Summary of diagnosis and clinical manifestation, kinds of sample, and the image of the pathogens observed by SEM
\r\n\tThere are generally two types of masonry: brick and stone masonry. Brick masonry: a type of masonry that uses bricks. However, masonry is further divided into "clay work," which uses clay to fill various joints with bricks to build walls, and "cement masonry," the cheapest type of masonry. Masonry: this is the art of building with bricks or stone. The ability of masonry to support the load imposed by the structural elements above it is called strength. The application of loads to masonry creates internal stresses and deformations. The brand of mortar and brick, the shape and size of masonry materials, and the thickness and density of mortar joints affect the strength of masonry. The ability of masonry to maintain its position under horizontal load is called stability. This property limits the height of masonry depending on its thickness and the magnitude of wind loads. The thermal conductivity of bricks of different types (silicate, ceramic, facing, refractory) is considered. A comparison of bricks in terms of their thermal conductivity is made; the thermal conductivity coefficients of refractory bricks are presented at different temperatures - from 20 to 1700°C. The thermal conductivity depends mainly on the density and the configuration of the voids. Architecture and construction consist of various elements for building works, and masonry is the main element with which these constructions are realized. Masonry is a piece of fired clay with a rectangular shape and is used to build walls and structures. Nowadays, eco-masonry can be made of different materials that offer a variety of advantages, but all of them offer benefits at the level of the environment and sustainability; some of these utensils are plastic bottles, clay, etc. The book addresses the holistic issue of using modern masonry in construction. This book interprets masonry as an essential theme of contemporary architecture and sustainable construction. It is one of the most valuable materials in the history of mankind.
",isbn:"978-1-83768-126-6",printIsbn:"978-1-83768-125-9",pdfIsbn:"978-1-83768-127-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"85ef86d046d15e7d4b1988f1ec5dd750",bookSignature:"Prof. Amjad Almusaed and Prof. Asaad Almssad",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12061.jpg",keywords:"Unreinforced Masonry Buildings, Masonry in Sustainable Building, Energy Saving and Masonry, Eco-Friendly Masonry, Modern Architecture and Masonry, Masonry and Human Behavior, Esthetic and Masonry, History of Advanced Masonry, Structural Masonry, Modeling of Masonry Structures, Modern Masonry Manufacturing, Masonry Walls",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 20th 2022",dateEndSecondStepPublish:"July 21st 2022",dateEndThirdStepPublish:"September 19th 2022",dateEndFourthStepPublish:"December 8th 2022",dateEndFifthStepPublish:"February 6th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Prof. Amjad Almusaed, affiliated with Jönköping University has carried out a great deal of research and technical survey work and has performed several studies in these areas. He has edited many international books and is an active member of many worldwide architectural associations. He has published more than 170 international academic works (papers, research, books, and book chapters) in different languages.",coeditorOneBiosketch:"Associate Prof. Asaad Almssad has more than thirty years of experience in industry, academia, and research at Umeå University, Sweden; Karlstad University, Sweden; and various European and non-European institutions. His research focuses on building structures, materials, sustainable building, and energy efficiency in building systems. He has authored and co-authored more than fifty research papers and many books. 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Differentiation and identification of these pathogens are a huge challenge and very important for the patient’s diseases diagnosis and treatment. Scanning electron microscope (SEM) is a very strong tool for detection and observation of pathogens from the clinical samples that helps us obtain a direct proof of the pathogen on the surface of the skin samples of the lesion. Based on the detailed morphologic image, we can recognize the ultrastructural of the pathogen and understand the pathogenesis of the skin-infected diseases. During recent years, we collected a lot of pathogenic microorganisms’ photographs taken by SEM. These pathogens include fungi (
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Tinea capitis | \n\t\t\tErythema, scales on the scalp; hair broken and hair loss | \n\t\t\tInfected hair | \n\t\t\tFungus ( | \n\t\t
Tinea capitis | \n\t\t\tExcessive scales and hair loss on the scalp | \n\t\t\tInfected hair | \n\t\t\tFungus ( | \n\t\t
\n\t\t\t\t | \n\t\t\tSlightly pruritic, monomorphic follicular papules and pustules | \n\t\t\tKeratotic plug of pustule of hair follicle | \n\t\t\tFungus ( | \n\t\t
Pityriasis versicolor | \n\t\t\tErythema and scaly, hyperpigmentation or hypopigmentation of skin | \n\t\t\tScales | \n\t\t\tFungus ( | \n\t\t
Mucormycosis | \n\t\t\tProgressive red plaque around the inner canthus | \n\t\t\tCultured colony | \n\t\t\tFungus ( | \n\t\t
Mucormycosis | \n\t\t\tPurulent granuloma of left forearm | \n\t\t\tCultured colony | \n\t\t\tFungus ( | \n\t\t
Cutaneous alternariosis | \n\t\t\tAn ulcer covered with crust on left anterior tibia | \n\t\t\tCultured colony | \n\t\t\tFungus ( | \n\t\t
Chromoblastomycosis | \n\t\t\tRed plaque in the left knee | \n\t\t\tCultured colony | \n\t\t\tFungus ( | \n\t\t
Primary laryngeal aspergillosis | \n\t\t\tHoarseness, severe paroxysmal coughing and tachypnea | \n\t\t\tBiopsy tissue | \n\t\t\tFungus ( | \n\t\t
Acne | \n\t\t\tRecurrent papule and pustule acne | \n\t\t\tPustule | \n\t\t\tBacteria ( | \n\t\t
Pediculosis pubis | \n\t\t\tIntense itching of the scalp | \n\t\t\tParasite | \n\t\t\tParasite ( | \n\t\t
Demodicid mites | \n\t\t\tItching, multiple erythema, papules, pustules | \n\t\t\tHair follicle plug | \n\t\t\tParasite ( | \n\t\t
Summary of diagnosis and clinical manifestation, kinds of sample, and the image of the pathogens observed by SEM
All samples for SEM were taken from clinical patients. These samples included infected hair, scales, colony of culture, and tissue of skin biopsy. The samples for SEM were fixed in 2% glutar-aldehyde for 4 h at 4 °C, dehydrated through four gradations of alcohol solutions (50%, 70%, 95%, 100%, progressively) for 15 min each, then soaked in isoamyl acetate for 30 min. The specimens were prepared after critical-point drying method, under which condition they were gilded in a vacuum chamber and observed under the SEM, FEI Inspect F50, equipped with an FEG gun operated at 30 kV at high vacuum.
Tinea capitis is a common superficial fungal infection of scalp hair follicles and surrounding skin. It often affects children rather than adults. Its pathogens are dermatophytes, usually species in the genera
a. A 9-year-old boy, weighing 25kg, presented to our clinic with slightly itching, multiple patchy areas of gray scaling lesions on the scalp and obvious hair loss.
We describe two cases of tinea capitis due to
a-b. Cuticle layers of hair shaft were seriously destroyed and a large part had been lost, exposing the fibril cortex inside which many arthrospores were noted. c. High magnification of “a” showing the cuticle layers completely destroyed with the residual cortex fully filled with arthroconidia (A) and distorted fibril bundles (F). d. An arthrospore (A) at high magnification showing irregular convex granules on the chitinous surfaces and the poles bordered by a protruding ring structure (R), bulged by a hemispherical convex (HC). Numerous residual fibril fragments (F) noted around the spore as well as on its surface.
