\r\n\tThis book chapter’s main theme will be focused on transmission dynamics, pathogenesis, mechanisms of host interaction and response, epigenetics and markers, molecular diagnosis, RNA interacting proteins, RNA binding proteins, advanced development of tools for diagnosis, possible development of concepts for vaccines and anti drugs for RNA viruses, immunological mechanisms, treatment, prevention and control. \r\n\t
",isbn:"978-1-80355-667-3",printIsbn:"978-1-80355-666-6",pdfIsbn:"978-1-80355-668-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"52f8a3a1486912beae40b34ac557fed3",bookSignature:"Ph.D. Yogendra Shah",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11369.jpg",keywords:"HIV, Dengue, Zika, West Nile Virus, Chikungunya, Rabies, SARS-CoV2, MERS-CoV, Hanta Virus, Influenza, Whole Genome Sequencing, DNA Sequencing",numberOfDownloads:168,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 4th 2021",dateEndSecondStepPublish:"November 1st 2021",dateEndThirdStepPublish:"December 31st 2021",dateEndFourthStepPublish:"March 21st 2022",dateEndFifthStepPublish:"May 20th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"8 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Dr. Shah obtained his Ph.D. degree in Veterinary Medicine from Hokkaido University, Japan. He was awarded the Young Science and Technology Award from the Nepal Academy of Science and Technology (NAST) in 2019. His research interests include infectious diseases, zoonotic infectious diseases, and vector-borne diseases.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"278914",title:"Ph.D.",name:"Yogendra",middleName:null,surname:"Shah",slug:"yogendra-shah",fullName:"Yogendra Shah",profilePictureURL:"https://mts.intechopen.com/storage/users/278914/images/system/278914.jpg",biography:"Dr. Yogendra Shah is a consultant microbiologist/virologist, senior research microbiologist, and lecturer at Seti Provincial Hospital, COVID-19 PCR laboratory, National Zoonoses and Food Hygiene Research Center, and Kathmandu College of Science and Technology, Nepal. 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1. Introduction
Confocal laser scanning microscopy (CLSM) is a useful image tool to study the fate of delivery systems and biomolecules applied into the skin. Through the use of fluorescence probes, it is possible to evaluate their behavior, like: i) interaction with the biological system [1]; ii) cellular uptake [2-3]; iii) depth of penetration [4]; iv) penetration routes into the skin [5]; v) quantification of skin penetration of drugs and biomolecules [6]; and vi) effect of topical therapies for several skin diseases by morphological analysis of the tissue [7].
In fluorescence microscopic terms, the skin tissue is difficult to investigate because it reflects and scatters incoming light, and melanin and other chromophores, significantly attenuate visible wavelengths [8]. CLSM has the potential to overcome these limitations, since it generates high-resolution imaging, non-invasive optical sectioning and three-dimensional reconstructions, in combination with sensitivity, selectivity and versatility of fluorescence measurements [1]. In this way, the expertise of the CLSM technique provides excellent opportunities for probing and better understanding the behavior and fate of pharmaceuticals in the skin, including in vivo monitoring of skin drug penetration, allowing a rational development of skin delivery systems.
This chapter intends to discuss important topics for pharmaceutical researchers related to the proper use of this technique to design and optimize skin delivery systems, as well as to diagnose skin diseases. The first topic will shortly comment about the skin structure for an easier understanding of the next ones. With the advent of nanotechnology, CLSM technique can be an imaging tool for better understanding of the fate of nanocarriers in delivering drugs into the skin layers and the efficacy of them in the therapies of different skin disorders. Furthermore, our previous experience in this technique [9-10] will allow a critical discussion on the main topics in this specific subject and to update the advances in this field application.
2. The skin structure
The human skin is the largest organ in the body that has many important physiological functions, such as, protection (physical, chemical, immune, pathogen, UV radiation and free radical defenses), major participant in thermoregulation, sensory organ and performs endocrine functions (vitamin D synthesis, peripheral conversion of prohormones) [11]. Its thickness varies from 0.05 mm to 2 mm and is composed of four main layers: the stratum corneum (SC) and the viable epidermis, the outermost layers; the dermis, and the subcutaneous tissue (Figure 1)[12].
Figure 1.
Schematic representation of skin structure and cell population. The skin comprises three main layers – the epidermis, dermis and hypodermis. The resident cell populations and various structures present throughout the skin allow for maintenance of an efficient barrier against water loss, and protection against threats such as ultraviolet radiation (UVR) and microbial pathogens. The blood and lymph vessels allow for the migration of immune cells in and out of the skin, so that the cell population of the skin is constantly in a state of flux, in response to the demands of the cutaneous inflammatory and immune systems. Reproduced from [13] with permission.
The SC is 10-20 μm thick, highly hydrophobic and contains 10-15 layers of interdigitated dead cells called corneocytes. It is the major barrier to penetration of drugs because its structure is highly organized [14]. Its “brick and mortar” structure is analogous to a wall. The corneocytes of hydrated keratin comprise the “bricks”, embedded in a “mortar”, composed of multiple lipid bilayers of ceramides, fatty acids, cholesterol and cholesterol esters. These bilayers form regions of semicrystalline, gel and liquid crystals domains. Most molecules penetrate through skin via this intercellular microroute and therefore many enhancing techniques aim to disrupt or bypass its highly organized structure [15].
The viable epidermis (~100-150 μm thick) is composed by multiple layers of keratinocytes at various stages of differentiation. Besides the keratinocytes, the epidermis also contain several other cells (melanocytes, Langerhans cells, dendritic T cells, epidermotropic lymphocytes and Merkel cells) and active catabolic enzymes (e.g. esterases, phosphatases, proteases, nucleotidases and lipases) [12].
The dermis is rich in blood vessels, nerves, hair follicles, and sebaceous and sweat glands. The elasticity of the dermis is due to the presence of collagen, elastin, glycosaminoglycans, collectively termed the extracellular matrix (ECM), as well as fibroblasts that elaborate the ECM. Dermal adipose cells, mast cells, and infiltrating leucocytes are also present in this skin layer [11-12].
3. Operational parameters for digital image capture for CLSM to assess the drug penetration into skin layers
3.1. Equipment characteristics
This technique is fluorescence-based image and offers greater resolution than fluorescence microscopy due to its point illumination and detection properties [16]. The illumination in a confocal microscopy is achieved by a collimated laser beam across the specimen [16-17]. This laser beam is reflected by a dichroic mirror and passes through the objective lens of the microscope in a focused manner on the specimen, which, and then, excites fluorescence probe in the sample. So, light is emitted at a longer wavelength which can come through the dichroic mirror and is again focused at the upper pinhole aperture (Figure 2) [16,18]. With CLSM, out-of-focus light (coming from places of the specimen above or below the focus) is cut off before the beam hits the electronic detector due to the addition of a spatial filter containing an aperture, – the pinhole or slit – the point detection. Just the light in-focus can pass through the pinhole (now termed confocal apertures), come to detector, and then form the image with more details because the blurring from out-of-focus has vanished. By using CLSM, it is possible to obtain high-resolution images (lateral, ~140 nm; axial, ~1 µm) from the samples, which increase to accuracy of the microscopic images [5,16-17].
Figure 2.
Schematic diagram of the principle of confocal laser scanning microscopy.
3.1.1. Measurement of different optical sections
Acquisition of several optical sections (x-y plane) taken at successive focal planes along the z axis is a usual practice to obtain a three-dimensional information from the skin that can be viewed as a simple image. Figure 3 shows the principle of z series acquisition.
Figure 3.
Confocal optical sectioning: (A) a schematic of a sample (hexagonal features) mounted on a microscope slide and covered with coverslip for a z series processing (sequential x-y sections as a function of depth (z); (B) confocal images of a z series through the porcine skin. Reproduced from [5] with permission.
Acquisition of x-z section is useful to obtain depth information from a specific surface. First, it is necessary to get an image in the x-y plane. Then, after choosing a region of interest, a horizontal line is drawn across this area in the z = 0 µm – x-y plane and is “optically sliced” through the digitalized image data of successive x-y sections; the results are (x-z planar) optical cross-sections (Figure 4).
Figure 4.
Confocal optical sectioning: (A) schematic of an x-z planar optical cross-section; (B) confocal x-z image of porcine skin. Reproduced from [5] with permission.
3.2. Sample processing techniques
The main goal of confocal microscopy is to explore the structure and structural relationship along the optical (z) axis as well in the x-y plane. For this, preservation of the tissues and cells during the preparation of the sample is necessary to obtain a reliable image. Optimum sample preparation is very dependent upon the cell or tissue type, the labeling technique and type of data to be collected [5,19].
Analyses by confocal microscopy can be done with living or preserved (fixed) samples. Working with living samples, is possible to analyze dynamic events and avoid some of the artifacts that can be introduced with preservation techniques and processing of the sample. It is easier to work with preserved samples since it does not have to concern about keeping the cells or tissue alive but; however, the impossibility to observe dynamic events and the presence of artifacts make this routine less attractive [19].
For skin samples, little or no sample preparation is necessary to visualize skin structures and the localization of fluorescent probes used within the tissue. Hence, the technique is rapid, with minimal physical perturbation or damage to the tissue. CLSM offers nearly accurate images with few artifacts contributing significantly in topical/transdermal permeation studies to elucidate and understand the mechanisms and pathways of drug penetration using different delivery technologies [5,19].
3.3. Main fluorescence probes used to assess skin penetration
Fluorescence is an important optical readout mode in biological confocal microscopy due to its sensitivity and specificity. These features can be influenced by the capability to tag onto biological systems to show your localization and by the local environment [20].
In some situations in which the drug carried by the delivery system studied has fluorescence properties, the use of a specific fluorophore is not necessary, once the drug can be excited and it emits fluorescence signals that are captured and transformed into image [9-10, 21].
Several fluorophores are used in skin visualization. The Table 1 shows the main used.
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tObjective of study\n\t\t\t
\n\t\t\t
\n\t\t\t\tFluorescence probe\n\t\t\t
\n\t\t\t
\n\t\t\t\tλexc/λem (nm)\n\t\t\t
\n\t\t\t
\n\t\t\t\tReference\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
Visualization and suggested mechanism of interaction between liposome-skin
\n\t\t\t
β-carotene and 5(6)carboxyfluorescein
\n\t\t\t
514/660-1250; 488/515-535
\n\t\t\t
[22]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation of composition and preparation methods of vesicles in skin penetration
\n\t\t\t
β-carotene and 5(6)carboxyfluorescein
\n\t\t\t
514/660-1250; 488/515-535
\n\t\t\t
[23]
\n\t\t
\n\t\t
\n\t\t\t
Penetration and distribution of lipophilic probes in the hair follicle
\n\t\t\t
Bodipy FL C5, Bodipy 564/570 C5 and Oregon Green
\n\t\t\t
488 /514; 564/574; 488/514
\n\t\t\t
[24]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of influence the use low-frequency ultrasound in skin penetration
\n\t\t\t
Calcein
\n\t\t\t
495/515
\n\t\t\t
[25]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation the composition of liposomes in skin penetration behavior
\n\t\t\t
Calcein , NBC-PC1 and Rhodamine B
\n\t\t\t
488/510; 488/530; 568/590
\n\t\t\t
[26]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of penetration and distribution of nanoparticles in the skin treated with microneedles
\n\t\t\t
Coumarin-6
\n\t\t\t
488/
\n\t\t\t
[27]
\n\t\t
\n\t\t
\n\t\t\t
Visualization the ability of cell penetrating peptides to improve skin penetration
\n\t\t\t
DID-oil
\n\t\t\t
644/665
\n\t\t\t
[28]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation the effect of polymeric nanoparticles with surface modified with oleic acid in skin permeation
\n\t\t\t
DiO
\n\t\t\t
484/501
\n\t\t\t
[29]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation of skin penetration of nano lipid carries with surface modified by polyarginine
\n\t\t\t
DiO
\n\t\t\t
484/501
\n\t\t\t
[30]
\n\t\t
\n\t\t
\n\t\t\t
Investigate the effect of pore number and depth on rate and extension of drug delivery through the skin using a novel laser microporation technology
\n\t\t\t
FITC2\n\t\t\t
\n\t\t\t
488/520
\n\t\t\t
[31]
\n\t\t
\n\t\t
\n\t\t\t
Influence and elucidation of the transport pathway of solute-water skin penetration with use low-frequency sonophoresis
\n\t\t\t
FITC-dextran and Rhodamine B
\n\t\t\t
488/520; 568/590
\n\t\t\t
[32]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation of microneedle shape by visualization of skin penetration of fluorescent dye
\n\t\t\t
Fluorescein
\n\t\t\t
488/515
\n\t\t\t
[33]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of skin penetration pathways of a novel micelle formulation
\n\t\t\t
Fluorescein
\n\t\t\t
505/530
\n\t\t\t
[34]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation the ability of dendrimers to facilitate transdermal drug delivery in vivo\n\t\t\t
\n\t\t\t
Fluorescein-PAMAM
\n\t\t\t
488/<560
\n\t\t\t
[35]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of nanoparticles deposition in a skin
\n\t\t\t
Fluospheres®\n\t\t\t
\n\t\t\t
405/420-480; 488/505-530; 633/647-754
\n\t\t\t
[36]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation the influence of a liposome surface charge in skin penetration
\n\t\t\t
NBD-PC and Rhodamine B
\n\t\t\t
488/530; 568/590
\n\t\t\t
[37]
\n\t\t
\n\t\t
\n\t\t\t
Visualization the effect of heat on skin permeability
\n\t\t\t
Nile red
\n\t\t\t
543/630
\n\t\t\t
[38]
\n\t\t
\n\t\t
\n\t\t\t
Visualization the behavior of microemulsion formulation in the skin stratum corneum
\n\t\t\t
Nile red
\n\t\t\t
543/630
\n\t\t\t
[39]
\n\t\t
\n\t\t
\n\t\t\t
Visualization the behavior of polymeric particles for drug delivery to the inflamed skin
\n\t\t\t
Nile red
\n\t\t\t
543/595
\n\t\t\t
[40]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of skin transport properties of model as a carrier for oligodeoxynucleotide (ODN) during iontophoresis
\n\t\t\t
Oregon Green and TAMRA
\n\t\t\t
488/505; 543/560
\n\t\t\t
[6]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation the interaction of quantum dots nanoparticles in the skin
\n\t\t\t
Quantum dots
\n\t\t\t
351, 364, 488/610-632
\n\t\t\t
[41]
\n\t\t
\n\t\t
\n\t\t\t
Visualization the skin penetration behavior of ethosomes
\n\t\t\t
Quantum dots (CdTe)
\n\t\t\t
488/<560
\n\t\t\t
[42]
\n\t\t
\n\t\t
\n\t\t\t
Investigate skin penetration of quantum dots in human skin
\n\t\t\t
Quantum dots (CdTe)
\n\t\t\t
800/
\n\t\t\t
[43]
\n\t\t
\n\t\t
\n\t\t\t
Evaluation of ultrasound and sodium lauryl sulfate for increase transdermal delivery of nanoparticles
\n\t\t\t
Cationic, neutral, and anionic quantum dots (CdSe/ZnS)
\n\t\t\t
\n\t\t\t
[44]
\n\t\t
\n\t\t
\n\t\t\t
Visualization of ethosome penetration in skin and mechanism of action study
\n\t\t\t
Rhodamine 6 GO
\n\t\t\t
543/<560
\n\t\t\t
[45]
\n\t\t
\n\t\t
\n\t\t\t
Exploration of the three-dimensional structure, organization and barrier function of the stratum corneum ex vivo and in vivo after transfersome permeation
\n\t\t\t
Rhodamine-DHPE3, Texas Red-DHPE4 and fluorescein-DHPE5\n\t\t\t
\n\t\t\t
543/590; 583/601; 496/519
\n\t\t\t
[46]
\n\t\t
\n\t
Table 1.
