Background:Pulmonary cavities (PC) are known to be more prevalent among multidrug-resistant pulmonary tuberculosis (MDR)/extensively drug-resistant tuberculosis (XDR) patients than among drug-sensitive tuberculosis (DS) patients. This study aims to clarify how the interaction between Mycobacterium tuberculosis aggressiveness and tuberculosis history causes the PC prevalence and pattern differences between DS patients and MDR/XDR patients. Methods:Eastern European patient data were from the NIAID TB (National Institute of Allergy & Infectious Diseases Tuberculosis) Portals Program registered before January 2019. Chinese patients were from Shenzhen, China, treated between April 2017 and February 2019. There were in total 244 DS cases (222 new patients and 22 previously treated patients), 344 MDR cases (188 new patients and 156 previously treated patients), and 155 XDR cases (36 new patients and 119 previously treated patients). The first chest computed tomography (CT) images were analysed. PC were counted only for those with a lumen diameter >5 mm. Multiple cavities in a single consolidation were counted as one cavity. Calcified lesions in the lungs, as a sign of chronicity, were also recorded. Results:In new patients, there was no difference in lung lesion calcification prevalence among DS (13.5%), MDR (14.4%), and XDR (13.9%). In previously treated patients, lung calcification prevalence was 36.4% for DS, 44.9% for MDR, and 45.4% for XDR. For new patients, the PC prevalence was higher for MDR cases than for DS cases (41% vs. around 25%). For treated patients, PC prevalence increased to 36.4% for DS cases, to 57% for MDX cases, and to 71.4% for XDR cases. For new patients, the mean PC number for positive cases was DS: 1.66, MDR: 2.79, XDR: 2.69. For treated cases, the mean PC number for positive cases was DS: 2.13, MDR: 2.58, XDR: 2.47. For new patients, the mean PC diameter (in mm) for positive cases was DS: 15.4, MDR: 16.9, XDR: 17.5. For treated cases, the mean PC diameter (in mm) for positive cases was DS: 19.0, MDR: 20.8, XDR: 25.6. The number of lung fields with PC lesion was higher for MDR cases than for DS cases. PC number ≥2 had a specificity of around 92.3% for new patients, and around 81.0% for previously treated patients, suggesting the diagnosis of MDR/XDR. Conclusions:MDR/XDR patients exhibit significantly higher PC prevalence and more extensive pulmonary involvement compared to DS patients, which are not totally determined by the length of disease history. Compared with literature reports, the prevalence of PC and the PC number per positive case were comparatively low in this study. Taking all results together, PC number ≥3 offers reasonable specificity for suggesting the diagnosis of MDR, though the sensitivity would be low.

DiscoVir is an automated pipeline for viral metagenomics available in National Institute of Allergy and Infectious Diseases (NIAID)’s free web application for microbiome analysis, Nephele. DiscoVir makes viral discovery, taxonomic and functional annotation, host predictions, and diversity analyses of the virome easily accessible to researchers at all levels of expertise.

The mycobacterial membrane exporter MmpL3 transports trehalose monomycolates (TMM) from the cytosol to the outer membrane of Mycobacterium tuberculosis, making it a potential drug target. Proton influx is believed to drive TMM efflux, suggesting that disrupting proton transfer (PT) could be therapeutic. However, the PT mechanism and its relation to function remain unclear. Recent MmpL3 structures reveal a potential proton channel in its hydrophobic core, which also binds potential antituberculosis compounds. We investigated the PT process using hybrid quantum-mechanical/molecular-mechanical and classical molecular dynamics simulations. We show that transient water chains form in two connected transmembrane cavities that act as proton conduits. Four consecutive PT events are necessary to alter the protonation states of acidic residues in the protein core, triggering conformational changes that affect the TMM binding site. The process begins with the tandem movement of two protons through an upper cavity, protonating two aspartate residues via a classical hydronium migration. After conformational shifts, PT proceeds through a lower cavity, protonating two glutamate residues near the cytosolic opening and inducing further conformational shifts; here, PT occurs sequentially via hydronium and proton-hole migration. The cycle ends with the release of protons into the cytosol. Based on the observed conformational changes, we propose a mechanism for TMM efflux.