The second patient is a 5-year-old boy in good health, weighing 19 kg and presented at our clinic with a 1-month history of excessive scales and hair loss on the scalp (Fig. 3a). He had been previously diagnosed with tinea capitis in a local hospital, and received oral itraconazole 100 mg per day for 14 days administered with water. However, the area of hair loss enlarged slightly. Additionally, he had a history of direct contact with a pet dog. Direct microscopic examination (with 10% KOH) of broken hair strands showed numerous spores inside as well as outside of the hair strand. Simultaneously, strands were observed under SEM, and there were many round spores in and around the hair strand (Fig. 4a, b). Fungal culture revealed yellow filamentous colonies, which were identified as
a. A 5-year-old boy presented with 1-month history of scalp scales and hair loss, who had received oral itraconazole 100 mg per day with water for 14 days; b. The patch with hair loss was smaller and without scale after oral itraconazole 100 mg per day with whole milk for 14 days; c. There was no apparent hair loss on scale after 40 days at end of treatment.
a-b. After oral itraconazole 100 mg per day with water for 2 weeks, broken hair strands with many round spores in and around the hair strands were evident under scanning electron microscopy (SEM); c-d. Oral itraconazole 100 mg per day with whole milk for 14 days, the number of spores were markedly reduced in broken hair strands, and spores appeared very irregular under SEM.
The following is a case of
a. A 25-year-old man with complains of slightly pruritic, monomorphic follicular papules, pustules, and secondary keloid on the upper trunk and neck. b-c. SEM of the hair follicle from the upper trunk. These demonstrated a large number of globular or orbicular-ovate yeasts of budding daughter cell, with collar structure around the budding. b. Globular yeast. c. Orbicular-ovate and globular yeast in the same sample.
Pityriasis versicolor is a superficial fungal infection of the skin and caused by
This is a case of pityriasis versicolor due to
a. A 27-year-old man presented in our clinic with extensive erythema and scaly for 6 months. b. Under SEM, numerous hyphae (H) went through the scaly, length of which is about 10–20 μm. c. Under SEM, abundant of 3–5 μm in diameter grapes-like spherical
Mucormycosis is a clinically rare and fatal opportunistic fungal infection, which invades nasal, brain, lung, gastrointestinal tract, skin, and other parts with acute, subacute, or chronic process. The routes of invasion contain respiratory tract, digestive tract, skin, and neonatal umbilical region [10]. Its pathogens are fungi in the order
The following is a description of two cases of primary cutaneous mucormycosis caused by
a. A 47-year-old farmer was presented to our clinic with 1-year history of progressive red plaque around the inner canthus. Faint yellow exudation was oozing from the ulceration at the center of plaque. Some scales were also observed on the plaque. b. SEM observations revealed non-apophysate sporangia with pronounced columellae and conspicuous collarette at the base of the columella following sporangiospore dispersal.
The other case is of a 69-year-old female farmer, who presented to our clinic with the history of a progressive purulent granuloma of her left forearm (Fig. 8a) following a fracture of left forearm about 11 months earlier. Broad, nonseparate hyphae were seen in pathologic study with methenamine silver stain (Fig. 8b). Fungal culture revealed white filamentous colonies that were identified as
a. A 69-year-old female was presented to our clinic with a progressive purulent granuloma of her left forearm. b. Broad nonseparate hyphae were seen in pathologic study (methenamine silver stain, ×200). c-d. The sporangiophores of
We describe in the following is a rare case of a healthy individual with cutaneous alternariosis due to infection with
a. Ulcer with an overlying crust on the patient’s skin of left anterior tibia. b. Fungal culture of the tissue revealed dark grey-white colonies with a dark-brown underside. c. SEM observation of slide culture revealed beaked conidia.
Chromoblastomycosis is a chronic fungal infection of the skin and subcutaneous tissue caused by dematiaceous fungi. Common pathogenic fungi are
In this part we describe a case of chromoblastomycosis due to
a. A 34-year-old male with a 12-year history of a red plaque in the left knee. b. Under SEM observation: dematiaceous hyphae with many well-defined septa, conidiophores, and oval brown spores arranged in a clump could be seen. The surfaces of conidiogenous cells were smooth. Oval spores were arranged around conidiophores.
Primary laryngeal aspergillosis is a rare opportunistic infection caused by
We describe in the following a case of primary laryngeal aspergillosis due to
a. A 23-year-old female undergraduate student presented with hoarseness, severe paroxysmal coughing, and tachypnea. Laryngoscopy revealed obvious white plaques on the swollen vocal cords and laryngeal ventricle. b. SEM of the biopsy tissue revealed hyphae branching at 45° angles destroyed the vocal cords tissue.
Acne is a chronic inflammatory disease of the sebaceous–pilosebaceous system. It is estimated to affect 9.4% of the global population [17]. Acne is closely related to the combination of genetic and environmental factors, among which
The following is a description of a case of acne in a 24-year-old girl. She suffered recurrent papule and pustuleacne for 6 months (Fig. 12a-b). We removed the follicular plug with sterile hemostatic forceps and observed it through SEM. Under SEM, abundant rod-shaped bacteria were closely spaced in follicular plug tissue (Fig. 12c). Treatment with oral minocyline 50 mg twice a day and topical use of adapalene gel was effective.
a. A 24-year-old girl who suffered recurrent papule and pustule acne for 6 mouths. b. Under the dermoscopy, the papule was semisphere with tawny follicular plug inside. c. Under SEM, rod-shaped bacteria were closely spaced in follicular plug tissue.
Pediculosis is a skin disease caused by arthropods. Its pathogens are three lice species including head louse, crab or pubic louse, and body louse, which cause the
We describe a case of pediculosis on the scalp of a 6-year-old boy caused by the crab louse [23]. The boy was presented to our clinic with the complaint of intense itching of the scalp. There were some small pieces of erythema (in the circle) and a brown dot-like substance on his scalp (Fig. 13a). The dermoscopy revealed a brown parasite (0.9 mm in horizontal axes and 1.2 mm in vertical axes) with two crab-like feet adhered to the scalp (Fig. 13b). Microscopic examination and scanning electron microscope showed the detail of this insect (Fig. 13c-d). Based on these morphological findings, the diagnosis of
a. There were some small pieces of erythema (in the circle) and a brown dot-like substance on the scalp (arrow). b. The dermoscopy revealed a brown parasite (0.9 mm in horizontal axes and 1.2 mm in vertical axes) with two crab-like feet adhered to the scalp. c. Under the microscope, the parasite was characterized by a flat body like a crab and three pairs of feet in different sizes. There was an area (red box) full of blood in the middle part of the parasite. A large number of short setae (arrow) were noted at the edge of the parasite abdomen. d. The SEM showed a vivid three-dimensional ultrastructure of the parasite: the whole body was composed of three parts including spherical head, chest, and elliptical abdomen; a pair of feelers was noted on the head; the three pairs of feet were in shaped section and curved serrated claws were noted at the end of foot; short setae in different length were not only at the edge of the abdomen but also on the feet.
Demodiciosis is a kind dermatitis caused by
The following is a description of a case of demodicosis due to
a. A 28-year-old man came to our clinic because of itching, multiple erythema, papules, pustules lesions on the nose and cheek. b. SEM revealed a
Comparison of cancer indicators across different regions and countries is important to understand the effectiveness of cancer prevention and control measures. Considerable care has to be taken however to ensure that the data are indeed comparable and have the necessary level of quality not to result in the production of biased or misleading statistics. Centralized processes to ensure comparability of data are costly in terms of time and resources and should ideally be supported with efficient and effective automated tools. The goal towards the eventual federation of such processes requires the means of formally ascertaining the level of the quality of the underlying data.
\nPopulation-based cancer registries (CRs) are information systems designed for the collection, storage, and management of data on cancer patients. They collate information on all cancer cases occurring in a defined population and play a critical role in the planning and evaluation of cancer control activities at population level (particularly via trends in incidence, mortality, prevalence, and survival), as well as in identifying good practices of patient care [1, 2]. They also provide the means for evaluating the effectiveness of screening programs and contribute actively to cancer epidemiological research.
\nCRs may be nationally based, covering the entire country (such as in Europe for Finland, Sweden, and Slovenia), or regionally based (such as in France, Italy, and Spain). Whereas regional CRs may provide total coverage of the country, in some cases they only provide partial coverage and estimations based on the partial coverage are used to provide national statistics. The production of reliable statistics is directly dependent on the quality of the underlying CR data.
