biological sensing modalities has started emerging and it aims at basically transforming the way we utilize bio-measurements in a truly customized monitoring platform that is smart and context-aware, yet imperceptible. An IoT healthcare sensor network (IoT-HSN) consists of one or more wireless sensor devices positioned on, in, or around the human body. The sensor devices sense and collect data from the human body and then transmit the data to a central device, called a Coordinating Sink Station (CSS) or simply sink, that can be implemented as an application in a smartphone or PDA. After collecting all information, this sink then forwards the data to the medical workers through external networks.
Thus, the idea behind an IoT-HSN is to perform the monitoring of human well-being in a “ubiquitous” and “pervasive” way keeping an eye on physiological, biochemical, and physical parameters in any environment – home or hospital, without constraint of activity [17, 18]. This idea is rapidly converting to reality with the key innovations in sensors, processor miniaturization, and wireless technologies for transmission of sensor data [19, 20].
Innovations in crucial areas such as miniaturization of power supply, enhanced battery time, lowered energy intake, and power scavenging are vital to the design of such systems and are fast becoming a reality [23]. Use of customized wireless sensor network (WSN) technology for creating pervasive healthcare systems will permit access to truthful medical information irrespective of place and time and will go a long way in improving the quality of healthcare services.
Due to the restricted bandwidth and power constraints in an IoT-HSN, the optimality of conventional method of data acquisition followed by post transmission digital conversion and signal processing is questionable. While it requires resources, bio-inspired local processing at the sensor front-end prior to transmission, combined with behavior profiling, pattern recognition, and machine learning can yield highly optimized bio-monitoring systems.
An IoT-HSN has more challenges than other wireless sensor networks because of several reasons, the most important of them all being the involvement of living subjects. The various design considerations for IoT-HSNs involve size, cost, reliability, data privacy, security, and intrinsic safety of the subject. This paper tries to address some of these issues concerning the intrinsic safety aspect of IoT-HSN design and the energy efficiency of an IoT-HSN.
WSN technology has benefited by miniaturization and cost reduction in creating sensors with computers and wireless transmission capability that are smaller than the size of the pin head [24, 25, 26, 27]. Sensors that can be combined, run on low power, communicate over wireless links, and self-organize into a network have been used in oil and petroleum exploration and industry [25, 28, 29], structural monitoring [29], habitat monitoring [30] and smart homes [31, 32]. Security and scalability of IoT applications and services could also be an issue as addressed in this project aimed at building a Smart Independent Living for Elders (SMILE) home [33, 34] that the author is a part of.
However, the equipment used for these applications cannot address the specific challenges related to human body monitoring. The human body comprises of a complex internal ecosystem that reacts to and interacts with its external environment while staying distinct and self-contained. Hence, although an IoT-HSN is similar in operation to a regular WSN, it comes with an additional set of new challenges. It involves a smaller scale network (made up of miniature sensor nodes each having a small processor, wireless transceiver, and power) that requires a different type and frequency of monitoring and is capable of seamlessly integrating with home, office, and hospital environments.
The IoT-HSN sensor node guarantees the perfect gathering of data from the transducer element used, performs low level local processing of transducer data, and then transmits this data to a Local Processing and Coordinating Sink Station Unit (CSS). The data from all the sensors is collected by the CSS by this method, processed further, fused, and transmitted wirelessly to a central monitoring server [35].
As pointed out earlier, while some of the challenges faced are common to IoT-HSNs and WSNs, there are intrinsic variations between the two, which require special consideration in case of IoT-HSNs. Some of these sensors need to be implanted inside living human tissue. The power source for IoT-HSNs, if exhaustible and hence with finite lifetime, could be inaccessible and difficult to replace in an implantable setting. Energy is more difficult to supply, hence lower the requirement (with options of energy scavenging), the better. Loss of data in an IoT-HSN can be intolerable and may necessitate extra actions to guarantee quality of service (QoS) and real-time data examination capabilities. Human body is capable of movement, so an IoT-HSN is a mobile and dynamically changing network. Motion artifact is a major challenge in IoT-HSNs. Early detection of adverse events is vital in IoT-HSNs because failure of human tissue cannot be reversed. High level security for wireless data transfer is necessary to safeguard patient information and privacy. All these factors change the sensing modalities for IoT-HSN.