Main fluorescence probes used to assess skin penetration
Unfortunately, there is not a comprehensive list of available fluorophores that is selective for a particular parameter of the cell and that also has a suitable wavelength. The choice of an ideal fluorophore is not a simple task; however, it is possible to realize the choice of the more appropriate fluorophore within a range of wavelengths. This selection is based on the objectives of the study taking into account the physicochemical properties of the fluorophore as well as the characteristics of the sample.
In the case of skin, various endogenous substances (Table 2) can interfere with analyzes because they are excited at a wavelength in the excitation band of the chosen fluorophores.
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tFluorochrome\n\t\t\t
\n\t\t\t
\n\t\t\t\tλexcitation (nm)\n\t\t\t
\n\t\t\t
\n\t\t\t\tλemission (nm)\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
Tryptophan
\n\t\t\t
295
\n\t\t\t
345
\n\t\t
\n\t\t
\n\t\t\t
Tyrosine
\n\t\t\t
275
\n\t\t\t
300
\n\t\t
\n\t\t
\n\t\t\t
Phenylalanine
\n\t\t\t
260
\n\t\t\t
280
\n\t\t
\n\t\t
\n\t\t\t
Melanin
\n\t\t\t
330-380
\n\t\t\t
400-700
\n\t\t
\n\t\t
\n\t\t\t
Keratin
\n\t\t\t
375
\n\t\t\t
430
\n\t\t
\n\t\t
\n\t\t\t
Collagen
\n\t\t\t
335
\n\t\t\t
390-405
\n\t\t
\n\t\t
\n\t\t\t
Elastin
\n\t\t\t
360
\n\t\t\t
460
\n\t\t
\n\t\t
\n\t\t\t
FAD
\n\t\t\t
390
\n\t\t\t
520
\n\t\t
\n\t\t
\n\t\t\t
NADH
\n\t\t\t
290, 364
\n\t\t\t
440, 475
\n\t\t
\n\t\t
\n\t\t\t
NADPH
\n\t\t\t
336
\n\t\t\t
464
\n\t\t
\n\t\t
\n\t\t\t
Flavoprotein
\n\t\t\t
450-490
\n\t\t\t
500-560
\n\t\t
\n\t\t
\n\t\t\t
Porphyrins
\n\t\t\t
476
\n\t\t\t
625
\n\t\t
\n\t
Table 2.
Endogenos substances responsible for the skin’s autofluorescence [5]
To minimize autofluorescence of the skin, configuration settings of fluorescence detection must be made so that it can separate the fluorescence signal of the skin from that of the used fluorophore, avoiding any interference of the specimen [43]. The imaging using dual-channel and subsequent overlapping of images obtained at each channel is a resource used for reducing potential problems with autofluorescence from the sample [5]. Another resource is the choice of fluorophores with distinct emission spectra from those presented by endogenous fluorophores of the skin [47].
Some fluorophores can be covalently bound to biomolecular targets as well as the components of formulations without changing their spectral properties. The wide variety of derivatives of rhodamine and fluorescein, both well-known dyes, has been used with this subject, which result in greater functionality of these dyes [20].
Rhodamine is a lipophilic dye and because of its physicochemical property it is widely used to evaluate the dynamic properties of living cells such as membrane potential and ion concentration. It is also used to study the behavior and mechanisms of permeation of lipid delivery systems like liposomes. It can be incorporated into the lipid bilayers promoting a marking and also mimicking lipophilic drugs such as betamethasone, since they both exhibit a similar partition coefficient (log P). In many cases, it is associated with a hydrophilic dye as, for example, calcein, which is expected to be encapsulated in the aqueous compartment of the liposome, promoting marking and functioning as a model for hydrophilic drugs that can be carried by this type of delivery system [26,37].
On the other hand, fluorescein has a wide use due to its ability to react with many substrates. One example of this, it is its conjugation with dendrimers, which enables the study of the behavior and properties of dendrimer on skin permeation [35].
Moreover, for a successful CLMS analysis of the skin samples, the fluorophores probe must present: (i) good quantum efficiency and persistence of the signal sufficient for the instrument to achieve image data; (ii) selectivity for the target molecule; (iii) high resistance to bleaching; (iv) minimal perturbation to the sample; (v) minimizing the cross-talk when multiple fluorophores are being used together. Another important consideration is that the fluorescence intensity of depth images depends on both the sample transparence or opacity and the interference of the fluorescence of upper layers with the fluorescence signal of the adjacent layers [1]. Instrumentally, fluorescence emission collects can be improved by careful selection of objectives, detector aperture dimensions, dichromatic and barrier filters, as well as keeping the optical train in precise alignment [48].
3.4. Non–invasive assessment
Noninvasive optical techniques, such as CLSM, have become widely used in recent years because they are an efficient tool for skin characterization, as additional technique or even as a replacement for invasive biopsies of skin [49].
In this technique, images are obtained by scanning the specimen with one or more focus of beams of light. The images produced by this way are called optical section. This term refers to the method of collecting images noninvasively, which uses light to section the sample out instead of performing mechanical sections [17]. Due to this important characteristic, CLSM has been used in dermatology diagnosis by assessing the skin in vivo, where a comparison is made between the healthy skin aspect with the pathological state of living human skin [50-53].
Two modes are established for the use of in vivo CLSM: reflectance and fluorescence modes. The reflectance mode is based on detection of own endogenous contrast by refractive indices of several cellular structures, like melanin or keratin, which have both a high refractive index. Generally, a laser with near-infrared wavelengths is used for this type of measurements. Backscattered in-focus signals are captured and transformed for the image visualization [50-51]. The fluorescence mode is based on detection of the distribution of an exogenous dye administrated before the measures. A laser beam with visible-light wavelengths is used to excite selectively the dye to produce contrast. Backscattered fluorescence signals are captured and transformed for the image visualization [50]. They are both optical non-invasive techniques that permit in vivo and ex vivo (biopsy) images without the fixing, sectioning and staining, which are common procedures for histology analysis [54].
CLSM facilitates the image of living specimens, provides data from the three-dimensional structure of the sample and improves the image resolution due to high sensitivity, selectivity and versatility in fluorescent measures resulting in a valuable opportunity to study the behavior of pharmaceutical systems.
4. Advantages and disadvantages of using CLSM as a tool in skin delivery studies
Skin delivery research involves determination of physicochemical parameters of the formulation and in vitro and in vivo release studies. The fate of the drug/carrier into the living skin, either in healthy or pathological condition, is very important information; and CLSM can be a useful tool for development of cosmetic and dermatological products. The main advantages for studies with these aims include: (i) the ability to obtain images in a noninvasive manner in vitro and in vivo conditions [5]; (ii) suitable for in vivo diagnosis through imaging of superficial skin layers [49]; (iii) same skin site can be imaged serially over time that is an important advantage in studies like hair follicle neogenesis following wounding where the analysis are processed directly on the skin and information about number, length and width of follicles neogenics are obtained concurrently and from one animal [55]; (iv) ability to produce serial thin (0.5 to 1.5 µm) optical sections without mechanical sections and preserving the skin structure [5,16,47]; (v) eliminates artifacts that occur during physical sectioning and staining of sample; (vi) visualization of images at multiple depths through the sample without mechanical sections which allows the visualization of the skin layers where the drugs appropriately marked or dyes were able to achieve after their topical application [5,48]; (vii) ability in monitoring the skin penetration of drugs and delivery systems appropriately marked or dyes in real time, enabling, in this ways, bioavailability studies. This is done by performing analysis immediately after topical application of the dyes, for example, and after certain periods of time. Immediately after application, the dye was observed only in the stratum corneum, distributed in the intercellular spaces of the first layer of skin. In all other times analyzed; it is possible to assess whether the dye could penetrate the skin and if succeeded, the depth achieved [49]; (viii) high-resolution images; (ix) three-dimensional reconstruction from a series of optical sections at different depths; (x) sensitivity; (xi) versatility and selectivity florescence measures; (xii) reduced blurring of the image from light scattering and improved signal-to-noise that resulting in improvement of contrast and definition; (xiii) more precise quantification of images using image analysis software; (xiv) allows to analyze of fixed and live samples under a variety of conditions and with greater clarity [1,5,16,18,47-48].
However, the limitations of fluorophores and equipments as well as inherent interference of the tissue, bring some disadvantages of CLSM technique, such as: (i) limited number of excitation wavelengths available with common lasers; (ii) possibility to cause damage to living cells and tissues due to high intensity laser irradiation; (iii) autofluorescence of skin samples which may interfere in the analysis; (iv) problems in adjusting the focus on the skin surface due to cellular components and hydration state of the skin; (iv) limited special resolution that interfere significantly in vivo analyzes in dermatological practice where the maximum depth of analysis is around 200 µm, allowing diagnosis of only superficial skin disorders; (v) slow scanning laser action for high quality images; (vi) quantification in concentration terms since it is possible just when the relationship between probe and the fluorescence emission is linear and when this signal is not attenuated differently for each depth localization in the sample; (vii) high cost of acquisition and operation of systems for confocal microscopy [1,5,16,18,48,53].
5. CLSM technique for assessment of drug and delivery systems penetration into the skin and diagnosis of skin disorders
5.1. CLSM technique for assessment of drug and delivery systems penetration into the skin
CLSM is a valuable technique that helps to elucidate the mechanism, depth and distribution of skin penetration for delivery systems loaded or not with drugs. Nowadays, there is an increasing attempting to determine the possible penetration pathways because, with this information in hand, researchers are able to propose some structural modifications in the delivery systems, aiming to improve the skin penetration into the target tissue and, consequently, the pharmacological action of the drug [56]. Furthermore, penetration pathway elucidation is useful to address toxicological issue, since the fate of some formulations, mainly non-biodegradable nanoparticles, can be harmful to the body [44].
The emission of fluorescence by the delivery system and/or the drug is a condition when using CLSM for assessment of drug and delivery systems penetration into the skin. For that, the delivery systems are made fluorescent using probes covalently linked to polymers or homogeneously distributed in the system. In the case of the drugs, some studies link them to probes or, in many cases; they use a model probe with similar physicochemical properties of the drug. The mechanism of skin penetration from flexible polymerosomes, vesicles composed by polymers, was determined through the in vitro cutaneous penetration of vesicles containing Nile Red as the model probe. The skin samples were stained with fluorescein prior to fixation. The images (Figure 5) showed a time-dependence penetration into the skin, as initially, the particles tended to penetrate between the corneocytes isles (green stained) and, further application time, the vesicles tended to penetrate via intercellular lipids and follicular regions (red stained) until a maximum depth of 60 µm [57]. CSLM showed that a fluorescein loaded micelle formulation penetrate via follicular pathway and accumulate in epidermis up to a depth of 40 μm [34].
Figure 5.