This work utilized an artificial intelligence (AI)-based image annotation tool, Smart Imagery Framing and Truthing (SIFT), to annotate pulmonary lesions and abnormalities and their corresponding boundaries on 452,602 chest X-ray (CXR) images (22 different types of desired lesions) from four publicly available datasets (CheXpert Dataset, ChestX-ray14 Dataset, MIDRC Dataset, and NIAID TB Portals Dataset). SIFT is based on Multi-task, Optimal-recommendation, and Max-predictive Classification and Segmentation (MOM ClaSeg) technologies to identify and delineate 65 different abnormal regions of interest (ROI) on CXR images, provide a confidence score for each labeled ROI, and various recommendations of abnormalities for each ROI, if the confidence score is not high enough. The MOM ClaSeg System integrating Mask R-CNN and Decision Fusion Network is developed on a training dataset of over 300,000 CXRs, containing over 240,000 confirmed abnormal CXRs with over 300,000 confirmed ROIs corresponding to 65 different abnormalities and over 67,000 normal (i.e., "no finding") CXRs. After quality control, the CXRs are entered into the SIFT system to automatically predict the abnormality type ("Predicted Abnormality") and corresponding boundary locations for the ROIs displayed on each original image. The results indicated that the SIFT system can determine the abnormality types of labeled ROIs and their boundary coordinates with high efficiency (improved 7.92 times) when radiologists used SIFT as an aide compared to radiologists using a traditional semi-automatic method. The SIFT system achieves an average sensitivity of 89.38%±11.46% across four datasets. This can significantly improve the quality and quantity of training and testing sets to develop AI technologies.

Background:Pulmonary nodular consolidation (PN) may represent an imaging sign potentially useful in differentiating multidrug-resistant (MDR) pulmonary tuberculosis (PTB) from drug-sensitive (DS) tuberculosis (TB) on chest computed tomography (CT). This study aims to confirm the difference in PN features between DS and MDR patients. Methods:Eastern European (Belarus, Moldova, Romania, Azerbaijan, and Georgia) patient data were obtained from the NIAID TB (National Institute of Allergy & Infectious Diseases Tuberculosis) Portals Program registered before January 2019. Chinese patients were obtained from Shenzhen, China, treated between April 2017 and February 2019. There were in total 244 DS cases (222 new patients and 22 previously treated patients), 344 MDR cases (188 new patients and 156 previously treated patients), 155 extensively drug-resistant (XDR) TB cases (36 new patients and 119 previously treated patients). The first CT scan's images were used. A PN was defined as rounded or oval with a relatively clear boundary measuring between 6 and 30 mm in diameter. Calcified lesions in the lungs, as a sign of chronicity, were also recorded. Results:In new patients, there was no difference in lung lesion calcification prevalence among DS (16.1%) and MDR (15.0%). In previously treated patients, lung calcification prevalence was 38.5% for DS, 48.3% for MDR, and 52.8% for XDR. For new patients, the PN prevalence was higher for MDR/XDR cases than for DS cases (around 70% vs. around 39%). PN prevalence increased for DS cases from around 39% for new patients to 59% for treated patients, but the increases for MDR/XDR cases were minimal. For new patients, the mean PN number for positive cases was DS: 2.38, MDR: 2.89, XDR: 2.72. For treated cases, the mean PN number for positive cases was DS: 2.54, MDR: 3.91, XDR: 4.99. For both new patients and treated patients, PN No. ≥3 had a specificity of around 85% suggesting the diagnosis of XDR/XDR. The number of lung fields with PN lesion was higher for MDR cases than for DS cases. PN lesions were even more widely spread in XDR cases than in MDR cases. Additional analysis of recent literature suggests that a trend may exist in the frequency of lung lesions: DS < RR (rifampicin-resistant) < MDR < XDR. Conclusions:MDR/XDR patients exhibit significantly higher PN prevalence and more extensive pulmonary involvement compared to DS patients and which is not totally determined by disease history length, suggesting that PN characteristics could serve as imaging biomarkers for drug resistance assessment.

