Description of daily average and weekly average input layers.
\r\n\tThere are a variety of approaches to reversing biodiversity loss, ranging from economic, to ecological and ethical. The utilitarian approach to conservation, bolstered by the concept of ecosystem services, can be utilized to improve the conservation case by supplementing the burgeoning biodiversity rhetoric. To address this issue, a pluralistic approach to biodiversity is required for conservation and sustainability.
",isbn:"978-1-80356-339-8",printIsbn:"978-1-80356-338-1",pdfIsbn:"978-1-80356-340-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"ab014f8ed1669757335225786833e9a9",bookSignature:"Dr. Gopal Shukla, Dr. Jahangeer Bhat and Dr. Sumit Chakravarty",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11460.jpg",keywords:"Ecosystem Services, Intrinsic Value, Global Trends in Biodiversity Loss, Convention on Biological Diversity, Utilitarian Value, Biodiversity Conservation, Perception, In Situ and Ex Situ Conservation, Nature Conservation, Sustainable Development Goals, Drivers of Degradation, Prioritizing Biodiversity",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 17th 2022",dateEndSecondStepPublish:"April 22nd 2022",dateEndThirdStepPublish:"June 21st 2022",dateEndFourthStepPublish:"September 9th 2022",dateEndFifthStepPublish:"November 8th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Gopal Shukla, prior to becoming an assistant professor, has worked under NAIP (National Agricultural Innovation Project), NICRA ( National Innovations on Climate Resilient Agriculture), and SERB (Science and Engineering Research Board) projects. The focus of his research and development work is forest conservation. He has authored 75 research papers, 10 book chapters and has edited 5 books.",coeditorOneBiosketch:"Dr. Jahangeer is a Guest Associate Editor in Frontiers in the Environmental Science journal and is the first researcher to report the first time growing of Acacia dealbata Link. (Silver Wattle), an invasive species in the high altitudes of the Himalayas. He has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute.",coeditorTwoBiosketch:"Dr. Chakravarty, Ph. D., has a wide experience in forestry training, research, and development. He is currently working as a Professor in Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India. He has conducted research on several aspects of forestry, agroforestry, medicinal plants, and climate change. 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He has been instrumental in developing HE and TVET streams of forestry and allied programmes and worked closely in accreditation with the Fiji Higher Education Commission and forestry stakeholders. Before joining Fiji National University, he worked for HNB Garhwal University, Srinagar, India, and has 11 years of research and 8 years of teaching experience with a publication record of more than 60, including research articles, review papers, conference papers, and books of national and international repute. Dr. Jahangeer reviews research articles for several scientific journals and has handled research projects in his capacity as Principal Investigator and Co-Principal Investigator. His major interests lie in emerging issues in forestry including conservation of biodiversity, traditional knowledge of plants, and sustainable management of forest resources. His focus of research is vegetation ecology, ethnobotany, and evaluation of ecosystem services, forest plant biodiversity, climate change, and socio-cultural issues in forestry. Dr. Jahangeer is currently working at the College of Horticulture and Forestry, Rani Lakshmi Bai Central Agricultural University, Jhansi, India.",institutionString:"Central Agricultural University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Central Agricultural University",institutionURL:null,country:{name:"India"}}},coeditorTwo:{id:"94999",title:"Dr.",name:"Sumit",middleName:null,surname:"Chakravarty",slug:"sumit-chakravarty",fullName:"Sumit Chakravarty",profilePictureURL:"https://mts.intechopen.com/storage/users/94999/images/system/94999.jpg",biography:"Dr. Sumit Chakravarty, Ph.D., has wide experience in forestry training, research, and development. 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Once exposed to air and water during and after mining, the oxidation of metal sulfides minerals releases acid and heavy metals to the environment. The oxidation of metal sulfides can be accelerated in the presence of microorganisms. The drainage from mine wastes may have high level of toxic elements and chemicals such as arsenic, selenium, lead, uranium, zinc etc. Over time, waste rocks are deposited in the storages which can contain over one hundred million tons and cover a few hundred hectares. The drainage water from waste rock storages and its impact on the surrounding environment are becoming critical challenges to both of mining companies and the public. The treatment of the drainage from waste rock storages may have to last decades, even centuries, and bring a significant cost to the mining sectors [1, 2].
For day-to-day mine site management, flood control and contaminant remediation plan are dependent on the evaluation and prediction of drainage flow rates and drainage chemistries. Various methodologies have been developed over the past several decades to predict the drainage from waste rock storages. For predicting drainage flow rates, a numerical model to simulate groundwater flow through unsaturated bed or layers of earth is developed in [3]. In reference [4], a water balance approach is proposed to calculate the conservation of total water flow through waste rock piles by dividing the whole hydrological process into independent components. In terms of predicting drainage chemistries, a numerous numerical models enabled with mass transport effect are developed to evaluate the geochemical reaction and transport inside waste rock piles [5, 6, 7]. Furthermore, using dimensional equation to correlate drainage chemistries with seepage flow rates from waste rock piles is explored in [8]. In reference [9], the effectiveness of a cover system is assessed and a Multiphysics model is developed to predict the iron loading and lime consumption for a full-scale waste rock pile.
However, there are several limitations with above predictive models. For example: 1. Many predictive models are based on lab testing and then scaled up to predict the result in the field, but there is little comprehensive understanding on how to scale up; 2. Simplification and assumptions of geo-bio-chemical processes for the geochemical reaction and leaching process in waste rock storages are critical to the accuracy of the predictive models; 3 Lab or field characterization of material and transport properties related to predictive models is essential, which is also very costly and time-consuming.
To understand and minimize the environmental impact from the contaminated drainage, routine monitoring of waste rock storages is required by many governmental regulators. With the rapid development of computer and sensing technologies, constant and comprehensive monitoring on the waste rock storages is now possible for many mine sites. Daily or even hourly monitoring data become available for many key parameters such as precipitation, temperature, wind, internal temperature, gas concentrations, air/water flow rates, drainage chemistries, etc. These monitoring data are accumulated to weekly, monthly and yearly datasets, and become so huge and complex that traditional data analysis approaches are inadequate to handle and investigate them. As one of the most famous machine learning technologies, artificial neural network not only has the advantages of high processing speed and high computational accuracy, but also enables a machine to mimic human learning behavior and problem solving functions. Thus using neural network to investigate the huge monitoring datasets and further predict drainage flow rates and drainage chemistries from waste rock storages shows very promising potentials. For example, the concentrations of sulphate, chlorine, total dissolved solids and total suspended solids in mine water are predicted by artificial neural network based on the input of pH, temperature and hardness in [10]. Heavy metal included in acid rock drainage is investigated by support vector machine and neural network for a copper mine in Iran [11]. Five machine learning approaches to predict copper concentration are compared in [12]. A feedforward neural network with weather input is proposed to predict drainage flow rates for a full scale waste rock pile [13].
