However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most.The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms. Molecular understanding of tumor heterogeneity is key to personalized medicine and effective cancer treatments. Numerous studies have identified molecularly distinct cancer subtypes among individual patients of the same histopathological type by performing a high-throughput gene expression analysis of the patient tumor samples . While mloess) [A crucial step in the translation of gene signatures derived from high-throughput platforms is validation in a clinical setting, using robust and quantitative assay platforms  without loss of any classification accuracy . A majorloess) ,16, rankloess) -19, haveData discretization is a popular data pre-processing step used in supervised learning for creating the training sets. Data discretization transforms continuous values of feature variables to discrete ones ,22. It cFeature selection algorithms seek for a subset of relevant features to use in model construction in order to simplify and reduce over-fitting of the models. The wrapper, filter, and embedded methods are the three main categories that have been widely used in biomedical research to deal with a large feature space ,27. BrieUsing a subset of most important genes (variables/features) screened by the variable selection methods, numerous classification methods have been applied to tackle disease sample classification problems. For example, SVM was applied for characterizing functional roles of genes in yeast genome and cancer tissues ,29, RF fk for the discretization, we explored various bin sizes including the optimal bin number (k = 11) based on Dougherty's formula [k = 10 as it consistently achieved good accuracy. Then we applied two advanced feature selection algorithms, SVM-recursive feature elimination (SVM-RFE) [th (257 samples) for training and 1/4th (85 samples) for testing. We describe below the classification performance for each variable selection method--CV, SVM-RFE and RF_based_FS.Because gene isoforms (variables) whose expression levels do not vary much across the samples are less useful for discriminating the four GBM subtypes, We selected 2,000 isoforms with the highest variability across the samples, using CV (coefficient of variation). To search for an optimal bin number  formula , and choSVM-RFE)  and RF bk-means clustering yielded the best accuracy of 90.6% , we trained the classifiers using the data from exon-array and tested on matched RNA-seq datasets for the same TCGA samples. First, we observed that the classification framework resulted in poor classification accuracies when the classification and feature selection algorithms were trained on FC data from exon-array data and tested on corresponding FC data from RNA-seq platform Table . The besk-means with SVM . Equal-W binning improved the accuracy for RF , but not for the other three classifiers. When using the top 100 features in the final model, RF with Equal-F binning correctly predicted 68 samples out of 76, achieving ~90% accuracy  was rather poor and ranged from 27.6 to 69.7% Figure . Howeverk-means clustering discretization, RF achieved the best performance. Using the top 100 features, RF with Equal-F binning achieved 86.8% accuracy that is 31.6% higher than the best accuracy with FC (55.2%).Similarly, for SVM-RFE features selection, the prediction accuracy of the models on FC data is quite low, within the range of 35.5-61.8%. While Equal-F binning improved the accuracy of all the four classifiers, Equal-W binning improved the accuracy for SVM and RF only  for classification methods that can handle only discrete variables, (2) for improving the human interpretation, (3) for faster computation process with a reduced level of data complexity, (4) for handling non-linear relations in the data, e.g., very highly and very lowly expressed genes are more relevant to cancer subtype, and (5) to harmonize the heterogeneous data. In this study, we showed that simple unsupervised discretization indeed improved the classification accuracy by harmonizing the data that come in different scale and magnitude from different gene expression platforms. The discretization step lead to numerically comparable measures of gene expression between different platforms, and translate the classification models (consisting of multiple transcript variables) across platforms. However, the discretization methods applied in this study have some limitations. For example, Equal-W binning is prone to outliers that may skew the distribution . The k-mThe choice of classification algorithms is often important dealing with a certain dataset as each of the algorithms has its own strengths and weaknesses. We experimented on the four state-of-the-art machine learning approaches based on maximum margin, decision tree, probabilistic and clustering classification. While SVM achieved the best accuracy, the performance of RF was more consistent when tested with various numbers of features and data types. We used the linear kernel SVM because it is known to be less prone to overfitting than nonlinear kernels such as radial basis function (RBF); intuitively, the RBF kernel could perform better when the data is linearly not separable or the feature and sample spaces are well balanced. PAM and NB also performed fairly well with the features chosen by RF_based_FS. NB is known to be robust with irrelevant features, but the performance would be quickly degraded when correlated features are added.k-means clustering. With Equal-F binning, RF_based_FS identified important features more efficiently than the CV and SVM-RFE when fewer gene isoforms are considered in classification. Based on these encouraging results, we propose an integrative machine learning framework that involves feature selection, data discretization, and classification model build up by training and testing for independent platform ; obtained the isoform expression of 114,930 transcript variants  using the Multi-Mapping Bayesian Gene eXpression program [We obtained isoform-level gene expression estimates and molecular subtype information for 342 and 155 GBM samples profiled by Affymetrix exon-arrays and RNA-seq, respectively. The four molecular subgroups are neural (N), proneural (PN), mesenchymal (M) and classical (CL). Gene expression profiles for 76  samples were available from both RNA-seq and exon-array platforms. The common samples were used to assess classification performance for platform transition. We followed the data pre-processing procedure and obtained patients' GBM subtype information (class labels) from our recent study ; briefly program . The estk-means clustering--on the continuous FC data.We processed the gene expression data to estimate the FC values, and then three unsupervised discretization techniques--Equal-W binning, Equal-F binning, and 2 (T/N), where T is estimated expression values of a tumor sample and N is median expression of normal brain samples.FC is a measure of a quantitative change of gene expression, defined by FC=loget al [k = max ), where l is the number of distinct values of the attribute. Boulle [k = 10, considered as a default for most cases. While the former approach resulted the maximum bin number k = 11, we extensively evaluated by exploring various bin numbers of k = 2i for i={1, 2, ..., 10}.To determine the number of bins for discretization, Dougherty et al  suggeste. Boulle  proposedi))-min(GE(i)))/number of bins, where GE is isoform-level transcript gene expression of sample i. Then, continuous variables are assigned into the corresponding bin numbers.Equal-W binning algorithm seeks for maximum and minimum values, and then divides the range into the user-defined equal width intervals defined as Equal-W=(max(GE(i))/number of bins.Equal-F binning algorithm sorts all continuous variables in ascending order, and then divides the range into the user-defined intervals so that every interval contains the same number of sorted values defined as Equal-F=sort/2 binary classifiers are trained and the appropriate class is determined by major voting. In this study, we used the latter approach with a linear kernel method as the size of features is larger than samples.SVM is primarily a two-class classifier that constructs a hyperplane to separate the data with maximum margin ,36. For RF is an ensemble learning method that builds decision trees with binary splits . Each trx1,...,nx) of an instance X are conditionally independent given the class C. For example, for a class C that maximizes the likelihood is P(X|C)=P. The conditional independence enables the conditional probability as a product of simpler probabilities defined by P(X|C)=Π P(iX|C).NB is a simple probabilistic classification method grounded in Bayes' theorem, for calculating conditional probabilities, with an independence assumption . For a gPAM is a sample classification method that uses the nearest shrunken centroid approach for transcript-variants gene expression data . Brieflyi, tnitp, and ifn, are true positive, true negative, false positive, and false negative for class iC, respectively.We estimated the overall classification accuracy based on the number of correct predictions divided by the total number of prediction samples defined as ACC=(number of correct predictions)/. In addition, sensitivity (Sn) and specificity (Sp) for each sub-group (one GBM sub-group vs the rest of the GBM groups together) are calculated as The authors declare that they have no competing interests.SJ participated in the design of the project, performed the bioinformatics and statistical analysis, and drafted the manuscript. YB and RVD conceived the idea and participated in the design of the project. SJ, YB, and RVD revised the manuscript. All authors read and approved the final manuscript.