\nThe data collected by a CR are in accordance with the purpose for which the registry has been established, dependent on the available information and resources. Nevertheless, the accent is on the quality of the data rather than on the quantity [3]. Whereas the initial focus was on monitoring cancer incidence and the trends over time, many registries now collect patient follow-up details in order to compute survival.
\nCRs need to register all cancers diagnosed in a defined area and have consequently to access multiple data sources, including hospital discharge and outpatient records, pathology laboratory results, oncology/radiotherapy/clinical hematology records and death certificates. The combination of such sources is the cornerstone of the data collection process [4]. Additional data sources include screening programs, communications from general practitioners, drug prescriptions, and insurance reimbursement claims.
\nSets of rules and linkage routines are normally used to create provisional incidence records, which are then verified within a few months to confirm or discard cases [5]. Once the incidence data set has been consolidated, the data are thereafter cleaned according to specific data-cleaning rules. Additional to the local CR procedures, wider standards for data collection, coding, reporting, and validation are required to facilitate data interoperability. Such standards are generally defined and agreed at national or transnational level, especially in relation to the data comprising the base denominator or the common data set.
\nWithin the last couple of decades, CR data have improved dramatically in quality and quantity, due largely to technological advances and the improved means for reliable record linkage [6, 7]. Owing to the fact that CRs collect and integrate data from very heterogeneous multiple information sources, a process of data harmonization is required both preceding and following linkage according to national and internationally accepted procedures. This process of harmonization has been defined as “all efforts to combine data from different sources and provide users with a comparable view of data from different studies” [8] and is a critical element for accurate and meaningful inter-comparison of CR data. It is also extremely important for the correct usage of anonymized or aggregated CR data in secondary-data analyses [9].
\nAn example of the importance of CR data harmonization relates to the implementation of the 1995 European Network of Cancer Registries’ (ENCR) recommendations for the coding of bladder tumors in the Scottish CRs in the year 2000. After the introduction of the recommendations, bladder tumor incidence rates halved [10] and became similar to those of other registries following the same rules. Notwithstanding such changes in coding, it always remains possible to calculate rates with the previous rules in order to assess time trends.
\nIn the US, the North American Association of Central Cancer Registries (NAACR) develops and promotes uniform data standards for cancer registration. These standardization efforts are of direct importance to the North American Surveillance, Epidemiology, and End Results (SEER) program [11] involving twenty-one North American CRs covering more than one third of the U.S. population.
\nWithin Europe, the standardization efforts of the ENCR, comprising over 150 individual registries, are similarly of importance to the European Cancer Information System (ECIS) [12]. The International Association of Cancer Registries (IACR), the International Agency for Research on Cancer (IARC), the European Commission, and ENCR have all played an essential role in European CR harmonization.
\nThe harmonization efforts ultimately benefit endeavors to compare cancer statistics at the global level [13, 14]. Data harmonization for inter-comparison purposes is generally achieved via the specification of common data sets in which the ranges and interdependencies of a core set of variables are defined by an agreed set of specific rules. The harmonization process is time consuming and requires consultation and agreement across a wide range of stakeholders, especially when the common data set serves multiple purposes. An example of a common data set comprising some fifty data variables and the rules specifying the variable values/ranges and the inter-variable relationships is provided in [15]. The ENCR common data set includes variables related to the patient, the tumor (including stage), treatment, and follow-up.
\nOwing to the need to ensure a high and consistent level of quality and harmonization, the CR common data sets are currently collected and processed centrally. Whereas centralized processes help control and ensure consistency, they add extra time delays in making the data available – not least from the overheads occasioned by increasingly stricter data-protection paradigms. Data cleaning and harmonization for CR inter-comparison purposes could be made more efficient by devolving the centralized processes to the local level – so long as consistency and data quality can be assured. Conformance of CR data to the FAIR data principles is key to realizing this aim.
\nThe four principles of FAIR data, encompassed in their felicitously named acronym, underlie the need for data to be: findable, accessible, interoperable, and reusable [16], also at a machine-readable and inferable level. The meaning of each term is elaborated by a set of three or four qualifying elements. The challenges to making data FAIR, in terms of the questions that have to be addressed, and some of the mechanisms towards meeting those challenges are summarized in \nTable 1\n.
\nFAIR data principle | \nQuestions to address | \nPossible means for addressing the needs | \n
---|---|---|
Findable | \nDo the data exist and where exactly? | \nData catalogs and inter-linkage of catalogs, with relevant search functions; registration of the data under unique identifiers; persistent links and identifiers; searchable metadata; appropriate synonym lists for search terms | \n
Accessible | \nIs authorization needed to access the data? How can the data be accessed physically? | \nData access and user identification controls; authorization request interfaces; application programing interfaces; data extraction scripts; file format metadata; identification of relevant application tools | \n
Interoperable | \nCan the data be integrated/combined fully/partially with another data set? Can the data be loaded from different applications? Are the data properly comparable with other data? What is the context of the data? How do the variables inter-relate? What are the measurement units of the variables? How can the measurement units be mapped to similar terms in another data set measured in different units? | \nMetadata descriptions of data variables; linkage of metadata terms to standard data dictionaries; mapping systems; knowledge organization systems; data quality contexts | \n
Reusable | \nDoes the data set contain limitations/disclaimers/assumptions? Are there data restrictions/licenses? Can the data be used for other purposes? Will the data still be accessible at a future date? May the data change over time? | \nContextual and provenance metadata; data-usage licenses; data persistence mechanisms; data-maintenance policies | \n
Challenges involved in making data FAIR, some of the questions that have to be addressed, and possible mechanisms for addressing them.
The foundations of FAIR were in fact laid down in several earlier initiatives [17] and the EU is actively supporting activities to progress the underlying concepts. Interoperability is arguably the most challenging of the four FAIR data principles outside of access to personalized data and is discussed further in Section 2. In relation to findable data, health data providers in many countries have started to create data portals and data catalogs.
\nWhereas a number of international CR portals provide access to anonymized and aggregated CR data sets [11, 12], it is not usually possible to provide secure access to record-level data through automated protocols due to the sensitive nature of health data, although SEER does provide an example of a way to access cancer data following a set of specific conditions. The challenges to CR data accessibility as far as record-level data are concerned are in fact less technical than administrative in view of the legal aspects of data-protection laws. Indeed, they are generic to all data where identification of a person is possible and, even with anonymized data sets, care has to be taken to ensure that persons cannot be re-identified using other data sources. Steps are being taken in the EU, where the data-protection laws are amongst the strictest in the world, to address mechanisms to facilitate authorized access to health data.
\nReusability for CR data mainly refers to their use for secondary-data purposes and hinges on accurate and comprehensive description of the data in both the contextual and semantic sense. In this regard, there is a close relationship with the principle of semantic interoperability (c.f. Section 2.1) – if the data are comprehensively described, the possibility for data reuse is greatly assisted. The latter may be appreciated to some extent by considering SEER data, which are well described in terms of metadata and draw from data adhering to the NACCR data standards. SEER data have consequently led to hundreds of scientific publications on cancer epidemiology. In contrast, the health data environment in Europe is extremely fragmented, but recent initiatives on data reuse are described in [18], including national initiatives in Finland, France, Portugal, and Italy. Within the EU as a whole, the first preparatory steps have been undertaken to create a European Health Data Space (EHDS) [19] for facilitating primary and secondary reuse of health data.
\nThe three qualifying elements defined under FAIR’s interoperability principle [16] are in relation to knowledge representation – with particular reference to the use of formal, shared languages and vocabularies as well as linkage to other data descriptors/metadata. Such aspects largely refer to syntactic and semantic interoperability.
\nMechanisms to address semantic interoperability include metadata schemas drawing on standard data dictionaries and thesauri, metadata catalogs (e.g. Data Catalog Vocabulary, DCAT [20]), metadata registries (e.g., ISO/IEC 11179 metadata registry standard [21]), knowledge organization systems (e.g. Simple Knowledge Organization System – SKOS [22]), linked open data (LOD) or any combination of these. Such mechanisms can be incorporated into frameworks and architectures designed for the purposes of supporting FAIR data processes.