In this book chapter, a refined feedforward neural network based on [13] will be introduced to learn from historical monitoring data and then predict the drainage flow rate, in addition, the refined neural network will also be extended to predict the drainage chemistries in the field. Compared with above traditional predictive models, the proposed neural network approach requires much less simplification and assumption of geo-bio-chemical processes involved and it can significantly reduce characterization cost for mining companies, as the monitoring data inherently contain the information of all the underlying physical mechanisms within real waste rock storages. However, the prediction accuracy is highly dependent on the quality of monitoring data as the proposed neural network is actually a mathematical regression process.
The proposed feedforward neural network selects the weather monitoring data from mine sites as the input to predict and the drainage as the output. The underlying logic for this approach is based on the fact that the water passing through the waste rock storage is mainly from two sources: 1 precipitation falls directly onto the storage and infiltrates into it; 2 groundwater originating from uphill precipitation flows into the storage from higher elevations. Both sources are highly dependent on rain, snow, temperature, hydrologic properties and geo-bio-chemical conditions in the field. As the hydrologic properties and geo-bio-chemical conditions are relatively stable than previous factors related to the weather, the evolution of total precipitation and mean temperature from ambient environment at the mine site is then adopted to correlate with the drainage flow rates and also drainage chemistries. The correlation can be gradually captured by machine learning through studying historical monitoring data from a specific waste rock storage. In addition, the reference [13] proposed to use the number of year and month as additional input to capture long-term fluctuation of drainage. As the number of month is naturally uncycled, the value of the month number has no meaning for machine learning but only brings learning issue when December transits to January. The chapter proposes to use the concept of accumulated days to capture the long term fluctuation instead. With further normalizing all input data, the refined feedforward neural network can better predict the drainage flow rates and further the drainage chemistries. A case study on a full-scale waste rock storage will be provided to validate the proposed approach in this chapter.
The Feedforward neural network is an artificial neural network wherein connections between the artificial neurons do not form a cycle, which is different from its variant: recurrent neural networks. The artificial neurons are capable of simulating basic learning behaviors through receiving inputs, calculating a weighted sum and then passing the sum through a transformation known as activation function to produce outputs. The mathematical calculation for an artificial neuron in a feedforward neural network is generally illustrated as follows:
where
The schematic of the proposed feedforward neural network structure is illustrated in Figure 1. As mentioned above, hydrologic properties and geo-bio-chemical conditions in waste rock storages are generally considered as a very slow evolution, which means they are relatively stable compared with weather conditions such as rain, snow and temperature in the field. The dynamics of weather conditions are powerful to act as driving input forces for the training process, leaving hydrologic properties and geo-bio-chemical conditions as coefficients within neural network to be determined during learning process. As the temperature controls not only the formation of rain/snow but also evaporation rate on the surface, total precipitation and mean temperature are then selected as two groups of neurons in the input layer. Current and preceding total precipitation and mean temperature are extracted from the weather monitoring database, then they are formatted into a time series in the input layer before entering the hidden layer. The length of time series determines the neuron number in each group of the input layer. For example, an input including previous 10 daily weather monitoring data indicates 10 neurons for previous daily total precipitation and 10 neurons for previous daily mean temperature in the input layer.
The proposed feedforward neural network structure.
An additional neuron in the input layer is composed of a time tag that represents the drainage measurement day. For example, the day with first weather monitoring data is considered as the first day, and the corresponding time tag is set to 1. Any future time tag for one drainage measurement is the accumulated day number adding from the first day to the measurement day. By introducing the concept of accumulated day number as the time tag, the geo-bio-chemical evolutions inside the waste rock storages no longer have to keep constant in the temporal scale, and become potentially time dependent. Thus this hybrid input structure enables the proposed neural network to capture the long-term trend of the drainage flow rates and drainage chemistries.
The output can be calculated by moving forward in the neural network based on Eq. (1). After the output is obtained, it is compared with drainage monitoring data (target) including flow rate and chemistry concentration. A cost function is then adopted to evaluate the difference between output and target. In this study, the mean squared errors (
where
As both of input and target data are different in terms of their scales, it is generally required to pre-process them to become normalized before the training starts. The normalization could accelerate the training process by making all undetermined coefficients in the neural network get updated in the same scales. For this study, the mean for each data set (total precipitation, mean temperature, flow rate, chemistry concentration) is set to 0 and the standard deviation is set to 1. The normalization is obtained by following calculations:
where
In the beginning, all coefficients within the neural network shown in Eq. (1) are randomly initiated. During the training process, they are automatically updated through a special data iteration technique called backpropagation algorithm, which calculates the gradient of the cost function based on comparing target with output. The proposed feedforward neural network should be trained with a fair amount of observation samples from historical monitoring database so that it can capture the correlation between input data and target data. Here an observation sample is defined as a combination of input and target from historical monitoring database. The training needs assessment to prevent both of underfitting and overfitting with various validation methods. The hold-out approach is adopted in this study. Among all observation samples, the training observation samples are those for actual training, the validation observation samples are for evaluating the generalization of the neural network and the training process continues until the generalization does not get improved, and the rest are called testing observation samples which do not impact on the training process but give independent assessment for the training performance.
After the training is completed, both of
Theoretically, the lower value for
In theory, a well-trained neural network proposed in this study is able to reasonably predict future drainage flow rate and drainage chemistry concentration for full-scale waste rock storages as long as the historical weather monitoring database, historical drainage monitoring database and the weather forecast onsite are available. Figure 2 shows the schematic diagram of the general functions for the proposed feedforward neural network approach. There are two processes involved in the implementation of the approach. After the training processed is completed, the correlation between weather and drainage for the waste rock storage is believed to be captured by the proposed neural network, and then the prediction process starts to utilize weather forecast to predict future drainage on site.
The functions of the proposed feedforward neural network.
To validate the proposed neural network approach, a full-scale case study is performed to predict real drainage flow rate and drainage chemistry in field condition. A real waste rock pile from an anonymous mine site in western Canada is adopted in this study. The proposed neural network is trained by historical monitoring data for 16 years (Year 1 to Year 16). After the training is completed, it will be used to predict the drainage in the next 2 years (Year 17 and Year 18). A comparison between real measurement and predicted value will be provided.
At this mine site, the drainage flow rates are not directly measured but they are calculated based on readings of the water level in v-notch weirs installed at the end of the drainage collecting ditch. Thus the v-notch is used as the one actual target of neural network training. In the following discussion, the drainage flow rate actually refers to the original measurement of v-notch from the weirs. Among all drainage chemistry data from this waste rock pile, acidity is selected for this case study as another target, because it is directly linked to the lime consumption for contaminant treatment. These drainage measurement are generally performed in a dynamic time frame at this mine site. During spring freshet and large precipitation periods, the measurements are usually more frequent than the remaining time in a year, as increased drainage flow rate is observed.
The weather monitoring data at this site is extracted from the website of Environment Canada (weather.gc.ca) on a daily basis, including minimum temperature, maximum temperature, mean temperature, total rain, total snow, total precipitation and snow thickness on ground, etc. For this case study, the total 16 years weather monitoring data have been obtained for the training purpose. As mentioned in the previous section, two independent weather parameters measured on a daily basis - the total precipitation and mean temperature are selected as the inputs for the proposed neural network.