IoT-HSN sensor nodes could be located on, around, or inside the human body, with each dissipating some part of its energy consumed as heat and causing temperature increase in its locality. Signals carrying sensor data need to travel through tissue (bones, flesh, and fluids). The longer a node works and transmits/receives data, the more energy is dissipated and converted into heat. Nodes not transmitting or in sleep with low power might not produce significant heat. However, continuous node operation over a period generates heat that cannot be ignored. When implanted nodes are being considered, this generates even higher concern. To balance the heat, the human body has a thermoregulatory system. If the rate at which heat is generated is greater than the rate of working of the thermoregulatory mechanism, the temperature rise can harm the human tissue that absorbs the heat. The temperature rise directly influences human safety and health adversely, as explored in [36, 37].
Due to the lossy nature of the human body, the sensor data might hop through intermediate sensor nodes before reaching the sink node instead of being communicated in a single hop. Natarajan et al. [38] attempted to compare the trustworthiness of single-hop and multi-hop network topologies.
The operation of node circuitry and radiation due to transmission from the antenna produce and discharge heat to the node’s surroundings, which can be injurious to the subject’s body cells beyond a safety threshold. Specific absorption rate (SAR) is a standard quantity that shows the power dissipated per unit mass of tissue. It is a well-known parameter regarding the electromagnetic safety towards the human body and is defined as a measure of the rate at which energy is absorbed by the body when exposed to a radio frequency electromagnetic field, expressed in W/kg [39]. For near-field exposures the upper bound of SAR is 1.6 W/kg for some tissue averaged over a gram according to the Federal Communications Commission (FCC) standard in the United States and is 2.0 W/kg for 10 g of tissue according to the International Commission on Non-Ionizing Radiation Protection (ICNIRP). These SAR values can be translated into temperature rise [36], with the maximum permissible temperature rise in the human head and brain being 0.31°C and 0.13°C (FCC) and 0.60°C and 0.25°C (ICNIRP). The report in [37] also suggests that a temperature increase of 0.1°C is sufficient to cause intense thermoregulatory responses in the human body.
According to a survey on thermal effects of bioimplants by Lazzi [39], the electromagnetic fields induced in the human body and the power dissipated by the implanted sensor nodes are the two main sources of temperature rise. The power dissipation is from three sources: caused by the stimulating electrodes, the implanted telemetry coil, and the implanted microchip.
The in-vitro (implanted) sensor nodes can transmit and receive the data only through a wireless system. The SAR measures the rate of energy absorption by the body per mass of tissue upon exposure to a radio frequency electromagnetic field. It is a standard parameter connected to the electromagnetic safety regarding the human body, expressed in W/kg. According to IEEE standards the acceptable value of SAR is 1.6 W/kg averaged over a gram of tissue and is used for cellular phones by the FCC.
A better hardware design with node and antenna running on lower power can reduce the heating effects. Also, a well-designed network routing protocol could reduce the bioeffects. This work tries to reduce these heating effects even further by reducing the amount of transmission, while trying to preserve the integrity and accuracy of the data within low limits of error as a trade-off of the suggested framework.
As briefly touched upon in the previous section, one of the topmost concerns in the design of IoT-HSNs involve monitoring the heat generated because of operation of sensor network nodes. Electronic activity in the sensor circuitry and antenna radiation dissipates as heat. Power is dissipated by the implanted sensor node electrodes, microchips and the electromagnetic fields induced in the human body from telemetry coils as heat which can cause harm to healthy cells and tissue [40, 41]. For burst data operations that do not last long, such heat can be overlooked. However, when the node is operating continuously, transmitting, and receiving data over a considerable period, the heat generated by the node cannot be neglected. This concern becomes even bigger when dealing with in vivo sensor nodes (i.e., implanted inside the human body). The human body has a thermoregulatory mechanism to balance the heat around the body. However, when the heat received rate is larger than the thermoregulatory mechanism rate, the temperature will rise and, in turn, damage the human tissue.
Routing overheads have a potential to cause additional heat damage. Also, extra energy might be required to implement thermally aware routing algorithms. The challenge is complicated by the fact that the heat and energy consumption, both these factors need to be lowered, because the sensor nodes run on the limited power resource of batteries, while the network throughput needs to be maximized. A trade-off needs to be reached in the design to address these diverse requirements.
There are three types of routing used on IoT-HSN protocols. First, proactive routing where each node has information about the neighbor nodes. Second, reactive routing where the node explores the information about the neighbors when there is a packet to be sent. Third, a hybrid which combines the benefits of two methods (e.g., protocols that use proactive in setup phase and reactive in data transmission phase).
Some approaches to reduce the risks of this heat damage involve designing routing protocols for IoT-HSNs that include temperature into the routing metric to decrease the heat.