\nA non-exclusive list of FAIR-supporting infrastructures include: beacons [23, 24], used primarily for discovering and sharing of genomic data; a federated semantic metadata registry framework [25], which also provides a potential model for population-based patient registries including CRs [26]; the MOLGENIS data platform for data sharing [27]; the Apache Atlas data governance and metadata framework [28]; the European Open Science Cloud (EOSC) interoperability framework [29]; and the FAIR digital object framework [30]. The way in which the FAIR digital object concept is able to support data interoperability, particularly with reference to EOSC, has been discussed in [31].
\nThe main challenges to semantic interoperability lie in the interlinkage, mapping, and maintenance of metadata between different standards and systems. The availability of standard dictionaries and ontologies together with knowledge organization systems such as SKOS allow data providers to describe their record-level metadata variables in ways meaningful for data users to combine data sets from different data sources. The fact that these standard resources are available in machine-readable ways opens up the possibility for automation of the data-linkage process by intelligent agents, especially when used in conjunction with data registration and cataloging systems.
\nAs important as the semantic context of data is, it does not fulfill all the requirements to make data interoperable. According to the Data Interoperability Standards Consortium [32], data interoperability concerns “the ability of systems and services that create, exchange and consume data to have clear, shared expectations for the contents, context and meaning of that data.”
\nWhereas semantic definitions and linkages of metadata can help describe the context and meaning of data, they cannot per se vouch for the quality of the data. Data quality is of prime importance for CRs whose data are compared between regions and countries for epidemiological purposes or for gauging the effectiveness of cancer healthcare policy initiatives.
\nWithout having some information regarding the quality and veracity of the data sets to be combined, any assumptions drawn from the data integration will at best be speculative. The FAIR data principles do not explicitly address such aspects, apart from in the sense that the usefulness of the data is somehow determinable by the user [33]. One of the qualifying elements under the reusable principle however does require that (meta)data meet domain-relevant community standards, of which quality could arguably form a part, and acknowledgement is given to the critical importance of the quality dimension as identified in the initiatives on which FAIR builds [17].
\nVarious ways for defining data quality have been propounded, particularly in relation to terms of classification/categorization. The ideas build on research conducted in the 1990s, mainly in relation to total data quality management (TDQM) for business processes. An overview of this early work [34] further developed the ideas and formulated a hierarchical data-quality framework in order to addresses the contemporary needs of big data with a view to developing data-quality evaluation algorithms. The hierarchy consists of fourteen elements (with a number of associated indicators) classified under the five dimensions of: availability, usability, reliability, relevance, and presentation quality. Most of these dimensions turn out to be closely aligned with the FAIR data principles and are therefore inherent to the objectives of the FAIR digital object framework (FDOF) [30]. The FDOF provides the means of resolving the identifier associated with a FAIR digital object into sets of information relating to the features required by the FAIR data principles. Factoring out these commonalities essentially removes all but the “reliability” dimension (equating to the trustworthiness of data) in the hierarchy of [34] and one of the elements (Timeliness) under the “availability” dimension as summarized in \nTable 2\n.
\nBig data quality dimension | \nBig data quality element | \nFAIR principle | \n
---|---|---|
Availability | \nAccessibility | \nA | \n
Timeliness | \n— | \n|
Authorization | \nA | \n|
Usability | \nDefinition/documentation | \nI,R | \n
Credibility | \nR | \n|
MetaData | \nF,I | \n|
Reliability | \nAccuracy | \n— | \n
Integrity | \n— | \n|
Consistency | \n— | \n|
Completeness | \n— | \n|
Auditability | \n— | \n|
Relevance | \nFitness | \nR | \n
Presentation quality | \nReadability | \nA,I | \n
Structure | \nA,I | \n
Cross-matrix of the quality dimensions (and associated elements) proposed for big-data quality [34] with the different FAIR principles.
Despite the lack of a universally agreed data-quality system, five of the resulting six elements are common to five of the six quality dimensions identified in [35], which also provides suggested metrics. The different sixth elements are “auditability” and “uniqueness” respectively. In total, the seven quality elements (which we refer to as quality dimensions in line with the terminology used in [35]) are described in \nTable 3\n together with the proposed means of measurement:
\nDimension | \nMeasure | \nUnit of measure | \n
---|---|---|
Completeness | \nDegree in which all the essential data are provided. Can be measured at both data level (missing data records) and variable level (missing variables within a record) | \nPercentage/ratio (e.g. proportion of captured data against potential of 100%) | \n
Integrity/Validity | \nDegree in which data types are standardized or conform to rules and relations encapsulated in the data. | \nPercentage/ratio (e.g. number of non-conformant data elements missing as a ratio of number of records). | \n
Consistency | \nDifferences found for data entities (or their representations) that should be identical or equivalent | \nNumber (e.g. number of differences) | \n
Accuracy | \nDegree in which the real-life situation is different from its representation | \nPercentage (e.g. percentage of records to that pass pre-specified data-accuracy rules; | \n
Timeliness | \nDegree in which the data are representative of the current situation | \nTime difference | \n
Uniqueness | \nRedundancy of data which could otherwise be derived, leading to maintenance and consistency issues | \nPercentage to total of duplicates data/data variables | \n
Auditability | \nEase in which/extent to which auditors can evaluate the quality of the data | \nAn agreed or standardized scale | \n
Description and proposed units of measurement of the seven generally agreed data-quality dimensions.
ISO 8000 is an international standard for managing, measuring, and improving the quality of data. Part 8 of the standard [36] (Information and data quality: Concepts and Measuring) can be used independently of the other parts and is specifically focused on providing the means for measuring the quality of data and information against scales that the standard requires the enterprise to establish. It can therefore be used as a means for auditing the data quality.
\nISO 8000-8 categorizes data/information quality under: syntactic quality, semantic quality, and pragmatic quality. Syntactic quality relates to the degree in which the data/information conforms to its metadata specifications and the standard requires the specification of a full set of syntactic quality rules. Semantic quality relates to the correspondence/relationships of data or information to other entities as represented in a conceptual model. The standard requires a documented conceptual model and a description of the means used for verification against the model. Pragmatic quality concerns usage-based requirements that have to be expressed as specific perspectives or dimensions not covered by the other two quality criteria. It can relate to such aspects as accessibility, completeness, security, etc. Using a standard such as ISO 8000-8 would address the issue of auditability as well as allow the means for formally specifying the other six quality dimensions and the metrics for their measurement.
\nRegarding the CR common data set, the metrics related to variable-completeness (i.e. completeness of the common data mandatory variable set), timeliness, and uniqueness can be relatively easily defined. The common data set specifies the permitted set of variables and qualifies which variables are mandatory. Timeliness can be ascertained from the most recent batch of case registration dates, and uniqueness can be addressed by ensuring that the common data-set template does not lead to duplication of data contained in another variable. The more intricate quality dimensions regard integrity, accuracy, consistency, and data-completeness (completeness of the cancer cases within the catchment area of the population).
\nWhereas integrity and consistency can be assessed from the data, accuracy and data-completeness have to be ascertained from the real-life situation [35]. It is a process followed by CRs when cross checking summary values against data from the primary data feeds (e.g. hospital/clinical records). There may also be accuracy issues within the primary records themselves, such as incorrect data entry, which may be difficult to ascertain at the CR level. Integrity and consistency checks may be able to serve as a proxy in some instances where data entry is incorrect and in violation of the data rules; more subtle, systematic errors could possibly be detected using variances in frequency measures on variables. Establishing a formal data-quality process such as ISO 8000-8 at the first point of data capture is however perhaps the only way in which to assess the steps taken to ensure data accuracy. Such a process if harmonized across the data sources could provide a standard metric to integrate into the quality stamp of further processing operations. Metrics for estimating data completeness of CR data have been summarized in [37]. The data-quality dimensions most relevant to each stage of the CR data throughput chain are depicted in \nFigure 1\n.