CLSM images showing distribution of polymer vesicles in cadaver epidermis with increasing durations: (a) 2 h, (b) 4 h, (c) 6 h, (d) 8 h and (e) 24 h. Inset depicts the accumulation of free Nile Red in the inter-corneocyte spaces. Being lipophilic in nature, the dye is seen to accumulate excessively in intercellular spaces (red stained). Vesicles initially (a) show localization in the ‘furrows’ between corneocyte groups (green stained) followed by the distribution in follicular (d) and intercellular spaces (e) (scale bar = 200 µm). Reproduced from [57] with permission.
It is possible to obtain CLSM images by sequential excitation using dual-labeled delivery systems, i.e., systems formulated with two different types of probes aiming to elucidate their mode of action. After ex vivo skin delivery, by sequential excitation of dual-labeled liposomes and vesicles carrying diclofenac, it was possible to show that the vesicles penetrated intact down to the epidermis and the fluorescence intensity was higher and predominantly accumulated in the inter-corneocytes spaces [22]. Teixeira et al. (2010) [58], taking the advantage that the vitamin A (retinyl palmitate) fluoresces due to the presence of cromophore in its structure, obtained dual-labeled images after in vitro studies using elastic polymeric nanocapsules, marked with Nile blue, carrying the vitamin. The images (Figure 6) showed that the nanocapsule did not penetrate the skin carrying the vitamin, but a deep permeation (around 30 µm) of both was observed, which suggests that the drug present in deep skin layers was released from nanocapsules in the superficial skin layers. The uniform permeation of both labels suggests an intercellular permeation as the main mechanism for this type of nanocapsules. The visualization of dually labeled nanoparticles by the combination of multiphoton and CLSM in human skin biopsies showed that the polymeric nanoparticles did not penetrate the skin, whereas the dye Texas red, used to mimic a drug loaded in the nanoparticles, slowly penetrated the skin up to the stratum granulosum [59].
Figure 6.
Confocal images of Nile blue-PLA nanocapsules. (a) xy image of the skin surface (initial fluorescence) and (b) deeper layers into skin, cross-sectional (xz mode) image. Red fluorescence, polymeric shell; green fluorescence, retinyl palmitate. Reproduced from [58] with permission.
Besides to determine the penetration in different skin layers, it is also possible to determine in which cellular site the drug preferentially penetrates. In this situation, Borowska et al. (2012) [35] stained skin sections with special dyes of high affinity to the nucleus before the in vitro skin penetration study using 8-MOP in formulations containing polyamidoamine dendrimers, conjugated with the probe fluorescein. CLSM images (Figure 7) revealed that 8-MOP encapsulated in dendrimers was able to penetrate into deep skin layers, epidermis and dermis, compared to standard formulation. In addition, the probe fluorescence (fluorescein) presented in the nucleus (blue stained) revealed that 8-MOP accumulated mostly in this cellular site important for its phototherapeutic activity.
Figure 7.
Distribution of 8-MOP (green) in rat’s skin samples obtained by confocal microscopy following skin application of tested 8-MOP formulation. Cellular nuclei were counterstained with 7 AAD (blue): (a) 8-MOP after 1 h; (b) 8-MOP after 2 h; (c) 8-MOP-G3 PAMAM after 1 h; (d) 8-MOP-G3 PAMAM after 2 h; (e) 8-MOP-G4 PAMAM after 1 h; (f) 8-MOP-G4 PAMAM after 2 h; (g) 8-MOP-G4 PAMAM after 2 h – all skin layers and subcutaneous fatty tissue. Reproduced from [35] with permission.
In order to compare the skin penetration enhancement ability of different carriers and formulations, probes are visualized in the skin samples by CLSM after permeation studies. The fluorescence signal of a probe in liposome and transfersome containing valsartan was scanned at different skin depths after in vitro skin permeation studies. The results showed an increased skin penetration up to the dermis when valsartan was loaded in transfersomes, compared to rigid liposomes. The fluorescence liposomes was visualized up to 50 µm, while transfersomes were assessed up to 150 µm with a high fluorescence intensity and homogeneous skin distribution, evidencing the transdermal potential of transfersomes compared to liposomes [56]. CLSM showed an improved and homogeneous skin penetration of the probe rhodamine B in the role epidermis using β- cyclodextrin composite ethosomal gel carrying the drug clotrimazole compared to gel and ethosomal gel formulations [60]. The in vitro percutaneous permeation of ethosomes, containing the drug 5- fluorouracil and labeled with rhodamine 6GO, in human hypertrophic scar and normal skin was assessed by CLSM. The images (Figure 8) showed a higher fluorescence intensity throughout the hypertrophic skin than normal skin compared to hydroethanolic solution, evidencing that the skin penetration of ethosomes was superior than the control in this type of skin [45]. Labeled (Nile red) solid lipid nanoparticles loaded with tacrolimus also showed improved skin penetration in various layers of skin (in the order of 5–6 times) compared to control, an ointment formulation [61].
Figure 8.
CLSM images of cross-sections of (A) human skin after application of rhodamine 6GO–hydroethanolic solution; (B) human skin after application of rhodamine 6GO–ethosomes; (C) human hypertrophic scar after application of rhodamine 6GO–hydroethanolic solution; and (D) human hypertrophic scar after application of rhodamine 6GO–ethosomes for 24 hours. Each image represents a 500 μm × 500 μm area. Reproduced from [45] with permission.
CLSM is extensively used to elucidate whether or not non-biodegradable nanoparticles penetrate the skin. Aiming to investigate the distribution of this type of nanoparticles (20 nm and 200 nm), covalently linked with FITC, across excised porcine skin, and to elucidate the pathways of penetration into/through the cutaneous barrier, Alvarez-Roman et al. (2004) [62] obtained CSLM images showing that FITC-nanoparticles preferentially accumulated in the hair follicles and furrows that demarcate clusters of corneocytes (Figure 9). Furthermore, images from the xz plane showed that, independently of size, both nanoparticles did not penetrate the skin being localized at the furrows. Similar results were obtained by Campbell et al. (2012) [36] who verified polystyrene nanoparticles in the top layers of the stratum corneum up to a depth of 2-3 µm.
Figure 9.
Dual label x–y images of the skin surface subsequent to treatment with FITC-nanoparticles (20 nm) for (a) 0 min, (b) 1 h, and (c) 2 h. The green fluorescence in figures a and c corresponds to furrows where nanoparticles accumulated. Follicular localization of FITC-nanoparticles subsequent to application of nanoparticles (200 nm) for (d) 30 min, (e) 1 h, and (f ) 2 h. The white circles correspond to hair follicles. The red color corresponds from the fluorescence emanating from the porcine skin and the yellow-green from the FITC-nanoparticles. Reproduced from [62] with permission.
Photodynamic therapy studies take the advantage that the drugs (photosensitizers) used for cancer treatment self-fluoresce and CLSM technique is extensively used to assess the skin localization of the photosensitizer after in vitro topical application of a delivery system, once its skin penetration, besides other factors, is responsible for the success of the treatment. De Rosa et al. (2000, 2004) [9,63] verified by CLSM images (Figure 10) that the skin penetration and accumulation of protoporphyrin IX, an endogenously photosensitizer obtained from 5-aminolevulinic acid (5-ALA) by the biosynthetic pathway of heme, depended on the presence of penetration enhancer substance in the vehicle and 5-ALA derivative used. The presence of 5-ALA in ethosomes influenced the penetration depth and fluorescence intensity obtained from protoporphyrin IX after in vivo skin penetration [64]. CLSM also confirmed the improved biodistribution of a photosensitizer, zinc pthalocyanine tetrasulfonate, when using a microemulsion [10].
Figure 10.
CLSM images of mechanical cross sections of hairless mouse skin (perpendicular series), optically sectioned 10 µm below the cutting surface: (a) control (untreated skin); skin treated with: (b) 10% 5-aminolevulinic acid (w/w); (c) 20% Dimethylsulphoxide (DMSO) (w/w); (d) 10% 5-aminolevulinic acid + 10% DMSO; and (e) 10% 5-aminolevulinic acid (w/w) + 20% Dimethylsulphoxide (w/w). All applied formulations contained 3% EDTA (w/w). Compared to controls (Figure 10 (a)), increases of red fluorescence in skins treated with 5-aminolevulinic acid (Figure 10 (b)), Dimethylsulphoxide (Figure 10 (c)), or with associations of these substances (Figures 10 (d) and 10 (e)), can be clearly seen as points with intense red fluorescence, indicating a PpIX accumulation. The association of 10% 5-aminolevulinic acid + 20% DMSO provided higher accumulation of PpIX, being considered more adequate for topical photodynamic therapy. Reproduced from [9] with permission.
CLSM is also used to assess the skin penetration of drugs after pre-treatment of the skin by physical methods, such as, sonophoresis, iontophoresis, microneedles and laser. Zhang et al. (2010) [27] showed that the pre-treatment of skin with microneedles permits the skin penetration of PLGA nanoparticles in the epidermis and dermis, and this would benefit a sustained drug release in the skin, supplying, in this way, the skin with drug over a prolonged period. The depth of skin penetration from the drug heparin (FITC-labeled) was assessed in vitro in skins that were previously subjected to pretreatment using enhancement strategies by physical methods, such as sonophoresis, iontophoresis and microneedles. The study showed (Figure 11) that microneedles pretreatment was the only enhancement strategy that permitted heparin to reach the epidermis and deeper dermal layers, since FITC-labeled heparin was observed to follow microchannels formed by the microneedle device [65]. The influence of the low fluence fractional laser on the penetration of high molecular weight model drug, a polypeptide, FITC and FITC-labeled dextran (MWs of 4 and 150 kDa), was assayed by CSLM, whose image of skin showed a fluorescence increase in the upper and middle dermis, as well as in hair shafts and hair sheaths, evidencing a transfollicular route for high molecular weight substances [66].
Figure 11.
CLSM (X10 objective) of the permeation of FITC-labeled heparin across hairless rat skin at various depths from the surface of the stratum corneum. Heparin transported across microchannel can be seen as a florescent green color up to a depth of 500 µm. Reproduced from [65] with permission.
5.2. Non–invasive method for diagnosis of skin disorders and diseases
As mentioned before, CLSM can be used in fluorescence and reflectance modes for studies of skin disorders and diseases. It allows optical en face sectioning with quasihistological resolution and good contrast within living intact human tissue. The resolution allows for imaging of nuclear, cellular and tissue architecture of epidermis and the underlying structures, including connective tissue, inflammatory infiltrates, tumour cells, capillaries, and even circulating blood cells, without a biopsy [67].
Technically, confocal microscopes used in dermatology are not very different from their counterparts used in basic sciences. Typically, confocal microscopy employs lasers as sources of illumination due to their capability to generate monochromatic, coherent beams [54]. Confocal reflectance microscopy (CRM) in dermatology uses a near-infrared laser at 830 nm operating at a power of less than 20 mW, which is harmless for the tissue [67], whereas fluorescent confocal microscopes use He/Ne, Kr or Ar lasers to illuminate fluorescent samples in a wavelength range of 400-700 nm [68].
The company Lucid Inc. (Rochester, NY, USA) introduced in 1997 the first generation confocal microscope called VivaScope 1000. This microscope had a bulky configuration impeding convenient attachment to certain anatomical areas and imaging was very time consuming. The most widely used confocal microscope for imaging of human skin, the VivaScope 1500, was commercialized in the year 2000. This device is considerably smaller, more flexible and portable in contrast to its stationary predecessor. Further developments are the VivaScope 3000, a handheld confocal microscope for imaging of difficult to access areas, and the ex vivo VivaScope 2500 designed for imaging of excised tissue, especially in Mohs surgery as well as Vivascope 1500 Multilaser, which combines fluorescence laser scanning microscopy and confocal reflectance microscopy (CRM) modes [7].
In dermatology, CRM has several uses, such as: i) to diagnose diseases non-invasively in vivo [69-72]; ii) to non-invasive monitoring of treatment response in vivo and permits early detection of subclinical disease [73]; iii) to improve the accuracy of clinical diagnosis [74]; iv) to improve clinical discrimination between benign and malignant lesions [75]; v) to evaluate the same skin site over time because it produces no tissue damage [76]; vi) to assess the boundaries of the lesion pre- or post-surgery [54]; vii) to evaluate the dynamics of structural and cellular changes that take place during the occurrence of the disease [77]; and viii) to study physiopathologic processes non-invasively over time [78-79].
Despite the benefits, CRM has some limitations, such as: i) not all lesions image well; ii) subject motion occurs frequently; iii) the depth of imaging is limited at present to only 200 to 500 µm (epidermis and superficial dermis); iv) the presence of refractive structures may also decrease contrast and make melanocyte visualization difficult; v) lesions with a thick epidermis or certain anatomic sites, such as the palm or sole, will image very superficially; vi) images are viewed in horizontal sections rather than vertical sections (as in conventional histopathology), which makes direct comparisons difficult and requires experience to properly interpret images [67,74]; and vii) the relative high cost of CRM (approximately $50,000). Although the upfront cost of the device is high, the supplies to image individual lesions cost only about $1 per lesion, allowing imaging of multiple lesions with minimal increased cost to the patient per lesion [80].