The world health organization's global tuberculosis (TB) report for 2022 identifies TB, with an estimated 1.6 million, as a leading cause of death. The number of new cases has risen since 2020, particularly the number of new drug-resistant cases, estimated at 450,000 in 2021. This is concerning, as treatment of patients with drug resistant TB is complex and may not always be successful. The NIAID TB Portals program is an international consortium with a primary focus on patient centric data collection and analysis for drug resistant TB. The data includes images, their associated radiological findings, clinical records, and socioeconomic information. This work describes a TB Portals' Chest X-ray based image retrieval system which enables precision medicine. An input image is used to retrieve similar images and the associated patient specific information, thus facilitating inspection of outcomes and treatment regimens from comparable patients. Image similarity is defined using clinically relevant biomarkers: gender, age, body mass index (BMI), and the percentage of lung affected per sextant. The biomarkers are predicted using variations of the DenseNet169 convolutional neural network. A multi-task approach is used to predict gender, age and BMI incorporating transfer learning from an initial training on the NIH Clinical Center CXR dataset to the TB portals dataset. The resulting gender AUC, age and BMI mean absolute errors were 0.9854, 4.03years and 1.67kgm2. For the percentage of sextant affected by lesions the mean absolute errors ranged between 7% to 12% with higher error values in the middle and upper sextants which exhibit more variability than the lower sextants. The retrieval system is currently available from https://rap.tbportals.niaid.nih.gov/find_similar_cxr.

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance – a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an in silico approach to design cationic AMPs de novo, followed by their experimental testing. The antibacterial potential of de novo designed cationic AMPs, along with their synergistic properties in combination with conventional antibiotics was examined. Furthermore, the effects of bacterial inoculum density and metabolic state on the antibacterial activity of AMPs were evaluated. Finally, the impact of several potent AMPs on E. coli cell envelope and genomic DNA integrity was determined. Collectively, this comprehensive analysis provides insights into the unique characteristics of cationic AMPs.

Africa faces both a disproportionate burden of infectious diseases coupled with unmet needs in bioinformatics and data science capabilities which impacts the ability of African biomedical researchers to vigorously pursue research and partner with institutions in other countries. The African Centers of Excellence in Bioinformatics and Data Intensive Science are collaborating with African academic institutions, industry partners, the Foundation for the National Institutes of Health (FNIH) and the National Institute of Allergy and Infectious Diseases (NIAID) at the National Institutes of Health (NIH) in a public-private partnership to address these challenges through enhancing computational infrastructure, fostering the development of advanced bioinformatics and data science skills among local researchers and students and providing innovative emerging technologies for infectious diseases research.

To assess a Smart Imagery Framing and Truthing (SIFT) system in automatically labeling and annotating chest X-ray (CXR) images with multiple diseases as an assist to radiologists on multi-disease CXRs. SIFT system was developed by integrating a convolutional neural network based-augmented MaskR-CNN and a multi-layer perceptron neural network. It is trained with images containing 307,415 ROIs representing 69 different abnormalities and 67,071 normal CXRs. SIFT automatically labels ROIs with a specific type of abnormality, annotates fine-grained boundary, gives confidence score, and recommends other possible types of abnormality. An independent set of 178 CXRs containing 272 ROIs depicting five different abnormalities including pulmonary tuberculosis, pulmonary nodule, pneumonia, COVID-19, and fibrogenesis was used to evaluate radiologists’ performance based on three radiologists in a double-blinded study. The radiologist first manually annotated each ROI without SIFT. Two weeks later, the radiologist annotated the same ROIs with SIFT aid to generate final results. Evaluation of consistency, efficiency and accuracy for radiologists with and without SIFT was conducted. After using SIFT, radiologists accept 93% SIFT annotated area, and variation across annotated area reduce by 28.23%. Inter-observer variation improves by 25.27% on averaged IOU. The consensus true positive rate increases by 5.00% (p=0.16), and false positive rate decreases by 27.70% (p<0.001). The radiologist’s time to annotate these cases decreases by 42.30%. Performance in labelling abnormalities statistically remains the same. Independent observer study showed that SIFT is a promising step toward improving the consistency and efficiency of annotation, which is important for improving clinical X-ray diagnostic and monitoring efficiency.