To evaluate how long the weather can impact on the drainage from this waste rock pile, two types of input layers are proposed for comparison in the study. When a target (flow rate or acidity) is selected to train the neural network, its measurement date is extracted. Daily average input layer consists of 21 neurons including the time tag of measurement day, daily total precipitation and daily mean temperature from previous 9 days and the measurement day, which mainly investigates short-term weather impact on the drainage. Furthermore, weekly average input layer has 21 neurons including the time tag of measurement day, weekly average total precipitation and weekly averaged mean temperature from previous 9 weeks and current week to investigate long-term weather impact. The weekly average input layer reflects longer weather monitoring data than the daily average input layer does, however, high frequent information is filtered in the weekly average input layer. The summary of daily average and weekly average input layers can be found in Table 1. Here 0 day means the measurement day, 0 week means measurement day and previous 6 days. Finally, both types of input layers are adopted to train the neural network to determine which input layer is more competent to capture the underlying pattern and make a better prediction.
Type of input layer | Daily Average | Weekly Average |
---|---|---|
Neuron number | 21 | 21 |
Description | Mean temperature of −9 day Total precipitation of −9 day Mean temperature of −8 day Total precipitation of −8 day … … … … Mean temperature of −1 day Total precipitation of −1 day Mean temperature of 0 day Total precipitation of 0 day Time tag of measurement day | Mean temperature of −9 week Total precipitation of −9 week Mean temperature of −8 week Total precipitation of −8 week … … … … Mean temperature of −1 week Total precipitation of −1 week Mean temperature of 0 week Total precipitation of 0 week Time tag of measurement day |
Description of daily average and weekly average input layers.
As the mine site is anonymous, the original monitoring data is confidential and not publicly accessed. To protect the site information, only normalized historical monitoring data including total precipitation, mean temperature, flow rate and acidity from the 16 years are provided in Figure 3. Total numbers of flow rate measurement and acidity measurement during the 16 years is 1741 and 320, respectively. It should be noticed that the weather data are extracted on a daily basis and any missing data is represented by a gap. The flow rate and acidity is not measured in a fixed time frame and the time interval is dynamic, so each measurement data is represented by a solid dot in the figure. As some weather data are missing, not all of drainage measurements are utilized for the training. Those drainage measurements that do not have a complete daily average or weekly average input will be excluded. In terms of the hold-out approach to avoid overfitting, 80% of the total observation samples are used for training and 20% for validation. No testing data is allocated in the training process, as a prediction of the drainage in the next 2 years will be performed after the training is completed. As the monitoring data of weather and drainage during the next 2 years are also available. The real weather data will be utilized as the input of the neural network. In real situation, weather forecasting data should be adopted for prediction purpose. The predicted drainage flow rates and acidities will be compared with the real monitoring values.
Normalized monitoring data for the 16 years (year 1 to year 16). (a) Year 1 to year 4; (b) year 5 to year 8; (c) year 8 to year 12 and (d) year 13 to year 16.
As shown in the Figure 1, only a single hidden layer is in the feedforward neural network adopted for this case study. So the number of neurons in the hidden layer is considered as a hyperparameter for the training process. A grid search is performed on 5, 10 and 20 neurons in the hidden layer to find the optimized size. The proposed neural network is trained through Levenberg–Marquardt backpropagation algorithm. The adaptive value (damping factor) is set to 0.001 initially, and will increase by 10 until the change of above results in a reduced performance value. The change is then made to the network and adaptive value is decreased by 0.1. The maximum adaptive value is set to 1e10. The maximum epochs before the stop of training is set to 1000. However, the training may be stopped early if the
The proposed neural network is implemented through the machine learning toolbox built in commercial software MATLAB. The weather monitoring data are pre-constructed for both types of input layer through Microsoft Excel Macro before exporting into the MATLAB.
After the training is completed, all training and validation observation samples go through Eq. (1) again and then the calculated output is called drainage regression. The
Number of Neurons in the Hidden layer | Flow Rate ( | Acidity ( | ||
---|---|---|---|---|
Daily Average | Weekly Average | Daily Average | Weekly Average | |
5 | 1.01, 0.62 | 0.25, 0.93 | 0.53, 0.65 | 0.35, 0.78 |
10 | 0.99, 0.63 | 0.18, 0.95 | 0.55, 0.65 | 0.41, 0.79 |
20 | 0.92, 0.67 | 0.18, 0.95 | 0.58, 0.67 | 0.28, 0.84 |
Results of the grid search.
The comparison between regressions of flow rate and acidity and real measurements (target) in temporal scale are provided in Figure 4. It is found that the proposed neural network is capable to capture the underlying correlation between the drainage and weather, as not only seasonal fluctuations in a year but also long term evolution across years are well reflected in the drainage regression.
Drainage regression vs. real measurement for the 16 years (year 1 to year 16). (a) Flow rate and (b) acidity.
In addition, the successful candidate neuron networks (weekly average input layer with 10 neurons in hidden layer for flow rate, and weekly average input layer with 20 neurons in hidden layer for acidity) are adopted to predict the future flow rate and acidity in the Year 17 and Year 18 based on the input extracted from the real weather monitoring data. The prediction and real measurement in temporal scale are compared in Figure 5.
Drainage prediction vs. real measurement for the next 2 years (year 17 and year 18). (a) Flow rate and (b) acidity.
It is observed that the general trend for both of flow rate and acidity are well predicted by the neural network. The proposed neural network is capable to predict the time and also the amount of peak flow rates in the spring freshet of both years, which is important for site water management. In terms of acidity, the long term downward trend shown in Year 11 to Year 16 is reflected in the prediction, which matches the trend of real measurement in both years. However, seasonal fluctuation has some mismatch. The reason is that the amount of observation samples for acidity is much less than those for flow rate, so the acidity prediction is not as good as the flow rate prediction in this case study. The accuracy can be improved when more monitoring data are accumulated for the training in the future.
A machine learning algorithm based on feedforward neural network is introduced in this chapter to correlate the drainage flow rate and drainage chemistry with the field precipitation and temperature for waste rock storages. Comparing with traditional predictive models, the neural network approach requires little simplification and assumptions of bio-geo-chemical processes involved, in additional, the cost and time for characterizations can be significantly reduced. The advantage of the neural network is that all underlying mechanisms have been naturally reflected in the monitoring data, which can be gradually captured during the machine learning process.
A case study on a full-scale waste rock storage is performed. The results show that the flow rate and acidity of the drainage discharged in the field have strong correlations with previous 10 weekly averaged weather data at this site. The capability of making prediction of future drainage in the field is also validated. However, the structure of input layer, hidden layer number, neurons in the hidden layer are all site specific, which may be adjusted for the applications to other waste rock storages.
It should also be addressed that the measurements of drainage flow rate and drainage chemistry may not always be accurate in the field, furthermore, they can fluctuate in a single day depending on the hydrogeological conditions. So the monitoring data may not represent the daily average in some cases, which means that some mismatch between the prediction and measurement does not necessarily indicate the prediction is wrong. High frequent (multiple in a single day or hourly) measurement is highly suggested, so that good quality monitoring data will be available to predict drainage for waste rock storages in the future.