Recently, there was a formation of an international CRM group (www.skinconfocalmicroscopy.org) constituted by clinicians who use this technique for diagnosis and monitoring, researchers and experts in many aspects of CRM to dermatology. By providing a forum for free communication of results, the establishment of meaningful collaborations and formation of trained personnel, this group aims to spread the use of CRM in dermatology [54].
5.2.1. CRM to diagnose melanocytic lesions
Langley et al., in 2001 [74], were the first to publish a study of benign and malignant melanocytic lesions by in vivo confocal microscopy. In the study, they discovered apparent differences in the CRM characteristics of nevi, dysplastic nevi, and melanomas (Figure 12), indicating that this tool could be helpful in the clinical discrimination of benign and malignant melanocytic lesions. The diagnostic applicability of CRM in melanocytic skin tumours, determined by evaluating sensitivity, specificity, positive and negative predictive value was first described by Gerger et al. in 2005 [81]. In 2006, Langley et al [75] determined that CRM has sensitivity and specificity compared to dermoscopy for the diagnosis of melanoma.
Figure 12.
Clinical photograph of superficial spreading melanoma, invasive to anatomic level II, with a measured depth of 0.44 mm on the right posterior shoulder of 47-year-old man. This lesion was clinically diagnosed as dysplastic nevus, but by CLSM showed features consistent with melanoma. b-d, Confocal images of melanomas. Key features noted in melanomas as viewed by CLSM were loss of keratinocyte cell border (asterisks in B-D); bright, granular, highly refractile particles (arrowheads in b); and atypical stellate cells (arrows in b and c). Scale bar = 25 μm. e, Histological section of intraepidermal component of superficial spreading malignant melanoma showing confluence of tumor cells at dermoepidermal junction as well as individual cells of varying levels of the epidermis, so-called “pagetoid” spread (asterisks). Scale bar = 50 μm. Reproduced from [74] with permission.
Benign nevi can be differentiated from melanomas by CRM and criteria proved to be valuable [81-84]. CRM features can distinguish lentigo maligna (Figure 13) from benign macules of the face such as solar lentigo, ephelis, actinic keratosis, and flat seborrheic keratosis [70,75,85].
Figure 13.
A, CRM image of control skin at the stratum spinosum displaying well-organized keratinocyte cell borders. B, CRM image of lentigo at the level of the stratum spinosum. The well-organized honey combed pattern of keratinocyte cell borders is preserved. C, CRM image of lentigo maligna at the stratum spinosum level. There is loss of keratinocyte cell borders, and a grainy image is noted. (Scale bar, 50 µm.). Reproduced from [75] with permission.
Even non-melanotic lesions can be recognized by CRM because of the presence of melanosomes and melanin granules in their cytoplasm [70,86-87].
5.2.2. CRM and non–melanocitic lesions
The clinical diagnosis of actinic keratosis by CRM (Figure 14) was first determined by Aghassi et al. (2000) [78]. Later, others confirmed the usefulness of this tool [73,88-89], although it has not been able to unequivocally differentiate actinic keratosis from squamous cell carcinomas [90].
Figure 14.
Left column is conventional histopathology of actinic keratosis (AK), center is CRM of AK, and right column is CRM of adjacent normal skin. Sections were obtained by means of conventional histopathology of AK (hematoxylin-eosin stain; original magnification ×20; 0.4 numerical aperture, dry objective lens), CM of AK (×30, 0.9 numerical aperture, water immersion objective lens, scale bar = 25 μm),and CM of adjacent normal skin (original magnification ×30, 0.9 numerical aperture, water immersion objective lens, scale bar = 25 μm). SC, Stratum corneum. Irregular hyperkeratosis of AK is evident on conventional histopathology and CRM, contrasting with smooth surface of normal skin. SG, Stratum granulosum. Conventional histopathology and CRM demonstrate uniform, evenly spaced, broad keratinocytes both in AK and normal skin. In CRM images, nuclei appear dark in contrast to bright, refractile cytoplasm. SSp., Stratum spinosum. Conventional histopathology and CRM of AK show enlarged, pleomorphicnuclei with haphazard orientation, contrasting with small, uniform, evenly spaced nuclei from normal skin. SB, Stratum basale. Conventional histopathology and CRM of AK show enlarged, pleomorphic nuclei with haphazard orientation, contrasting with small, uniform, evenly spaced nuclei from normal skin. In CRM images, dermal papillae appear as well-demarcated, dark holes in epidermis (arrowheads), containing blood vessels (arrow). Reproduced from [78] with permission.
CRM offers a sensitive and specific tool for the noninvasive diagnosis of basal cell carcinoma in vivo (Figure 15) [69,91-92] showing good correlation with histology.
Figure 15.
Real-time in vivo confocal images of a basal cell carcinoma (BCC) showing a uniform population of elongated cells (arrows, A and B) oriented along the same principal axis (x-y). Mononuclear cells (black arrowheads, B) are seen intermixed with BCC cells (arrows, B) with some of the BCC cells showing nucleoli (white arrowheads, B). C, High-power magnification of one BCC cell demonstrating a large elongated nucleus with a low refractility (asterisk), surrounded by a bright cytoplasm. (A-C, Hematoxylin-eosin stain; original magnifications: A, x30, 0.9 numerical aperture (NA) water-immersion objective lens; B and C, x100, 1.2 NA water-immersion objective lens. Scale bar, 25 µm). Reproduced from [91] with permission.
Non-melanoma skin cancers (NMSC) were evaluated by fluorescence confocal microscopy aiming to diagnose and monitor the lesions in reference to normal skin and correlation with routine histology. The results suggest that fluorescence confocal microscopy may allow a systematic, noninvasive histomorphometric evaluation of actinic keratosis and basal cell carcinoma, potentially aiding in the detection of subclinical actinic keratosis and early therapeutic management [93]. CRM was also evaluated to diagnose NMSC early and the authors concluded that in vivo CRM is a promising and innovative technology for the early diagnosis of skin cancer, which may ultimately play an important role in skin cancer screening and prevention as well as in the early detection of progression or recurrence after therapy [94].
5.2.3. CRM and other skin diseases
In vivo CRM resolves changes at the cellular and subcellular level comparable to that obtained with standard histology. In this case, this tool was successfully used to evaluate the dynamics of structural and cellular changes that take place during the occurrence of allergic contact dermatitis [77]. Furthermore, it is a promising tool for dynamic, noninvasive assessment and may help to differentiate acute and induced contact dermatitis (Figure 16) [76,79].
Figure 16.
Features common to allergic contact dermatitis and irritant contact dermatitis observed with reflectance confocal microscopy CRM and correlated by routine histology. a, Spongiosis: increased intercellular brightness apparent on CRM. b, Inflammatory cell infiltrate: bright structures 12- to 15-µm size interspersed between keratinocytes. Arrows denote inflammatory cells. c, Intraepidermal vesicle (arrow) formation: dark spaces in epidermis containing inflammatory cells and necrotic keratinocytes. Scale bars = 50 µm. H&E, hematoxylin-eosin stain. Reproduced from [76] with permission.
CRM was also used to investigate lesions of progressive macular hypomelanosis (Figure 17) showing that the ‘‘pigmented ring’’ is intact, but its melanin content is decreased compared with the surrounding normal skin [95].
Figure 17.
a) Lesion areas and (b) perilesional normal skin were observed under CRM. The ‘‘pigmented ring’’ around dermal papilla show completed, but compared with the surrounding normal skin its melanin content was decreased (the same horizon). Reproduced from [95] with permission.
Chiavèrini et al., 2001 [96] have shown crystalline deposits of cystine within the papillary dermis (Figure 18) by in vivo CRM, in patients with infantile cystinosis. The study concluded that CRM scoring of dermal cystine deposition could be used as a marker of treatment response complementary to the leukocyte cystine level.
CRM is a valuable tool to evaluate histological features of psoriasis (Figure 18), and the findings correlate with histology [97-99].
CRM imaging elucidates and monitors the dynamic pathophysiologic response, such as those that occur subsequent to laser treatment of cutaneous lesions, like cherry angiomas [78] and sebaceous gland hyperplasia [100-101]. Moreover, it facilitates the discrimination of sebaceous hyperplasia from basal cell carcinoma in vivo [102].
Noninvasive diagnosis of pemphigus foliaceus by CRM was consistent with the routine histology of the preexisting lesions [52] and criteria for diagnosing pemphigus (Figure 20) was established [72].
Diagnostic CRM criterion for mycosis fungoids was first determined by Agero et al. in 2007 [103] and later on confirmed by Lange-Asschenfeldt et al. (2012) [104].
Others diseases diagnosed by CRM in vivo were: discoid lupus erythematosus [105]; dermatophyte infections [106-108]; lichen sclerosus [109] and folliculitis [110].
Figure 18.
In vivo CRM of skin in infantile cystinosis. Confocal images of skin: block image of 4 x 4 mm. Numerous (+++) (A), few (++) (B), and some (+) (C) bright particles were seen around or in vessels (arrows) at level of dermis, in patients with cystinosis compared with control subjects (D). Reproduced from [96] with permission.
Figure 19.
CRM image (0.5 × 0.5 mm) of the basal layer: absence of papillary rings in plaque psoriasis skin (a), compared to normal skin taken on symmetrical anatomical sites (b). Reproduced from [98] with permission.
Figure 20.
Dilated blood vessels (thin arrows) containing highly refractive peripheral blood cells (thick arrows) in pemphigus vulgaris (a) and pemphigus foliaceous (b) (0.5 x 0.5 mm). Reproduced from [72] with permission.
6. Pathways involved in penetration of drugs and biomolecules in the skin
The SC is the outmost layer of the skin and provides an efficient barrier for the ingress of extraneous substances and egress of endogenous substances [111]. The packing of the lipids (ceramides, cholesterol and free fatty acids) present in the SC are responsible for this barrier as they are arranged surrounding the corneocytes forming a "brick and mortar" model with the corneocytes as the bricks and the lipids as the mortar [112].
Drugs and biomolecules can penetrate the skin to the viable tissue by three main routes: i) the intercellular (between the corneocytes, through the lipid matrix); ii) the transcellular (through the corneocytes); and iii) the shunt (through the skin appendages, like hair follicles, sweat ducts and sebaceous glands). Generally, the intercellular pathway is predominantly for most molecules, and the shunt pathway is important for polymers and colloidal nanoparticles and large polar molecules [15].
CLSM helps to elucidate not only the extension and depth of skin penetration of drugs and biomolecules [10,45] but also the main pathways involved in their penetration. In this sense, the use of CLSM with other physicochemical techniques such as Raman [113], infrared spectroscopy [39], differential scanning calorimetry and small- and wide-angle X-ray scattering [114], occlusive studies [115], dermatopharmacokinetic analysis [39] and transmission electron microscopy [116], help to further elucidate the exact drug/skin interaction, i.e., the mechanism of skin penetration. In addition, these techniques also permit to determine the skin interaction of delivery systems [34,117-120] and the skin modification promoted by physical methods [32,121-123], aiding to clarify the exact mechanism by which these techniques increase the skin penetration of drugs and biomolecules.
Interesting study was carried out with microemulsion containing Nile red dye, showing that this delivery systems lead to a dye accumulation between the coneocytes of the role stratum corneum, evidencing an intercellular penetration route (Figure 21). Moreover, to address whether the microemulsion penetrated the stratum corneum or not, a dermatopharmacokinetic analysis of the microemulsion components across this skin layer helped to clarify that every component diffuses separately [39].
Figure 21.
CLSM images of the microemulsion fluorolabelled with Nile red (a) and (b) Normal xy images, (c) Reconstructed xz image after sectioning through z-plane using one projection at 0⁰ angle (sectioned from 0 to 20 µm with 1 µm increments) and (d) z-stack image. Reproduced from [39] with permission.
Table 3 shows the main skin permeation pathways uncovered by CLSM studies using different delivery systems, probes and methods of application.
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t
\n\t\t\t
\n\t\t\t\tMain Permeation Pathways\n\t\t\t
\n\t\t\t
\n\t\t\t\tDelivery system/probe\n\t\t\t
\n\t\t\t
\n\t\t\t\tMechanism of skin application\n\t\t\t
\n\t\t\t
\n\t\t\t\tReference\n\t\t\t
\n\t\t
\n\t\t
\n\t\t\t
Follicular
\n\t\t\t
Ethosomes/β-carotene Micelle/Fluorescein Polystyrene nanoparticles/FITC Nanoparticles/5-fluoresceinamine Polypeptide, FITC and FITC-labeled dextran
Calcein dissolved in phosphate buffer Fluorescein isothiocyanate dextran dissolved in phosphate buffer
\n\t\t\t
Iontophoresis and high voltage pulsing Low-frequency sonophoresis
\n\t\t\t
[122] [32]
\n\t\t
\n\t\t
\n\t\t\t
Channels in the stratum corneum
\n\t\t\t
Elastic liposomes/Rhodamine red
\n\t\t\t
Passive delivery
\n\t\t\t
[124]
\n\t\t
\n\t\t
\n\t\t\t
Lacunar regions of the intercellular lipids and follicular
\n\t\t\t
Quantum dots (cationic, neutral and anionic)
\n\t\t\t
Ultrasound and sodium lauryl sulphate
\n\t\t\t
[44]
\n\t\t
\n\t\t
\n\t\t\t
Intercellular and follicular
\n\t\t\t
Flexible polymerosomes/Nile red
\n\t\t\t
Passive delivery
\n\t\t\t
[57]
\n\t\t
\n\t
Table 3.