Antiviral peptides (AVPs) are bioactive peptides that exhibit the inhibitory activity against viruses through a range of mechanisms. Virus entry inhibitory peptides (VEIPs) make up a specific class of AVPs that can prevent envelope viruses from entering cells. With the growing number of experimentally verified VEIPs, there is an opportunity to use machine learning to predict peptides that inhibit the virus entry. In this paper, we have developed the first target-specific prediction model for the identification of new VEIPs using, along with the peptide sequence characteristics, the attributes of the envelope proteins of the target virus, which overcomes the problem of insufficient data for particular viral strains and improves the predictive ability. The model's performance was evaluated through 10 repeats of 10-fold cross-validation on the training data set, and the results indicate that it can predict VEIPs with 87.33% accuracy and Matthews correlation coefficient (MCC) value of 0.76. The model also performs well on an independent test set with 90.91% accuracy and MCC of 0.81. We have also developed an automatic computational tool that predicts VEIPs, which is freely available at https://dbaasp.org/tools?page=linear-amp-prediction.

Purpose: This study compares performance of Timika Score to standardized, detailed radiologist observations of Chest X rays (CXR) for predicting early infectiousness and subsequent treatment outcome in drug sensitive (DS) or multi-drug resistant (MDR) tuberculosis cases. It seeks improvement in prediction of these clinical events through these additional observations.Method: This is a retrospective study analyzing cases from the NIH/NIAID supported TB Portals database, a large, trans-national, multi-site cohort of primarily drug-resistant tuberculosis patients. We analyzed patient records with sputum microscopy readings, radiologist annotated CXR, and treatment outcome including a matching step on important covariates of age, gender, HIV status, case definition, Body Mass Index (BMI), smoking, drug use, and Timika Score across resistance type for comparison.Results: 2142 patients with tuberculosis infection (374 with poor outcome and 1768 with good treatment outcome) were retrospectively reviewed. Bayesian ANOVA demonstrates radiologist observations did not show greater predictive ability for baseline infectiousness (0.77 and 0.74 probability in DS and MDR respectively); however, the observations provided superior prediction of treatment outcome (0.84 and 0.63 probability in DS and MDR respectively). Estimated lung abnormal area and cavity were identified as important predictors underlying the Timika Score's performance.Conclusions: Timika Score simplifies the usage of baseline CXR for prediction of early infectiousness of the case and shows comparable performance to using detailed, standardized radiologist observations. The score's utility diminishes for treatment outcome prediction and is exceeded by the usage of the detailed observations although prediction performance on treatment outcome decreases especially in MDR TB cases.

Tuberculosis (TB) drug resistance is a worldwide public health problem. It decreases the likelihood of a positive outcome for the individual patient and increases the likelihood of disease spread. Therefore, early detection of TB drug resistance is crucial for improving outcomes and controlling disease transmission. While drug-sensitive tuberculosis cases are declining worldwide because of effective treatment, the threat of drug-resistant tuberculosis is growing, and the success rate of drug-resistant tuberculosis treatment is only around 60%. The TB Portals program provides a publicly accessible repository of TB case data with an emphasis on collecting drug-resistant cases. The dataset includes multi-modal information such as socioeconomic/geographic data, clinical characteristics, pathogen genomics, and radiological features. The program is an international collaboration whose participants are typically under a substantial burden of drug-resistant tuberculosis, with data collected from standard clinical care provided to the patients. Consequentially, the TB Portals dataset is heterogenous in nature, with data representing multiple treatment centers in different countries and containing cross-domain information. This study presents the challenges and methods used to address them when working with this real-world dataset. Our goal was to evaluate whether combining radiological features derived from a chest X-ray of the host and genomic features from the pathogen can potentially improve the identification of the drug susceptibility type, drug-sensitive (DS-TB) or drug-resistant (DR-TB), and the length of the first successful drug regimen. To perform these studies, significantly imbalanced data needed to be processed, which included a much larger number of DR-TB cases than DS-TB, many more cases with radiological findings than genomic ones, and the sparse high dimensional nature of the genomic information. Three evaluation studies were carried out. First, the DR-TB/DS-TB classification model achieved an average accuracy of 92.4% when using genomic features alone or when combining radiological and genomic features. Second, the regression model for the length of the first successful treatment had a relative error of 53.5% using radiological features, 25.6% using genomic features, and 22.0% using both radiological and genomic features. Finally, the relative error of the third regression model predicting the length of the first treatment using the most common drug combination varied depending on the feature type used. When using radiological features alone, the relative error was 17.8%. For genomic features alone, the relative error increased to 19.9%. The model had a relative error of 19.0% when both radiological and genomic features were combined. Although combining radiological and genomic features did not improve upon the use of genomic features when classifying DR-TB/DS-TB, the combination of the two feature types improved the relative error of the predictive model for the length of the first successful treatment. Furthermore, the regression model trained on radiological features achieved the best performance when predicting the treatment length of the most common drug combination.