Asthma is a chronic inflammatory disease that has reached epidemic proportions worldwide. It is treatable in the majority of patients, but there is no cure. Moreover, a proportion of patients suffer from severe, difficult-to-control disease with daily symptoms and high morbidity, making it imperative that we continue to improve our understanding of the underlying mechanisms of this disease. Severe asthma is a heterogeneous condition. A systematic approach to identify specific asthma phenotypes, including clinical characteristics and inflammatory processes, is the first step toward individualized, logical therapy. This review focuses on the need to characterize severe asthma phenotypes and on novel, targeted molecular treatment options currently under development. Asthma is associated with significant morbidity and mortality and is one of the most common long-term conditions worldwide . The preA well-defined, unbiased approach that defines the clinical characteristics that specify distinct phenotypes of asthma is critical for a better understanding of disease pathogenesis and thus successful management.It is essential to differentiate severe asthma from difficult-to-control asthma. Assessment of each and every patient presenting with recurrent episodes of wheezing, chest tightness, and/or cough requires a careful history focusing on symptoms, exacerbating triggers and occupational as well as environmental factors. With the correct diagnosis established, systematic evaluation has the potential to identify up to half of previously deemed severe asthmatics as difficult-to-treat asthmatics , 5. PatiConsistently poor symptom control: Asthma Control Questionnaire (ACQ) score is consistently >1.5 or Asthma Control Test (ACT) score is <20.Frequent severe exacerbations requiring at least courses of oral corticosteroids for a minimum of 3 days in the previous year.One or more hospitalization, intensive care unit admission, or invasive ventilation in the previous year.1)<80% predicted  reduced to less than the lower limit of normal).Forced expiratory volume , sleep apnea, and obesity are well-known comorbidities that can aggravate and perpetuate and/or masquerade as asthma. Epidemiological studies report less atopy by skin test in severe asthma, but a high prevalence of chronic rhinosinusitis and nasal polyposis , 8. Seve2 agonists  emerged in the late 1970s . Snoringirements , 30.and patients with chronic diseases. Patients with severe asthma are under protracted distress and are at increased risk of developing psychiatric disorders, most commonly panic disorder, depression, and anxiety -driven disease characterized by reversible airway obstruction, thickened airway smooth muscle cells, subepithelial fibrosis, and a characteristic aberrant immune regulation with a predominance of Th2 cells secreting cytokines IL-4, IL-5, and IL-13 . IL-4 isSeveral approaches have been taken to characterize asthma subgroups. The Severe Asthma Research Program (SARP) identified five asthma subphenotypes by unbiased cluster analysis, three of which are severe asthma : the earIn another study using cluster analysis, sputum eosinophilia was incorporated. Consequent upon this, a phenotype of early-onset severe atopic asthma, late-onset asthma with persistent eosinophilia, and a cluster of obese females with late-onset asthma without eosinophilia were identified .POSTN), chloride channel regulator 1 (CLCA1), and serpin peptidase inhibitor, clade B, member 2 (SERPINB2) as epithelial genes that were specifically induced in asthma and directly regulated by IL-13 in vitro , but not to IgE that is already bound by the high-affinity IgE receptor (FcɛRI) on the surface of mast cells, basophils, and antigen-presenting dendritic cells. Steric hindrance by the receptor means the receptor is not accessible to omalizumab binding, thus averting anaphylaxis. In a number of studies conducted so far, patients treated with omalizumab reported significant improvements in asthma-related symptoms, allowing for a reduction in corticosteroid and rescue inhaler use –51. It ahttp://clinicaltrial.gov/ct2/show/NCT01582503).The success of anti-IgE therapy is limited by the fact that IgE production is not affected. Treatment has to be given regularly and on a long-term basis. Brightbill et al. have successfully created a monoclonal antibody against the M1′ segment of membrane IgE on human IgE-switched B cells, resulting in depletion of IgE-switched B cells via apoptosis or/and antibody-dependent cell-mediated cytotoxicity . Total IPromising newer treatments targeting the Th2 pathway with steroid-sparing potential include the anti-IL-5 antibody mepolizumab. The first randomized controlled trials with anti-IL-5 were conducted in patients with mild to moderate asthma , 57. TheClinical trials have also investigated targeted therapies against the Th2 cytokines IL-4 and IL-13. Pitrakinra is an IL-4 mutein, which binds to the IL-4Rα subunit and prevents the inflammation induced by IL-4 and IL-13. It has been shown to reduce allergen-induced airway responses when given in inhaled or subcutaneous form in a study of mild asthmatics and to reduce exacerbation rates in those with eosinophilia , 61. In 1 in patients with a high serum periostin level , there is no correlation at all between blood neutrophilia and airway neutrophilia . Further1, and bronchial hyperreactivity. However, the effect ceased as soon as the drug was discontinued. The fully human anti-TNF antibody golimumab did not have any clinical benefit, but resulted in an increase of respiratory infections and malignancies leading to an early discontinuation of the trial (Anti-TNFα treatment was tested in severe (etanercept) , 75 and he trial .In a randomized controlled trial, the human anti-IL-17 receptor monoclonal antibody brodalumab was not superior to placebo in terms of asthma control . InteresDeficiency in serum vitamin D has been linked to predisposing individuals to chronic inflammatory lung disease, such as asthma and viral respiratory infections, with higher rates of hospital admission for respiratory diseases –86. RepoMacrolide antibiotics have anti-inflammatory and immune-modulatory effects. They also increase gastrointestinal motility and to that extent might prove beneficial in patients with significant GERD. They have been proven effective in chronic respiratory diseases such as cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). They have been shown to reduce severe exacerbations in patients with non-eosinophilic asthma and to have significantly improved the Asthma Quality of Life Questionnaire score . HoweverMethotrexate has proven a valid agent in patients who, despite long-term treatment with oral glucocorticosteroids, fail to gain satisfactory control of their asthma . Due to 2 agonists (LABAs), it is currently unclear which phenotypes respond best to the treatment. Studies are needed to identify the phenotype of patients who will derive significant clinical benefit most from this invasive procedure.Smooth muscle hyperplasia is a distinctive feature of asthma. Applying radiofrequency energy to subsegmental airways has been shown to reduce muscle mass at the site of thermoplasty. Trials demonstrated a reduction in the number of severe asthma exacerbations and an improvement in asthma-specific quality of life , and a rAsthma is common, and in most cases it is a treatable disease. However, patients with difficult-to-treat asthma remain a challenge for every healthcare professional, and there is a significant unmet clinical need. It has become abundantly clear that asthma is a heterogeneous disease in its course and variation in response to treatment. A systematic approach to the complexity and diversity of asthma pathophysiology is essential. Up until recently, trials testing anti-inflammatory therapy based on targeting cytokines have, despite highly promising results in animal models, proven to be disappointing. However, in carefully selected patients, when asthma phenotypes are taken into account, novel biological treatments may lead to significant advances. Discriminatory biomarkers and genetic profiling may aid identification in support of personalized pharmacotherapy. Within the next 5 to 10 years, logical, targeted therapies will be available for patients with Th2-high severe asthma, and our understanding of the mechanisms driving Th2-low asthma will be significantly advanced.


Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data.Database URL: http://www.regnetworkweb.org Gene regulatory events play crucial roles in a variety of physiological and developmental processes in a cell, in which macromolecules such as genes, RNAs and proteins are coordinated to orchestrate operative responses under different conditions . TherefoEscherichia coli  spreads out in various databases and literature. It is desirable to develop a unified database and provide users with the necessary tools for information access or retrieval. However, only limited efforts such as RegulonDB for hia coli  have beehia coli  and KEGGhia coli , the TF hia coli , 17 can hia coli  and ubiqhia coli .http://www.regnetworkweb.org.Although there exist many computational methods for deciphering the transcriptional regulatory interactions between TFs and genes, the integrative analysis considering both TF and miRNA as regulators is still very limited due to the lack of a ready-to-use regulatory network database . In recoBoth transcriptional and post-transcriptional regulatory relationships are important, we thus consider both TFs and miRNAs as regulators. As shown in More specifically, for transcriptional regulatory relationships, we first compile a list of TFs for human and mouse, respectively, from FANTOM , UniProtFor post-transcriptional regulations, the experimentally validated ‘miRNA–gene’ pairs in human and mouse from miRTarBase , TarBaseThe documented ‘TF–miRNA’ regulatory relationships in TransmiR  are alsoWe have developed a web tool of RegNetwork for users to query and download the regulatory relationships and networks. RegNetwork is implemented in Java, JavaScript and Python together with the PostgreSQL database. All raw data ETL  are carried out with Python scripts on the back end. The frontend interface is developed using JSP and JavaScript.et al.  . Moreove.5×10−5) , 46.TabSecond, the node degrees of the established networks are calculated and found to satisfy the power law distributions as shown in ale-free , 46. Theale-free . A formaDifferent from the existing regulatory relationship databases such as TRED , RegNetwIn this article, we developed a database, RegNetwork, of the knowledge-based genome-wide regulatory networks in human and mouse by integrating various data sources. A comprehensive set of interplays among TFs, miRNAs and target genes were collected and reorganized for public access. The established regulatory networks from RegNetwork provide genome-wide regulatory interactions, which lay an initial foundation and establish a prior background network to identify or verify molecular and functional regulations in pathways or subnetworks corresponding to different phenotypes. Also, combined with high-throughput expression data under specific physiological and developmental conditions , one can identify differential subnetworks and pathways from the background networks in RegNetwork, which will lead to novel and interesting insights into regulatory mechanisms in context-specific processes.Rattus norvegicus (rat), Drosophila melanogaster (fruit fly), Caenorhabditis elegans (worm), Escherichia coli (E. coli) and Saccharomyces cerevisiae (yeast).At the time when the current version of RegNetwork was developed, the ENCODE project published thousands of regulatory interactions in human inferred from high-throughput datasets , which cNational Natural Science Foundation of China (NSFC) (Grant Nos. 61572287 and 61533011 to Z.P.L.); the Shandong Provincial Natural Science Foundation of China (Grant No. ZR2015FQ001 to Z.P.L.); the Fundamental Research Funds of Shandong University (Grant No. 2014TB006 to Z.P.L); the Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China (to Z.P.L.); University of Rochester Center for Biodefense Immune Modeling Grant (NIH/NIAID) (HHSN272201000055C to H.W.); University of Rochester Center for AIDS Research Grant (NIH/NIAID) (P30AI078498 to H.W); NIH Grant (R01GM100788 to H.M.). Funding for open access charge: Dr. Hulin Wu's start-up fund.Conflict of interest. None declared.