Skin permeation pathways uncovered by CLSM studies
7. Future perspectives
The use of confocal microscopy in recent years has revolutionized many aspects in the pharmaceutical and medical sciences. It is an useful tool that aids to develop and improve delivery systems aimed to reach certain targets in the skin; it permits to elucidate the mechanisms of penetration of these systems in relevant skin models and to uncover the cellular uptake of drugs; and help to diagnose cutaneous pathologies. The ability to obtain non-invasively images with high resolution and sensitivity, at different depths of the sample and three-dimensional reconstruction are important advantages of this technique. The assessment of various delivery systems in vitro by CLSM provides a better characterization of their physicochemical properties and their behavior and the ability to view them ex vivo and in vivo allows us to elucidate their mechanisms in situ. With this, the rational development of topical delivery systems becomes more effective.
The use of CLSM in the reflectance mode to diagnose skin disorders is relatively recent and the challenge in this area is related to the presence of substantial clinical studies that compares morphological confocal features with histology and establishes their correlations, the formation of trained and experienced personnel and the cost of the equipment. Although its usefulness related to the collection of images fast and relatively ease, some limitations of the technique related to the equipment capability still limits its wide use in dermatology.
In this context, improvements in equipment, fluorescent probes and confocal microscopy techniques will enhance and promote new opportunities for studies involving skin, cosmetic and pharmaceutical products, since it is clear the important contribution of CLSM in this application area.
\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/43810.pdf",chapterXML:"https://mts.intechopen.com/source/xml/43810.xml",downloadPdfUrl:"/chapter/pdf-download/43810",previewPdfUrl:"/chapter/pdf-preview/43810",totalDownloads:5306,totalViews:936,totalCrossrefCites:2,totalDimensionsCites:17,totalAltmetricsMentions:0,impactScore:11,impactScorePercentile:98,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"July 11th 2012",dateReviewed:"January 29th 2013",datePrePublished:null,datePublished:"March 20th 2013",dateFinished:"March 19th 2013",readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/43810",risUrl:"/chapter/ris/43810",book:{id:"3538",slug:"confocal-laser-microscopy-principles-and-applications-in-medicine-biology-and-the-food-sciences"},signatures:"Fábia Cristina Rossetti, Lívia Vieira Depieri and Maria Vitória Lopes Badra Bentley",authors:[{id:"164997",title:"Prof.",name:"M. Vitoria",middleName:null,surname:"Bentley",fullName:"M. Vitoria Bentley",slug:"m.-vitoria-bentley",email:"vbentley@usp.br",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Sao Paulo",institutionURL:null,country:{name:"Brazil"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. The skin structure",level:"1"},{id:"sec_3",title:"3. Operational parameters for digital image capture for CLSM to assess the drug penetration into skin layers",level:"1"},{id:"sec_3_2",title:"3.1. Equipment characteristics",level:"2"},{id:"sec_3_3",title:"3.1.1. Measurement of different optical sections",level:"3"},{id:"sec_5_2",title:"3.2. Sample processing techniques",level:"2"},{id:"sec_6_2",title:"3.3. Main fluorescence probes used to assess skin penetration",level:"2"},{id:"sec_7_2",title:"3.4. Non–invasive assessment",level:"2"},{id:"sec_9",title:"4. Advantages and disadvantages of using CLSM as a tool in skin delivery studies",level:"1"},{id:"sec_10",title:"5. CLSM technique for assessment of drug and delivery systems penetration into the skin and diagnosis of skin disorders",level:"1"},{id:"sec_10_2",title:"5.1. CLSM technique for assessment of drug and delivery systems penetration into the skin",level:"2"},{id:"sec_11_2",title:"5.2. Non–invasive method for diagnosis of skin disorders and diseases",level:"2"},{id:"sec_11_3",title:"5.2.1. CRM to diagnose melanocytic lesions",level:"3"},{id:"sec_12_3",title:"5.2.2. CRM and non–melanocitic lesions",level:"3"},{id:"sec_13_3",title:"5.2.3. CRM and other skin diseases",level:"3"},{id:"sec_16",title:"6. Pathways involved in penetration of drugs and biomolecules in the skin",level:"1"},{id:"sec_17",title:"7. Future perspectives",level:"1"}],chapterReferences:[{id:"B1",body:'PygallS. RWhetstoneJTimminsPMeliaC. DPharmaceutical applications of confocal laser scanning microscopy: The physical characterisation of pharmaceutical systems. Advanced Drug Delivery Reviews 2007'},{id:"B2",body:'WhiteP. 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Biomaterials 2006'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Fábia Cristina Rossetti",address:null,affiliation:'
Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café, s/n, Ribeirão Preto, SP, Brazil
Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Avenida do Café, s/n, Ribeirão Preto, SP, Brazil
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1. Introduction
Sepsis is an enigmatic clinical condition that occurs when the patient’s body reacts adversely to infection and as a consequence develops organ dysfunction. Sepsis can practically affect all organ systems however, the organs involved and the degree of dysfunction varies distinctly among patients and can even lead to death in most cases [1, 2]. In the early stages of the disease, the treatment of sepsis seems to be relatively easy with the availability of broad-spectrum antibiotics [3]. While in the later stages of the disease, diagnosis of sepsis becomes much easier but extremely hard to treat. Therefore, early diagnosis of sepsis is the need of the hour for better clinical management [4].
Current manual assessment of sepsis using screening tools, like the Sequential Organ Failure Assessment (SOFA) score for ICU-patients, are complex in terms of measured clinical signs and even lack adequate sensitivity [5, 6]. On the other hand, AI and machine learning-based automated clinical decision support systems that use easily accessible clinical data have reflected a significant improvement in agreement with these treatment protocols in ICUs by guiding physicians through predefined work-flows [7, 8, 9, 10, 11]. In the current era wherein we have abundant availability of electronic medical records (EMRs) has brought more feasibility to such automated realizations [12]. However, almost every machine learning (ML)-based AI model and automated decision support system lack proper explainability because of their uninterpretable black-box nature [13, 14]. This is where Explainable Artificial Intelligence (XAI) comes in rescue to address some of these restrictions imposed by a Black-box AI system by adding explainability. And thus assist clinicians in the interpretation of their diagnosis, and recommend future actions to be taken thereby improving the quality of predictions [15, 16, 17]. The development of such an explainable ML framework for sepsis onset prediction is an important and active area of investigation.
This work presents a novel clinical application of developing an explainable ML framework for sepsis onset prediction among ICU patients based on the physiological medical knowledge of given clinical signs, obtained via extensive analysis, and using popular gradient boosting ML techniques. The framework’s design includes an optimal explainable gradient boosting architecture for clinical decision making that investigates questions of generalizability and interpretability of the proposed system.
2. Methods
An overview of the proposed methodology from raw data to explainable decision framework is shown in Figure 1.
Figure 1.
Graphical overview: From given raw clinical data to explainable decision framework. (a & b) clinical data from two ICU cohorts is imputed. (c) Physiological inter-relations and time lag differences are computed as features. (d) an optimal sepsis onset prediction architecture is developed using LightGBM models via bayesian optimization. (e) the predictions are rendered to explanations and potentially their predictive power is increased by refining the threshold that drove the prediction at every time-point. (f) Final decision.
2.1 Dataset and study population
The publically available training set consists of data from two cohorts [18]. Cohort A has 790,215 records of 20,336 patients. Cohort B has 761,995 records of 20,000 patients. Particularly, data for every patient record contains 40 clinical covariates i.e. 8 vital signs, 26 laboratory values, and 6 demographic values. The labeling of the patient data was done adhering to Sepsis-3 clinical criteria. Table 1 presents the details of various clinical covariates used under study together with their missing information in percentage [18, 19].
Sr. no.
Covariates
Missing values (%)
Units
1
Heart rate
9.8
beats/min
2
O2Sat
13
%
3
Temperature
66
oC
4
SBP
14.5
mm Hg
5
MAP
12.45
mm Hg
6
DBP
31.34
mm Hg
7
Resp
15.35
breaths/min
8
EtCO2
96.28
mm Hg
9
Excess bicarbonate
95.57
mmol/L
10
Bicarbonate
94.81
mmol/L
11
FiO2
91.66
%
12
pH
93.06
—
13
PaCO2
94.44
mm Hg
14
SaO2
96.54
%
15
Asparatate transaminase
98.37
IU/L
16
BUN
93.13
mg/dL
17
Alkaline phosphatase
98.39
IU/L
18
Calcium
94.11
mg/dL
19
Chloride
94.46
mmol/L
20
Creatinine
93.90
mg/dL
21
Direct bilirubin
99.8
mg/dL
22
Gl
82.89
mg/dL
23
Lactic acid
97.32
mg/dL
24
Magnesium
93.68
mmol/dL
25
Phosphate
93.98
mg/dL
26
Potassium
90.68
mmol/L
27
Total bilirubin
98.50
mg/dL
28
Troponin I
99.04
ng/mL
29
Hematocrit
91.14
%
30
Hemoglobin
92.61
g/dL
31
PTT
94.05
s
32
WBC
93.59
count/L
33
Fibrinogen concentration
99.34
mg/dL
34
Platelet count
94.05
count/mL
35
Age
—
yr
36
Gender
—
Male (1) or Female (0)
37
Unit 1
39.42
true (1) or false (0)
38
Unit 2
39.42
true (1) or false (0)
39
HospAdmTime
—
hours
40
LOS
—
hours
41
SepsisLabel
—
septic (1) nonseptic (0).
Table 1.
Details of the various clinical variables used under study along with missing values information in percentage.
2.2 Feature extraction
Feature extraction takes place on the imputed version of given clinical data that generates features sample-wise on an hourly time grid. Two types of features ware generated namely:
Physiological features: In literature, inter-relations among the clinical values have been proven to enhance the capability of anomaly detection tasks [7, 20]. By reviewing various studies that justify the clinical significance of well-established physiological inter-relations among the given clinical signs 10 such physiological relations are derived from the given covariates: Three Shock Indices firstly the well defined Shock Index (SIndex) using Systolic BP and the other two are its modified versions proposed in this study for Diastolic BP (DPBSIndex) [21] and Mean Arterial Pressure (MAPSIndex) [22] followed by ratios BUN/Creatinine (BUNCr) [7], Bilirubintotal/Creatinine (BILTcr), SaO2/FiO2 [23], PaO2/FiO2 [24], Platelets/Age (PlaAge), the difference between SBP and DBP called Pulse Pressure (PP) [25], and lastly Cardiac Output (CO) [26]. Table 2 gives a detailed description of Physiological features.
Sl. no
Abbreviation
Description
Formula
1
SIndex
Shock Index (SIndex) is the proportion of heart rate (HR) being divided by systolic blood pressure (SBP), normalized by age.
(HR/SBP)*Age
2
DBPSIndex
Diastolic Shock Index is the proportion HR being divided by systolic blood pressure (DBP), normalized by age.
(HR/DBP)*Age
3
MAPSIndex
It is defined as the proportion of HR being divided by Mean Arterial Pressure (MAP), normalized by age.
(HR/MAP)*Age
4
BUNCr
It is the ratio of Blood Urea Nitrogen(BUN) to Creatinine
BUN/Creatinine
5
BILTCr
It is the ratio of Direct Bilirubin (Bilirubin_total) to Creatinine
Bilirubin_total/Creatinine
6
SaO2 -FiO2
It is the ratio of oxygen saturation of arterial blood in percentage (SaO2) to the fraction of inspired oxygen (FiO2).
SaO2/FiO2
7
PaO2 -FiO2
It is defined as proportion of the partial pressure of oxygen PaO2 divided by the fraction of inspired oxygen (FiO2).
PaO2/FiO2
8
Pla_Age
It is the ratio of platelets to age
Platelets/Age
9
PP
Pulse Pressure (PP) is the difference between SBP and DBP
SBP-DBP
10
CO
Cardiac Output is the product of pulse pressure (PP) and HR.
PP * HR
Table 2.
Detailed definitions of the physiological features.
Time-Lag difference features: A set of 35 time-lag features are computed with 6 hours of time-lag difference among vital signs and lab values from the given 40 clinical variables excluding the last 5 demographic values.
Finally, the obtained 45 features are combined with the given 40 clinical signs, thereby increasing the final feature count to 85 features. The resultant feature set is then fed to train the proposed xMLEPS framework.
2.3 Implementation of xMLEPS
Together with Bayesian optimization and the refinement of prediction risk threshold an optimal disease onset detection method before six hours for sepsis called xMLEPS is developed. As shown in Figure 1 the given clinical sepsis data has large amount of missing information (approximately 20%). So at the onset of the algorithm computation, filling of these missing values is carried out as as a pre-processing step. The data imputation to fill in the missing values is done by employing forward fill imputation on the given EHR clinical data. In the real-time scenario, the current missing values encountered are to be filled with previous available measurements. Thus only the previous clinical values of given EHR data are fetched for data imputation of current observation.
In this study, imputation is carried out into two rounds: first local imputation, for each individual record, and then global imputation for all the combined records together. In the case of local imputation, the trailing missing values in a row for a particular clinical covariate (or feature vector) are forward filled with the nearest past non-missing value in that row locally for the given record. Ipso facto, if the record encounters ‘NaN’ values, in the beginning, i.e. for the first alone measurement at t = 0, they are retained as it is initially and then later replaced with ‘global mean’ for that covariate row obtained by combining all records [19].
