PMLB v1.0: an open-source dataset collection for benchmarking machine learning methods.

MOTIVATION: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that provide rapid access to many of these datasets through a standardized, user-friendly interface that integrates well with popular data science workflows. RESULTS: This release of PMLB (Penn Machine Learning Benchmarks) provides the largest collection of diverse, public benchmark datasets for evaluating new machine learning and data science methods aggregated in one location. v1.0 introduces a number of critical improvements developed following discussions with the open-source community. AVAILABILITY AND IMPLEMENTATION: PMLB is available at https://github.com/EpistasisLab/pmlb. Python and R interfaces for PMLB can be installed through the Python Package Index and Comprehensive R Archive Network, respectively.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

treeheatr: an R package for interpretable decision tree visualizations.

SUMMARY: treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. AVAILABILITY AND IMPLEMENTATION: The treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration.

Pitfalls in brain age analyses.

Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the "brain age gap." Researchers have identified that the brain age gap, as a linear transformation of an out-of-sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline Optimization Tool (TPOT) was developed using strongly typed genetic programing (GP) to recommend an optimized analysis pipeline for the data scientist's prediction problem. However, like other AutoML systems, TPOT may reach computational resource limits when working on big data such as whole-genome expression data. RESULTS: We introduce two new features implemented in TPOT that helps increase the system's scalability: Feature Set Selector (FSS) and Template. FSS provides the option to specify subsets of the features as separate datasets, assuming the signals come from one or more of these specific data subsets. FSS increases TPOT's efficiency in application on big data by slicing the entire dataset into smaller sets of features and allowing GP to select the best subset in the final pipeline. Template enforces type constraints with strongly typed GP and enables the incorporation of FSS at the beginning of each pipeline. Consequently, FSS and Template help reduce TPOT computation time and may provide more interpretable results. Our simulations show TPOT-FSS significantly outperforms a tuned XGBoost model and standard TPOT implementation. We apply TPOT-FSS to real RNA-Seq data from a study of major depressive disorder. Independent of the previous study that identified significant association with depression severity of two modules, TPOT-FSS corroborates that one of the modules is largely predictive of the clinical diagnosis of each individual. AVAILABILITY AND IMPLEMENTATION: Detailed simulation and analysis code needed to reproduce the results in this study is available at https://github.com/lelaboratoire/tpot-fss. Implementation of the new TPOT operators is available at https://github.com/EpistasisLab/tpot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding.

SUMMARY: Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes. AVAILABILITY AND IMPLEMENTATION: Available at: https://insilico.github.io/npdr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.

Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/31400.].

STatistical Inference Relief (STIR) feature selection.

MOTIVATION: Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data. RESULTS: We demonstrate the statistical power and control of type I error of the STIR family of feature selection methods on a panel of simulated data that exhibits properties reflected in real gene expression data, including main effects and network interaction effects. We compare the performance of STIR when the adaptive radius method is used as the nearest neighbor constructor with STIR when the fixed-k nearest neighbor constructor is used. We apply STIR to real RNA-Seq data from a study of major depressive disorder and discuss STIR's straightforward extension to genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: Code and data available at http://insilico.utulsa.edu/software/STIR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

MOTIVATION: Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. METHODS: We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. RESULTS: On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. AVAILABILITY AND IMPLEMENTATION: Code available at http://insilico.utulsa.edu/software/privateEC . CONTACT: brett-mckinney@utulsa.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An osmotin from the resurrection plant Tripogon loliiformis (TlOsm) confers tolerance to multiple abiotic stresses in transgenic rice.

Osmotin is a key protein associated with abiotic and biotic stress response in plants. In this study, an osmotin from the resurrection plant Tripogon loliiformis (TlOsm) was characterized and functionally analyzed under abiotic stress conditions in T. loliiformis as well as in transgenic Nicotiana tabacum (tobacco) and Oryza sativa (rice) plants. Real-time PCR analysis on mixed elicitor cDNA libraries from T. loliiformis showed that TlOsm was upregulated a 1000-fold during the early stages of osmotic stresses (cold, drought, and salinity) in both shoots and roots but downregulated in shoots during heat stress. There was no change in TlOsm gene expression in roots of heat-stressed plants and during plant development. The plasma membrane localization of TlOsm was showed in fluorescent-tagged TlOsm tobacco plants using confocal laser scanning microscopic analysis. Transgenic rice plants expressing TlOsm were assessed for enhanced tolerance to salinity, drought and cold stresses. Constitutively expressed TlOsm in transgenic rice plants showed increased tolerance to cold, drought and salinity stress when compared with the wild-type and vector control counterparts. This was evidenced by maintained growth, retained higher water content and membrane integrity, and improved survival rate of TlOsm-expressing plants. The results thus indicate the involvement of TlOsm in plant response to multiple abiotic stresses, possibly through the signaling pathway, and highlight its potential applications for engineering crops with improved tolerance to cold, drought and salinity stress.