This indicates that there are different predictors impacting the transmission range of primary MDR-TB and primary DS-TB in the general population.To find out the reason why some people get infected directly with multidrug-resistant tuberculosis (MDR-TB), whereas some get infected with drug-susceptible tuberculosis (DS-TB), a 1:1:1 matched-pairs case-control study was conducted to identify predictors associated with primary MDR-TB and primary DS-TB against the control in Jiangsu Province, China. All three groups were geographically matched (by neighborhood) and matched on sex and age (±5 years). In total, 110 participants were enrolled in each of three matched groups. Conditional logistic regression analysis showed that predictors independently associated with primary MDR-TB were illiteracy or primary school education, annual per capita income ≤ US$2,000, per capita living space < 40 m Mycobacterium tuberculosis (MTB) that are resistant to at least isoniazid (INH) and rifampicin (RFP), is a form of TB infection linked with high morbidity and mortality, and it is of great importance to the National TB Control Program (NTP). According to the World Health Organization (WHO) report, China accounted for about 22% of the estimated 390,000–510,000 cases of incident MDR-TB (primary and acquired) arising globally in 2008.Multidrug-resistant tuberculosis (MDR-TB), which is defined as TB caused by strains of Many surveys around the world have been conducted to identify risk factors for MDR-TB. Previous treatments for TB as a risk factor associated with MDR-TB were found in some surveys conducted in Brazil, Burkina Faso, Georgia, Spain, and Turkey.As noted above, despite lower prevalence of primary MDR-TB from primary TB cases than that of acquired MDR-TB from retreatment TB cases, the absolute numbers of primary MDR-TB cases estimated are larger than those of acquired MDR-TB cases. We have to hypothesize that there exists some risk factors associated with primary MDR-TB that are different from risk factors associated with primary DS-TB, although a survey in Peru found a high rate of primary MDR-TB in a general population with no identifiable risk factors.By using the formula of sample size for a 1:1 matched-pairs case-control study,2, one-half of whom came from rural settings. Each of the primary MDR-TB cases was matched individually with a primary DS-TB case and a control. The primary DS-TB case was specially defined as the primary SS+ case who was susceptible to all first-line drugs mentioned in this study. All three groups were geographically matched by living in the same village or community and matched on sex and age (± 5 years). The matched primary DS-TB cases were selected from the remaining 1,283 primary SS+ TB cases. The controls who were selected through the local population management system were not confirmed with TB or other pulmonary diseases according to diagnostic criteria of the NTP.From June to December of 2011, a 1:1:1 matched-pairs case-control study was conducted to identify risk factors associated with primary MDR-TB and primary DS-TB versus controls in Jiangsu Province, China. The laboratory procedures included two steps: first, suspected TB cases were diagnosed with TB mainly by sputum smear microscopy and chest imaging according to the guidelines for implementing the NTP in China,To explore risk factors associated with primary MDR-TB and primary DS-TB, we used a structured questionnaire covering sociodemographic characteristics, living environment, dietary patterns, daily life, mental status, past medical history, and other potential risk factors in the year before they became MDR-TB or DS-TB cases, which were markers of socioeconomic status and general health status. All data were collected by self-reporting, except height and weight. The height and weight were collected from previous medical records or physical examination records of participants. The questionnaire was pre-tested before the study participants were interviewed. All of the interviews were conducted in participants' homes or rooms of local CDCs or TB dispensaries according to the participants' demands. The matched participants were interviewed on the same day by the same interviewer. Privacy was ensured while interviews were being conducted.This study was approved by the Ethics Review Committee (ERC) of Jiangsu Province CDC and the ERC of China CDC. Before the interviews, all participants provided their written informed consent. If participants were less than 18 years of age, their guardians provided the written informed consent.2 test were used for paired comparison of characteristics of three groups according to Bonferroni correction. Compared with control, risk factors with crude and adjusted matched ORs and their 95% CIs associated with primary MDR-TB and primary DS-TB were identified by univariate and multivariate conditional logistic regression models, respectively. A two-sided P value of 0.05 or less was regarded as the significance that was assessed by the Wald χ2 test. All data were analyzed using the SAS statistical package .For descriptive statistics, medians and interquartile ranges were calculated for continuous variables, and frequencies and percentages were calculated for discrete variables. The rank-sum test and the Pearson χIt is displayed in 2 , living below the second floor , interval ≥ 7 days of eating fruits , interval ≥ 2 days of physical exercise , and feeling higher life pressure . The multivariate analysis showed that risk factors independently associated with primary MDR-TB were illiteracy or primary school education , annual per capita income ≤ US$2,000 , per capita living space < 40 m2 , and interval ≥ 7 days of eating fruits .According to 2 , interval ≥ 2 days of eating meats , and feeling higher life pressure . The multivariate analysis showed that risk factors independently associated with primary DS-TB were BMI ≤ 20  and feeling higher life pressure .As shown in Although MDR-TB is initially associated with irregular anti-TB treatment,However, our results were contrary to the specific transmissions and groups noted above. We found in this study that primary MDR-TB cases, primary DS-TB cases, and controls had similar proportions of prior exposure to TB cases, which were similar with findings of a study in Peru and another study in China.This study attempted to extensively explore the possible clues to above-mentioned problems. There were only few studies of risk factors for primary MDR-TB, although many studies of risk factors for MDR-TB were found.Except less fruit consumption, other risk factors for primary MDR-TB in our study were involved in risk factors for MDR-TB in previous studies.29Poor education, low income, and small living space as the external factors of society orient toward lower socioeconomic groups, whereas low BMI and great psychological pressure as the internal factors of an individual cannot specifically orient toward a social group. This maybe indicate that, compared with risk factors of primary DS-TB, primary MDR-TB more likely happens in the low social group. Previous studies displayed that the extensive transmission of MDR-TB had been indicated by molecular epidemiology and happened in urban and rural areas.The WHO advocated that rapid DST of both INH and RFP at the time of diagnosis is the most cost-effective strategy for any TB patient group or setting.Several limitations of this study should be considered in the interpretation of the results. First, we obtained information about the year before participants became DS-TB or MDR-TB cases, and therefore, recall biases were unavoidable and potentially affected the results. Second, some information, such as annual per capita income, per capita living space, interval days of eating foods or physical exercise, and prior exposure to TB cases, might be inaccurate, because it was collected by self-reporting. Despite these limitations, the risk factors associated with primary MDR-TB may provide important information for additional studies in China.This matched-pairs case-control study identified risk factors associated with primary MDR-TB in China, which included poor education, low income, small living space, and less fruit consumption. They are different from risk factors associated with primary DS-TB, which are low BMI and great psychological pressure. It may be inferred from these findings that, compared with risk factors of DS-TB, the transmission range of MDR-TB is not very extensive and perhaps still confined to the low social group. Additionally, these findings can provide useful information for controlling transmission of MDR-TB.


Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy . Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions . Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. Alzheimer’s disease (AD) is the leading cause of dementia in the aging population, affecting more than 30 million people worldwide . AD is aIncreasing efforts have focused on building predictive models of AD dementia using pattern classification methods based on clinical, imaging, genetic, and fluid biomarkers –11. ThisWe use a kernel-based classifier to predict future dementia status of MCI patients by incorporating heterogeneous  data. Kernel-based learning algorithms use “kernel functions” to encode the degree of similarity between examples in a dataset based on their features ,21, suchThe prevailing approach in the literature has been to consider prediction of MCI-to-dementia progression as a non-probabilistic binary classification task, where all patients are unequivocally assigned to either the progressive MCI (P-MCI) or the non-progressive MCI (N-MCI) group ,26–28. SThe objectives of this study were to determine whether: (a) clinical, MRI, and plasma proteomic data capture complementary information regarding the progression from MCI to dementia; (b) this information is more effectively learned using a multiple-kernel classifier as opposed to a single-kernel classifier; (c) the performance of our prognostic model is sensitive to patient heterogeneity; (d) model performance can be improved by taking into account the confidence of the predictions; and (e) the model's probabilistic predictions reflect any information regarding the time to progression for P-MCI patients.http://adni.loni.usc.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations as a public-private partnership. ADNI is an observational study with both cross-sectional and longitudinal follow-up components. The primary goal of ADNI has been to test whether neuroimaging, fluid and genetic biomarkers, and cognitive assessments can be combined to measure the progression of MCI and early AD. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California–San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. The first phase of ADNI (ADNI-1) was completed in 2010 and has been followed by ADNI-GO and ADNI-2. For up-to-date information, see www.adni-info.org.Data used in this study were obtained from the ADNI database  and are summarized in section 1.1 in The general eligibility, inclusion, and exclusion criteria for ADNI subjects can be found on the ADNI website  of these subjects were further excluded due to partially missing baseline data. APOE) genotyping and proteomic analysis. Subjects were then followed longitudinally at specific time points . The clinical status of each MCI subject was re-assessed at each follow-up visit and updated to reflect one of several outcomes . The N-MCI and P-MCI group designations were based on this follow-up clinical diagnosis and used as the “ground truth” in our classification experiments.At study entry (baseline), all subjects underwent a comprehensive clinical evaluation, cognitive/functional assessments, and a structural brain MRI scan. Subjects also provided a blood sample for apolipoprotein E  as potential predictors of MCI-to-dementia progression in our classification analyses. Clinical features were of two types: risk factors (16 features) and assessments/markers (170 features). Risk factors included: age, sex, education, MRI offers a non-invasive, widely available, and more cost-effective alternative for obtaining imaging biomarkers of AD-related neurodegeneration (e.g. atrophy measures) compared to positron emission tomography (PET) . We conshttp://surfer.nmr.mgh.harvard.edu) [Subjects received high resolution T1-weighted MRI scans of the brain at 1.5 Tesla acquired using a variety of scanners  and a standardized protocol . Each MRard.edu) –43, an iard.edu) –46. See http://adni.loni.usc.edu). We also considered the plasma levels of amyloid-β proteins , which were assayed by the ADNI Biomarker Core Laboratory at the University of Pennsylvania. Aβ42 and Aβ40 have been identified as the major molecular species contributing to the amyloid (“senile”) plaques, a pathological hallmark of AD [Plasma-based proteomic biomarkers have been proposed as an alternative for the early diagnosis of AD to cerebrospinal fluid (CSF)-based biomarkers ,48. Howerk of AD .http://www.cs.man.ac.uk/~gbrown/fstoolbox) [Analyses were conducted using MATLAB R2010b . We applied a series of transformations to the feature data prior to conducting feature selection and classification analyses (see section 1.4 in toolbox) . JMI-bashttp://www.dcs.gla.ac.uk/inference/pMKL) [In this study, we use the probabilistic multiple kernel learning (pMKL) classification approach proposed by Damoulas et al. (ce/pMKL) ,56,57 toce/pMKL) ,21. Kernce/pMKL) . For furE is the expectation with respect to the standard normal distribution p(u) = N and Ф is the cumulative distribution function. This function computes the probability P that example n belongs to class/outcome i (as opposed to class j) given the feature data  regression framework using the multinomial probit likelihood  given byails see ) and capWe built and examined a series of nine predictive models, each designed to classify individual patients as belonging to either the N-MCI or the P-MCI group. Models 1–5 were constructed using a single, linear kernel and were designed to assess the predictive utility of different data sources, alone and in combination. First, a separate single-source model was constructed for clinical risk factors , clinical assessments / markers , MRI markers , and plasma proteomic markers . Second, a multi-source model was constructed where all features across the four data sources  were concatenated and considered jointly during feature selection and classifier training steps . We also constructed a set of multiple-kernel, multi-source models (models 6–9) to examine whether multiple kernel learning can be used to improve upon the predictive performance achieved with the single-kernel model . Finally, we examined whether there is an association between the predicted probabilities and time to progression for P-MCI patients.In subsequent analyses, we studied the best performing model from the set of nine models examined. First, we examined the extent to which patient heterogeneity affects model accuracy; we examined the effects of age, sex, educational level, For each model (1–9), we report several cross-validated measures of predictive performance. We report sensitivity (percent of P-MCI subjects correctly classified) and specificity (percent of N-MCI subjects correctly classified) as measures of classification accuracy . The balt-test with 10 degrees of freedom calibrated for 10x10 CV experiments [P < 0.05 level.We used a nested stratified cross-validation (CV) procedure  to avoideriments  to test P < 0.01, one-sample t-test), although they varied from a low of 53.2% for PPM to a high of 76.1% for CAM. The CAM model outperformed the other three single-source models on accuracy . The CAM and MRI models were well-calibrated, as indicated by high positive CCC (both P < 0.001) while the PPM model showed poor calibration . The single-kernel, multi-source model 5 (CONCAT), in which all features across the four data sources were considered jointly, outperformed all four single-source models  with an accuracy of 80.0%. The calibration of the CONCAT model, as measured by the CCC, was statistically similar to that of CAM and MRI models (both P > 0.3) and better than that of the PPM model (P < 0.001). None of the four multiple-kernel, multi-source models considered (models 6–9) outperformed the single-kernel CONCAT model in terms of classification accuracy. However, model 6  outperformed the single-kernel CONCAT model in terms of calibration, as indicated by a higher CCC (P < 0.05), while maintaining a similar accuracy of 79.9%. Based on its classification accuracy and calibration, model 6 (MKL-Gaussian) was selected as the best performing model to be studied in subsequent analyses. APOE epsilon 4 alleles was selected with a high degree of consistency. Other candidate CRF features, including age, were selected infrequently. The features most frequently selected in the CAM model included total scores and sub-scores on three assessments: Alzheimer's Disease Assessment Scale–Cognitive sub-scale (ADAS-Cog), Functional Activities Questionnaire (FAQ), and Rey Auditory Verbal Learning Test (RAVLT). In the MRI model, the most frequently selected features included volume and cortical thickness measures for several temporoparietal brain regions with a preference toward the left hemisphere (8/10 features). In the PPM model, the most frequently selected features included proteins associated with vascular processes, immune function and inflammation, and lipid metabolism. In the case of multi-source models–both the single-kernel (CONCAT) model and the best performing, multiple-kernel (MKL-Gaussian) model–only CAM and MRI features were consistently selected as predictors . P-MCI subjects were more cognitively and functionally impaired at baseline than N-MCI subjects, as indicated by higher scores on the ADAS-Cog and FAQ. Relative to N-MCI subjects, P-MCI subjects had a more pronounced verbal memory impairment at baseline, as indicated by lower scores on the RAVLT. P-MCI subjects also showed signs of atrophy in temporoparietal brain regions at baseline, as indicated by reduced hippocampal volume as well as reduced middle temporal and inferior parietal cortical thickness relative to N-MCI subjects. In this study, we excluded data from 131 out of 390 (~34%) MCI subjects in the ADNI-1 database because they either did not meet our inclusion criteria or due to missing data. No differences were found between included (n = 259) and excluded (n = 131) subjects on any of the baseline predictor variables  model revealed that overall the model generated more accurate predictions regarding MCI-to-dementia progression for subjects with the following characteristics : older adementia : 0–6 monP < 0.05, Spearman correlation).We investigated whether probabilistic outputs from the pMKL classifier could be used to improve the classification accuracy of our prognostic model by permitting only "high confidence” predictions to be made. As we raised the level of confidence required to make predictions, the accuracy of the model gradually increased . HoweverCognitive and functional (CF) assessments proved to be the most accurate (76.1%) in predicting MCI-to-dementia progression. Likewise, other studies have reported that CF markers are more predictive of MCI-to-dementia progression than structural MRI and CSF biomarkers during a two-year period ,26. PlasInterestingly, we found that our best performing model (MKL-Gaussian) could identify MCI patients who progressed to AD dementia within 18 months of baseline with substantially higher accuracy than patients who progressed after 18 months. Thus, CF and MRI markers appear to be most sensitive to incipient AD during the 18 months prior to the onset of dementia. This finding is consistent with the AD biomarker model proposed by Jack and colleagues , which sThe predictors of MCI-to-dementia progression identified in the multi-source models included baseline scores on cognitive (ADAS-Cog and RAVLT) and functional (FAQ) assessments as well as morphometric measures for three brain regions . The selection of ADAS-Cog scores as predictors, in addition to RAVLT scores, suggests that baseline impairment in multiple cognitive domains–not just memory function–is predictive of future progression to dementia. Consistent with this finding, previous studies have reported that MCI patients with both memory and non-memory deficits have a greater risk of progression to AD dementia than those with isolated memory deficits . A largeIt is important to note that in this study, MCI subjects recruited as part of ADNI-1 were diagnosed based on the original Petersen (Mayo Clinic) criteria for amnestic MCI . Thus, MThe selection of hippocampus, middle temporal gyrus, and inferior parietal cortex as predictors of MCI-to-dementia progression is consistent with the known pattern of grey matter atrophy associated with incipient AD, which begins in the medial temporal lobes and then spreads to temporoparietal association cortices . In bothThe effect of MKL on model performance was modest in this study. MKL did not improve classification accuracy but modestly improved the calibration of the multi-source model when using five Gaussian kernels. We used a relatively small number (3–5) of kernels in our MKL models, which could account for the limited benefit we observed with MKL. Using a larger number of kernels, as done in some recent studies (e.g. ), could APOE genotype, obtaining an AUC of 0.86. Devanand et al. [d et al.  proposedd et al. .APOE markers and had an AUC of 0.80 (74.1% accuracy). Two methodological differences may account for the superior predictive accuracy of our model compared to that of Young et al. Unlike their model, our model incorporated CF markers. Moreover, while their model was trained on data from healthy control and AD subjects (and then used to classify MCI subjects), we trained our model using MCI data to specifically classify N-MCI and P-MCI subjects. Using a model that incorporated only baseline MRI data, Wee et al. [Other recent studies using the ADNI dataset have developed models based on various combinations of MRI, PET, and CSF markers, attaining AUCs in the 0.74–0.80 range ,24,28,75e et al.  were ablAPOE epsilon 4 allele. A possible mechanism for this interaction between predictive accuracy and patient characteristics is that variables such as age and APOE genotype may be exerting moderator effects on the CF and MRI predictors in our model. Supporting this explanation is evidence that aging and AD exert independent but partially overlapping effects on cognitive function and brain structure, including converging effects on the hippocampus and temporoparietal cortex [APOE genotype, epsilon 4 allele has been linked with temporal