The challenges related to IoT-HSNs have been proposed to be addressed through numerous routing protocols. Some approaches have tried to tackle the issue of extreme and dynamic path loss observed in intra IoT-HSN communication caused by postural movement of the subject’s body. The routing scheme by Quwaider and Biswas [42] proposes division of the sensor field combined with store and flood mechanism to route the sensor data towards CSS. Their work in [43] uses a store and forward approach for a delay tolerant intra IoT-HSN communication.
The proposal in [44] uses a field partitioning with store and forward like in [42] based on if or not the sensors have a clear line of sight for communication. The storage of packets in these works makes the routing non real time making the scheme impractical for vital medical applications. The proposals do not take the heterogeneous nature of IoT-HSN data and the thermal effects into account.
In [45] the routing protocol uses a Temperature Aware Routing Algorithm (TARA) to reduce the thermal effects IoT-HSN operation by estimating the temperature rise in neighboring nodes to avoid hotspot nodes. The trade-off involves a delay in routing sensor data packets and additional energy requirement. The Least Temperature Rise (LTR) algorithm [46] tries to address this limitation by associating a hop-count with each data packet and use it for deciding to discard the packet if the hop count reaches a limiting value. The trade-off in this case is poor packet delivery ratio. Adaptive Least Temperature Rise (ALTR) algorithm proposed in [47] is also a thermal aware scheme that uses shortest hops to route packets instead of dropping them. Least Total Route Temperature (LTRT) algorithm [48] observes the temperature across the entire route instead of individual nodes or hop-count for routing decisions. None of these schemes consider the dynamic intra network path loss or the QoS parameters of heterogeneous IoT-HSN data, making their utility questionable.
Djenouri and Balasingham [49] propose to divide the vital sign data into four categories based on data criticality, thus allowing for delay in some parameters and employ two sinks for all data. The latter feature increases network traffic. Razzaque et al. [50] tried to improvise on [49] by using multi-hop transmission to meet QoS requirements of data packets but their algorithm performs poorly on data packet delivery. QoS aware routing used in two proposals by Khan et al. [51, 52] involves classification approaches that are variants of [49]. None of the QoS-aware routing schemes take inter IoT-HSNs communication, path loss or temperature issues into account.
Monowar et al. [53] and Bangash et al. [54] claim to propose QoS as well as thermal aware routing schemes for intra IoT-HSNs. Both schemes classify the sensor data as in [49, 50]. Monowar et al. [53] propose to send multiple copies of data to counter delay issues. This generates redundant additional network traffic, causes congestion and packet drops despite higher energy requirements and rise in temperature while neglecting the dynamic path loss. The proposal by Bangash et al. [54] performs better on these factors but fails to address the issue of reliable, timely delivery of critical data.
Critical Data Routing (CDR) proposed in [55] classifies data into critical and noncritical categories while considering path loss, temperature rise and QoS with decent performance. However, the scheme could benefit by considering additional measures for conserving network energy, which it does not focus on.
The approach in [56] suggests a Media Access Control (MAC) protocol that resorts to shortest hop routing of sensor data packets based on hop counts using a duty cycle decided upon by using the current temperature rise. The duty cycle is calculated using four probability distribution functions- Poisson, Binomial, Lognormal and Laplace. This protocol was chosen by the author for the current article as no other protocol blends thermal awareness with efficient duty cycles. The work uses three models. Of these, the Sensor-Centric Monte-Carlo model (SCMC) involves random sampling from a given finite space [57] while acquiring any temperature rise right from the sensor and not from the surrounding tissues. In the Tissue-Based Fixed Coordinator (TBFC) model, a grid divided control volume of tissue space is considered, like [39, 45, 58] which assumes that the entire IoT-HSN or a major portion of it is within this tissue control volume. The results indicate least packet loss of 30% for Poisson distribution on the duty cycle with the trade off with 80% active nodes that need more energy for IoT-HSN operation. The packet loss was further reduced by enhancing the working of TBFC by adding 1-hop caching mechanism (TBFC-1HC) in which data packets are cached before the node goes to sleep state if the node has not reached its sampling state while the next hop node might be mere one hop from the CSS.
None of these approaches address the issue of improving upon network lifetime. As the approach in [56] provides for best possible compromise for intrinsically safe, thermal and energy aware IoT-HSN design, the author chose on using it for further optimization and improving upon the energy scenario and network operation lifetime.