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome

BACKGROUND Drug-resistant (DR) tuberculosis treatment is challenging and frequently leads to poor outcomes. An international collaboration, the National Institute of Allergy and Infectious Diseases (NIAID) TB Portals develops, maintains, and supports a multi-national database of tuberculosis cases, with an emphasis on drug-resistant tuberculosis. Patient records include clinical, radiological, genomic, and socioeconomic features. Establishing factors associated with unsuccessful treatment may help optimize treatment for the most challenging infections. METHODS Association analysis and machine learning algorithms were applied to identify important factors associated with treatment outcome and predict the outcome for three patient cohorts, selected by drug resistance level representing 1575 patients in total. The predicted probabilities of poor treatment outcome from models were calibrated as a risk score ranging from 0 to 100 corresponding to confidence level of the model for treatment outcome. RESULTS The features most associated with treatment success in all cohorts were body mass index (BMI), onset age, employment, education, smear-negative microscopy, and percent of abnormal volume in X-ray images, confirming previously reported findings, and identifying novel factors such as pathogen genomic markers. CONCLUSIONS The identified features might help in establishing high-risk patients at the time of admission for tuberculosis treatment. This study integrates clinical, radiological, and pathogen genomics into a patient risk model, a way of determining risk through the application of machine learning on real-world data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project has been funded in part with Federal funds from the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Department of Health and Human Services under BCBB Support Services Contract HHSN316201300006W/75N93022F00001 to MEDICAL SCIENCE & COMPUTING. This research was supported in part by the Office of Science Management and Operations of NIAID at the NIH. No additional external funding was received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study used de-identified data stripped of all PHI/PII, which is made publicly available through the TB Portals Program, a trans-national initiative led by the NIAID (https://tbportals.niaid.nih.gov/). Before public-sharing and reuse of the de-identified data, each participating clinical research institution (https://tbportals.niaid.nih.gov/where-do-our-cases-come-from) receives approval from the participating institution's IRB and must follow strict adherence to ethics rules requirements of CRDF Global and the International Science and Technology Center who are the grant-issuing institutions (https://journals.asm.org/doi/10.1128/JCM.01013-17). The data was analyzed in accordance to the guidelines specified in TB Portals Data Use Agreement (https://tbportals.niaid.nih.gov/pdf/TB-Portals-Data-Use-Agreement.pdf). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The TB portals program necessitates all users of the data sign a DUA before access to the underlying, de-identified clinical data is provided and the data can be requested at the following URL (https://tbportals.niaid.nih.gov/download-data). Therefore, this study provides code used in the analysis without the underlying raw data (https://github.com/niaid/tb-portals-association-and-prediction) in compliance with the DUA. * DR : Drug resistant BMI : Body mass index NIAID : National Institute of Allergy and Infectious Diseases NIH : National Institute of Health TB : Tuberculosis MDR-TB : Multidrug resistant TB XDR-TB : Extensively drug-resistant TB WHO : World Health Organization TB DEPOT : Tuberculosis Data Exploration Portal DUA : Data usage agreement DS : Drug-sensitive ANOVA : Analysis of variance UC : Uncertainty coefficient SMOTE : Synthetic Minority Oversampling Technique AUROC : Area Under the Receiver Operating Characteristics Curve PRAUC : Area Under the Precision and Recall Curve SHAP : Shapely Additive Explanations