lobe atrophy, an effect seen even in healthy control subjects [APOE genotype as predictors of progression and examined their interaction with other predictor variables insofar as these interactions were identified using the JMI-based multivariate feature selection technique used in this study. Future studies may be able to improve predictive accuracy further by removing moderator-related variability from the data via stratification or regression methods [We found that the predictive accuracy of our multi-source model (MKL-Gaussian) varied with demographic, genetic, and clinical characteristics even though none of these variables were selected as predictors of progression. For example, accuracy tended to be higher when classifying older MCI patients but lower for carriers of the l cortex ,78. In tsubjects . We did  methods . NeverthA unique aspect of this study is our adoption of a probabilistic kernel-based classifier (pMKL) for the prediction of MCI-to-dementia progression. Calibration analysis revealed that the probabilistic predictions generated by our model reliably reflect the actual risk of progression. Thus, the model could be used to stratify MCI patients according to the risk of progression. The probabilistic predictions also reflected some information about the time to progression for P-MCI patients, a surprising finding since the model was not explicitly trained to predict time to progression but rather to classify P-MCI versus N-MCI subjects. It may be possible to adapt our pattern classification approach to explicitly predict time to progression, which would allow staging of MCI patients along the MCI-AD continuum.Importantly, we showed that the probabilistic outputs could be used as a measure of predictive confidence to further improve the accuracy of the model. When using the model in conventional, non-probabilistic mode, where no information about predictive confidence was taken into account, we obtained an accuracy of 79.9% . When using the model in probabilistic mode, where predictions were allowed to be made only for the top ~73% most confident patient cases, we obtained an improved accuracy of 87.4% . By assuming a 30% risk of progression over a three-year period  as the pre-test probability . This meOur probabilistic prognostic model could be used to stratify MCI patients into high and low risk groups as a way to enrich a patient sample in a clinical trial, resulting in up to a 57% reduction in the required sample size to detect the effect of a potential treatment. The extent of amyloid deposition in the brain based on CSF proteomic analysis or PET imaging is already being used as a biomarker to select “amyloid positive” individuals in clinical trials. For example, the ongoing A4 clinical trial (Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease) is designed to examine whether an anti-amyloid treatment can slow down cognitive decline among non-demented older adults who have amyloid deposition in their brains, as determined using PET-based amyloid scans . Our preAn inherent limitation of this and other pattern classification studies using the ADNI dataset is the reliance on the clinical diagnosis of AD as the "ground truth" (gold standard). The clinical diagnosis of probable AD has an accuracy of 70–90% relative to the pathological diagnosis  The implAPOE genotype as a generic predictor of progression, genome-wide association studies have been used to identify several other genes that likely contribute to the development of AD [Another limitation of this study is the relatively short follow-up period of three years. Although the development of prognostic models for long-term dementia prediction is warranted, short-term dementia prediction can be useful for selecting high-risk MCI patients in clinical trials. For example, some recent clinical trials investigating disease-modifying anti-amyloid agents for the treatment (e.g. ) or prevnt of AD . These AThe present work can be extended in several ways. First, our model was developed using data only from patients with the single-domain amnestic subtype of MCI, based on the inclusion criteria of ADNI-1. Thus, the use of this relatively narrow inclusion criteria means that multiple-domain amnestic MCI patients, who tend to be more severely impaired and likely closer in their transition to AD-type dementia, were excluded. From a predictive modeling standpoint, the exclusion of these MCI cases likely made the task of predicting MCI-to-dementia progression more challenging. To enhance the clinical utility of our predictive model, future work should incorporate data from patients with both amnestic and non-amnestic as well as single-domain and multiple-domain MCI subtypes. Second, our model was specifically designed to predict progression from MCI to AD. In practice, there are multiple other types of dementia in addition to AD , and many cases of dementia are of a mixed etiology (e.g. AD combined with vascular dementia). The probabilistic pattern classification approach adopted in this study can be naturally extended for use in the differential diagnosis of dementia, such that a multi-class classifier could be designed to assign a probability for each type of dementia. Third, we considered only clinical, structural MRI, and plasma proteomic data in this study. Our pattern classification approach could also be applied to biomarker sources such as CSF, PET, and other neuroimaging data. The incorporation of imaging measures of brain connectivity, such as those based on diffusion tensor imaging  and restIn summary, we developed a model for predicting progression from MCI to AD-type dementia during a three-year period using a probabilistic, kernel-based pattern classification approach and data from 259 patients with MCI. Using cognitive/functional markers and morphometric MRI markers, the model predicted progression in individual patients with a cross-validated accuracy of 80% and reliably estimated the actual risk of progression. The predictive accuracy of the model varied with demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Our prognostic model can potentially improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.S1 FigError bars are 95% confidence intervals. No group differences were found for any of the predictor variables . Vol. = volume, CT = cortical thickness, ADAS-Cog = Alzheimer's Disease Assessment Scale–Cognitive sub-scale, FAQ = Functional Activities Questionnaire, RAVLT = Rey Auditory-Verbal Learning Test, L. = Left, Constr. = Constructional(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 TableIn terms of classification accuracy (T-BAR), the CONCAT model performed similarly to MKL-LPG, MKL-Poly, and MKL-Gaussian models  and outperformed the MKL-Linear model (P < 0.001). While MKL-LPG and MKL-Poly models were as equally well-calibrated as the CONCAT model , the MKL-Linear model was less well calibrated (P < 0.01) and the MKL-Gaussian model was better calibrated (P < 0.05) than the CONCAT model.(PDF)Click here for additional data file.


Pre-eclampsia (PE) is a complex, multi-systemic condition of pregnancy which greatly impacts maternal and perinatal morbidity and mortality. MicroRNAs (miRs) are differentially expressed in PE and may be important in helping to understand the condition and its pathogenesis.Case-control studies investigating expression of miRs in PE were collected through a systematic literature search. Data was extracted and compared from 58 studies to identify the most promising miRs associated with PE pathogenesis and identify areas of methodology which could account for often conflicting results.Some of the most frequently differentially expressed miRs in PE include miR-210, miR-223 and miR-126/126* which associate strongly with the etiological domains of hypoxia, immunology and angiogenesis. Members of the miR-515 family belonging to the imprinted chromosome 19 miR cluster with putative roles in trophoblast invasion were also found to be differentially expressed. Certain miRs appear to associate with more severe forms of PE such as miR-210 and the immune-related miR-181a and miR-15 families. Patterns of miR expression may help pinpoint key pathways (e.g. IL-6/miR-223/STAT3) and aid in untangling the heterogeneous nature of PE. The detectable presence of many PE-associated miRs in antenatal circulatory samples suggests their usefulness as predictive biomarkers. Further progress in ascertaining the clinical value of miRs and in understanding how they might contribute to pathogenesis is predicated upon resolving current methodological challenges in studies. These include differences in diagnostic criteria, cohort characteristics, sampling technique, RNA isolation and platform-dependent variation in miR profiling.Reviewing studies of PE-associated miRs has revealed their potential as informants of underlying target genes and pathways relating to PE pathogenesis. However, the incongruity in results across current studies hampers their capacity to be useful biomarkers of the condition. Candidaogenesis ,54. On tell line . Given tThe inclusion of GA into PE classification is critical. A significant increase in maternal mortality has been associated with eoPE, with the symptomatic chasm between eoPE and loPE such that separate pathogenetic mechanisms with overlapping phenotypes have long been suspected ,131. Altet al. [et al. [H19 gene, to be downregulated significantly in eoPE. This miR was demonstrated to inhibit trophoblast cell growth, possibly by targeting Nodal Modulator 1 (NOMO1), an antagonist of the Nodal signalling pathway which is important for regulating cell growth [A screening study by Weedon-Fekjær et al.  detectedet al. . Lastly, [et al.  found mil growth .et al. [Where the contribution of a miR cannot be attributed to PE alone, as in studies involving cases of PE co-morbid with conditions such as SGA, animal models can serve as a powerful means to help resolve ambiguity. An example of this is in the screening study by Pineles et al. , who detet al. , who detAmong case and control groups from included studies, matching of characteristics generally occurred for both GA at delivery and maternal age. Nulliparity was the most explicitly stated characteristic to be additionally matched across screening studies. However, as with other characteristics (e.g. ethnicity and BMI) matching infrequently reached significance after use of statistical testing, such as the Mann-Whitney rank-sum test. The most consistent differences in PE cases as compared to controls, outside of diagnostic criteria, were increased nulliparity, decreased GA at delivery and decreased foetal birth weight.et al. [Ethnic composition was rarely indicated in studies. A study by Goodwin and Mercer  found Afet al. , PE caseet al. . Given tet al. . In combThe vast majority of screening studies used PE cohort sizes of less than 10 individuals, particularly those involving microarrays .In considering the apparent heterogeneous nature of PE pathogenesis, with multiple risk factors and inherent genetic/environmental variability, these smaller cohort sizes may have a reduced power to detect genuine differential expression of miRs.Studies would therefore benefit from prior analysis to determine recommended sample sizes for achieving the necessary statistical power. Lastly, few studies mentioned prior or on-going treatment for PE in included cohorts, and