Optimizing electrochemically active microorganisms as a key player in the bioelectrochemical system: Identification methods and pathways to large-scale implementation.

The rapid global economic growth driven by industrialization and population expansion has resulted in significant issues, including reliance on fossil fuels, energy scarcity, water crises, and environmental emissions. To address these issues, bioelectrochemical systems (BES) have emerged as a dual-purpose solution, harnessing electrochemical processes and the capabilities of electrochemically active microorganisms (EAM) to simultaneously recover energy and treat wastewater. This review examines critical performance factors in BES, including inoculum selection, pretreatment methods, electrodes, and operational conditions. Further, authors explore innovative approaches to suppress methanogens and simultaneously enhance the EAM in mixed cultures. Additionally, advanced techniques for detecting EAM are discussed. The rapid detection of EAM facilitates the selection of suitable inoculum sources and optimization of enrichment strategies in BESs. This optimization is essential for facilitating the successful scaling up of BES applications, contributing substantially to the realization of clean energy and sustainable wastewater treatment. This analysis introduces a novel viewpoint by amalgamating contemporary research on the selective enrichment of EAM in mixed cultures. It encompasses identification and detection techniques, along with methodologies tailored for the selective enrichment of EAM, geared explicitly toward upscaling applications in BES.

Outcomes of balloon angioplasty and stent placement for iliac artery lesions classified as TASC II A, B: a single-center study.

Background: Iliac artery stenosis or occlusion is a critical condition that can severely impact a patient's quality of life. The effectiveness of balloon angioplasty and intraluminal stenting for the treatment of iliac artery lesions classified as TASC II A and B was evaluated in this single-center prospective study. Methods: Conducted between October 2016 and September 2020 at Cho Ray Hospital's Vascular Surgery Department, this prospective study involved PAD patients categorized by TASC II A and B classifications who underwent endovascular intervention. Intervention outcomes were assessed peri-procedure and during short-term and mid-term follow-ups. Results: Of the total of 133 patients, 34.6% underwent balloon angioplasty, while 65.4% received stenting. The immediate technical success rate was 97.7%, while the clinical success rate was 62.4%. Complications were minimal, with major limb amputation reported in 1.5% of the cases. There was a significant improvement in Rutherford classification and ABI at short-term follow-up, with a patency rate of 90.2%. The mid-term post-intervention follow-up yielded similar results with an 86.1% patency rate. The mortality rates associated with arterial occlusion were 2.3% during short-term follow-up and 1.7% during mid-term follow-up. Conclusion: Balloon angioplasty and stent placement are effective and safe interventions for TASC II A and B iliac artery occlusions with favorable short and mid-term outcomes. Further, multi-center studies with larger sample sizes are recommended for more comprehensive conclusions, including long-term follow-up assessment.

Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Clinicopathological features and prognostic outcomes of molecularly defined entities in the new edition of the WHO classification of sinonasal carcinoma.

Introduction: We investigated the clinicopathological features and prognoses of the new molecularly defined entities in latest edition of the World Health Organization (WHO) classification of sinonasal carcinoma (SNC). Methods: Integrated data were combined into an individual patient data (IPD) meta-analysis. Results: We included 61 studies with 278 SNCs including 25 IDH2-mutant, 41 NUT carcinoma, 187 SWI/SNF loss, and 25 triple negative SNCs (without IDH2 mutation, NUTM1 rearrangement, and SWI/SNF inactivation) for analyses. Compared to other molecular groups, NUT carcinoma was associated with a younger age at presentation and an inferior disease-specific survival. Among SNCs with SWI/SNF inactivation, SMARCB1-deficient tumors presented later in life and were associated with a higher rate of radiotherapy administration. SMARCA4-deficiency was mostly found in teratocarcinosarcoma while SMARCB1-deficient tumors were associated with undifferentiated carcinoma and non-keratinizing squamous cell carcinoma. Conclusion: Our study facilitates our current understanding of this developing molecular-defined spectrum of tumors and their prognoses.