The author proposes a model which not only addresses temperature rise but is also energy aware and helps in improving network lifetime. For this study, the author used the same IEEE 802.15.4 Wireless based IoT-HSN schematic modeled for [59] to run in the CSS. The 24 channels in the IoT-HSNs were used to mimic relaying of physiological parameters from the subject such as parietal and occipital electroencephalogram (EEG), electroculography, respiratory airflow, oxygen saturation %, heart rate, pacemaker diagnostics, electrocardiogram (ECG), arterial and central venous pressures, respiration rate, thoracic and abdominal resistance, blood pressure and temperature, blood sugar and insulin levels, urine creatinine, nerve conduction, musculature actuator and electromyography (EMG). The study did not involve any human subjects directly because the data utilized were obtained from Physionet [60], a public research database. Of the 24 channels, 3 were used for bioactuators and the remainder were utilized by sensors. Figure 1 shows the biosensors and bioactuators using an adhoc link to communicate with the coordinating sink station which was connected over an adhoc link with the body area network (BAN) gateway which in turn links the biosensors to a IoT-HSN base station. To demonstrate the in-depth analysis and to evaluate the performance of the thermal and energy aware framework proposed in this article, the author has used the arterial pressure parameter from the 24-channel model in the following sections.
Four 24-channel IoT-HSNs in action.
Figure 1 shows four different 24-channel IoT-HSNs P1 to P4 in the vicinity of each other trying to send data to the base station with their performance possibly affected by radio interference. The channels in the IoT-HSN have 802.15.4 adhoc links to the BAN gateway for data transmission. Subsequently, the data is sent through a router to the base station. IoT-HSN P2 transmits its data to a different wired base station that exists on the same subnet. IoT-HSN P3 attempts to send data to its base station which is in a different subnet and uses a second router for connections. The base station for IoT-HSN P4 is a wireless node linked to the wired network via an access point. As human subjects can have different sizes, the placement distance for biosensors varies in the four IoT-HSNs.
While assessing the accomplishment of an IoT-HSN, it becomes vital to evaluate the intrinsic safety aspect of the wireless system and the possible risks of damage to healthy human cells and tissues. As pointed out earlier, the heat generated because of dissipation of wireless energy can cause discomfort to the subject and has the capacity to damage healthy human cells and tissues if endured for long times. For instance, the incessant monitoring of peripheral capillary oxygen saturation (SPO2) levels using a pulse oximeter for over 8 hours would cause a rise of temperature of 43 degrees Centigrade and is hence deemed risky as it could cause burns [61]. The detrimental effects of such sensor radiation caused heating can be evaluated by applying Penne’s bio-heat Equation [62] that offers the heat transfer relationship between the temperature of blood vessels and the tissue surrounding the vessels. IoT-HSNs can follow temperature-aware routing algorithms [63, 64, 65] that consider parameters like antenna radiation and the ensuing power dissipation as temperature rise in the surrounding tissue and make routing decisions to minimize the generation of heat. Combined with an efficient MAC protocol, the thermal-aware routing algorithms can be used for generating transmission and sleep duty cycles that allow a reduced rise in temperature than individual schemes [56]. Although the outcomes in [56] are improved over the other attempts at temperature-aware routing, the approach does not take into consideration the base network energy requirement and additional energy consumption required for retransmissions of lost sensor data. The author attempted to estimate the implementation of the three models in [56] with regards to energy in a network involving actuator control applications with sensors for Internet of Things Healthcare Sensor Networks. The model in [56] uses up to 25 sensors in its IoT-HSN, which is very close to the author’s model involving 24 sensors [59].
All the models considered in the present evaluation study the effect of four probability distributions for network parameters in addition to temperature rise, namely Poisson, Laplace, Binomial and LogNormal. Of the three, the SCMC model is a sensor-centric model that permits a random generation of packets based on a probability distribution while presuming fixed rise and fall in temperatures. A stable solver comprising of a fixed CSS is employed in the TBFC model for a stepped packet generation to offer improved heat performance than the SCMC. The trade-off for the TBFC model is a higher packet loss which is improved in the third model (TBFC-1HC). This modified TBFC model employs ‘one-hop caching’ in sensors to cache data packets for transmission delays up to their one hop neighbor that is nearest to the CSS. Data packets wait for a clear-to-send signal after which they are transmitted to the CSS.
The thermal aware routing algorithms for reducing the amount of heat generated have a trade-off in the form of loss of packets. The lost packets need to be retransmitted. The author tried to assess the data overhead due to retransmission resulting from packet loss for the four distributions across the three models. The results of the comparison can be seen in Figure 2 below. It is evident from the results that of the three models, TBFC fared the worst on the retransmission of packets that were dropped, while SCMC was found to be the best. Comparing the retransmission overhead for the distributions, the Poisson distribution had the lowest values while Log-Normal had the highest retransmission overhead among the four distributions. The work has the potential to be extended by including other distributions involving a more realistic human model.
Number of retransmitted packets in unit time for the three models.