\nData-quality dimensions relevant to the different stages in the CR common data set throughput process. Auditability can span all processes.
The decision processes underlying the choices to combine data sets dependent on their quality metrics will depend largely on the intended purpose of the end application. The means for one possible decision-making framework is proposed in [38]. The framework is presented in terms of business-related data but raises a number of important considerations. It lays down five requirements for data-quality metrics and argues these requirements in practical examples of metrics proposed by others for measuring the specific quality dimensions of timeliness, completeness, reliability, correctness, and consistency (where correctness corresponds to accuracy and the metric for consistency can be applied also to integrity). The five requirements are:
provision of minimum and maximum values;
provision of interval-scaled values;
means of determining the metric values on the basis of the associated configuration parameters and also whether the quality-criteria objectivity, reliability, and validity of the metric are fulfilled;
consistent aggregation of metric values on different data-view levels; and
economic efficiency of the metric (i.e. the cost incurred by the metric).
Ontologies are relevant for describing the semantic relationships between entities in a data model. Bioportal [39] provides a comprehensive repository of biomedical ontologies. The Web Ontology Language (OWL) [40] underlies many of these ontologies and represents the concept definitions and relations between them as sets of Resource Description Framework (RDF) [41] graphs.
\nInterestingly however, ontologies formulated on description logic (such as OWL) can also be made to provide a basis for ascertaining the quality of data sets. A single tool can thereby be developed to handle both the semantic and the data-quality contexts. Whereas we present a model for achieving this for CR data, the concept is sufficiently generic to be applied to other data domains. An important requirement is that some form of data-validation rules are specified a priori.
\nFor the purposes of comparing CR data, a common data set specifies the metadata of a minimum set variables to be included. Whereas, the availability of a common data set is not necessarily an essential aspect of the data-quality model, it does however aid the process to provide data-quality metrics easily interpretable by the end application.
\nDescription logics (DLs) are a family of languages used to represent in a structured and formal sense knowledge about a given domain [42]. They also provide the means for a degree of machine-reasoning allowing automated inferences to be made on the basis of statements concerning that knowledge.
\nDL languages are classified by language expressivity. Expressivity basically determines the richness of the modeling capacity of the language; a language with greater expressivity is able to model more complex relationships but at a cost of computing performance. In view of the latter, it is generally preferable to limit the DL expressivity to the minimum needed for the modeled aspects of the domain.
\nKnowledge about a domain can be captured in an OWL ontology using DL statements that are be classified into TBox and ABox axioms. TBox axioms refer to the terminological part of the ontology and ABox axioms, to the assertional part. The terminological part is analogous to the database concept of a database schema, which describes the structure or layout of the database while the assertional part is analogous to a particular instance or population of a database described by that schema [43]. Thus, OWL TBox axioms describe the hierarchies and relationships between OWL classes and ABox axioms describe specific instances of classes, also referred to as individuals.
\nThe primary two semantic constructs DLs use are: unitary predicates (or concepts) describing entities equating to OWL classes/individuals; and binary predicates (or roles, equating to OWL properties) that describe relationships between entities. DLs are termed as decidable fragments of first-order logic [42] and TBox and ABox statements can in fact be expressed as first-order logic statements. The expressivity of a DL language determines the set of operators permitted. The Attributive Language with Complement (ALC) expressivity allows quite a rich modeling language to handle most of the validation checks in the ENCR common data set. ALC includes: subclasses (⊑), intersections (⊓), unions (⊔), negation (⌐), existential restrictions (∃), and universal restrictions (∀). The restriction operators are used for qualifying the entities on which a given role acts, with ∃ specifying the notion of an “at-least-one relationship” and ∀ the notion of an “only relationship” and are similar to the existential and universal quantifiers of first-order logic.
\nThe data-validation rules encapsulate the part of the domain model that minimally needs to be modeled. The challenge lies in designing the ontology in a way that is straightforward to understand, easy to maintain, and models the data relationships satisfactorily whilst performing efficiently under automatic reasoning. Consideration should also be given to its potential reuse and extensibility. In practice, the interplay between all these factors may lead to a number of compromises.
\nProtégé [44] is a convenient, free, and open-source ontology-editing tool that provides a friendly user interface for creating and testing axioms. Such editing tools are particularly useful for aiding the design process in which the most appropriate design patterns may not be immediately obvious. Taking the example of the ICD-O-3 [45] spindle cell sarcoma with morphology code 8801 and tumor behavior code 3 (malignant behavior), the compound code (morphology-behavior) can be modeled in the ontology in several ways (where the morphology code has been prepended with the letter “\n
\nEqs. (1) and (2) are similar apart from the fact that behavior in Eq. (2) has been expressed in terms of an existential restriction. Behavior may not even need to be modeled at all and just left implicit in the name of the class (since the trailing digit denotes the behavior code). The choice ultimately depends on how the morphology-behavior class will be used in other classes. For instance, a prostate tumor can have ICD-O-3 topography code C619, morphology code 8801, and behavior code 3 and may be modeled in a similar fashion to Eqs. (1)–(3):
\nIt could also be modeled as an Abox axiom to denote that this is a specific instance of a more general prostate cancer class. It is not necessarily a simple choice since there are advantages and disadvantages to each approach. With Eq. (5) the concepts of topography and morphology can be declared disjoint (a topography is not a morphology), but then modeling a tumor type or signature (e.g. \n
and thereby makes it a harder task to access the code values without increasing the language expressivity (such as including inverse operations or complex role inclusion axioms or other rules). It would be even harder to access the behavior code had Eq. (2) been used owing to the chain of existential restriction. Eq. (6) results in automatic class subsumption of the conjunction \n
Nevertheless, subsumption is a primary mechanism used by automatic reasoners to make inferences on a knowledge base and is perhaps the most critical factor to take into account in the design of an ontology that models validation rules predominantly using TBox axioms. OWL uses the open world assumption (OWA) in which the truth of a statement is unknown unless it is expressly known to be true/false – the philosophy being that there may always be extra information not yet declared in the knowledge base that has further bearing on the statement. The consequence is that an entity having topography \n
Also relevant is the balance between pre- and post-coordination of the ontology [48] – in pre-coordination, all the relationships are explicitly declared a priori, whereas in post-coordination a reasoner is used to infer relationships between entities a posteriori. In addition, other types of rules can be incorporated into OWL ontologies using the Semantic Web Rule Language (SWRL). SWRL extends the expressivity of OWL DLs using Horn-like logic rules (in which logic statements are written in terms of an implication) and can overcome some limiting cases in OWL at the potential cost of decidability and interoperability [49]. \nTable 4\n summarizes some of the more important mechanisms that can be employed in validation-type tests.
\nPre/post coord | \nMechanism | \nUtilization | \nAdvantages/disadvantages | \n
---|---|---|---|
Post | \nSubsumption | \nDefined classes (TBox) | \nEnsures subsumption (since classes are equivalent). Can give rise to unintended equivalences | \n
Post | \nSubsumption | \nGeneral Concept Inclusions (TBox) | \nEnsures subsumption if the ontology design is correct. Needs careful ontology design to ensure the specific order of subsumption, which may conflict with other requirements | \n
Post | \nSubsumption | \nIndividuals and higher DL expressivities (ABox) | \nGreater flexibility and functionality. More difficult to control logic, and computationally expensive | \n
Post | \nInconsistency of class structure | \nDisjoint class definitions | \nStraightforward to catch any validation errors. Can lead to unintended class inconsistencies for ontologies with many class inter-relations | \n
Post | \nAdditional logic (internal to ontology) | \nSWRL | \nProvides extra functionality. Difficult to control if many rules and can lead to portability issues | \n
Both | \nAdditional logic (external to ontology) | \nProgramming logic | \nConsiderable control and extra functionality. Requires a dedicated computer program and extra maintenance | \n
Pre | \nComprehensive assertions | \nPredefinition of all entities and relationships | \nAll the relationships are known a priori. Ontology can be very large and lead to performance issues if interfaced with ontologies requiring automatic reasoning | \n
Summary of the most important ontology-based mechanisms that can used for data validation purposes with their main associated advantages/disadvantages.