it is unclear how treatment options, such as use of anti-hypertensive medications, could impact expression patterns.et al. [Technical and ethical reasons necessitate placental samples being taken after delivery, and it is unknown how accurate miRs detected at this stage are in representing earlier pathological mechanisms. Many studies have highlighted the tissue-specific, cell-specific and temporal nature of miR expression during placental development ,139. Thiet al.  who deteet al. [et al. [rd trimester, in plasma and serum samples respectively, but with no overlap in results. While plasma and serum results are comparable, serum samples have been noted to contain a higher level of miRs, suggested to be due to additional release of RNA from red blood cells during the coagulation process [ICAM-1 [et al. [eNOS) and inhibit the migration of HTR8/SVneo trophoblast cells, although Western blot and Transwell insert assays showed the effect to only be slight with use of miR-584 mimics alone, in comparison to synergistic use with miR-355 which shares a target in eNOS. Studies using samples from early in gestation have great potential in revealing miRs which may only be transiently expressed, but with significance towards pathogenesis. This, in turn, could prove important for the development of miR biomarkers to assist with the prediction and diagnosis of PE.Across screening studies which profiled circulatory miRs, only Ura et al.  and Akeh [et al.  included process . The pla process   can result in increased activity of the free radical generating xanthine oxidase, in addition to gene expression changes [In examining differences in sampling methodology which could account for variability in results, crucial aspects include the timing and mode of delivery, as well as the sampling technique. Placental sample collection is a complex matter; collecting, processing and storage of samples all require attention to maximize a sample’s representation of the  changes . Most stet al. [One of the most significant sources of bias is in the sampling region. An oxygen gradient is purported to exist from the central placental region to the periphery, where the periphery also composes a larger portion of the total volume . Therefoet al.  found maet al. . Differeet al. . ImportaCTGF) under normal physiological conditions, it is expected that hypoxia-regulated miRs increase in expression from medial to lateral regions—particularly in the chorionic plate. Given that CTFG has been shown to increase VEGF-dependent angiogenesis via miR-210, it would be interesting to see if any overlap in regional expression exists in PE [Spatial patterns of miR expression represent a key consideration when examining contributions to pathogenesis. Interestingly, there appears to be a clear dichotomy in direction of change of differentially expressed miRs between chorionic and basal plates in PE, perhaps reflecting changes in vasculature and oxygenation across placental regions. Localization of miR-210 has previously been shown in the basal plate interstitial trophoblasts and in the syncytiotrophoblasts of the chorionic villi, which sprout towards the basal plate—reinforcing studies of its observed differential expression in basal plate samples ,70. Overts in PE .et al. [et al. [et al. [The timing and storage of collected samples poses an addition layer of methodological challenge which could account for variability. Bravo et al.  emphasiset al. . Differe [et al.  and Noac [et al. , profile [et al. ,165. It Prior to profiling, RNA extraction and isolation methods can significantly influence determinations by defining the set of miRs available for discrete hybridization, amplification or sequencing ,167. Theet al. [Podolska et al.  demonstret al. . While iet al. .et al. [The most utilised approaches for miR profiling are microarrays, qRT-PCR and NGS—each bearing advantages and caveats which can have profound effects on results. Microarrays are the most frequently employed across screening studies. Probe design for microarrays is a delicate matter; probes must differentiate between precursor and mature miRs, highly-related miRs  and account for differences in thermo-stability between miRs. Considerable inter-platform differences between microarrays have been noted, and the lack of consensus in microarray platform across screening studies may account for observed variability ,172. Kuoet al. , in a coet al. . The lacet al. . These fet al. [The second most prolific profiling method is NGS, considered the new “gold standard” technique in large-scale studies. The advantages of NGS compared to array-based platforms include a determination of absolute miR quantity, uncovering novel miRs without a reliance on pre-defined capture probes and detection of miR isoforms, or isomiRs . For exaet al.  was ableet al. [Interestingly, many of the overlapping miRs from SOLiD-based studies appear to be almost exclusively from NGS-based studies. This perhaps reflects the sensitivity of this technology in the detection of particular miRs, and is indicative of the comparability in results between SOLiD-based studies. An additional candidate study utilising SOLiD sequencing by Pan et al.  also detet al. .Lastly, qRT-PCR has been considered the “gold standard” of small-scale miR measurement due to its accuracy, sensitivity and specificity. Hence, qRT-PCR is typically used to validate microarray or NGS results. Good intra-platform comparability reportedly exists between microarray and qRT-PCR results, with variability in FC values attributed to differences in cDNA preparation or data normalization ,175. By et al. [Normalization is an imperative step to eradicating technical bias not attributable to genuine biological difference—for example, with RNA sampling and input. Lack of normalization with microarrays has been shown to result in low conformity with qRT-PCR results . For micet al.  noted thMost qRT-PCR-based studies used small RNAs as internal controls for normalization: either spiked-in cel-miR-39 (for circulatory determination) or the small nuclear RNA, U6 .Conformity of internal controls can be considered a positive when collating results. However, most studies did not state prior validation of internal controls to ensure constant expression and suitability before use. The most frequently used endogenous control, U6, is processed differently to miRs, meaning that expression could be altered under different experimental conditions . PresumpWhere normalization of NGS data is concerned, both LOESS and quantile-based normalization have been indicated to correlate well with qRT-PCR results . A more in vivo are unclear. Threshold values for FC significance varied between studies; significant upregulation was generally considered to be a FC >2 or >1.5, with few differentially expressed miRs showing a FC >3. However, even a large FC value for a miR expressed at low levels does not confer any meaning for functional significance. This represents an area of considerable uncertainty when comparing results and a weakness of this review is in the inclusion of results proposed significant by each study, as determining an arbitrary global significance threshold for FC would require access to raw data.Lastly, although FC is frequently used to quantify the differential expression of miRs, values constituting meaningful change et al. [PE appears to be a heterogeneous condition within which many pathogenetic routes converging on similar phenotypes have been implicated—permitting it well deserving of its often-claimed title as the “disease of theories”. Since the initial study delving into miR expression in PE by Pineles et al. , a wave In spite of this, and methodological variation among studies, certain miRs have emerged repeatedly and appear to associate strongly with PE. Many elucidated in this review, such as miR-210, miR-223 and miR-126/126*, impinge on a spectrum of diseases. Through candidate studies each can be intertwined with some proposed aspect of PE pathogenesis—as with hypoxia and the HIF-1α/miR-210/ISCU pathway towards mitochondrial abnormalities. Other PE-associated miRs segregate into distinct families with predominately placental expression patterns, such as the prolific miR-515 family clustered within the expansive, primate-specific C19MC. Epigenetic alterations may hold the key to aberrant expression of this cluster. This paternally-imprinted cluster, whose members appear prevalent in secreted exosomes, could be a means by which the fetus modulates adverse maternal immune responses. However, many C19MC miRs, such as miR-518b, still lack verified target genes outside of cancer-based studies, despite frequent association with PE. Further investigation is therefore essential in order to understand their precise contributions. By expanding the question of contribution towards PE classification, certain miRs appear to correlate with PE severity—as with miR-210, -223 and -181a. Some miR families and clusters may also be closely tied to more severe forms of PE, such as the miR-106a/363 cluster and miR-15 precursor family from which the prevalent miR-195 and -16 emerge. Patterns of miR expression could help link similar pathological processes to particular classifications of PE, resolving ambiguity, for example, between eoPE and sPE cases. Exploring these questions further may therefore prove useful in disentangling the web of PE definitions through strongly associating miRs linked to pathogenic pathways (e.g. IL-6/miR-223/STAT3) and beyond this, for ascertaining the clinical value of miRs.PE-associated miRs have the potential to serve as viable biomarkers of the condition. This could be through the creation of miR-based panels with the capacity to aid in diagnosis, heralding the onset and/or severity of PE. In a therapeutic context, transiently modulating the expression of genes implicated in pathways relating to pathogenesis using miR mimics and inhibitors may also have clinical value, as with miR-126 mimics and the angiogenesis-related PIK3R2/PI3K/Akt pathway. However, a current lack of consensus across studies with regards to PE-associated miRs limits their capacity to be of wider, practical clinical value. Ultimately, any clinical value is predicated upon resolving current methodological challenges which may account for variability in results, allowing for a greater degree of standardization among studies. Challenges include the definition and classification of PE used, sampling technique, RNA extraction method and crucially, the chosen platform for miR profiling. Furthermore, accurate PE animal models will continue to prove important for allowing more rigorous determinations of miR functionality in the context of entire biological systems.Overall, this systematic review touches upon a promising and expanding area of research, propelled by new technologies, such as NGS, which are better able to clarify the pictures formed by prior studies, and driven by the ever-present threat of the condition. It is hoped that further exploration of miR expression in PE, including a systems-level approach to unearthing target genes and pathways, will continue to inform domains which may be of relevance to pathogenesis and advance our present understanding of PE.S1 File(XLSM)Click here for additional data file.S2 File(DOC)Click here for additional data file.S3 File(DOC)Click here for additional data file.