During model development, a ten-fold cross-validation scheme is employed wherein 10 LightGBM classifiers with the same complexity of model hyper-parameters obtained during Bayesian optimization are developed for the corresponding fold. The total feature set used to develop these models comprises of 85 features as described in sub-Section 2.2. Generally, hyper-parameter optimization aims at looking for the best hyper-parameter values to minimize the objective loss function. The hyper-parameter settings maximizing the custom-defined challenge metric- utility score on the subset of training data during the Bayesian optimization phase are later used to build models. These built models generate the predictions on the hold out 10% of validation data in each fold. The training process of the model in each fold stops when the utility score of the validation set does not show further improvements over 32 consecutive iterations, i.e. early stopping to best iteration is achieved to reset the model and thereby to avoid over-fitting.
The initial predictions generated by each optimal model on the corresponding validation data of each fold undergo refinement of the prediction risk threshold to enhance the utility score. The search space for the prediction risk thresholds lies in the range of 0 to 1 and is varied in steps of 0.05. Thus the threshold search space has 20 values. So the initial predictions of validation data of each fold are compared with each of these 20 values. After comparison, the threshold value that gives the maximum utility score for the set of predictions of that fold is said to be optimal. Such 10 optimum threshold values are later used by the corresponding models to refine the predictive power in terms of utility score for generated labels in each fold.
This LightGBM based gradient boosting framework serves with a specific processing method for sparse data which is important in our classification task with class imbalance problem [27]. For the interpretability of the proposed framework, the LightGBM uses its feature importance attribute to quantify each variable, and the explainability component is addressed by employing SHAP summary and dependency plots wherein the distribution of the variable importance is illustrated [28, 29].
3. Results
The proposed framework performs the prediction from the given patient-records to determine the risk of development of sepsis onset in the next 6 hours. This is achieved using a continuous-valued utility score as defined by challenge organizers for each prediction [18]. The utility function rewards or penalizes classifiers for their predictions within 12 hours before and 3 hours after sepsis onset time and was normalized as described in [18]. Using a ten-fold cross-validation scheme 10 LightGBM models are designed based on patient-wise stratified ten-folds each containing unique 10% of the entire training set. The hyper-parameters of the above models that minimize cross-validation loss are obtained by using automatic hyper-parameter optimization utility ‘bayesopt’ in Python [30, 31]. The underlying objective function formulated for the optimization is intended to maximize the AUROC. The given software utility finds optimal parameters automatically using Bayesian optimization. At the outset, the optimized models includes: 60 ‘num_leaves’, 120 ‘min_data_in_leaf’, ‘max_depth’ of 2, ‘learning_rate’ of 0.01, ‘scale_pos_weight’ of 20, ‘min_samples_split’ of 4.
Table 3 gives a summary of the results by the proposed framework on the entire training data in a ten-fold cross-validation scheme. Results also include performances of inter-cohort and baseline studies. To ensure that the models trained in the proposed study learn dependencies not only between the patient-records but also among the cohorts, we considered inter-cohort training and testing scheme. i.e. model trained with the data of cohort A was scored on cohort B data and vice versa. This certainly avoids the doubt of the over-fitting, thus increasing the robustness of the framework. Inter-cohort scores for A and B were 0.3191 and 0.3284 respectively.
Fold
AUROC
F1
Utility
Threshold
1
0.8456
0.1420
0.4234
0.35
2
0.8436
0.1501
0.4029
0.35
3
0.8605
0.2208
0.4452
0.40
4
0.8610
0.1737
0.4331
0.35
5
0.8568
0.1507
0.4069
0.35
6
0.8607
0.1290
0.4253
0.25
7
0.8628
0.1475
0.4302
0.25
8
0.8649
0.1294
0.4205
0.20
9
0.8648
0.1299
0.3973
0.20
10
0.8704
0.1285
0.4282
0.20
Average (Std)
0.8591 (0.0085)
0.1502 (0.0286)
0.4214 (0.0148)
—
Baseline 1
0.8560
0.1517
0.3870
—
Baseline 2
0.8502
0.1376
0.3509
—
Baseline 3
0.8124
0.1197
0.3198
—
xMLEPS
Set A (Training) and Set B (Test)
0.3191
xMLEPS
Set B (Training) and Set A (Test)
0.3284
Table 3.
Results summary of the proposed framework.
3.1 Comparison of xMLEPS with baseline
Further, to emphasize the clinical relevance of the derived features under this proposed method, a comparative analysis of results is done by carrying out three baseline studies as shown in Figure 2.
Figure 2.
Comparison of results by xMLEPS with the three base-line studies. US: Utility score, F1: F1 score.
As a part of comparative analysis three well-tuned baseline studies are performed: Firstly, the proposed method with feature set of 85 features is tested without optimal threshold refinement (default threshold value of 0.5 with no skill is used) in a 10-fold cross-validation scheme. In the second and third methods, the given 40 clinical variables only are directly fed to LightGBM models with and without refinement of optimal threshold respectively in a 10-fold cross-validation scheme. Table 3 presents the results of these three baseline studies accordingly. As expected the proposed method xMLEPS outperforms these three studies. The third study carried out without derived features and optimal threshold refinement shows worst performance. Even for the first baseline study, results are significantly lower by 3% in terms of the utility score as compared to the proposed method.
3.2 Explanation and visualization of feature importance
The cumulative feature importance of the first top 50 features is shown in Figure 3. Here the LightGBM feature importance attribute is used for the gradient boosting framework developed. The approach used is to count the number of times a feature gets involved to split the dataset across all trees. The failure of such an approach is that it accounts for different impacts due to different splits. The next best approach is to attribute the gain achieved with the reduction in average training loss when using a feature for splitting. This “Gain” measure used for feature importance recovers the correct mutual information between feature inputs and label outputs [32]. The limitation of this approach is that it gets easily biased when greedy trees are built in the finite ensembles. So other methods are designed to compensate for the bias in feature selection using gain approach [33, 34].
Figure 3.
Cumulative feature importance of the top 50 features using the feature importance attribute of LightGBM.
SHAP summary plot with the 20 most important clinical features that cause sepsis onset identified by the xMLEPS framework is shown in Figure 4(a). Here the approach used for the feature importance is to sort all the relevance scores across the entire population in decreasing order of mean relevance as computed for local, but considering only those individuals who were positive for sepsis. The mean relevance is displayed as blue horizontal bars in Figure 4(a). While local explanations summary is shown in Figure 4(b), wherein all the individual data points are displaced by mean relevance for sepsis and are colored by feature values. As shown from Figure 4(b) we can draw that the increase in the length of stay (ICULOS) and higher value of clinical ratio’s like PaO2/FiO2, Shock indices: DBPSIndex and SIndex, etc. leads to the development of sepsis, whereas lower Platelets, DBP and Magnesium levels cause sepsis. These findings are found to be consistent with previous studies on it [7, 21, 35, 36].
Figure 4.
Results from the SHAP explanation module showing the global feature importance together with local explanation summary. PaO2: partial pressure of oxygen. FiO2: fraction of inspired oxygen. HR: Heart Rate. DBP: Diastolic Blood Pressure. SBP: Systolic Blood Pressure. SIndex: Shock Index. DBPIndex: Diastolic Blood Pressure Shock Index. PaCO2: Partial pressure of carbon dioxide. PTT: Partial thromboplastin time. WBC: Leukocyte count. BUN: Blood Urea Nitrogen.
Further, the impact of each feature and the interactions among them for sepsis development can also be illustrated using SHAP dependency plots. As an example, in Figure 4(c) the dependency plot showing the interaction of Heart rate with ICULOS is depicted. As seen the xMLEPS model seems to associate high heart rate values in the range 120–180 with increased ICULOS and hence causing sepsis. Further Figure 4(d) shows lower values of SBP (approx. Below 90) is associated with increase ICULOS causing sepsis. A summary plot of a SHAP interaction value matrix is shown in Figure 5 wherein the diagonal reflects the main effects, while across the diagonal show interaction effects. The explainable model will produce a high probability when it is confident about a decision, resulting in larger relevance scores due to the availability of more relevance for distributing backward. On the contrary, the model will output a lower probability when it is less confident about the patient to develop sepsis and as a result, yields lower relevant scores. This summary of scores distribution assists the clinicians with the hints to what to be expected from the designed model for clinical practice.
Figure 5.
Summary plot of a SHAP interaction value matrix.
4. Discussion
This study justifies the clinical significance of the derived physiological inter-relations among the clinical signs via feature importance and SHAP plots for visualized interpretation. Though SHAP values cannot be used as a generalized approach for early prediction of sepsis, they certainly help in generating relevant clinical hypotheses for desired events. The SHAP illustrations indeed assist in mitigating the concerns of the black-box issue associated with prediction models and might assist clinicians with a better understanding of the important features of the xMLEPS framework. The the proposed framework has the ability to establish the significance of the individual features contributing to enhance prediction of the utility score. Thus ensuring interpretablity of the framework to its clinical users. Furthermore, the proposed prediction framework, deploying clinical ICU data in the routine practice care can be potentially integrated into a computerized clinical decision support system instead of employing advanced molecular biomarkers.
The recent research literature relevant to early diagnosis of sepsis comes from the articles of various submission entries to PhysioNet Challenge 2019 [18]. This challenge aimed at the design and development of algorithms for early and automated prediction of sepsis onset with the optimal window definition of six hours before the actual clinical recognition of disease onset. The predictions of the machine learning algorithms were rewarded if they were able to detect true positives correctly up to 12 hours before disease onset and were slightly penalized if they were false positive. However predictions were strongly penalized if they were incorrect near disease onset. The reason for choosing the optimal prediction window to be six hours comes from the clinical fact that the ratio of observed median time to antimicrobial therapy is found to be 6 hours [37]. Furthermore, delay in each hour of treatment results in average decrease of survival rate of 7.6% [37].
The comparative analysis of the results obtained by the proposed method with our previous works [19, 38] and submission approaches [39, 40, 41, 42, 43, 44, 45, 46] that reported the best results in the PhysioNet 2019 Challenge [18] is listed in Table 4. Most of these approaches utilized 5 or 10 fold cross-validation scheme and yielded utility scores in the range of 0.36–0.45.
Summary of the results obtained by our previous works and the submitted solutions to PhysioNet 2019 challenge under 5/10-fold cross-validation scheme using training data.
This study supports the usage of the Utility score as an effective metric on ICU data for sepsis onset. However, experiments showed that even the F1 score gave reliable results aligning with utility score. i.e. the increase and decrease of F1 score follow accordingly with the Utility score. However, the bounds for utility score vary from −2 to 1 whereas the F1 Score has bounds from 0 to 1. The other conventional metrics namely AUROC, AUPRC, and Accuracy are insignificant to use with such a highly unbalanced dataset and are misleading for sepsis onset. Further, the fact that the interpretation of these results together with utility score is quite difficult cannot be ignored as mentioned by Roussel et al. [47].
The limitation of this study is, it constrains only to a two-center cohort design from the available training data, which might create doubt that the trained models may get over-fit towards the particular cohort data and it’s patient-records. However, the analyzed ICU patient admissions originate from a diverse population covering the entire spectrum of ICU patients, and further, the validation in terms of inter-cohorts train-test approach along with optimum threshold refinement demonstrates the deployment of our framework in other ICUs.