There are thus a number of careful choices to be made dependent upon how the ontology will be used. The consequence of these design decisions may compromise the ability to reuse existing ontologies as well as render the ontology developed unsuitable for wider purposes.
\nAn alternative to using an ontology for data validation, but which still draws directly from the data model, is to use a data shapes language such as the Shapes Constraint Language (SHACL) [50] or Shapes Expressions (ShEx) [51]. Both languages benefit from the possibility of formulating the rules under the closed world assumption (CWA) which, contrary to the OWA, considers a statement to be false unless it has otherwise explicitly been declared to be true.
\nThe degree of complexity that can be handled for the inter-variable validation checks is more limited, but in cases where this does not pose a problem, SHACL in particular provides a number of advantages. SHACL is specifically intended as a language for describing constraints on RDF data and has been used to describe ontology design patterns for validating data in Electronic Health Records (EHRs) conforming to Clinical Information Modeling Initiative (CIMI) models [52].
\nShEx can also be used to validate data but the underlying philosophy is different from that of SHACL. As noted in [53], ShEx is more grammar-related whilst SHACL is more constraint-related with the result that ShEx puts greater focus on validation results in contrast to SHACL that gives more attention to validation errors. As discussed in Section 4.4, ShEx is particularly useful in detecting syntactic and range errors in the preprocessing stages of CR data validation. \nFigure 2\n provides an overview of the applicability of the semantic-web tools to the different data-validation steps and the quality dimensions they are able to address.
\nApplicability of the semantic-web tools to the different steps of the validation process and the quality dimensions they are able to measure. Shape languages such as ShEx and SHACL provide the means for finding non-compliance to the more straightforward data validation rules. More complex validation checks require the increased functionality offered by DLs maybe in combination with SWRL and dedicated program logic.
Before CR data can be compared at inter-regional or international level, they have to pass through a rigorous cleaning process. From the point of view statistical analysis, assessment of the quality and reliability of data hinges on the basic requirement of the representativeness of the data. A large CR data set for which reasonable doubts exist concerning the data representativeness has less value than a small CR data set with high representativeness.
\nMore specifically for statistical analyses to derive incidence and survival indicators from CR data, the two required dimensions are completeness (the confidence that all diagnosed cancers in the population are actually included in the data set) and accuracy (the confidence that the proportion of cases with a given set of characteristics truly reflects reality [37, 54]). Whereas timeliness is another important dimension [54], it may lead to some trade-off with the degree of data completeness [55].
\nOne cause of incompleteness observed in cancer survival studies results from the varying risk of death from other causes than cancer, and is more pronounced for the older age brackets [56] (competing-risks phenomenon). Other observational studies performed with the availability of additional, post factum data reveal that the level of incompleteness can also be cancer-site specific [55].
\nIn addition, high-quality cancer data should have high comparability between different populations over time, which can best be achieved using up-to-date, homogeneous, and consistent data collection and recording procedures [54]. Application of the standard data validation rules is one way of ascertaining the comparability of data between different CRs, as discussed in the following sub-sections.
\nTNM (Tumor, Nodes, Metastases) is a globally recognized cancer staging classification system for describing the extent and spread of solid tumors in terms of tumor size, invasion of lymph nodes, and presence of metastases. One of the validation checks relates to the validity of TNM stage on the basis of the associated TNM parameters (including: topography, morphology, pathological/clinical T, N, and M codes, TNM edition, as well as age, and grade for certain tumor sites). Validity can be ascertained using the automatic reasoner to infer the stage from the parameters and compare it with the value provided by the registry. Axioms to model stage can be defined along the lines of the example taken for stage I prostate cancer:
\nin which:
\nand all the ICD-O-3 morphologies associated with carcinoma have the form similar to:
\nin which, for example:
\nThe resulting subsumption process for a CR case record passed in with the values: topography C619, morphology 8140, TNM edition 7, and TNM parameters: T2a, N0, M0 would be the following:
morphology \n
topography C619 together with the subsumed morphology \n
the subsumpton result of (b) together with the specified TNM parameters, are finally subsumed under the stage class
The value of stage inferred by the reasoner can then be compared with the stage value provided with the CR case record in order to validate the record. Axioms described in this manner can be developed to provide a modular structure to model TNM stage for all editions of TNM.
\nFor the purpose of deriving cancer incidence indicators, it is important in patients with multiple cancer case records to distinguish between tumors that are linked with an existing case and those that are not. The latter are referred to as multiple primary tumors and they need to be validated.
\nAn international set of rules provides the definition of multiple primary tumors [57]. Transcribing the rules into DL requires a higher expressivity owing to the need for ABox statements, inverse relationships, and qualified number restrictions. These requirements arise from the need to analyze the different permutations of the possible tumor pairings according to the rules. The latter can be transcribed as a set of TBox axioms which are used by the reasoner to test the dependencies of multiple tumor cases defined as a set of ABox axioms. TBox axioms take the form of constructs encapsulated in Eqs. (13)–(16) below (described in greater detail in [58]):
\n\nEq. (13) models the conjunction of two dependent morphology groups as a sub-class of the class depicting a duplicate morphology, according to one of the multiple primary Tumor rules:
\n\nEq. (14) models the conjunction of a previously-determined duplicate morphology with a hematological morphology type as a duplicate primary tumor condition, according to another of the multiple primary Tumor rules.
\n\nEq. (15) models the rule that if the two topographies of a tumor pairing are in any of the “other or ill-defined” topography groups or subgroups they are considered a duplicate topography group.
\n\nEq. (16) models a resulting duplicate primary tumor for the case of a duplicate morphology and a duplicate topography.
\nABox axioms are built up using permutations of tumor morphologies and topographies, where a tumor is defined by the TBox axiom as the conjunction of one morphology and one topography:
\nAccessing the morphologies from two tumor individuals to derive a morphology permutation can be performed using the ABox axiom:
\nwhere the name of the individual \n
ABox axioms for the tumor pairings (containing a morphology pairing and a topography pairing) can then be specified according to the template:
\nin which \n
The axioms can be constructed automatically from the input records since the cancer-case records have a patient identifier and a tumor identifier and therefore all the tumor-pairing permutations can be ascertained in a preprocessing step. On the basis of the TBox axioms, the reasoner classifies the ABox axioms under the class \n
The third batch of validation checks concerns the specificities of tumor types, particularly in relation to parameters including basis of diagnosis, grade, age at diagnosis, sex, and topography-morphology-behavior inter-dependencies. These checks concern many of the rule tables provided in [15] and are examples of rules that be modeled in a variety of ways as discussed in Section 3.2 and which ultimately can be related to the balance between pre- and post-coordination of classes [48].
\nA tumor type, which we refer to as a tumor signature, comprises a topography/set of topographies in association with a set of morphologies. The topographies and morphologies may additionally specify a number of restrictions on values of associated variables such as age of patient at diagnosis, sex, basis of diagnosis, grade, etc.
\nPre-coordination allows the greatest control over the definition of tumor signatures since it allows each tumor signature at its most granular level to be defined independently. Consequently, the permissible ranges of values of all the dependent variables can be specified for each tumor signature individually. The drawback to this approach is that it would result in over 200,000 unique tumor-signature classes and could have implications on reasoning speeds of other ontologies that use them.
\nThe design used by SNOMED CT [59] to handle all the possible clinical terminology class definitions is to create a number of general classes in a pre-coordinated way and capture the specializations of those classes either in equivalent classes or GCI expressions that would be determined in a post-coordinated way by means of the reasoner [48]. Emulating such a design would allow, for instance, the qualifying rule of age on a given morphology/set of morphologies to be expressed as a specialization. Taking as an example the morphology
The resulting subsumption for hepablastomas thereby provides a mechanism through which it can be ensured that all qualifying rules are respected in the data.