Oxytocin receptor gene single nucleotide polymorphisms have been associated with structural and functional alterations in brain regions, which involve social-emotional processing. Therefore, oxytocin receptor gene polymorphisms may contribute to individual differences in alexithymia, which is considered to be a dysfunction of emotional processing. The aim of this study was to evaluate the association between oxytocin receptor gene single nucleotide polymorphisms or haplotypes and alexithymia in patients with obsessive-compulsive disorder. We recruited 355 patients with obsessive-compulsive disorder . Alexithymia was measured by using the Toronto Alexithymia Scale. We performed single-marker and haplotype association analyses with eight single nucleotide polymorphisms  in the oxytocin receptor gene. There were no significant associations between any of the eight single nucleotide polymorphism of the oxytocin receptor gene and alexithymia. In addition, a six-locus haplotype block (rs237885-rs237887-rs2268490-rs4686301-rs2254298-rs13316193) was not significantly associated with alexithymia. These findings suggest that genetic variations in the oxytocin receptor gene may not explain a significant part of alexithymia in patients with obsessive-compulsive disorder. Alexithymia is characterized by difficulties in recognizing and effectively expressing one's own feelings . It is dLarge-scale twin studies reported that genetic factors could account for 30–42% of the individual differences in alexithymia , 7. EarlOxytocin (OT) is a neuropeptide synthesized primarily in the paraventricular and supraoptic nuclei of the hypothalamus and plays a role as both a neurotransmitter and a neuromodulator. It is an important regulator of complex social behavior and emotional states, such as empathy, attachment, trust, social cognition, and emotional regulation–12 ThereThe effects of OT are modulated by the expression and function of oxytocin receptors (OXTR). Several single nucleotide polymorphisms (SNPs) on the OXTR gene have been documented in association with various aspects of social-affective behavior as well as psychopathology –21. OXTRWith regard to obsessive-compulsive disorder (OCD), there have been several studies on the implications of alexithymia on OCD. Alexithymia is prevalent in OCD  and assoWe recruited 355 patients  from the OCD clinic at Severance Hospital, Yonsei University Health System  from August 2006 to May 2015. All participants met the criteria for OCD, as determined by the Structured Clinical Interview for the DSM-IV (SCID)  assessedThe degree of alexithymia was measured with the Toronto Alexithymia Scale (TAS-20), which is a 20-item self-report scale with a five-point Likert-type scale. The TASThe clinical symptoms and the severity of OCD symptoms were evaluated by using the Yale-Brown obsessive-compulsive scale (Y-BOCS) . The Y-B2 = 0.97) with rs2254298 in our sample. Therefore, eight SNPs with an r2 threshold < 0.8 in ‘pair-wise tagging only’ mode using the ‘Tagger’ program in Haploview [First, we selected OXTR SNPs which showed significant association with empathy, prosocial behavior, sociability, emotionality, social cognition, and social deficit by using a PubMed search of previous studies , 33–42. aploview  were incχ2 tests. Generalized estimating equations (GEE) were used to analyze the relationship between total or three subdimension scores of TAS-20 and SNPs, adjusting for age, total Y-BOCS, and MADRS scores. An additive model was applied by coding genotypes as 0, 1, or 2, depending on carrier status of the minor allele.Hardy-Weinberg equilibrium values of the SNPs were tested using http://www.broadinstitute.org/haploview/haploview) [http://cran.r-project.org/src/contrib/Descriptions/haplo.stats.html) controlling for age, total Y-BOCS and MADRS scores. Permutation tests  were performed to estimate the global significance of the results for all haplotypes analyzed and to validate the expectation-maximization values.Haploview v4.0 (ploview)  was usedDemographic and clinical characteristics of the subjects are presented in We used GEE approaches to examine the effect of the OXTR gene polymorphisms on the total or subdimension scores of the TAS-20 for each of eight SNPs. There were no significant associations found between any of the OXTR SNPs and alexithymia. In addition, a six-locus haplotype block (rs237885-rs237887-rs2268490-rs4686301-rs2254298-rs13316193) also was not significantly associated with the severity of alexithymia. The results are shown in Tables Initially, we hypothesized that OXTR variants might account for the individual differences of alexithymic traits seen in OCD patients. However, contrary to our initial hypothesis, there were no differences of alexithymic traits according to alleles or genotypes of eight SNPs within OXTR. Haplotype analyses also did not show any association between various haplotypes of OXTR and alexithymia. Alexithymic individuals show impaired emotional experience, deficits of emotional interpretation and emotional face recognition, and lack of empathy  and are Recently, Laursen et al. found that subjects with the CC genotype at OXTR rs2268498 and AA genotype at OXTR rs53576 showed higher empathic accuracy . MeanwhiHowever, not all of the studies consistently reported a positive association between OXTR variants and alexithymia-related traits. Nyffeler et al. did not find any associations between single SNPs of OXTR  and autism . Tansey However, the resulting lack of association in our study should be interpreted with caution because there are several factors, which are required to be considered. First, the ethnic and cultural backgrounds might influence the results of our study. In fact, several inconclusive results about autism have been reported from different ethnic and cultural backgrounds across studies. In Japanese  and ChinWhen we simulated the power of our study  using the JPT + CHB panel of a 1,000 genome database and phenotypic variance-covariance matrix estimated from this study, the power of the sample size in this study was only 0.342, and was sufficient for detecting only an effect size larger than 0.9, as its regards to an association between rs237885  and three subdimension scores of TAS-20. However, the effect size of rs237885 in this study was only 0.454 , suggesting the possibility of type II errors. Therefore, to confirm our reported findings, a study with a much larger number of samples with stronger statistical power is needed.Therefore, it would be necessary to evaluate the effects of OXTR variants on alexithymia with particular consideration towards those confounding factors and limitations.S1 Table(PDF)Click here for additional data file.


Aim: To evaluate the effect of an acute L-dopa administration on eye-closed resting state electroencephalographic (EEG) activity of cognitively preserved Parkinsonian patients.Methods: We examined 24 right-handed patients diagnosed as uncomplicated probable Parkinson’s disease (PD). Each patient underwent Unified Parkinson’s Disease Rating Scale (UPDRS)-part-III evaluation before and 60 min after an oral load of L-dopa-methyl-ester/carbidopa 250/25 mg. Resting condition eyes-closed EEG data were recorded both pre- and post L-dopa load. Absolute EEG power values were calculated at each scalp derivation for Delta, Theta, Alpha and Beta frequency bands. UPDRS scores  and EEG data  were submitted to a statistical analysis to compare Pre and Post L-Dopa conditions. Finally, a correlation analysis was carried out between EEG spectral content and UPDRS scores.Results: Considering EEG power spectral analysis, no statistically significant differences arose on Delta and Theta bands after L-dopa intake. Conversely, Alpha and Beta rhythms significantly increased on centro-parietal scalp derivations, as a function of L-dopa administration. Correlation analysis indicated a significant negative correlation between Beta power increase on centro-parietal areas and UPDRS subscores (Rigidity of arms and Bradykinesia). A minor significant negative correlation was also found between Alpha band increase and resting tremor.Conclusions: Assuming that a significant change in EEG power spectrum after L-dopa intake may be related to dopaminergic mechanisms, our findings are consistent with the hypothesis that dopaminergic defective networks are implicated in cortical oscillatory abnormalities at rest in non-demented PD patients. Parkinson’s disease (PD) is a movement disorder finding its origin from the degeneration of pigmented dopaminergic neurons within the substantia nigra pars compacta and their striatal projections . These techniques analyze brain activity by using electromagnetic properties of the brain and are characterized by a relatively low spatial resolution in the face of a high temporal resolution (in the order of milliseconds). Thus, compared to neuroimaging techniques , that explore hemodynamic properties of the brain with high spatial and low temporal resolution, they allow to assess changes in neuronal response in a brief time scale  frequencies in non-demented PD patients  increase in spectral power over all frequency bands, as a consequence of chronic L-dopa replacement therapy  to evaluate global cognitive efficiency (mean score at MMSE = 27.37 ± 2.80) and to exclude the coexistence of dementia.Disease staging (mean score: 1.90 ± 0.64) was expressed by the Hoehn and Yahr  grading The study protocol had the approval of the local Ethical Committee and all participants gave their written informed consent.We collected clinical information about the progression of the disease and treatment  were submitted to a Students’ r = 0.57), the alpha level was then adjusted to p ≤ 0.006 (corresponding to a t ≥ 3.01).A similar model of analysis was applied to EEG data. For each scalp derivation, pre- vs. post- L-dopa administration EEG power values of different frequency bands  were compared. To correct for multiple comparisons, the Bonferroni correction was applied: considering the mean correlation between the variables  and changes of UPDRS scores , after L-dopa administration (Post-Pre). Electroencephalogram differential values have been calculated independently for each scalp derivation.t test on UPDRS scores showed statistically significant improvement of clinical features as a function of L-dopa administration. More specifically, a general improvement of almost all UPDRS part III items was observed , except for postural stability and resting tremor.Students’ The analysis carried out on EEG power spectral content showed differential effects for each EEG band with marked topographical differences. As depicted in Figure p = 0.003 and p = 0.0002, respectively), T5 (p = 0.005), and P3, P4 and Pz . No other recording sites reached the statistical significance.Conversely, after the intake of L-dopa, the Alpha rhythm showed a sharp increase on centro-temporo-parietal scalp derivations. More specifically, a significant increase in Alpha band was observed on C3 and C4 (p = 0.006), and P3, P4 and Pz . No other recording sites reached the statistical significance.On the same way, also the analysis on Beta band indicated a significant increase of EEG spectral power after L-dopa ingestion. Again, such an effect was localized and limited to centro-parietal areas and particularly to C4 . No other significant correlations were observed. In the same vein, the increase of Beta spectral power in C3 and C4 negatively correlated with Rigidity of arms  and Bradykinesia . Also, Beta increase in P3 negatively correlated with Rigidity of arms , while Beta increase in P4 with Rigidity of arms  and Bradykinesia .Correlation analysis indicated some significant effects, once more circumscribed to centro-parietal areas and to faster rhythms. Alpha band increase on C4 after L-dopa intake negatively correlated with Rest tremor of arms  in PD are due to a disruption in thalamo-cortical circuits, we might expect that dopamine replacement treatment, by re-establishing a more physiological thalamo-cortical coupling, will reverse the EEG slowing. Therefore, we conducted a study aimed to investigate the effect of DRT on the oscillatory brain activity of cognitively preserved Parkinsonian patients. In particular, we analyzed eye-closed resting state activity and quantitative EEG modifications before and after an acute L-dopa administration, and found that L-dopa determines a significant modification of EEG power spectrum, leading to a significant increase of alpha and beta power. Alpha and beta frequencies constitute the leading characteristic of normal EEG activity at rest, frequently referred to as the “idling rhythms” of the adult brain  derivations, suggesting that