Even if lost transmissions cause additional data traffic due to retransmissions, a data transmission scheme that involves reducing the frequency on transmissions of the sensor data and sending alternate samples as suggested by the author in the next section would effectively cut down the heating effects in the same proportion. Merely skipping alternate samples would reduce the amount of heat generated to half, thereby allowing longer node operation. If the final recreation does not alter the doctor’s initial diagnosis, the sample cut rate can be increased, thereby improving the heat performance to three or even four folds of the default.
A key question related to IoT-HSNs entails the energy-fidelity trade-off. When sensor data is transmitted after processing and transformation, it is expected that the fidelity level of the received data must be acceptable and appropriate to be useful. Any data transformation and transfer need to be done in an energy efficient manner. This requirement advocates for selective processing of collected physiological data samples.
Another major operation and design issue with IoT-HSNs involves improving the lifetime of sensing for sensor nodes and thus that of the networks. The issue is caused due to the constraints on batteries that need to be small in size and cannot pack a lot of power due to this constraint [66, 67]. The sensor nodes collect data samples and relay them to the CSS at an acceptable rate as dictated by the QoS of the physiological parameter. However, the total number of samples collected and transmitted by the sensor does not take the nature and frequency of variations in the physiological parameter into account by default. In this work, an attempt has been made to address the energy-fidelity trade-off [68] by reducing this data content through signal processing techniques. The approach involved selective exclusion of some sample data from transmission. Prediction techniques were used to recreate the missing samples that were not transmitted. The approach used in this work was different from the dual prediction technique proposed by Mishra et al. [69]. Prediction techniques involve approximations that come with errors but if the error is negligible, the recreated signals can be used for an early diagnosis if not for full diagnosis, while the patient is on the way to hospital.
The fidelity of data and the accuracy of information contained would undoubtedly be better if all the data samples sensed by the physiological sensor were transmitted. Although this sampling approach would satisfy the Nyquist criterion, it would result in transmission of several samples which could be predicted with reasonable accuracy using numerical techniques within some range of error. While such data might not truthfully reflect what continuous monitoring would reveal, the medical personnel would still be helped by early diagnosis, planning or determination on the course of action.
The author first reduced the transmitted samples for each of the parameters in the 24-channel IoT-HSNs to half by skipping transmitting alternate samples and tried predicting the skipped samples at the receiving end by using a simple proportional-integral-derivative (PID) scheme and a more computationally involved prediction using non-linear regression involving an artificial neural network (ANN-NLR). The results are shown for a couple of cycles of prediction for the ART parameter in Figure 3. The approximation used in the two prediction strategies generates some error, which is still not too big to alter the characteristics of the ART signal appreciably. This error is shown in Figure 4.
Plots of comparison of prediction performance by PID and ANN-NLR algorithms for arterial pressure.
Error plots of comparison of prediction performance by PID and ANN-NLR algorithms for arterial pressure.
The amount of data was reduced by periodically skipping those samples from the original set and predicting the missed samples at the receiving end. The bulk of data marked for transmission could be further used by delta encoding to pack more amount of data in every transmission [59].
Sample data sets for the 24-channel IoT-HSN involving critical physiological parameters such as ECG, central venous pressure, pulmonary artery pressure and arterial pressure signals obtained from Physionet [60], were used as the source to progressively cut down samples and create four different subsets of the original sets like the approach used in [70]. For the sample analysis and graphical evaluation, the programs were written in in MATLAB r2020 [71].
Alternate samples of each of the original sample sets were used to create the first subset, every third sample was picked up to create the second, every fourth sample for the third set and every fifth sample for the fourth set. Thus, the sample sizes of these sets were half, one-third, one-fourth, and one-fifth of the original, respectively. The four sets were transmitted and recreation of original by a variety of numerical interpolation algorithms was attempted at the receiving end. The reconstructed sets were compared with the original set of samples with all samples intact, and the error was calculated. Figure 5 shows the results of the recreation for the representative ART signal using ANN-NLR prediction after four different rates of sample reductions, with only a few cycles covered for the sake of conciseness. Figure 6 shows the error in prediction for the four sample reductions. A similar analysis was also done on the other signals of the 24-channel IoT-HSN with comparable results. Table 1 shows the particulars of the ART signal used as a representative of the results.
Prediction of skipped samples for four sample elimination rates through pChip.
Error plots for prediction of skipped samples for four sample elimination rates through pChip.
Characteristics | Signal | Signal | Signal |
---|---|---|---|
Minimum | Maximum | Span | |
ART (mV) | 52.35 | 89.6935 | 37.343 |
Signal specifications for the arterial pressure vital sign IoT-HSN parameter.