Background Epidemics and pandemics are causing high morbidity and mortality on a still-evolving scale exemplified by the COVID-19 pandemic. Infection prevention and control (IPC) training for frontline health workers is thus essential. However, classroom or hospital ward based training portends an infection risk due to the in-person interaction of participants. We explored the use of Virtual Reality (VR) simulations for frontline health worker training since it trains participants without exposing them to infections that would arise from in-person training. It does away with the requirement for expensive Personal Protective Equipment (PPE) that has been in acute shortage and improves learning, retention and recall. This represents the first attempt in deploying VR-based pedagogy in a Ugandan medical education context. Methods We used animated VR-based simulations of bedside and ward-based training scenarios for frontline health workers. The training covered the wearing and stripping of PPE, case management of COVID-19 infected individuals and hand hygiene. It used VR headsets and Graphics Processing Units (GPUs) to actualize an immersive experience, via a hybrid of VR renditions and 360degrees videos. We then compared the level of knowledge acquisition between individuals trained using this method to comparable cohorts previously trained in a classroom setting. That evaluation was supplemented by a qualitative assessment based on feedback from participants about their experience. Results The effort resulted into a well-designed COVID-19 IPC VR curriculum, equivalent VR content and a pioneer cohort of trained frontline health workers. The formalized comparison with classroom-trained cohorts showed relatively better outcomes by way of skills acquired, speed of learning and rates of information retention ( P-value =4.0e-09) - suggesting the effectiveness and feasibility of VR as a medium of medical training. Additionally, in the qualitative assessment 90% of the participants rated the method as very good, 58.1% strongly agreed that the activities met the course objectives, and 97.7 % strongly indicated willingness to refer the course to colleagues. Conclusion VR-based COVID-19 IPC training is feasible, effective and achieves enhanced learning while protecting participants from infections within a pandemic context in Uganda. It is a delivery medium transferable to the contexts of other highly infectious diseases.

Background: Tuberculosis (TB) drug resistance is a worldwide public health problem that threatens progress made in TB care and control. Early detection of drug resistance is important for disease control, with discrimination between drug-resistant TB (DR-TB) and drug-sensitive TB (DS-TB) still being an open problem. The objective of this work is to investigate the relevance of readily available clinical data and data derived from chest X-rays (CXRs) in DR-TB prediction and to investigate the possibility of applying machine learning techniques to selected clinical and radiological features for discrimination between DR-TB and DS-TB. We hypothesize that the number of sextants affected by abnormalities such as nodule, cavity, collapse and infiltrate may serve as a radiological feature for DR-TB identification, and that both clinical and radiological features are important factors for machine classification of DR-TB and DS-TB. Methods: We use data from the NIAID TB Portals program (https://tbportals.niaid.nih.gov), 1,455 DR-TB cases and 782 DS-TB cases from 11 countries. We first select three clinical features and 26 radiological features from the dataset. Then, we perform Pearson's chi-squared test to analyze the significance of the selected clinical and radiological features. Finally, we train machine classifiers based on different features and evaluate their ability to differentiate between DR-TB and DS-TB. Results: Pearson's chi-squared test shows that two clinical features and 23 radiological features are statistically significant regarding DR-TB vs. DS-TB. A ten-fold cross-validation using a support vector machine shows that automatic discrimination between DR-TB and DS-TB achieves an average accuracy of 72.34% and an average AUC value of 78.42%, when combing all 25 statistically significant features. Conclusions: Our study suggests that the number of affected lung sextants can be used for predicting DR-TB, and that automatic discrimination between DR-TB and DS-TB is possible, with a combination of clinical features and radiological features providing the best performance.

The TB Portals program is an international collaboration for the collection and dissemination of tuberculosis data from patient cases focused on drug resistance. The central database is a patient-oriented resource containing both patient and pathogen clinical and genomic information. Herein we provide a summary of the pathogen genomic data available through the TB Portals and show one potential application by examining patterns of genomic pairwise distances. Distributions of pairwise distances highlight overall patterns of genome variability within and between Mycobacterium tuberculosis phylogenomic lineages. Closely related isolates (based on whole-genome pairwise distances and time between sample collection dates) from different countries were identified as potential evidence of international transmission of drug-resistant tuberculosis. These high-level views of genomic relatedness provide information that can stimulate hypotheses for further and more detailed research.