L-dopa influence on cortical oscillatory activity, in the early stages of PD, may be conveyed mainly through dopamine depleted nigro-striatal-thalamo-cortical networks, rather than through direct dopaminergic mesocortical pathways projecting to frontal cortex.These findings display clinical implications: our correlation study investigating the relationship between EEG power spectrum modifications and patients’ motor performances found that an improvement in specific motor subtests of the UPDRS significantly correlated with an increase in beta power spectrum on centro-parietal derivations. The more motor performances improved, the more beta power increased on the above-mentioned regions. A minor correlation effect was also found for alpha power and resting tremor. In other words, beta and alpha power increase on centro-parietal regions was not a simple epiphenomenon, but correlated with motor improvement, further sustaining the hypothesis that EEG slowing in non-demented PD patients is related to dopaminergic defective networks strictly involved in motor control.The currently accepted view on the pathophysiology of PD primarily involves a dysfunction of the basal ganglia circuits, which determines a reduced excitatory thalamic outflow, and, eventually, a hypoactivity of several cortical areas, i.e., the primary motor cortex and other non-primary motor areas (Dick et al., At first glance, our study might seem in disagreement with other studies claiming for an inverse correlation between synchronization in beta frequencies and motor aspect of PD. Different research groups found an association between elevated cortical and subthalamic beta-band synchrony and bradykinesia, as well as a correlation between dopaminergic medication or subthalamic stimulation, decreased beta-band synchrony and improvement in motor performances (Brown and Marsden, Future studies would investigate functional brain connectivity as a consequence of DRT, assessed during different motor actions, in order to evaluate the effective changes in brain functioning after a L-dopa dose.Jean-Marc Melgari conceived the study, carried out the data collection, contributed to data analysis and statistical analysis, played a major role in the general idea of the paper and wrote the paper. Giuseppe Curcio analyzed the data, performed statistical analysis and helped to write the paper. Francesca Mastrolilli carried out the data collection, contributed to data analysis and statistical analysis. Gaetano Salomone carried out the data collection, contributed to data analysis and with the general idea of the paper, and helped to write the paper. Laura Trotta carried out the data collection, contributed to data analysis and helped to write the paper. Mario Tombini contributed to interpret the results and critically revised the paper. Lazzaro di Biase carried out the data collection and contributed to data analysis. Federica Scrascia carried out the data collection and contributed to data analysis. Rita Fini carried out EEG data collection. Emma Fabrizio carried out EEG data collection. Paolo Maria Rossini helped with the general idea of the paper, contributed to interpret the results and critically revised the paper. Fabrizio Vernieri helped with the general idea of the paper, contributed to statistical analysis, to interpret the results and critically revised the paper. The work presented here was carried out in collaboration between all authors. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Drug hypersensitivity reactions (DHR) have been present since the advent of drugs. In particular T-cell mediated delayed-type hypersensitivity reactions represent a heterogeneous clinical entity with a diverse pathogenesis and result in a considerable burden of morbidity and mortality not only driven by the reactions themselves but also by the use of alternatives which are sometimes less effective or even more dangerous. Diagnostic procedures rely on clinical history, skin testing and potential provocation testing, whereas validated in vitro diagnostic procedures are still lacking for most of them. Recent work in the field of pharmacogenomics combined with basic scientific research has provided insights in the pathogenesis of abacavir and carbamazepine hypersensitivities linked with certain human leucocyte antigen risk alleles. Nevertheless, important scientific questions on how other DHR arise and how host-drug interactions occur, remain unanswered. Recent work indicates an intricate relation between host, drug and pathogens in severe cutaneous and systemic reactions and provides more insights in the role of regulatory T-cells and viral reactivation in these reactions. In this review we focus on type IV delayed-type DHR, and address recent advances in the pathogenesis, pharmacogenomics, and diagnosis of these reactions with an emphasis on the understandings arising from basic research. Drug hypersensitivity reactions (DHR) are defined as adverse events resembling clinical allergy to an otherwise safe and effective therapeutic agent. Only those in which an immunological mechanism can be demonstrated, mostly after an allergy workup, are termed drug allergies . They coClinically, DHR are classified as immediate  or delayed-type DHR  . ImmediaDHR are classified according to the Gell and Coombs classification, with type IV hypersensitivity reactions accounting for the T-cell mediated, and the majority of DTH. Pichler et al. has subdivided type IV reactions into 4 groups according to the clinical presentation and the involvement of different types of drug-responsive T-cells Table  [4].In this review we focus on type IV DTH and address recent advances in the pathogenesis, pharmacogenomics, and diagnosis of these reactions with an emphasis on the understandings arising from basic research.Scrutinizing the pathogenesis of DTH focuses on the interaction of drugs with the immune system. Multiple mutually non-exclusive hypotheses explaining the pathogenesis of DHR, including DTH, exist and include the (pro)hapten hypothesis, the pharmacological interaction (p-i) concept and the altered self-repertoire model hapten hypothesis is still considered the primary pathway by which chemical sensitizers cause allergic contact dermatitis, and it is also identified as a well-known pathway for β-lactam hypersensitivity , certain drugs interfere with the antigen presenting process, without the requirement of a specific peptide ligand, but through non-covalent interaction with human leucocyte antigen (HLA) alleles and/or T-cell receptors (TCR), to trigger an immune response [Second, according to the response . This hyresponse . Yang etresponse , but detresponse . Next, aresponse . An incrresponse .altered self-repertoire model emerged based on findings explaining the increased risk for DTH upon abacavir-exposure in HLA-B*57:01 positive individuals. Here, mass spectrometry on HLA-associated peptides in the presence or absence of abacavir along with crystal structure data, demonstrate that abacavir lies across the bottom of the HLA-B*57:01-binding groove, interacting with several peptide binding pockets  but predominantly protrudes into the F pocket, thereby altering the repertoire of usual presented peptides  induced DRESS or SJS/TEN, mostly in Han Chinese  with a PRecently, HLA-B*13:01, present in 28% of Papuans and Australian aborigines, 2–20% of Chinese, 1.5% of Japanese, 1–12% of Indian, 2–4% Southeast Asians but largely absent in Europeans and Africans, was identified as a risk factor for dapsone hypersensitivity syndrome, developing in 0.5–3.6% of treated individuals. HLA-B*13:01 had an estimated PPV of 7.8% and NPV of 99.8% .The findings from pharmacogenomic studies are highly translational. Current HIV guidelines recommend HLA-B*57:01 testing prior to initiating abacavir treatment . The FooRecent work suggests an intricate relation between the host, drug and pathogen in severe cutaneous and systemic reactions, in particular in DRESS or DIHS . Drugs theterologous immunitymodel [What might be the relation of viral reactivation with DRESS/DIHS? It has been hypothesized that in the presence of viral replication, co-stimulatory molecules are up-regulated, lowering the threshold that is required for T-cell activation. Also, virus-specific T-cells from previous immunization may cross-react with drug-altered HLA-presented peptides according to the itymodel . Picard itymodel , althougitymodel . Next, titymodel . Also, titymodel . FinallyRecent studies have shown that after viral infection, a small fraction of memory T-cells persist in peripheral tissues such as the skin and are considered tissue-resident memory T-cells  , 48, 49.FoxP3+ regulatory T-cells (Tregs) suppress effector T-cells and play a pivotal role in the balance between increased susceptibility for infections and autoimmunity and the role of Tregs in DTH is increasingly being recognized. Tregs expand at the acute stages of DRESS/DIHS but decrease and become functionally deficient upon resolution of DRESS/DIHS, possibly explaining the delayed onset and viral activation at the acute stage and the risk of subsequent autoimmune disease upon resolution . AlternaDiagnosis of DTH after systemic administration remains difficult in everyday clinical practice. Currently, once a patient presents with a potential DTH, assessment of causality is based on a clinical judgment evaluating the relationship between drug intake and the time of onset, type of drug and aspect of the adverse reaction. This approach often leads to the eviction of numerous drugs and drug classes and/or the switch towards suboptimal alternatives. Clarification of the culprit drug(s) and/or chemicals, identification of potential cross-reactive molecules and safe alternatives require a complete allergy workup including skin testing –57 follo+ T-cell mediated reactions [The presence of drug-specific T-cells that are not detected in tolerant (exposed) controls plays a central role in the pathogenesis of almost all known forms of DTH. Therefore, the most commonly used in vitro test, the LPA, aims to detect these drug-specific T-cells as a marker of sensitization. The assay is based on the incubation of freshly isolated peripheral blood mononuclear cells (PBMCs) from hypersensitive patients with titrated concentrations of the suspected drug or a vehicle control enabling the determination of a stimulation/proliferation-index 5–7 days later. Although safe and able to test many different drugs in various types of DTH, the assay is technically demanding, subject to toxicity issues, and unable to evaluate drug metabolites or most drug antigens acting as haptens. Moreover, although specificity of the LPA is high (at least 85% ), the seeactions .To improve the sensitivity of this in vitro assay, several strategies have been evaluated recently. Addition of anti-CTLA4 and anti-PDL1 to LPA cultures increased the sensitivity of the assay with more positive LPA assays and an increased proliferation index in already positive cases . SimilarIn order to evaluate the profile of drug-activated T-cells, alternative techniques have been developed such as flow cytometry–based methods using carboxyfluorescein diacetate succinimidyl ester staining alone  or in coNext, Adachi et al. suggested combined testing for DTH using the basophil activation test and LPA and observed that samples that yielded positive results for LPA and basophil activation test did not overlap, suggesting that the two analyses might compensate each other for false-negativity. Combined sensitivity was higher compared with the sensitivity for the single assays  .Recently, an alternative assay using the determination of IL-6 production early (20 min) after in vitro exposure of PBMCs to suspected drugs in a heterogeneous patient population  was reported to have a sensitivity and specificity of respectively 85 and 82%, yet it awaits further validation .In conclusion, considerable progress has been made in our understandings of the disease mechanisms in various DTH, with the best examples coming from HLA-associated DHR with abacavir, carbamazepine and allopurinol. Whether or not these findings can be extrapolated to other DTH remains to be elucidated. The complex interplay between host, drug and other potential factors such as infectious diseases and/or environmental factors remains to be explored. Nevertheless, pharmacogenomics studies have paved the way for pre-treatment screening for potentially severe DHR and further work in this field is highly anticipated. Also, improvements in the in vitro diagnostics will most likely enhance our understandings of DTH, and aid in the daily clinical practice of diagnosis and proper management of drug hypersensitivities.