The signals recreated at the receiver using five different interpolation techniques over reduced samples for the arterial pressure parameter were compared with the original full sample sets for error in prediction by interpolation. The results of the prediction for the ART signal and the associated error analysis for just the cubic interpolation technique are presented in Table 2.
1–6 Sample Reduction | Halved | 1/3rd | 1/4th | 1/5th | 1/6th |
---|---|---|---|---|---|
ART (mV) | 1.58 | 1.67 | 6.01 | 5.65 | 1.02 |
%Error | 0.04 | 0.04 | 0.16 | 0.15 | 1.02 |
Peak error with sample reduction for arterial pressure using cubic interpolation.
Five numerical interpolation techniques – linear, near, Spline, Pchip and cubic were employed for rebuilding the missing IoT-HSN sample data for the parameters of the 24-channel IoT-HSNs at the receiving end. Table 3 shows the comparison between the five techniques for the ART parameter.
Reduced | Nearest | Linear | Spline | Pchip | Cubic |
---|---|---|---|---|---|
1/2 | 0.102 | 0.043 | 0.049 | 0.043 | 0.034 |
1/3rd | 0.104 | 0.082 | 0.039 | 0.045 | 0.057 |
1/4th | 0.12 | 0.166 | 0.158 | 0.161 | 0.162 |
1/5th | 0.212 | 0.159 | 0.143 | 0.152 | 0.152 |
Maximum percentage error values for ART from the five numerical interpolation techniques.
From Table 3, it is evident that the nearest neighbor interpolation algorithm performs the poorest of all but the other four yield lesser error, almost in the same range, with the linear spline interpolation performing better across the sample sets.
Twenty random sets (with 3600 samples transmitted in 10 seconds) of the signals for the 24 channel IoT-HSNs from healthy individuals and patients were employed to assess the performance of the prediction algorithms. The physiological parameters are all different in range, wave-shape, and type of variations. Figure 3 shows the results of error evaluation after reconstructing the signal using the five interpolation algorithms for one such set of values for the ART signal.
The first column in Figure 5 illustrates the signal sets with successive reduction in samples. The second column indicates the reconstruction of lost samples for the corresponding row after data reception done using the Pchip interpolation prediction.
A key requirement of IoT-HSNs is low power wireless which in turn makes signal detection difficult. Low power wireless is required, which makes signal detection more challenging. Common and proven technologies such as Bluetooth, ZigBee, General Packet Radio Service (GPRS) and Wireless Local Area Network (WLAN) might not offer good and optimal solutions to the low power requirement problem.
The growing miniaturization and cost drop on IoT-HSN sensors, circuits and wireless communication electronics is establishing new opportunities for wireless sensor networks in wearable applications. Nevertheless, for sensors to be untethered, the design needs to use wireless communication between nodes along with wireless powering of sensors. This requirement is fulfilled by batteries in most of the portable electronic devices, making them an obvious answer for IoT-HSN wireless applications. However, the batteries have a finite life and require to be replaced or recharged. This limitation presents a cost and convenience penalty which is undesirable in wireless applications including IoT-HSN while the market for such applications and demands grows. One possible solution to this problem involves harvesting energy from the environment for recharging of power sources. Energy scavenging from motion (vibration) and thermal (body heat) sources offer some options for recharging mechanisms that are being investigated. While the power demands of many electronic functions including wireless communication are being actively reduced, energy efficiency of power sources remains a problem because IoT-HSN nodes are intended to operate for a long period of time, especially if they are implanted.
Wireless devices can be powered by primary, or rechargeable batteries. Of these, primary batteries are better in energy densities, shorter in leakage rates and lower in cost. The energy density of Lithium-ion batteries that are most used in electronics, is around 700–1400 J/cc for rechargeables [72], and the figure for primary cells is higher. Batteries used for IoT-HSN applications are preferred to last at least a year. A lifetime of 1 year corresponds to 32 J/micro-watt of average power for an average power requirement of some tens of micro-watts.
Hence, a finite battery-life of some tens of microwatt-years is attainable for a battery under 1 cc. Search for better alternatives is on because such batteries require replacement and have issues related to toxicity, safety, and operating temperature range. While ultracapacitors are drawing rising interest for powering electronics as their energy densities are much higher than those of conventional capacitors, the density still are way lower than those of batteries [73].