The work is financially supported by the Environmental Advances in Mining Program and New Beginnings Initiative in the National Research Council Canada.
Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) belong to severe cutaneous adverse drug reactions (SCARs). Although their incidence is rare, around 2–3 per million per year, their mortality rate can be up to 5–30%. These reactions are life-threatening due to internal organ failures, disseminated skin detachment, and necrolysis. The most common causative medicines inducing SJS/TEN are allopurinol, carbamazepine, sulfamethoxazole, and other antibiotics, even traditional medicine. The pathogenesis of SJS/TEN is not fully understood, but some immunological and genetic factors are believed to be involved. The treatment of SJS/TEN is still controversial in which several studies showed variable results, including systemic corticosteroid, cyclosporine, intravenous immunoglobulin (IVIG), etanercept, thalidomide, and plasmapheresis.
In the SCARs group, in addition to SJS/TEN, there are other forms of drug allergy such as drug reaction with eosinophilia and systemic symptoms (DRESS syndrome), acute generalized exanthematous pustulosis (AGEP), drug-induced hypersensitivity syndrome (DIHS), maculopapular exanthema (MPE).
Previously, the classification between erythema multiforme (EM), SJS, and TEN was still inconsistent due to unclear pathogenesis. There are different opinions on the differential diagnosis of severe EM from SJS/TEN. Since 1983, SJS has been considered synonymous with severe EM, both with the involvement of more than one mucosa with skin involvement [1]. In 1993–1994, Bastuji-Garin and Roujeau proposed the distinction of the two diseases based on clinical and etiological factors. In severe EM, there are mucosal lesions, bullae, skin lesions less than 10% of body surface area [2]. But unlike SJS, the skin lesions in severe EM are typical and/or atypical target lesions that are prominent compared to normal skin, distributed mainly in the extremities. Stevens-Johnson syndrome is characterized by widespread blisters due to drug reactions, which appear on the background of erythema, necrosis, and pruritus, concentrated mainly on the face and trunk. Etiologically, EM is often associated with herpes simplex virus reactivation, rarely drug-induced, SJS/TEN mainly drug-induced, rarely infection [3, 4]. There are few reports of stomatitis caused by
Based on the area of epidermal detachment (blister, erosion), Bastuji-Garin classified the spectrum of SJS/TEN into SJS, overlapping SJS/TEN and TEN. According to this classification, there are four subgroups as follows: (1) SJS with an epidermal detachment of less than 10% of the body area, red, pruritic, atypical target macules that are flat with normal skin; (2) overlap SJS/TEN with 10–30% epidermal detachment; (3) TEN with spots (spots) when the lesions are epidermal detachment over 30% of the body area, with red, widespread pruritus, atypical target macules; (4) TEN without spot with epidermal detachment lesions of more than 30% of the body area, no individual macules, no atypical target lesions [1, 2].
Several microorganisms are considered to be the cause of SJS/TEN. For example, SJS/TEN can occur after vaccination with chickenpox, measles [8],
The majority of cases of SJS/TEN were related to drug hypersensitivity. Some studies show that in people infected with HIV, the prevalence of the disease is about 1 in 1000, which is a thousand times higher than in the population without HIV [6]. In sub-Saharan Africa, where HIV prevalence is high, there is an association between SJS/TEN and HIV due to the use of antiretroviral and anti-tuberculosis drugs [18]. The drugs most commonly causing SJS/TEN are nevirapine, lamotrigine, carbamazepine, phenytoin, phenobarbital, cotrimoxazole, sulfonamides, sulfasalazine, allopurinol, and non-steroidal anti-inflammatory drugs (oxicam group) [6, 19]. Other less common drugs are aminopenicillin, cephalosporin, quinolone. In Asian countries, traditional medicine can also be a causative drug of SJS/TEN [20]. Currently, SJS/TEN is reported to be associated with pembrolizumab [21], and itraconazole [22]. Patch test, lymphocyte transformation test, and enzyme-linked immunospot can be used to detect culprit drugs in SJS/TEN but none of them is considered as standard [1, 23].
Sassolas et al. developed an algorithm for drug causality for epidermal necrolysis (ALDEN). This algorithm rates each drug with a score of −12 to 10 based on six criteria: (1) delay from initial drug component intake to the onset of reaction (index day); (2) drug present in the body on the index day; (3) pre-challenge/rechallenge; (4)dechallenge; (5) type of drug (notoriety); (6) other cause. Allergenic possibilities are given based on the following total score: ≥ 6: very probable; 4–5: probable; 2–3: possible; 0–1: unlikely; < 0: very unlikely [19].
Stevens-Johnson syndrome and TEN are equally common in men and women. The frequency of the disease increases with age, peaking in people over 50 years of age [24, 25]. In the population of people living with HIV/AIDS, the prevalence of SJS/TEN is higher than in the general population [6].
In SJS/TEN, keratinocytes are necrotic to varying degrees. The pathogenesis of SJS/TEN is related to the mechanism of death of keratinocytes [26, 27, 28]. These cells undergo apoptosis or necroptosis, causing the entire epidermal structure to necrosis, detachment, form blisters [26, 28].
Toxic epidermal necrolysis is a T-cell-mediated disease, TCD8+ lymphocytes are found in bullous fluid [29, 30, 31], perivascular in the superficial dermis [6, 32]. TCD8+ lymphocytes together with NK cells are considered to be the main agents of apoptosis of keratinocytes [29, 30, 31]. TCD4+ lymphocytes and other immune cells such as dendritic cells and mast cells also play an important role in TEN [6]. Caproni’s study of cell infiltration in the skin of TEN patients showed a high density of CD40 ligand (CD40L) staining cells in the dermis, some infiltrating the epidermis [32]. CD40L is a molecule expressed on the surface of activated CD4+ T cells, and is a co-stimulator of macrophages, dendritic cells, B lymphocytes, and endothelial cells, leading to the release of tumor necrotic factor-alpha (TNF-α), nitric oxide (NO), interleukin (IL)-8, and cell adhesion molecules. Soluble CD40L is elevated in the serum of TEN patients. The TCD4+ lineage in the epidermis and dermis of TEN patients has a balance between T helper (Th) 1 and Th2 as well as cytokine levels from these two cell types [33].
Macrophages, neutrophils, and NK cells are also involved in the pathogenesis of TEN, and studies have shown that macrophages infiltrate in predominant numbers in skin samples [32, 33]. Tohyama noted the presence of CD14+ CD16+ monocytes in the epidermis and the dermal-epidermal junction in skin lesions of SJS/TEN patients. This reinforces the proliferation and cytotoxicity of TCD8+ lymphocytes through the CD137/CD137L system. Monocytes and macrophages contribute to apoptosis through the production of TNF-α, TNF-related apoptosis ligands [34]. Neutrophils and dendritic cell factor XIIIa+ were also found in these skin samples, but their role in TEN has not been elucidated [32, 33, 35]. NK cells are present in the bullous fluid along with highly cytotoxic T cells that express the NK cell CD56 receptor. Both of these cell types are considered to be major contributors to keratinocyte apoptosis [30]. The important role of TCD8+ lymphocytes in the pathogenesis of TEN has been recently demonstrated with the murine model of TEN, which tends to resemble that of humans [36].