\nOnce the rules have been established in the ontology, the individual data records can be validated according to the various groups of tests (e.g. stage, multiple primaries, tumor signature, etc.). Of the seven generally agreed dimensions for data quality listed in Section 2.2, integrity, consistency, and variable-completeness of CR common data sets are ascertained in a relatively straightforward manner for each of the tests and scored in percentage terms of conforming records using the metric:
\nwhere \n
Variable-completeness would describe the extent of the availability of information/variables necessary for running the specific test. Integrity would provide information on the number of records passing the test. Consistency would then be a measure of data conformity across tests – e.g. consistency of the morphology-topography code combinations not just within one individual test but across all tests (TNM, multiple primary and tumor signature).
\nThe syntactic part of the integrity dimension (as differentiated in ISO 8000-8) can be measured from a preprocessing stage which in general is necessary to ensure the correct format of the cancer-case records before passing them into the DL-based validation checks. This preprocessing stage can itself be performed also with direct reference to the data model using a shape language such as ShEx as discussed in Section 3.3. ShEx is particularly appropriate for validating the format and ranges of the variable values and benefits from the possibility of formulating the rules under the closed world assumption. The output of this stage can therefore provide a metric for data-type integrity also in percentage terms of records conforming to the ShEx schema.
\nAs noted in Section 2.2.1, the quality dimensions posing greatest difficulty are data-completeness and accuracy. Various metrics to estimate the former have been proposed [37] and those based on mortality-incidence ratios or survival probabilities conform well to the data-metric requirements of [38]. Whereas accuracy issues may be insinuated from the result of the integrity/validity checks, the surest way of detecting them would be through a data-auditing process such as that advocated by ISO 8000-8.
\nThe chain of processes from preparation of the CR common data set to reading cancer case records into the ontology and performing the validation checks and counting the non-conforming records can be automated using the OWL application program interface (OWL-API) [61]. The OWL-API provides methods for accessing the ontology axioms, invoking the reasoner, and polling the results of the reasoning process. The API also allows the incorporation of program logic to permit greater expressivity although at the expense of increased maintenance.
\nThe strength of the ontological approach is that the data model and the data-quality model – at least for the integrity and consistency dimensions remain in synchronization owing to the fact that they are integrated in the same sets of ontologies. Not only does this aid transparency of the validation process but it also simplifies maintenance and version control via the URIs pointing to the most current version of the ontology.
\nMoreover, the outputs of the validation process are readily verifiable by a trusted third party since it would basically be a matter of rerunning the checks on the CR file and comparing the outputs. For situations where the integrity of the quality metrics is important, the trusted third party can provide such assurance by integrating the validation checks together with the tests for data completeness and accuracy into a data-quality certification scheme such as ISO 8000-8.
\nThe quality context is as important as the semantic context for interoperability of CR data and as applicable to machine-based reasoning as it is to human-based reasoning; even though the semantics might admit the apparent compatibility of data sets, any inferences drawn from their combination could be legitimately challenged without due attention to the data quality. The importance of taking CR data completeness into consideration when comparing survival estimates between different populations has been emphasized previously [62]. In short, data quality is a critical issue for health data where erroneous inferences could lead to potentially dire consequences [63]. Encapsulating quality metrics in the metadata associated with the data set would adapt well to the FAIR digital object framework, and indeed such a model was proposed as far back as 1999 [64] and more recently in [65].
\nAgreeing a common set of data-quality metrics is however not an easy task and perhaps explains the lack of an overall framework. Whereas the difficulties are more acute for unstructured data [66] and require complicated semantic enrichment techniques [66], processes dealing with structured data pose less difficulty. The key to a potentially elegant solution able to unify both semantic and quality aspects of interoperability may lie in the use of OWL ontologies for describing common data models, or at least relevant parts of them.
\nIf designed carefully, OWL axioms can be used for validating CR data sets against predefined rules as discussed in Section 4, thereby providing a quantitative quality index or set of indices for certain quality dimensions on which to base pragmatic decisions regarding the compatibility/comparability of different data sets. The availability of such a decision framework is critical to any eventual devolution of the centralized data-cleaning processes to the local level. It is also critical for purposes of secondary-data usage where the end user/application has to be aware of issues limiting the extent and purpose for which different data sets can be used.
\nWith respect to the generally agreed seven quality dimensions, completeness of the mandatory variable set (variable-completeness), integrity and consistency are ascertainable from the validation process of the CR common data sets with each dimension being measured in percentage terms of conforming records as suggested in [35] and according to Eq. (21). Uniqueness can be ensured by a correct definition of the common data set template and therefore be provided as a default measure for all CR common data sets. Timeliness can be determined directly from the data set variable relating to cancer-case registration date providing a metric easy to measure. Data-completeness can be estimated in several ways as discussed in [37], one of which also provides a quantifiable metric along the lines of Eq. (21). The metrics for these quality dimensions would therefore all fulfill the requirements stipulated for a data metric supporting a decision-based framework [38].
\nThe remaining quality dimension, accuracy, is dependent on the primary-data capture process, which is outside the control of cancer registries. Whereas, performance of the validation checks and frequency analyses of selected variables may provide some proxy measures for systematic errors, a more robust method would need a data-auditing process in the various stages of the data pipeline. The resulting accuracy metrics could then be passed along through each stage to form a compound accuracy measure on the data set.
\nIn this way, a comprehensive and structured data-quality context could be constructed and thereafter provided as an additional component of the associated FAIR digital object, as illustrated in \nFigure 3\n. This component would provide a direct means for decision-based mechanisms to compute quantitative differences between quality measures of data sets and thereby infer the suitability of their integration in some fashion.
\nDepiction of a FAIR cancer-registry data set in terms of a FAIR digital object (FDO). The FDO comprises the data itself and an associated set of metadata components that describe the data and their context. The FDO is registered in a catalog to make it findable. One of the metadata components provides information on how to access the data. Another metadata component describes the metadata and semantics of the data-set variables and links to standard dictionaries using the semantic relations of knowledge organization systems (e.g. SKOS). The semantic context provides an essential part of data interoperability and reusability. A further metadata component provides the data-quality context and “dots the ‘i’ of interoperability” by adding the second vital ingredient towards making the data interoperable and reusable.
Achieving data interoperability, at least in the widest sense, is a major challenge. In order to be able to integrate or compare heterogeneous data sets, data users non-expert in the respective data domains need a considerable amount of contextual information. Whereas these needs can be met partially by semantic linkage of metadata, the aspect of data quality is crucial especially in quality-critical disciplines such as health. The FAIR data principles acknowledge the importance of data quality but do not address it directly.
\nA means of quantifying the data quality context in CR data sets along a number of representative and widely accepted quality dimensions has been presented. These metrics provide a quality context that can serve as an additional set of metadata within the associated FAIR digital object and made available with any aggregated data derived from it. The latter is an important consideration for entities having access only to the aggregated data sets for which the information is no longer available to verify the data quality directly from the validation rules themselves.
\nHaving access to this type of data-quality information, even if measured in relatively simple terms, would enable data-processing entities to make certain informed decisions on the likely compatibility with other data sets. Not only is this a fundamental prerequisite to being able ultimately to federate the CR data-harmonization processes themselves but also to promoting the availability of CR data in ways that would prove useful and informative for secondary-data purposes. It would also allow more scrutiny and transparency on the results of secondary analyses that may have potentially far-reaching consequences.
\nAlthough the focus has been on CR data, the ideas are sufficiently generic to apply as a general framework to other data domains and is amenable to formalization in a data-quality auditing process such as ISO 8000-8 by providing a conceptual model and the defined means of verification against the model.
\nAll the work was performed solely by the authors and there was also no grant finding to acknowledge. All authors are employed by governmental or supranational entities and report no additional funding for the development of this manuscript.