Hydrocarbon fuels are known to have very high specific energy in the range of 16 kJ/cc for pure methanol [74] or 31 kJ/cc for iso-octane [75]. For miniature electronics, exhaustible sources of energy that use hydrocarbon fuel of some type are also under review, although primarily for greater power levels. Small, micro-machined and only few inches big external combustion heat engines have been built to provide power for portable electronics that can generate up to 200 micro-watts [76]. Such engines have a disadvantage of moving parts and very high temperatures, and hence fuel cells are also being widely investigated for applications involving low power. In the pure methanol-based device used in [74], the electrochemical reaction of methanol with water after passing through a polymer membrane results in oxidation of methanol producing free electrons and protons and generating high power levels of 195 mW/sq.cm.
Miniature fuel cells for implantable sensors as small as 0.5 mm thick can also utilize energy harvesting to provide inexhaustible power up to 4.4 micro-watts/sq.cm if they use body fluids such as oxygen dissolved in blood and glucose as the fuel source [77]. A crucial challenge for such power sources that needs to be addressed is their operational lifetime.
Due to difficulties in changing or recharging batteries in IoT-HSN sensors (some implanted), the management of energy consumption for network longevity and resourceful network operation is an important design consideration. The network design methods utilize a sleep-awake cycle for conserving energy and increasing the network operation time because the power requirement for the communications unit in a sensor node is several orders higher in comparison to the transducer and A/D converter unit in the sensor electronics. The author attempted to assess the lifetime of the proposed IoT-HSN framework created using commercial sensors and power supplies focusing on the period that the sensors would remain powered on.
The sample rate for the ART signals used in the representative evaluation was 360 samples per second with the samples encoded in 8-bits and the more popular 12-bits. The total energy necessary for the operation of a IoT-HSN sensor node varies based on factors such as sleep-awake cycle, inter-sensor distances, the time for which the node stays in a specific mode, as well as a system constant.
Based on Heinzelman’s sensor node transceiver model [78], the transmission energy required to transmit a k-bit message to a distance of
where,
Their model has the below assumptions:
To receive a k bit message, the energy expended in the receiver is
The energy expended in the transceiver electronics for most sensor nodes is identical for transmission and reception circuitry and in a few tens of nJ/bit.
The energy required for transmitting all the samples (and not skipping any of the 360 samples) while continuously operating for a minute was 8.65 mJ when the samples are encoded in 8-bits and 12.97 mJ when the samples are encoded in 12-bits.
The author attempted to assess the life cycles of wireless networks comprising of two commercially available low-power ultra-compact sensor nodes. The minute-long sensor duty cycle comprises of 10 seconds each of transmission and reception succeeded by 40 seconds of sleep. The author used three sensor modes for this evaluation – the Eco [79], Texas Instruments’ TI CC3100 [80] in Direct Sequence Spread Spectrum mode (1DSSS) and the TI CC3100 in Orthogonal Frequency Division Multiplexing mode (54OFDM).
The Eco sensor required a current of 16 mA during transmission, 22 mA during reception and mere 2 μA while sleeping.
The TI CC3100 sensor fared fine in the 1DSSS mode while performing amply better in the 54OFDM mode. The author evaluated the performance of these sensors based on three commercially available batteries that supply 3.0–3.6 volts, 0.5A – the CR2032, CR123A, iXTRA and ER34615. Table 4 summarizes the battery characteristics and the findings for transmission power requirements of the two sensor nodes without any power management applied.
Battery | CR2032 | CR2447 | CR123A | iXTRA | ER34615 |
Sensor Node | 225 mAH | 1000 mAH | 1550 mAH | 1700 mAH | 19000 mAH |
ECO (16 mA) | 1.76 | 7.82 | 12.11 | 13.28 | 148.62 |
TI – DSSS (21 mA) | 1.34 | 5.96 | 9.23 | 10.12 | 113.16 |
TI – OFDM (9.39 mA) | 2.99 | 13.29 | 20.63 | 22.63 | 252.49 |
Life in days for the different battery models as per their capacities and node power requirements if continuous power drawn.
Table 4 indicates that the innovations in low-power sensor design and battery technology enhance the lifetime of the IoT-HSN network.
If the sample reduction algorithm suggested by the author is used, the sensor, and hence the network lifetime would improve in accordance with the sample chop rate. Figure 7 shows the network lifetime improvement for the sample chop rates.
Comparison plot of battery lives for 5 batteries and four sample rates.
The author also attempted to evaluate the performance of the three thermal aware routing models for network lifetime with the three batteries that were shortlisted and considered by [56]. Of the battery models evaluated, the model based on ECO sensor nodes running on the 19000 mAH ER34615 battery had the best performance for network lifetime without any power management.