T cells are hyperactive due to decreased T regulatory (Treg) cell function and upregulation by monocytes. CD8+ T cells themselves are not specific for TEN; they are also present in other drug-induced reactions. Treg’s function in increasing TCD8+ activity is an important factor in TEN, which causes epidermal injury. The mechanism by which Treg cells degrades function is unknown, but the loss of TCD8+ inhibition has been documented. Treg cells from the peripheral blood of TEN patients do not inhibit T cells. Treg cell counts in TEN patients do not differ from that in normal subjects, but their function is impaired during the acute phase of TEN [37].
Th17 cells, which are subtypes of TCD4+ cells, are present in SJS/TEN at a higher rate than other ordinary drug skin reactions (ODSRs), producing IL-17 and IL-22. In SJS/TEN patients, there are more IL-17-producing CD4+ T cells than in EM patients and healthy subjects. As the disease improves, the Th17 cell count decreases. They can regulate the mobilization of neutrophils and other inflammatory leukocytes, causing inflammation and skin damage. Furthermore, neutropenia, a cause of death in TEN, may be due to the action of Th17 [37].
Many studies show an association between HLA class I and SCARs. In Han Chinese patients with SJS/TEN, there is a strong association between antiepileptic aromatics such as carbamazepine, phenytoin, oxcarbazepine, and lamotrigine with HLA-B*15:02 [38], and between allopurinol and HLA-B* 58:01 [39, 40]. An association between HLA-B*15:02 and carbamazepine was also seen in Thai, Vietnamese, Malaysian, and South Indian SJS/TEN patients, but not in Japanese, Korean, or European populations [41]. In patients of European descent, there was an association between HLA-B*57:01 and abacavir-induced SJS/TEN [42], and between HLA-A*31:01 and carbamazepine-induced SJS/TEN [43]. A study in Japan showed that the HLA-B*15:11 allele is a risk factor for carbamazepine-induced SJS/TEN, with an association between HLA-A*02:06 and acetaminophen-induced SJS/TEN [44]. The significant association between SJS/TEN and certain HLA haplotypes has led to speculation that these haplotypes play a role in the pathogenesis of TEN [45]. This concept was proposed when investigating the role of HLA-B*57:01 in the pathogenesis of abacavir-induced DIHS [46]. From the above studies, it is recommended to screen for some HLA alleles before prescribing some drugs that cause allergies, related to HLA. For example, HLA-B*58:01 allele carriers should not take allopurinol, and HLA-B*15:02 allele carriers should not take carbamazepine [41, 43].
In SJS/TEN, keratinocytes undergo massive, widespread necrosis. The main reason is due to the apoptosis mechanism. There are many cytotoxic proteins and molecules involved in apoptosis initiation in SJS/TEN, of which granulysin has been shown to play a major role. Other factors including FasL, TNF-α, perforin, granzyme B, and NO also play a certain role [6, 26, 47].
Granulysin is a molecule found in the granules of cytotoxic cells (along with granzyme B and perforin) such as TCD8+, NK, and natural killer T cells, that act as a tumor killer and kills bacteria. When there is an interaction between the drug and the specific HLA and T cell receptor of TCD8+, granulysin is released from the granules of TCD8+, causing apoptosis of keratinocytes. Granulysin can cut through the target cell membrane, causing ion imbalance, damage to mitochondria, releasing oxidants and caspase cascade, causing apoptosis [48].
To investigate the role of granulysin in TEN, Chung et al. compared the gene expression of bullous fluid cells. The results showed that the expression of the granulysin gene of bullous cells increased 10–20 times, granzyme B increased 8 times, perforin increased 3 times, serum FasL increased 2 times [26]. When measuring granulysin concentrations in bullous fluid following a similar pattern, granulysin levels were 2–4 times higher than perforin, granzyme B, and soluble FasL, which correlated with disease severity. On immunohistochemical staining, the skin tissues of patients with TEN were strongly stained with granulysin, while the skin tissues of patients with ODSRs were weakly stained [26]. Abe showed that serum granulysin levels were elevated in 4 out of 5 patients with SJS/TEN before epidermal detachment or mucosal lesion. Meanwhile, this concentration increased only in 1 in 24 patients with ODSRs [49].
The above studies demonstrate that granulysin is an important cause of apoptosis in TEN, it is also a marker for early diagnosis and prognosis of disease severity [24, 26].
Fas ligand (FasL) is a transmembrane protein of the TNF family, found on the surface of cytotoxic T cells, NK cells. When these cells are activated, FasL is expressed, binds to its receptor on the target cell, and activates the intracellular caspase, leading to uncontrolled destruction of the target cell. In addition, Fas can be separated from the cell membrane by metalloproteinase enzymes, producing soluble Fas from FasL, still maintaining the ability to bind to Fas receptors, causing apoptosis [6, 50].
The Fas receptor has a region of repeat cysteines and an intracellular region of 80 amino acids, identical to the region in TNF-R1, named the death domain. This region is required for Fas to induce apoptosis. Mutations in this region destroy apoptosis induction. The only known physical Fas ligand is FasL (CD95L), which belongs to the family of TNF-related cytokines. Like its relatives, FasL is synthesized as transmembrane and trimer-soluble forms, by the enzyme metalloprotease. Fas signaling plays a decisive role in lymphocyte homeostasis. Repeated activation of antigen receptors on T cells induces FasL expression, leading to apoptosis via Fas signaling. When this process fails, due to mutations in Fas or FasL, lymphomas and autoimmune diseases occur [51]. FasL induces apoptosis by binding to the Fas receptor, causing activation of caspases [52]. Fas is expressed mainly on activated T lymphocytes and NK cells. Viard showed that FasL is also expressed in keratinocytes in TEN lesions [27]. Serum soluble FasL levels are elevated in patients with SJS/TEN before skin detachment, mucosal lesion, or both [53].
Granzyme B and perforin have roles in the apoptosis of keratinocytes and endothelial cells [52]. Cytotoxic T cells, once activated, secrete perforin and granzyme B, create channels on target cell membranes, and activate apoptosis-inducing caspase [6]. In TEN, monocytes from bullous fluid induce apoptosis in the presence of anti-Fas antibodies, but not apoptosis in the presence of perforin/granzyme B inhibitors, indicating perforin/granzyme B is the causative agent of apoptosis [30, 31, 50]. The concentrations of these molecules correlate with the severity of the drug reactions. Therefore, testing for perforin and granzyme B may help differentiate TEN from other drug reactions [47]. Recent studies on skin biopsies of TEN patients showed that endothelial cells were apoptosis, and immunohistochemical staining showed granzyme B and TNF-α around the dermis vessels. Although not found on biopsies, the possibility that soluble FasL is the cause of apoptosis of endothelial cells cannot be excluded. The reason is that the biopsy samples were taken 2–4 days after the onset of the disease when the soluble FasL concentration was greatly reduced [54, 55].