5. Conclusion
This study presents xMLEPS – an explainable machine learning framework for the early prediction of sepsis using clinical data in the ICU setting. These predictive explanations justify the clinical significance of physiological inter-relations among the given clinical signs via visualized interpretation. And thus assist the clinicians in decision making for diagnosis and recommend future actions to be taken to improve the quality of predictions. This certainly ensures that the data-driven automated ML models have the potential to make the paradigm shift from conventional detection and treatment to an automated early prediction that prevents the failure of the organ system due to sepsis.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"sepsis, early prediction, machine learning, explainable AI, electronic health records, clinical informatics, critical care, model-based diagnosis",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77653.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77653.xml",downloadPdfUrl:"/chapter/pdf-download/77653",previewPdfUrl:"/chapter/pdf-preview/77653",totalDownloads:168,totalViews:0,totalCrossrefCites:1,dateSubmitted:"February 23rd 2021",dateReviewed:"June 17th 2021",datePrePublished:"July 23rd 2021",datePublished:"October 27th 2021",dateFinished:"July 23rd 2021",readingETA:"0",abstract:"Early identification of individuals with sepsis is very useful in assisting clinical triage and decision-making, resulting in early intervention and improved outcomes. This study aims to develop an explainable machine learning model with the clinical interpretability to predict sepsis onset before 6 hours and validate with improved prediction risk power for every time interval since admission to the ICU. The retrospective observational cohort study is carried out using PhysioNet Challenge 2019 ICU data from three distinct hospital systems, viz. A, B, and C. Data from A and B were shared publicly for training and validation while sequestered data from all three cohorts were used for scoring. However, this study is limited only to publicly available training data. Training data contains 15,52,210 patient records of 40,336 ICU patients with up to 40 clinical variables (sourced for each hour of their ICU stay) divided into two datasets, based on hospital systems A and B. The clinical feature exploration and interpretation for early prediction of sepsis is achieved using the proposed framework, viz. the explainable Machine Learning model for Early Prediction of Sepsis (xMLEPS). A total of 85 features comprising the given 40 clinical variables augmented with 10 derived physiological features and 35 time-lag difference features are fed to xMLEPS for the said prediction task of sepsis onset. A ten-fold cross-validation scheme is employed wherein an optimal prediction risk threshold is searched for each of the 10 LightGBM models. These optimum threshold values are later used by the corresponding models to refine the predictive power in terms of utility score for the prediction of labels in each fold. The entire framework is designed via Bayesian optimization and trained with the resultant feature set of 85 features, yielding an average normalized utility score of 0.4214 and area under receiver operating characteristic curve of 0.8591 on publicly available training data. This study establish a practical and explainable sepsis onset prediction model for ICU data using applied ML approach, mainly gradient boosting. The study highlights the clinical significance of physiological inter-relations among the given and proposed clinical signs via feature importance and SHapley Additive exPlanations (SHAP) plots for visualized interpretation.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77653",risUrl:"/chapter/ris/77653",signatures:"Naimahmed Nesaragi and Shivnarayan Patidar",book:{id:"10729",type:"book",title:"Infections and Sepsis Development",subtitle:null,fullTitle:"Infections and Sepsis Development",slug:"infections-and-sepsis-development",publishedDate:"October 27th 2021",bookSignature:"Vincenzo Neri, Lixing Huang and Jie Li",coverURL:"https://cdn.intechopen.com/books/images_new/10729.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83969-458-5",printIsbn:"978-1-83969-457-8",pdfIsbn:"978-1-83969-459-2",isAvailableForWebshopOrdering:!0,editors:[{id:"170938",title:"Prof.",name:"Vincenzo",middleName:null,surname:"Neri",slug:"vincenzo-neri",fullName:"Vincenzo Neri"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"349984",title:"Dr.",name:"Shivnarayan",middleName:null,surname:"Patidar",fullName:"Shivnarayan Patidar",slug:"shivnarayan-patidar",email:"shivnarayan.patidar@nitgoa.ac.in",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"349994",title:"Mr.",name:"Naimahmed",middleName:null,surname:"Nesaragi",fullName:"Naimahmed Nesaragi",slug:"naimahmed-nesaragi",email:"naimahmed.nesaragi@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methods",level:"1"},{id:"sec_2_2",title:"2.1 Dataset and study population",level:"2"},{id:"sec_3_2",title:"2.2 Feature extraction",level:"2"},{id:"sec_4_2",title:"2.3 Implementation of xMLEPS",level:"2"},{id:"sec_6",title:"3. Results",level:"1"},{id:"sec_6_2",title:"3.1 Comparison of xMLEPS with baseline",level:"2"},{id:"sec_7_2",title:"3.2 Explanation and visualization of feature importance",level:"2"},{id:"sec_9",title:"4. Discussion",level:"1"},{id:"sec_10",title:"5. Conclusion",level:"1"},{id:"sec_14",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'M. Singer, C. S. Deutschman, C. W. Seymour, M. Shankar-Hari, D. Annane, M. Bauer, R. Bellomo, G. R. Bernard, J.-D. Chiche, C. M. Coopersmith, R. S. Hotchkiss, M. M. Levy, J. C. Marshall, G. S. Martin, S. M. Opal, G. D. Rubenfeld, T. van der Poll, J.-L. Vincent, D. C. Angus, The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3), JAMA 315 (2016) 801'},{id:"B2",body:'L. Giesen, M. Singer, What Is Sepsis?, in: W. J. Wiersinga, C. W. Seymour (Eds.), Handbook of Sepsis, Springer International Publishing, Cham, 2018, pp. 3–14'},{id:"B3",body:'H. Nishie, Guidelines for management of severe sepsis and septic shock, Okayama Igakkai Zasshi (Journal of Okayama Medical Association) 125 (2013) 153–157'},{id:"B4",body:'C. W. Seymour, J. N. Kennedy, S. Wang, C.-C. H. Chang, C. F. Elliott, Z. Xu, S. Berry, G. Clermont, G. Cooper, H. Gomez, D. T. Huang, J. A. Kellum, Q. Mi, S. M. Opal, V. Talisa, T. van der Poll, S. Visweswaran, Y. Vodovotz, J. C. Weiss, D. M. Yealy, S. Yende, D. C. Angus, Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis, JAMA 321 (2019) 2003'},{id:"B5",body:'R. Goulden, M.-C. Hoyle, J. Monis, D. Railton, V. Riley, P. Martin, R. Martina, E. Nsutebu, qSOFA, SIRS and NEWS for predicting inhospital mortality and ICU admission in emergency admissions treated as sepsis, Emergency Medicine Journal 35 (2018) 345–349'},{id:"B6",body:'V. Anand, Z. Zhang, S. S. Kadri, M. Klompas, C. Rhee, Epidemiology of Quick Sequential Organ Failure Assessment Criteria in Undifferentiated Patients and Association With Suspected Infection and Sepsis, Chest 156 (2019) 289–297'},{id:"B7",body:'K. E. Henry, D. N. Hager, P. J. Pronovost, S. Saria, A targeted real-time early warning score (TREWScore) for septic shock, Science Translational Medicine 7 (2015) 299ra122–299ra122'},{id:"B8",body:'J. Calvert, T. Desautels, U. Chettipally, C. Barton, J. Hoffman, M. Jay, Q. Mao, H. Mohamadlou, R. Das, High-performance detection and early prediction of septic shock for alcohol-use disorder patients, Annals of Medicine and Surgery 8 (2016 a) 50–55'},{id:"B9",body:'J. S. Calvert, D. A. Price, U. K. Chettipally, C. W. Barton, M. D. Feldman, J. L. Hoffman, M. 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Bakker, Diastolic shock index and clinical outcomes in patients with septic shock, Annals of Intensive Care 10 (2020) 41'},{id:"B22",body:'Y.-c. Liu, Modified shock index and mortality rate of emergency patients, World Journal of Emergency Medicine 3 (2012) 114'},{id:"B23",body:'A. Linder, R. Arnold, M. Zindovic, I. Zindovic, A. Lange-Jendeberg, M. Paulsson, P. Nyberg, B. Christensson, P. Åkesson, Heparin-binding protein improves prediction of severe sepsis in the emergency department, Critical Care 17 (2013) P3'},{id:"B24",body:'Y. Wang, H. Yang, L. Qiao, Z. Tan, J. Jin, J. Yang, L. Zhang, B. M. Fang, X. Xu, The predictive value of PaO2/FIO2 and additional parameters for in-hospital mortality in patients with acute pulmonary embolism: an 8-year prospective observational single-center cohort study, BMC Pulmonary Medicine 19 (2019) 242'},{id:"B25",body:'A. Y. A. Amer, J. Vranken, F. Wouters, D. Mesotten, P. Vandervoort, V. Storms, S. Luca, B. Vanrumste, J.-M. 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Cora, N. de Freitas, A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning, arXiv:1012.2599 [cs] (2010). ArXiv: 1012.2599'},{id:"B31",body:'J. Snoek, H. Larochelle, R. P. Adams, Practical bayesian optimization of machine learning algorithms, in: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2, NIPS’12, Curran Associates Inc., Red Hook, NY, USA, 2012, p. 2951–2959'},{id:"B32",body:'G. Louppe, Understanding Random Forests: From Theory to Practice, arXiv:1407.7502 [stat] (2015). ArXiv: 1407.7502'},{id:"B33",body:'S. Chebrolu, A. Abraham, J. P. Thomas, Feature deduction and ensemble design of intrusion detection systems, Computers & Security 24 (2005) 295–307'},{id:"B34",body:'V. A. Huynh-Thu, A. Irrthum, L. Wehenkel, P. Geurts, Inferring Regulatory Networks from Expression Data Using Tree-Based Methods, PLoS ONE 5 (2010) e12776'},{id:"B35",body:'X. Li, Y. Kang, X. Jia, J. Wang, G. Xie, Tasp: A time-phased model for sepsis prediction, in: 2019 Computing in Cardiology (CinC), pp. Page 1–Page 4'},{id:"B36",body:'D. W. Shimabukuro, C. W. Barton, M. D. Feldman, S. J. Mataraso, R. Das, Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial, BMJ Open Respiratory Research 4 (2017) e000234'},{id:"B37",body:'A. Kumar, D. Roberts, K. E. Wood, B. Light, J. E. Parrillo, S. Sharma, R. Suppes, D. Feinstein, S. Zanotti, L. Taiberg, et al., Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock, Critical care medicine 34 (2006) 1589–1596'},{id:"B38",body:'N. Nesaragi, S. Patidar, Early prediction of sepsis from clinical data using ratio and power-based features, Critical Care Medicine 48 (2020) e1343–e1349'},{id:"B39",body:'J. H. Morrill, A. Kormilitzin, A. J. Nevado-Holgado, S. Swaminathan, S. D. Howison, T. J. Lyons, Utilization of the signature method to identify the early onset of sepsis from multivariate physiological time series in critical care monitoring, Critical Care Medicine 48 (2020) e976–e981'},{id:"B40",body:'M. Zabihi, S. Kiranyaz, M. Gabbouj, Sepsis Prediction in Intensive Care Unit Using Ensemble of XGboost Models'},{id:"B41",body:'M. Yang, X. Wang, H. Gao, Y. Li, X. Liu, J. Li, C. Liu, Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization'},{id:"B42",body:'Y. Chang, J. Rubin, G. Boverman, S. Vij, A. Rahman, A. Natarajan, S. Parvaneh, A multi-task imputation and classification neural architecture for early prediction of sepsis from multivariate clinical time series, in: 2019 Computing in Cardiology (CinC), IEEE, pp. Page1–Page4'},{id:"B43",body:'B. T. Lee, O.-Y. Kwon, H. Park, K.-J. Cho, J.-M. Kwon, Y. Lee, et al., Graph convolutional networks-based noisy data imputation in electronic health record, Critical Care Medicine 48 (2020) e1106–e1111'},{id:"B44",body:'S. Lyra, S. Leonhardt, C. Hoog Antink, Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data'},{id:"B45",body:'X. Li, X. Xu, F. Xie, X. Xu, Y. Sun, X. Liu, X. Jia, Y. Kang, L. Xie, F. Wang, et al., A time-phased machine learning model for real-time prediction of sepsis in critical care, Critical Care Medicine 48 (2020) e884–e888'},{id:"B46",body:'J. A. Du, N. Sadr, P. d. Chazal, Automated prediction of sepsis onset using gradient boosted decision trees, in: 2019 Computing in Cardiology (CinC), pp. Page 1–Page 4'},{id:"B47",body:'B. Roussel, J. Behar, J. Oster, A recurrent neural network for the prediction of vital sign evolution and sepsis in icu, in: 2019 Computing in Cardiology (CinC), pp. Page 1–Page 4'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Naimahmed Nesaragi",address:null,affiliation:'
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The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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Dissemination and Promotion
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Proven world leader in Open Access book publishing with over 10 years experience
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+5,700 OA books published
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Most competitive prices in the market
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Fully compliant with OA funding requirements
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Optimized processes that assure your research is made available to the scientific community without delay
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Personal support during every step of the publication process
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+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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10,000 GBP Monograph - Long Form
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
\n\n
Services included are:
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\n\t
An online manuscript tracking system to facilitate your work
\n\t
Personal contact and support throughout the publishing process from your dedicated Author Service Manager
\n\t
Assurance that your manuscript meets the highest publishing standards
\n\t
English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
\n\t
XML Typesetting and pagination - web (PDF, HTML) and print files preparation
\n\t
Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\n
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Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
\n\t
Long-term archiving
\n\t
Visibility on the world's strongest OA platform
\n\t
Live Performance Metrics to track readership and the impact of your chapter
\n\t
Dissemination and Promotion
\n
\n\n
Benefits of Publishing with IntechOpen
\n\n
\n\t
Proven world leader in Open Access book publishing with over 10 years experience
\n\t
+5,700 OA books published
\n\t
Most competitive prices in the market
\n\t
Fully compliant with OA funding requirements
\n\t
Optimized processes that assure your research is made available to the scientific community without delay
\n\t
Personal support during every step of the publication process
\n\t
+184,650 citations in Web of Science databases
\n\t
Currently strongest OA platform with over 175 million downloads