We studied the variability of resistance of MTB for MDR/XDR-TB patients in the Kharkiv region,Ukraine, based on full genome sequencing and drug-sensitivity test (DST) data. Methods: Bacterial DNA was isolated from 223 patient samples. Following the complete genome sequencing, we analyzed reads using the TB Profiler (version 3.0.6) software to compile detailed information about drug resistance variants. Patients were categorized as MDR or XDR based on standard microbiological DSTs. Results: Most of the patients were predicted to be highly resistant to anti-TB drugs (XDR:62,Pre-XDR:63,MDR:63,Pre-MDR:4,Sensitive:16,Other:15). Most of the samples are from the Beijing sub-lineage 2.2.1 (183). Other lineages include:4.1.2 (9),4.2.1(10),4.3.3(14),4.8 (3),M.bovis(4). Of the 223 patient samples,17 were identified as having 10 or more drug resistance variants. Resistance SNPs were found for most anti-TB drugs: rifampicin (17/17), isoniazid (17/17), pyrazinamide (17/17), ethambutol (17/17),streptomycin(17/17),fluoroquinolones(16/17),kanamycin(16/17),ethionamide (16/17),capreomycin(6/17), aminoglycosides (5/17),para-aminosalicylic_acid (4/17),linezolid (2/17), bedaquiline(2/17),clofazimine(2/17). Conclusions: Our results confirm the importance of full genome sequencing to provide a detailed view of the growing threat of drug-resistant tuberculosis. Tracking TB lineages allows for monitoring epidemiological routes of tuberculosis and pinpointing the sources of outbreaks. Clinical data collected together with corresponding genomic and radiological information help to optimize treatment practices and proactively identify difficult-to-treat cases.

Abstract The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is an open-access, comprehensive database containing information on amino acid sequences, chemical modifications, 3D structures, bioactivities and toxicities of peptides that possess antimicrobial properties. DBAASP is updated continuously, and at present, version 3.0 (DBAASP v3) contains >15 700 entries (8000 more than the previous version), including >14 500 monomers and nearly 400 homo- and hetero-multimers. Of the monomeric antimicrobial peptides (AMPs), >12 000 are synthetic, about 2700 are ribosomally synthesized, and about 170 are non-ribosomally synthesized. Approximately 3/4 of the entries were added after the initial release of the database in 2014 reflecting the recent sharp increase in interest in AMPs. Despite the increased interest, adoption of peptide antimicrobials in clinical practice is still limited as a consequence of several factors including side effects, problems with bioavailability and high production costs. To assist in developing and optimizing de novo peptides with desired biological activities, DBAASP offers several tools including a sophisticated multifactor analysis of relevant physicochemical properties. Furthermore, DBAASP has implemented a structure modelling pipeline that automates the setup, execution and upload of molecular dynamics (MD) simulations of database peptides. At present, >3200 peptides have been populated with MD trajectories and related analyses that are both viewable within the web browser and available for download. More than 400 DBAASP entries also have links to experimentally determined structures in the Protein Data Bank. DBAASP v3 is freely accessible at http://dbaasp.org.

TB Portals program (TB-PP), supported and spearheaded by NIAID NIH, coordinates efforts of doctors, researchers, and IT specialists from 13 countries, aimed at collecting and studying patient-centric clinical, radiological and genomics data. Ukraine recently joined TB-PP and we present our first results of comparative analyses and discuss next steps aimed at optimizing TB care and treatment. The purpose of our study is to illustrate and discuss how Ukraine aligns its efforts in combatting TB with other countries having heavy burden of drug-resistant (DR) DR-TB within the framework of multi-national TB-PP. Materials and methods: 15 regions of Ukraine take part in the study. The data is entered into the portal through the platform: data.tbportals.niaid.nih.gov. Led by a steering committee of treating physicians, TB Portals collect original clinical, IT, imaging and genomics data, and results of bioinformatics and radiological analysis. Results: As of today (02/07/2021) 976 cases of TB have been entered: 490 (50.2%) cases of MDR-TB, 189 (19.4%)-XDR-TB, 225 (23.1%)-susceptible TB, 11 (1.1%)-Polyresistant-TB, 61 (6.2%)-Monoresistant-TB were presented. Due to the challenges of long-term treatment of TB, especially of resistant forms, 186 such cases (19.1%) were chosen, and submitted to the TB-PP. According to the results of treatment, we observed: 63 (33.9%) Died, 26 (14%) Cured, 15 (8%) completed, 42 (22.6%) failure, 40 (21.5%) Lost to follow up. Conclusions: TB Portals now provide de-identified, open-access to individual patient cases and collections of related patient groups in Ukraine, along with data from 13 countries. All patient cases may be instantaneously and interactively retrieved, visualized and analyzed using Data Exploration Portal.