Other molecules, cytokines such as TNF-α and NO have certain roles in apoptosis. TNF-α acts on the “death receptor” TNF-R1, causing activation of caspases, causing cell death. TNF-α is elevated in the bullous fluid, skin, and serum of patients with TEN. Its role, though, is unclear. In addition to its ability to induce apoptosis, TNF-α also has a protective role by activating the anti-apoptosis pathway with nuclear factor-kappaB (NF-κB). This may explain why the mortality of TEN is increased with treatment with anti-TNF-α (thalidomide) [56]. NO induces apoptosis through its effect on the
Cell death resembles necrosis but is regulated by a specific intracellular program known as necroptosis [57]. Experimental studies by Saito et al. showed that apoptosis is not the only mechanism of keratinocyte death in SJS/TEN. When pan-caspase inhibitors were added to the supernatant cultures of peripheral blood monocytes (PBMCs) of patients with SJS/TEN, incubated with keratinocytes, cell death still occurs. Thus, the PBMCs cultures of patients with SJS/TEN contain a specific molecule that causes the death of keratinocytes, which is independent of the apoptosis mechanism. The authors found that the concentration of annexin A1 protein in PBMCs cultures was significantly higher in SJS/TEN patients than in ODSR patients. Anti-annexin A1 antibody inhibits the death of keratinocytes [28]. Annexin A1 is a member of the family of 13 annexin proteins, which bind to acidic phospholipids with high affinity in the presence of Ca2+ ions [58]. When annexin A1 binds to the formyl peptide receptor 1 (FPR1) receptor on the cells, it causes necroptosis. The level of FPR1 expression was significantly different between the SJS/TEN group and the ODSR group. Therefore, it can determine the probability of SJS/TEN or ODSRs. Although there is no gene difference in the FPR1 promoter region between SJS/TEN, ODSRs, and healthy subjects, the annexin A1-FPR1 interaction may predict the occurrence of SJS/TEN and hold promise for targeted therapy in which necrosulfonamide inhibits the necroptosis pathway related to the annexin A1-FPR1 complex [28, 52].
Stevens-Johnson syndrome and TEN develop acutely, with epidermal necrosis, mucositis at multiple sites, accompanied by systemic and other organ changes [1, 2]. In general, the first symptoms are fever, fatigue, discomfort in the upper respiratory tract, appearing a few days before skin and mucosal lesions [1, 6]. Often, it is difficult to diagnose ODSRs and predict the likelihood of progression to SJS/TEN at this stage. Many patients have EM-like lesions with atypical target lesions. However, according to Abe’s study, serum granulysin levels were elevated during this time, based on which could predict the possibility of SJS/TEN [49], helping to treat early avoid complications, and reduce the risk of death.
The time from taking a suspected drug to the onset of symptoms varies widely, ranging from a few days to two months. Therefore, it is necessary to carefully review all drugs used by the patient within the previous two months, including over-the-counter drugs and dietary supplements [1].
Lesions of the ocular mucosa may precede skin lesions. Manifestations include eye discomfort, conjunctivitis, and scleritis. After a few days, the conjunctiva becomes ulcerated, oozing. Lesions on the ocular mucosa may occur concurrently with lesions of the oral mucosa and genital mucosa [1]. According to Revuz’s research, 97% of SJS/TEN patients had mucosal lesions, of which, oral mucosal lesions were found in 93% of patients, ocular mucosa 78%, genital mucosa 63%, and other mucous membranes 66% [59].
Skin pain is an early symptom in SJS/TEN, the presence of which signals an epidermal necrolysis event [1]. There are many types of skin lesions with varying degrees of severity. The earliest lesions are atypical target lesions and/or itchy erythematous macules. The first sites where lesions appear are usually the upper half of the body, the head near the extremities, and the face. After that, the skin lesions spread to the rest of the body and distal extremities. Lesions on the palms and soles of the feet are quite prominent. In many patients, the first symptom is an intensely red, erythematous rash on the palms of the hands. In severe cases, the erythema may coalesce into large macules. The skin becomes tender, vulnerable, and mild pressure can cause epidermal detachment (positive Nikolsky test). The maximum extent of skin lesions after onset is 5–7 days. Necrotic blisters appear when the necrotic epidermal detachment separates from the underlying skin [1, 59]. Extensive necrosis epidermal detachment leaves open dermis, serous exudates, susceptible to infection and bleeding (see Figures 1–4) [60].
A male patient with carbamazepine-induced SJS. He had erosions and dark crust on the lips, pruritic erythematous lesions on the face and the upper trunk. (photo: Tran Thi Huyen).
A lot of blisters were formed on the dark erythematous rashes on the trunk of the patient with carbamazepine-induced SJS. (photo: Tran Thi Huyen).
Epidermal detachments, blisters, and necrotic rashes on the trunk of a patient with allopurinol-induced TEN (photo: Tran Thi Huyen).
The extensive epidermal necrosis in a patient with allopurinol-induced TEN (photo: Tran Thi Huyen).
Multiple organs are affected in SJS/TEN, with necrosis and erosion occurring in the conjunctiva, trachea, bronchi, kidneys, and intestines [52]. There have been reports of acute renal failure with increased microalbuminuria, renal tubular enzymes in the urine, demonstrating glomerular and proximal tubular damage. Lung and respiratory lesions include tracheobronchitis, subcutaneous emphysema, dyspnea, and respiratory failure. Other systemic manifestations may include anemia, leukopenia, hepatitis, abdominal pain, diarrhea, transient elevation of liver enzymes, hypoalbuminemia, hyponatremia, and myocarditis [1].
The diagnosis of SJS/TEN is mainly clinical. However, skin biopsy for histopathology is necessary to further confirm the diagnosis and rule out other bullous skin diseases [1]. On histopathology, there are different degrees of epidermal lesions, the keratinocytes are necrotic individually or in plaques, forming blisters. Appendical structures such as sweat ducts and hair follicles may be affected. The dermis has an inflammatory infiltrate (mainly perivascular) of lymphocytes, histocytes, and a few eosinophils [61]. In addition, there may be liquid degeneration of the basal layer, squamous separation, and spongiosis [62]. Depending on the stage of the disease, the histopathological picture can be different. In the early stages, a histopathological picture is a group of necrotic keratinocytes with some inflammatory cells (monocytes and neutrophils). In the late and severe stages, the keratinocytes are more necrotic, the basal epithelial cells degenerate, leading to the separation of the epidermis from the dermis, the entire layers of keratinocytes of the epidermis are necrotic, only intact horny layer. In some cases, the superficial layer of the epidermis is more necrotic than the deeper layers, forming slits between the two layers of the epidermis [29]. Monocytes and neutrophils can infiltrate areas of necrosis [29, 62].