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She is now a lecturer at the University of Witwatersrand, South Africa, and a principal researcher at the Health Economics and Epidemiology Research Office (HE2RO), South Africa. Dr. Moolla holds a Ph.D. in Psychology with her research being focused on mental health and resilience. In her professional work capacity, her research has further expanded into the fields of early childhood development, mental health, the HIV and TB care cascades, as well as COVID. She is also a UNESCO-trained International Bioethics Facilitator.",institutionString:"University of the Witwatersrand",institution:{name:"University of the Witwatersrand",country:{name:"South Africa"}}},{id:"342152",title:"Dr.",name:"Santo",middleName:null,surname:"Grace Umesh",slug:"santo-grace-umesh",fullName:"Santo Grace Umesh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/342152/images/16311_n.jpg",biography:null,institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"333647",title:"Dr.",name:"Shreya",middleName:null,surname:"Kishore",slug:"shreya-kishore",fullName:"Shreya Kishore",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333647/images/14701_n.jpg",biography:"Dr. Shreya Kishore completed her Bachelor in Dental Surgery in Chettinad Dental College and Research Institute, Chennai, and her Master of Dental Surgery (Orthodontics) in Saveetha Dental College, Chennai. She is also Invisalign certified. She’s working as a Senior Lecturer in the Department of Orthodontics, SRM Dental College since November 2019. She is actively involved in teaching orthodontics to the undergraduates and the postgraduates. Her clinical research topics include new orthodontic brackets, fixed appliances and TADs. She’s published 4 articles in well renowned indexed journals and has a published patency of her own. Her private practice is currently limited to orthodontics and works as a consultant in various clinics.",institutionString:null,institution:{name:"SRM Dental College",country:{name:"India"}}},{id:"323731",title:"Prof.",name:"Deepak M.",middleName:"Macchindra",surname:"Vikhe",slug:"deepak-m.-vikhe",fullName:"Deepak M. Vikhe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/323731/images/13613_n.jpg",biography:"Dr Deepak M.Vikhe .\n\n\t\n\tDr Deepak M.Vikhe , completed his Masters & PhD in Prosthodontics from Rural Dental College, Loni securing third rank in the Pravara Institute of Medical Sciences Deemed University. He was awarded Dr.G.C.DAS Memorial Award for Research on Implants at 39th IPS conference Dubai (U A E).He has two patents under his name. He has received Dr.Saraswati medal award for best research for implant study in 2017.He has received Fully funded scholarship to Spain ,university of Santiago de Compostela. He has completed fellowship in Implantlogy from Noble Biocare. \nHe has attended various conferences and CDE programmes and has national publications to his credit. His field of interest is in Implant supported prosthesis. Presently he is working as a associate professor in the Dept of Prosthodontics, Rural Dental College, Loni and maintains a successful private practice specialising in Implantology at Rahata.\n\nEmail: drdeepak_mvikhe@yahoo.com..................",institutionString:null,institution:{name:"Pravara Institute of Medical Sciences",country:{name:"India"}}},{id:"204110",title:"Dr.",name:"Ahmed A.",middleName:null,surname:"Madfa",slug:"ahmed-a.-madfa",fullName:"Ahmed A. Madfa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204110/images/system/204110.jpg",biography:"Dr. Madfa is currently Associate Professor of Endodontics at Thamar University and a visiting lecturer at Sana'a University and University of Sciences and Technology. He has more than 6 years of experience in teaching. His research interests include root canal morphology, functionally graded concept, dental biomaterials, epidemiology and dental education, biomimetic restoration, finite element analysis and endodontic regeneration. Dr. Madfa has numerous international publications, full articles, two patents, a book and a book chapter. Furthermore, he won 14 international scientific awards. Furthermore, he is involved in many academic activities ranging from editorial board member, reviewer for many international journals and postgraduate students' supervisor. Besides, I deliver many courses and training workshops at various scientific events. Dr. Madfa also regularly attends international conferences and holds administrative positions (Deputy Dean of the Faculty for Students’ & Academic Affairs and Deputy Head of Research Unit).",institutionString:"Thamar University",institution:null},{id:"210472",title:"Dr.",name:"Nermin",middleName:"Mohammed Ahmed",surname:"Yussif",slug:"nermin-yussif",fullName:"Nermin Yussif",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/210472/images/system/210472.jpg",biography:"Dr. Nermin Mohammed Ahmed Yussif is working at the Faculty of dentistry, University for October university for modern sciences and arts (MSA). Her areas of expertise include: periodontology, dental laserology, oral implantology, periodontal plastic surgeries, oral mesotherapy, nutrition, dental pharmacology. She is an editor and reviewer in numerous international journals.",institutionString:"MSA University",institution:null},{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. He is now Head of the TMD Clinic at Prosthodontic Department of Faculty of Dentistry , Istanbul Aydın University , Turkey.",institutionString:"Istanbul Aydin University",institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"240870",title:"Ph.D.",name:"Alaa Eddin Omar",middleName:null,surname:"Al Ostwani",slug:"alaa-eddin-omar-al-ostwani",fullName:"Alaa Eddin Omar Al Ostwani",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/240870/images/system/240870.jpeg",biography:"Dr. Al Ostwani Alaa Eddin Omar received his Master in dentistry from Damascus University in 2010, and his Ph.D. in Pediatric Dentistry from Damascus University in 2014. Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",country:{name:"India"}}},{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null},{id:"178412",title:"Associate Prof.",name:"Guhan",middleName:null,surname:"Dergin",slug:"guhan-dergin",fullName:"Guhan Dergin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178412/images/6954_n.jpg",biography:"Assoc. Prof. Dr. Gühan Dergin was born in 1973 in Izmit. He graduated from Marmara University Faculty of Dentistry in 1999. He completed his specialty of OMFS surgery in Marmara University Faculty of Dentistry and obtained his PhD degree in 2006. In 2005, he was invited as a visiting doctor in the Oral and Maxillofacial Surgery Department of the University of North Carolina, USA, where he went on a scholarship. Dr. Dergin still continues his academic career as an associate professor in Marmara University Faculty of Dentistry. He has many articles in international and national scientific journals and chapters in books.",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"178414",title:"Prof.",name:"Yusuf",middleName:null,surname:"Emes",slug:"yusuf-emes",fullName:"Yusuf Emes",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178414/images/6953_n.jpg",biography:"Born in Istanbul in 1974, Dr. Emes graduated from Istanbul University Faculty of Dentistry in 1997 and completed his PhD degree in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery in 2005. He has papers published in international and national scientific journals, including research articles on implantology, oroantral fistulas, odontogenic cysts, and temporomandibular disorders. Dr. Emes is currently working as a full-time academic staff in Istanbul University faculty of Dentistry Department of Oral and Maxillofacial Surgery.",institutionString:null,institution:{name:"Istanbul University",country:{name:"Turkey"}}},{id:"192229",title:"Ph.D.",name:"Ana Luiza",middleName:null,surname:"De Carvalho Felippini",slug:"ana-luiza-de-carvalho-felippini",fullName:"Ana Luiza De Carvalho Felippini",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192229/images/system/192229.jpg",biography:null,institutionString:"University of São Paulo",institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"256851",title:"Prof.",name:"Ayşe",middleName:null,surname:"Gülşen",slug:"ayse-gulsen",fullName:"Ayşe Gülşen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256851/images/9696_n.jpg",biography:"Dr. Ayşe Gülşen graduated in 1990 from Faculty of Dentistry, University of Ankara and did a postgraduate program at University of Gazi. \nShe worked as an observer and research assistant in Craniofacial Surgery Departments in New York, Providence Hospital in Michigan and Chang Gung Memorial Hospital in Taiwan. \nShe works as Craniofacial Orthodontist in Department of Aesthetic, Plastic and Reconstructive Surgery, Faculty of Medicine, University of Gazi, Ankara Turkey since 2004.",institutionString:"Univeristy of Gazi",institution:null},{id:"255366",title:"Prof.",name:"Tosun",middleName:null,surname:"Tosun",slug:"tosun-tosun",fullName:"Tosun Tosun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255366/images/7347_n.jpg",biography:"Graduated at the Faculty of Dentistry, University of Istanbul, Turkey in 1989;\nVisitor Assistant at the University of Padua, Italy and Branemark Osseointegration Center of Treviso, Italy between 1993-94;\nPhD thesis on oral implantology in University of Istanbul and was awarded the academic title “Dr.med.dent.”, 1997;\nHe was awarded the academic title “Doç.Dr.” (Associated Professor) in 2003;\nProficiency in Botulinum Toxin Applications, Reading-UK in 2009;\nMastership, RWTH Certificate in Laser Therapy in Dentistry, AALZ-Aachen University, Germany 2009-11;\nMaster of Science (MSc) in Laser Dentistry, University of Genoa, Italy 2013-14.\n\nDr.Tosun worked as Research Assistant in the Department of Oral Implantology, Faculty of Dentistry, University of Istanbul between 1990-2002. \nHe worked part-time as Consultant surgeon in Harvard Medical International Hospitals and John Hopkins Medicine, Istanbul between years 2007-09.\u2028He was contract Professor in the Department of Surgical and Diagnostic Sciences (DI.S.C.), Medical School, University of Genova, Italy between years 2011-16. \nSince 2015 he is visiting Professor at Medical School, University of Plovdiv, Bulgaria. \nCurrently he is Associated Prof.Dr. at the Dental School, Oral Surgery Dept., Istanbul Aydin University and since 2003 he works in his own private clinic in Istanbul, Turkey.\u2028\nDr.Tosun is reviewer in journal ‘Laser in Medical Sciences’, reviewer in journal ‘Folia Medica\\', a Fellow of the International Team for Implantology, Clinical Lecturer of DGZI German Association of Oral Implantology, Expert Lecturer of Laser&Health Academy, Country Representative of World Federation for Laser Dentistry, member of European Federation of Periodontology, member of Academy of Laser Dentistry. Dr.Tosun presents papers in international and national congresses and has scientific publications in international and national journals. He speaks english, spanish, italian and french.",institutionString:null,institution:{name:"Istanbul Aydın University",country:{name:"Turkey"}}},{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"256417",title:"Associate Prof.",name:"Sanaz",middleName:null,surname:"Sadry",slug:"sanaz-sadry",fullName:"Sanaz Sadry",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/256417/images/8106_n.jpg",biography:null,institutionString:null,institution:null},{id:"272237",title:"Dr.",name:"Pinar",middleName:"Kiymet",surname:"Karataban",slug:"pinar-karataban",fullName:"Pinar Karataban",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272237/images/8911_n.png",biography:"Assist.Prof.Dr.Pınar Kıymet Karataban, DDS PhD \n\nDr.Pınar Kıymet Karataban was born in Istanbul in 1975. After her graduation from Marmara University Faculty of Dentistry in 1998 she started her PhD in Paediatric Dentistry focused on children with special needs; mainly children with Cerebral Palsy. She finished her pHD thesis entitled \\'Investigation of occlusion via cast analysis and evaluation of dental caries prevalance, periodontal status and muscle dysfunctions in children with cerebral palsy” in 2008. She got her Assist. Proffessor degree in Istanbul Aydın University Paediatric Dentistry Department in 2015-2018. ın 2019 she started her new career in Bahcesehir University, Istanbul as Head of Department of Pediatric Dentistry. In 2020 she was accepted to BAU International University, Batumi as Professor of Pediatric Dentistry. She’s a lecturer in the same university meanwhile working part-time in private practice in Ege Dental Studio (https://www.egedisklinigi.com/) a multidisciplinary dental clinic in Istanbul. Her main interests are paleodontology, ancient and contemporary dentistry, oral microbiology, cerebral palsy and special care dentistry. She has national and international publications, scientific reports and is a member of IAPO (International Association for Paleodontology), IADH (International Association of Disability and Oral Health) and EAPD (European Association of Pediatric Dentistry).",institutionString:null,institution:null},{id:"202198",title:"Dr.",name:"Buket",middleName:null,surname:"Aybar",slug:"buket-aybar",fullName:"Buket Aybar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/202198/images/6955_n.jpg",biography:"Buket Aybar, DDS, PhD, was born in 1971. She graduated from Istanbul University, Faculty of Dentistry, in 1992 and completed her PhD degree on Oral and Maxillofacial Surgery in Istanbul University in 1997.\nDr. Aybar is currently a full-time professor in Istanbul University, Faculty of Dentistry Department of Oral and Maxillofacial Surgery. She has teaching responsibilities in graduate and postgraduate programs. Her clinical practice includes mainly dentoalveolar surgery.\nHer topics of interest are biomaterials science and cell culture studies. She has many articles in international and national scientific journals and chapters in books; she also has participated in several scientific projects supported by Istanbul University Research fund.",institutionString:null,institution:null},{id:"260116",title:"Dr.",name:"Mehmet",middleName:null,surname:"Yaltirik",slug:"mehmet-yaltirik",fullName:"Mehmet Yaltirik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/260116/images/7413_n.jpg",biography:"Birth Date 25.09.1965\r\nBirth Place Adana- Turkey\r\nSex Male\r\nMarrial Status Bachelor\r\nDriving License Acquired\r\nMother Tongue Turkish\r\n\r\nAddress:\r\nWork:University of Istanbul,Faculty of Dentistry, Department of Oral Surgery and Oral Medicine 34093 Capa,Istanbul- TURKIYE",institutionString:null,institution:null},{id:"172009",title:"Dr.",name:"Fatma Deniz",middleName:null,surname:"Uzuner",slug:"fatma-deniz-uzuner",fullName:"Fatma Deniz Uzuner",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/172009/images/7122_n.jpg",biography:"Dr. Deniz Uzuner was born in 1969 in Kocaeli-TURKEY. After graduating from TED Ankara College in 1986, she attended the Hacettepe University, Faculty of Dentistry in Ankara. \nIn 1993 she attended the Gazi University, Faculty of Dentistry, Department of Orthodontics for her PhD education. After finishing the PhD education, she worked as orthodontist in Ankara Dental Hospital under the Turkish Government, Ministry of Health and in a special Orthodontic Clinic till 2011. Between 2011 and 2016, Dr. Deniz Uzuner worked as a specialist in the Department of Orthodontics, Faculty of Dentistry, Gazi University in Ankara/Turkey. In 2016, she was appointed associate professor. Dr. Deniz Uzuner has authored 23 Journal Papers, 3 Book Chapters and has had 39 oral/poster presentations. She is a member of the Turkish Orthodontic Society. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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