\nThe authors declare that they have no competing interests.
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In addition, biostimulants applied to plants enhance nutrition efficiency, abiotic stress tolerance and/or plant quality traits, regardless of its nutrient contents. Several researches have been developed in order to evaluate the biostimulants in improving plant development subjected to stresses, saline environment, and development of seedlings, among others. Furthermore, various raw materials have been used in biostimulant compositions, such as humic acids, hormones, algae extracts, and plant growth-promoting bacteria. In this sense, this chapter aims to approach the use of biostimulants in plant growth according to the raw material used in their compositions as well as their effects on plants subjected to abiotic stresses.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Ana Carolina Feitosa de Vasconcelos and Lúcia Helena Garófalo Chaves",authors:null},{id:"32894",doi:"10.5772/27308",title:"Antimicrobial and Antioxidant Activities of Some Plant Extracts",slug:"antimicrobial-and-antioxidant-activities-of-some-plant-extracts",totalDownloads:7084,totalCrossrefCites:7,totalDimensionsCites:20,abstract:null,book:{id:"1958",slug:"phytochemicals-as-nutraceuticals-global-approaches-to-their-role-in-nutrition-and-health",title:"Phytochemicals as Nutraceuticals",fullTitle:"Phytochemicals as Nutraceuticals - Global Approaches to Their Role in Nutrition and Health"},signatures:"Elita Scio, Renata F. 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In small concentrations, these substances are efficient, favoring the good performance of the plant’s vital processes, and allowing high yields and good quality products. In addition, biostimulants applied to plants enhance nutrition efficiency, abiotic stress tolerance and/or plant quality traits, regardless of its nutrient contents. Several researches have been developed in order to evaluate the biostimulants in improving plant development subjected to stresses, saline environment, and development of seedlings, among others. Furthermore, various raw materials have been used in biostimulant compositions, such as humic acids, hormones, algae extracts, and plant growth-promoting bacteria. In this sense, this chapter aims to approach the use of biostimulants in plant growth according to the raw material used in their compositions as well as their effects on plants subjected to abiotic stresses.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Ana Carolina Feitosa de Vasconcelos and Lúcia Helena Garófalo Chaves",authors:null},{id:"69577",title:"Role of Fungi in Agriculture",slug:"role-of-fungi-in-agriculture",totalDownloads:1648,totalCrossrefCites:6,totalDimensionsCites:7,abstract:"Fungi are a group of eukaryotic organisms and source of food, organic acids, alcohol, antibiotics, growth-promoting substances, enzymes, and amino acids. They include microorganisms like molds, yeasts, and mushrooms. They live on dead or living plants or animals’ tissue. Fungi are very different from other living organisms; they are the primary decomposers of substances in the ecological system. Fungi are tremendous decomposer of organic waste material and most readily attack cellulose, lignins, gums, and other organic complex substances. Fungi can act also under a wide range of soil reaction from acidic to alkaline soil reactions. Fungi conjointly play a basic role in different physiological processes as well as mineral and water uptake, chemical change, stomatal movement, and biosynthesis of compounds termed biostimulants, auxins, lignan, and ethylene to enhance the flexibility of plants to ascertain and cope environmental stresses like drought, salinity, heat, cold, and significant metals.",book:{id:"8954",slug:"biostimulants-in-plant-science",title:"Biostimulants in Plant Science",fullTitle:"Biostimulants in Plant Science"},signatures:"Muthuraman Yuvaraj and Murugaragavan Ramasamy",authors:[{id:"280193",title:"Dr.",name:"Muthuraman",middleName:null,surname:"Yuvaraj",slug:"muthuraman-yuvaraj",fullName:"Muthuraman Yuvaraj"},{id:"289410",title:"Dr.",name:"Murugaragavan",middleName:null,surname:"Ramasamy",slug:"murugaragavan-ramasamy",fullName:"Murugaragavan Ramasamy"}]},{id:"62653",title:"Phytochemical Composition: Antioxidant Potential and Biological Activities of Corn",slug:"phytochemical-composition-antioxidant-potential-and-biological-activities-of-corn",totalDownloads:2214,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Corn seeds are used as a nutritional source for humans, and the stem and leaves are utilized as fodder for cattle throughout the world. Corn silk and corn cob are usually discarded as waste. This chapter highlights the nutritional as well as medicinal importance of various parts of corn plant. All parts of corn plant are good source of a variety of bioactive phytochemical compounds which possess antioxidant potential. The principal phytochemicals present in corn seed and corn silk include polyphenols, phenolic acids, flavonoids, anthocyanins, glycosides, carotenoids, and polysaccharides of biological importance, reducing compounds and some water-soluble vitamins. The presence of these phytochemicals makes corn a medicinal plant which shows various biological activities particularly the antioxidant, antimicrobial, antidiabetic, anti-obesity, antiproliferative, hepatoprotective, cardioprotective, and renal-protective activities. On the account of its high antioxidant potential, all parts of corn plant can be used for the management of oxidative stress and the treatment of various diseases.",book:{id:"7206",slug:"corn-production-and-human-health-in-changing-climate",title:"Corn",fullTitle:"Corn - Production and Human Health in Changing Climate"},signatures:"Haq Nawaz, Saima Muzaffar, Momna Aslam and Shakeel Ahmad",authors:[{id:"230900",title:"Mr.",name:"Haq",middleName:null,surname:"Nawaz",slug:"haq-nawaz",fullName:"Haq Nawaz"},{id:"244066",title:"Prof.",name:"Saima",middleName:null,surname:"Muzaffar",slug:"saima-muzaffar",fullName:"Saima Muzaffar"},{id:"263041",title:"Ms.",name:"Momna",middleName:null,surname:"Aslam",slug:"momna-aslam",fullName:"Momna Aslam"},{id:"263042",title:"Dr.",name:"Shakeel",middleName:null,surname:"Ahmad",slug:"shakeel-ahmad",fullName:"Shakeel Ahmad"}]},{id:"70950",title:"Role of Biofertilizers in Plant Growth and Soil Health",slug:"role-of-biofertilizers-in-plant-growth-and-soil-health",totalDownloads:1436,totalCrossrefCites:4,totalDimensionsCites:11,abstract:"Biofertilizers nowadays have been realised for shifting fortunes in agriculture. It has been proven successful technology in many developed countries while in developing countries exploitation of bioinoculants is hampered by several factors. Scientific knowledge on bioinoculants and its usage will pave way for its effective usage. At the same time overlooking the significance of ensuring and maintaining a high quality standard of the product will have negative impact. Hence a proper knowledge of bioinoculants and its functioning will pave way to tape the resources in a better way. Thus the chapter provide overview knowledge about different bacterial, fungal and algal biofertilizers, its associations with plants and transformations of nutrients in soil. Adopting a rational approach to the use and management of microbial fertilizers in sustainable agriculture thrive vast potential for the future.",book:{id:"8004",slug:"nitrogen-fixation",title:"Nitrogen Fixation",fullTitle:"Nitrogen Fixation"},signatures:"Murugaragavan Ramasamy, T. Geetha and M. Yuvaraj",authors:[{id:"289410",title:"Dr.",name:"Murugaragavan",middleName:null,surname:"Ramasamy",slug:"murugaragavan-ramasamy",fullName:"Murugaragavan Ramasamy"}]},{id:"67454",title:"Nitrogen Fertilization I: Impact on Crop, Soil, and Environment",slug:"nitrogen-fertilization-i-impact-on-crop-soil-and-environment",totalDownloads:1620,totalCrossrefCites:7,totalDimensionsCites:17,abstract:"Nitrogen (N) is a major limiting nutrient to sustain crop yields and quality. As a result, N fertilizer is usually applied in large quantity to increase crop production throughout the world. Application of N fertilizers has increased crop yields and resulted in achievement of self-sufficiency in food production in many developing countries. Excessive application of N fertilizers beyond crops’ demand, however, has resulted in undesirable consequences of degradation in soil, water, and air quality. 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. 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