In the early stages of SJS/TEN, necrotic keratinocytes are scattered in the lower epidermis, similar to those seen in severe EM: extensive necrotic keratinocytes with vacuoles at the dermal-epidermal junction [6, 61]. When SJS/TEN is evident, the entire epidermis is necrotic, forming subepidermal bullae. Meanwhile, in severe EM, the epidermis is less necrotic, the change occurs mainly in the basal layer. The Japanese diagnostic criteria suggested that in SJS/TEN at least 10 necrotic keratinocytes were seen at 200x magnification [63]. In the superficial dermis, perivascular inflammatory infiltration and exocytosis are usually absent. In SJS/TEN, inflammation in the dermis occurs less frequently than in severe EM [61]. The degree of inflammation correlates with the disease severity, the number of infiltrating mononuclear cells in the dermis has the same prognostic value as the SCORe for TEN (SCORTEN) index to assess the severity of TEN [35].
Serology for diagnosis of
In SJS/TEN, the blood count may be normal or there may be disorders such as leukocytosis, leukopenia, and anemia. Many patients have a transient elevation of liver enzymes, increased urea, creatinine, blood bicarbonate, blood glucose, C-reactive protein, procalcitonin.
In severe cases of SJS/TEN, acute systemic disorders can lead to multi-organ failure and death. In 2000, Bastuji-Garin et al. published a valuable prognostic score for SJS/TEN, called SCORTEN, which used seven clinical factors to predict in-hospital mortality. Each factor is worth one point, the higher the total score, the higher the risk of death [65]. Several studies have shown a gradual increase in SCORTEN scores during patient hospitalization, with a significant change observed on day 1 and day 4 (see Table 1) [66].
Risk factors | 0 point | 1 point |
---|---|---|
1. Age | <40 | ≥ 0 |
2. Have a malignancy | No | Yes |
3. Heart rate (beats/minute) | <120 | ≥120 |
4. Area of skin detachment | <10% | ≥10% |
5. Blood urea (mmol/l) | ≤10 | >10 |
6. Blood glucose (mmol/l) | ≤14 | >14 |
7. Blood bicarbonate (mmol/l) | ≥20 | <20 |
It is a disease with a high risk of death, but with time management and treatment, SJS/TEN can be cured. However, it is necessary to note visceral complications (liver, kidney) [52], eye complications, nail disorders [67], skin pigmentation changes after the disease as well as the psychological trauma of the patient. Among them, eye complications are noted the most, with different degrees [68]. Mild degree with eyelid edema, conjunctivitis; a moderate degree of membranous conjunctivitis, corneal epithelial loss, corneal ulceration, corneal infiltrates; in severe cases, there is the irreversible loss of corneal epithelium, loss of vision [1, 68, 69].
Erythema multiforme.
Generalized fixed drug eruption.
Staphylococcal scalded skin syndrome.
Graft versus host disease.
Pemphigus Vulgaris.
Other bullous autoimmune diseases.
Experts recommend that patients with more than 10% of their body area peeled off should be treated in an emergency care unit with doctors and nurses in a variety of specialties. Some patients are treated and cared for as burn patients. Many studies have shown that prompt admission to burn centers improves survival, while delay increases mortality [70, 71]. In the ward, room temperature should be maintained to reduce the patient’s energy consumption. Energy consumption is increased to 40% of basal metabolic rate when the area of skin loss is 10%, increasing to 120% when the area of skin loss is 80% [72]. The patient’s drug history should be taken, and possible tests performed to identify and discontinue the allergen. Limit the use of drugs during the treatment of SJS/TEN.
Medical staff should use protective equipment when in contact with patients to avoid oral and respiratory infections. It is important to avoid holding or pulling the patient strongly and to limit injury to the epidermis (blood pressure measuring tape, electrocardiogram) [73]. Bacteria, viruses, and Candida fungi from three skin lesions should be cultured. Herpes infection should be excluded, especially in the case of severe mucous membrane lesions. Systemic antibiotics should be used if there is evidence of infection. In patients who have diarrhea or are unable to move, avoid getting dirty stools into skin lesions. Pay attention to using pain relievers if the pain is severe [1, 74].
Skin lesions should be washed with sterile warm water or physiological saline or with an antiseptic solution such as chlorhexidine (1/5000). A moisturizer with fatty properties such as vaseline, paraffin all over the skin, including the area that is growing granules should be applied. Scaly skin could be improved with topical antimicrobial drugs. Peeled epidermal fragments should be kept as a bio-bandage. The blisters should be aspirated and drained. Areas of skin that have lost the epidermis should be covered with non-stick gauze. Necrotic and infected epidermal fragments should be removed.
Eye mucosa is often damaged in SJS/TEN, if not detected, timely treatment can leave complications such as corneal ulceration, eye corner adhesions, pterygium adhesions, blindness [68]. Patients with SJS/TEN should be examined, treated, and monitored by an ophthalmologist from the acute stage of the disease until the disease has recovered. The mucous membrane of the vulva, vagina needs to be regular check-ups and cleaning with antiseptic solutions, moist gauze. Topical corticosteroids can reduce inflammation [75]. Oral mucosa needs to be cleaned with antiseptic solutions such as chlorhexidine. Lips and mouth should be covered with moist gauze, corticosteroid solution can be applied to rinse the mouth, oral hemorrhages need to be controlled [76].
Peripheral and central lines should be placed in preserved areas. The fluid balance will be monitored by a catheter. The amount of fluid to compensate could be calculated by referring to the formula of Shiga and Cartotto [77]:2 ml kg−1 body weight/% of epidermal area detachment, necrotic.
If the patient can drink, oral rehydration should be maintained. Patients with SJS/TEN need more nutrition than usual. If the mouth is severely damaged, eating is difficult, a nasogastric tube should be placed or parenteral nutrition. In the acute phase, the required calorie intake is 20–25 kcal kg−1 per day. During the recovery phase, the calorie requirement is 25–30 kcal kg−1 per day [1].
Patients with SJS/TEN have pain in the skin, especially at epidermal detachment sites. There are no studies on analgesic regimens in SJS/TEN. Therefore, analgesics according to the World Health Organization tiers can be used. Paracetamol or synthetic opiate pain relievers (tramadol) should be used, but not non-steroidal anti-inflammatory analgesics because of the risk of kidney and stomach damage. Some care procedures such as bathing and changing clothes require the use of analgesics.
Other treatments including proton pump inhibitors, anticoagulants, and drugs to treat leukopenia, anemia (granulocyte colony-stimulating factor, G-CSF) should be used appropriately.
The basis for the use of IVIG in SJS/TEN are studies that show the role of Fas-FasL interaction in the mechanism of apoptosis of squamous cells [78]. FasL is a transmembrane protein of the TNF family that is expressed on the surface of cytotoxic T cells and natural killer cells. When cytotoxic T cells are activated, FasL is expressed, binds to its receptor on the target cell, activates the intracellular caspase, leading to uncontrolled destruction of the target cell. In addition, Fas can be separated from the cell membrane by metalloproteinase enzymes, producing soluble Fas from FasL, still maintaining the ability to bind to Fas receptors, causing apoptosis [6, 79]. High concentrations of normal immunoglobulin inhibit Fas-Fas ligand and apoptosis interactions through activation of anti-Fas antibodies.