How can climate change and its interaction with other compounding risks be considered in evaluation? Experiences from Vietnam.

While evaluations play a critical role in accounting for and learning from context, it is unclear how evaluations can take account of climate change. Our objective was to explore how climate change and its interaction with other contextual factors influenced One Health food safety programs. To do so, we integrated questions about climate change into a qualitative evaluation study of an ongoing, multi-sectoral program aiming to improve pork safety in Vietnam called SafePORK. We conducted remote interviews with program researchers (n = 7) and program participants (n = 23). Based on our analysis, researchers believed climate change had potential impacts on the program but noted evidence was lacking, while program participants (slaughterhouse workers and retailers) shared how they were experiencing and adapting to the impacts of climate change. Climate change also interacted with other contextual factors to introduce additional complexities. Our study underscored the importance of assessing climate factors in evaluation and building adaptive capacity in programming.

Knowledge, attitude, and perception regarding COVID-19-related prevention practice among residents in Vietnam: a cross-sectional study.

Background: Vietnam was one of the countries pursuing the goal of "Zero-COVID" and had effectively achieved it in the first three waves of the pandemic. However, the spread of the Delta variant was outbreak first in Vietnam in late April 2021, in which Ho Chi Minh City was the worst affected. This study surveyed the public's knowledge, attitude, perception, and practice (KAPP) toward COVID-19 during the rapid rise course of the outbreak in Ho Chi Minh City. Methods: This cross-sectional survey was conducted from 30th September to 16th November 2021, involving 963 residents across the city. We asked residents a series of 21 questions. The response rate was 76.6%. We set a priori level of significance at α = 0.05 for all statistical tests. Results: The residents' KAPP scores were 68.67% ± 17.16, 77.33% ± 18.71, 74.7% ± 26.25, and 72.31% ± 31, respectively. KAPP scores of the medical staff were higher than the non-medical group. Our study showed positive, medium-strong Pearson correlations between knowledge and practice (r = 0.337), attitude and practice (r = 0.405), and perception and practice (r = 0.671; p < 0.05). We found 16 rules to estimate the conditional probabilities among KAPP scores via the association rule mining method. Mainly, 94% confident probability of participants had {Knowledge=Good, Attitude=Good, Perception=Good}, as well as {Practice=Good} (in rule 9 with support of 17.6%). In opposition to around 86% to 90% of the times, participants had levels of {Perception=Fair, Practice=Poor} given with either {Attitude=Fair} or {Knowledge=Fair} (according to rules 1, 2, and rules 15, 16 with a support of 7-8%). Conclusion: In addition to the government's directives and policies, citizens' knowledge, attitude, perception, and practice are considered one of the critical preventive measures during the COVID-19 pandemic. The results affirmed the good internal relationship among K, A, P, and P scores creating a hierarchy of healthcare educational goals and health behavior among residents.

Pretreatments of lignocellulosic and algal biomasses for sustainable biohydrogen production: Recent progress, carbon neutrality, and circular economy.

Lignocellulosic and algal biomasses are known to be vital feedstocks to establish a green hydrogen supply chain toward achieving a carbon-neutral society. However, one of the most pressing issues to be addressed is the low digestibility of these biomasses in biorefinery processes, such as dark fermentation, to produce green hydrogen. To date, various pretreatment approaches, such as physical, chemical, and biological methods, have been examined to enhance feedstock digestibility. However, neither systematic reviews of pretreatment to promote biohydrogen production in dark fermentation nor economic feasibility analyses have been conducted. Thus, this study offers a comprehensive review of current biomass pretreatment methods to promote biohydrogen production in dark fermentation. In addition, this review has provided comparative analyses of the technological and economic feasibility of existing pretreatment techniques and discussed the prospects of the pretreatments from the standpoint of carbon neutrality and circular economy.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

PURPOSE: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. METHODS: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. RESULTS: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). CONCLUSION: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection.

Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.