The TBFC thermal-aware routing model was found to offer the poorest economy on the battery power in these evaluations as compared to the other two models for the four probabilistic packet distributions. Figure 8 shows the battery and network lifetime for the model despite retransmissions using the mentioned battery-sensor combination in the number of hours of operation, in conjunction with the details in the tables.
A comparison of lifetime hours for the three models across four duty cycle distributions.
The three models pave a way for a study towards efficient and intrinsically safe, thermal-aware IoT-HSNs for wearable computing. Figure 9 shows the improvement for the sample chop rate of 3, if the reduction in samples is used with prediction for recreating the original signal. This was done for the ECO sensor when used in conjunction with the ER34615 battery, evaluated over the four probability distributions for the three thermal aware routing algorithms. The author’s findings indicated that the results were the best for the SCMC routing algorithm with Poisson sample distribution where the sensor and battery combination lasted for almost 66500 hours (7.6 years) with the results for other distributions not very different for the combination. The sensor and network lifetime are seen to be improved in accordance with the sample chop rate.
A comparison of improvement in lifetime hours for the three models for best sensor and battery combination.
In this article, the author has presented a comprehensive survey of the different types of routing models used for IoT-HSN data. The author has also proposed a thermal and energy aware model that enhances the lifetime of IoT-HSN for intra-network as well as inter-network traffic and evaluated the performance of the model. The author has also demonstrated energy savings by reduction in transmission using a linear elimination algorithm and recreating the missed data at the receiver using a variety of techniques involving a variety of interpolation techniques and prediction using PID and NLR-ANN with very low error values. The savings shown from the model and the enhancement of network lifetime have been demonstrated in quantified as well as graphical forms.
While the basic factors of the network look good for employing energy optimization in IoT-HSN applications, the dynamic execution of the proposed model needs to be studied in better detail for real life HSN applications. An extension of this work could focus on a clinical implementation covering several vital parameters with varying rates of change in them. More possibilities could emerge if the model is tested on well-founded and strong applications such as pacemakers, insulin monitors or movement sensors and prosthetic control.
This article opens the arena for further probing of thermal, QoS and energy aware design of micro-hardware for wearables and implantable bionics. The thermal and energy aware model offers an encouraging prospect to be selected as a design standard for IoT-HSN applications whereas none exists at this time.
The author expresses his heartfelt thanks towards Physionet [60] and the doctors from the Department of Pediatric Cardiology at Cincinnati Children’s Hospital for their judgment on the tolerable drop in volume of physiological data for competent supervision of vital body parameters for human subjects.
This work was not supported by any funding whatsoever from any sources.
The author declares no conflict of interest.
IoT | Internet of Things |
HMI | Human-machine interface |
IoT-HSN | IoT healthcare sensor network |
MEMS | Micro Electro-mechanical Systems |
PDA | Personal digital assistant |
WHO | World Health Organization |
ART | Arterial pressure |
WSN | Wireless sensor network |
SMILE | Smart Independent Living for Elders |
CSS | Coordinating Sink Station Unit |
QoS | Quality of service |
SAR | Specific absorption rate |
FCC | Federal Communications Commission |
ICNIRP | International Commission on Non-Ionizing Radiation Protection |
TARA | Temperature Aware Routing Algorithm |
LTR | Least Temperature Rise |
ALTR | Adaptive Least Temperature Rise |
LTRT | Least Total Route Temperature |
CDR | Critical Data Routing |
MAC | Media Access Control |
SCMC | Sensor-Centric Monte-Carlo |
TBFC | Tissue-Based Fixed Coordinator |
1HC | One-hop caching |
EEG | Electroencephalogram |
ECG | Electrocardiogram |
EMG | Electromyography |
BAN | Body area network |
SPO2 | Peripheral capillary oxygen saturation |
PID | Proportional-integral-derivative |
ANN-NLR | Artificial neural network – non-linear regression |
GPRS | General Packet Radio Service |
WLAN | Wireless Local Area Network |
TI | Texas Instruments |
1DSSS | Direct Sequence Spread Spectrum |
54OFDM | Orthogonal Frequency Division Multiplexing |
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. 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This will ensure that we discover ways to live in our world that allows us and other beings to flourish. We can no longer rely on medicalized approaches to health that wait for people to become ill before attempting to treat them. We need to live in harmony with nature and rediscover the beauty and balance in our everyday lives and surroundings, which contribute to our well-being and that of all other creatures on the planet. This topic will provide insights and knowledge into how to achieve this change in health care that is based on ecologically sustainable practices.
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In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},subseries:[{id:"3",title:"Bacterial Infectious Diseases",keywords:"Antibiotics, Biofilm, Antibiotic Resistance, Host-microbiota Relationship, Treatment, Diagnostic Tools",scope:"