In a systematic review and meta-analysis published by Huang in 2012 (all studies included at least 8 IVIG-treated SJS/TEN patients), cumulative estimates of risk mortality were determined, comparing IVIG and supportive care alone in patients with TEN and overlapping SJS/TEN. Statistical analysis was performed on the raw data to compare the clinical differences between high- and low-dose treatment in adult patients, and between pediatric and adult patients receiving IVIG. The mortality rate in the group of TEN and overlapping SJS/TEN patients treated with IVIG was 19.9%. Pediatric patients treated with IVIG had a lower mortality rate than adults (0% vs. 21.6%, p = 0.01). Adult patients treated with high dose IVIG had a lower mortality rate than those treated with low dose (18.9% vs. 50%, p = 0.02). However, the multivariable logistic regression model showed that IVIG dose was not correlated with mortality. But these results should be interpreted with caution due to limitations in the study design [79]. Following Huang’s publication, a further study performed by Firoz et al. including 23 TEN patients treated with IVIG, demonstrated that IVIG did not improve survival compared with supportive care simply [80]. In 2013, Lee et al. published a retrospective analysis of 64 patients with overlapping SJS/TEN and TEN receiving IVIG. Based on the actual mortality compared with the SCORTEN estimated mortality, IVIG therapy showed no benefit. In addition, there was no difference between the high dose (>3 g/kg) and the low dose (<3 g/kg) [81].
Corticosteroids have been used to treat SJS/TEN for many years. Advocates emphasize the anti-inflammatory role of high doses of corticosteroids in the early stages of the disease. Opponents argue that systemic corticosteroids increase the risk of infection. Retrospective analysis of EuroSCAR data showed a lower mortality rate in the German group of patients receiving corticosteroids than in the supportive care group alone. To limit the side effects of long-term corticosteroid use, some authors use very high doses for a short time (pulse therapy) [82]. In the study by Kardaun and Jonkman, 12 patients treated with intravenous dexamethasone 100 mg or 1.5 mg/kg for 3 days had a lower mortality rate compared with the estimated mortality according to the SCORTEN [83]. Hirahara et al. had 8 patients with SJS/TEN treated with intravenous methylprednisolone 1000 mg for three consecutive days, followed by dose reduction with oral prednisolone or 2 days of methylprednisolone at half the initial dose. No patient died although the SCORTEN estimated mortality was 1.6. Serum biomarkers IFN-γ, TNF-α, IL-6, and IL-10 were measured in 5/8 patients. On the fourth day post-treatment, the mean concentrations of these cytokines decreased compared with pre-treatment, but only significantly changed for interferon-gamma (IFN-γ) and IL-6. On day 19, there was a significant reduction in both IFN-γ, TNF-α, and IL-6, whereas IL-10 levels were higher than before treatment [84].
Cyclosporine is an immunosuppressive drug, indicated in many diseases such as rheumatoid arthritis, psoriasis, Crohn’s disease, nephrotic syndrome, anti-rejection in organ transplantation. The mechanism of action of the drug is to reduce the function of lymphocytes by forming a complex with cyclophilin to inhibit the activation of calcium channel phosphatase, thereby reducing the production of cytokines by T lymphocytes.
Its inhibitory effect on lymphocytes defines cyclosporine as the theoretical standard drug of action in SJS/TEN [1]. A study by Valeyrie-Allanore showed that in 29 SJS/TEN patients treated with cyclosporine (1.5 mg/kg/day in 2 divided doses for the first 10 days, then reduced to 1 mg/kg/day days for the next 10 days, the last 10 days is 0.5 mg/kg/day) is effective. No patient died, although the SCORTEN estimated number of deaths was 2.75/29 [85]. Singh reported 11 patients with SJS/TEN who were treated with cyclosporine 3 mg/kg/day for 7 days, followed by dose reduction. This group was compared with 6 corticosteroid-treated SJS/TEN patients. In the cyclosporine group with a shorter hospital stay, the epithelialization rate was faster. Cyclosporine was more effective than corticosteroids when comparing SCORTEN estimated mortality. Kirchhof retrospectively studied 64 SJS/TEN patients treated with cyclosporine or IVIG (dose varied from patient to patient). Comparison of SCORTEN estimated mortality with actual mortality suggests a benefit of cyclosporine over IVIG [86]. Cyclosporine is well-tolerated, despite treatment in patients prone to hemodynamic instability and prerenal hypovolemia. It contributed to improved patient survival. Disease progression was slow and halting in the majority of patients [85, 87].
Other therapies were used in SJS/TEN but in small sample sizes, no comparisons were made. Plasmapheresis is used in difficult-to-treat cases, and some reports have shown it to be effective [88, 89]. The immunoregulatory and regenerative role of G-CSF is used in the treatment of SJS/TEN (helps to stop hypersensitivity, stimulate epithelialization, control neutropenia) [90]. Biologics such as TNF-alpha antagonists have been conducted to improve the prognosis of SJS/TEN [23, 52]. Paradisi reported 10 patients with SJS/TEN treated with etanercept with a single dose of 50 mg subcutaneously. The study did not have a control group. All patients responded with a mean time of epithelialization of 8.5 days. Although the SCORTEN estimated mortality rate was 50%, no patient died [91].
Notes on suspected culprit drugs. Advise patients to avoid over-the-counter medications if the cause of SJS/TEN is unknown. If the patient has damage to the ocular mucosa, an ophthalmologist should be examined a few weeks after discharge from the hospital. Monitor for complications after discharge such as skin, oral, urogenital, respiratory, digestive, and psychological problems.
Stevens-Johnson syndrome and TEN have aggressive, acute, severe clinical manifestations, diagnosis is mainly based on clinical characteristics. Tests are mainly used to find probable etiology, assess severity, and differentiate from other bullous skin diseases. Treatment includes discontinuation of the suspected allergen/drug, supportive care, and/or a combination of specific drugs such as corticosteroids, cyclosporine, IVIG, and others.
We sincerely thank the medical staff and the Board of Directors of National Hospital of Dermatology and Venereology, Hanoi, Vietnam for supporting this manuscript.
The author declares no conflict of interest.
ALDEN | algorithm for drug causality for epidermal necrolysis |
CD | cluster of differentiation |
DIHS | drug-induced hypersensitivity syndrome |
EM | erythema multiforme |
FasL | Fas ligand |
FPR1 | formyl peptide receptor 1 |
G-CSF | granulocyte colony-stimulating factor |
HHV 6 | human herpesvirus 6 |
HIV | human immunodeficiency virus |
HLA | human leukocyte antigen |
IFN-γ | interferon-gamma |
IL | interleukin |
iNOS | inducible nitric oxide synthase |
IVIG | intravenous immunoglobulin |
MPE | maculopapular exanthema |
NK | natural killer |
NO | nitric oxide |
ODSRs | ordinary drug skin reactions |
OR | odd ratio |
PBMCs | peripheral blood monocytes |
SCARs | severe cutaneous adverse drug reactions |
SCORTEN | SCORe for TEN |
SJS | Stevens-Johnson syndrome |
TEN | toxic epidermal necrolysis |
Th | T helper |
TNF-α | tumor necrosis factor-alpha |
Treg | T regulatory |
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He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/136743",hash:"",query:{},params:{id:"136743"},fullPath:"/profiles/136743",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()