Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies.

Background: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximising differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Rationale: Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear.

Objectives: Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics.

Methods: We defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets.

High-precision regressors for particle physics.

Monte Carlo simulations of physics processes at particle colliders like the Large Hadron Collider at CERN take up a major fraction of the computational budget. For some simulations, a single data point takes seconds, minutes, or even hours to compute from first principles. Since the necessary number of data points per simulation is on the order of - , machine learning regressors can be used in place of physics simulators to significantly reduce this computational burden.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema.

Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels.

Identifying the Toxidrome of Ivermectin Toxicity.

Ivermectin is an antiparasitic drug that has been used as an alternative for prophylaxis and treatment of COVID-19 infection. The adverse effects from supratherapeutic doses of ivermectin can include non-neurological and neurological symptoms. In this study, we report the case of a 52-year-old Filipino male with newly diagnosed diabetes mellitus who developed a subacute history of fever, cough, and generalized weakness, causing him to self-medicate with supratherapeutic doses of ivermectin and thereafter subsequently developed a decrease in sensorium, restlessness, and complex visual hallucinations.

Deep Learning Utilizing Suboptimal Spirometry Data to Improve Lung Function and Mortality Prediction in the UK Biobank.

Background: Spirometry measures lung function by selecting the best of multiple efforts meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory volume in 1 second (FEV) and forced vital capacity (FVC). We hypothesize that discarded submaximal and QC-failing data meaningfully contribute to the prediction of airflow obstruction and all-cause mortality.

Methods: We evaluated volume-time spirometry data from the UK Biobank.

Transforming Complex Problems Into K-Means Solutions.

K-means is a fundamental clustering algorithm widely used in both academic and industrial applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the equivalence of K-means to principal component analysis, non-negative matrix factorization, and spectral clustering.

Self-supervised denoising of Nyquist-sampled volumetric images via deep learning.

Purpose: Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling provides guarantees about the maximum difference between adjacent slices in a volumetric image, which allows denoising to be performed without access to clean images.

Explainable deep learning for insights in El Niño and river flows.

The El Niño Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions.

Tertiary lymphoid structures accompanied by fibrillary matrix morphology impact anti-tumor immunity in basal cell carcinomas.

Tertiary lymphoid structures (TLS) are specialized lymphoid formations that serve as local repertoire of T- and B-cells at sites of chronic inflammation, autoimmunity, and cancer. While presence of TLS has been associated with improved response to immune checkpoint blockade therapies and overall outcomes in several cancers, its prognostic value in basal cell carcinoma (BCC) has not been investigated. Herein, we determined the prognostic impact of TLS by relating its prevalence and maturation with outcome measures of anti-tumor immunity, namely tumor infiltrating lymphocytes (TILs) and tumor killing.

Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.

Purpose: To compare the efficacy and efficiency of training neural networks for medical image classification using comparison labels indicating relative disease severity versus diagnostic class labels from a retinopathy of prematurity (ROP) image dataset.

Design: Evaluation of diagnostic test or technology.

Participants: Deep learning neural networks trained on expert-labeled wide-angle retinal images obtained from patients undergoing diagnostic ROP examinations obtained as part of the Imaging and Informatics in ROP (i-ROP) cohort study.

Influenza vaccination for hospital Employees: A 3-year success-story of a flu-pod drive-thru at a Veterans affairs medical center.

The Advisory Committee on Immunization Practices recommends all healthcare practitioners and hospital staff receive an annual influenza vaccination. Many challenges were noted in achieving this goal; especially during the last 2 influenza seasons throughout the COVID-19 pandemic. Over the past 3 years our institution has implemented a Drive-Thru fixed Point of Distribution (POD) event for this purpose.

Prevalence of SARS-CoV-2 infection among obstetric patients in Ottawa, Canada: a descriptive study.

Background: There is limited information on the prevalence of SARS-CoV-2 infection in obstetric settings in Canada, beyond the first wave of the COVID-19 pandemic (February to June 2020). We sought to describe the prevalence of SARS-CoV-2 infection in pregnant people admitted to triage units at a tertiary care hospital in Ottawa, Canada.

Methods: We conducted a descriptive study of pregnant people admitted to obstetric triage assessment units at The Ottawa Hospital between Oct.

Consensus and Diversity in the Management of Varicocele for Male Infertility: Results of a Global Practice Survey and Comparison with Guidelines and Recommendations.

Purpose: Varicocele is a common problem among infertile men. Varicocele repair (VR) is frequently performed to improve semen parameters and the chances of pregnancy. However, there is a lack of consensus about the diagnosis, indications for VR and its outcomes.

Machine Learning Prediction of Progression in Forced Expiratory Volume in 1 Second in the COPDGene® Study.

Background: The heterogeneous nature of chronic obstructive pulmonary disease (COPD) complicates the identification of the predictors of disease progression. We aimed to improve the prediction of disease progression in COPD by using machine learning and incorporating a rich dataset of phenotypic features.

Methods: We included 4496 smokers with available data from their enrollment and 5-year follow-up visits in the COPD Genetic Epidemiology (COPDGene) study.

A Computational Neural Model for Mapping Degenerate Neural Architectures.

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach.

Deep Bayesian Unsupervised Lifelong Learning.

Lifelong Learning (LL) refers to the ability to continually learn and solve new problems with incremental available information over time while retaining previous knowledge. Much attention has been given lately to Supervised Lifelong Learning (SLL) with a stream of labelled data. In contrast, we focus on resolving challenges in Unsupervised Lifelong Learning (ULL) with streaming unlabelled data when the data distribution and the unknown class labels evolve over time.

Induction of labour in patients with prior caesarean births or uterine surgery.

The current evidence favours trial of labour after one caesarean in the absence of any other contraindications, recognizing that risks with both trial of labour after caesarean (TOLAC) and elective repeat caesarean section (ERCS) birth are relatively uncommon. When the need for induction of labour (IOL) following a previous caesarean arises, shared decision-making should be based on the current available evidence. This approach, however, needs to be tailored, taking into account the individual's history, initial examination and response to the ongoing process of induction to optimize the maternal and foetal outcomes.

β3-Adrenoceptor Agonist for the Treatment of Bladder Dysfunction in Children: A Systematic Review and Meta-Analysis.

Purpose: ß3-adrenergic receptor agonists (ß3 agonists) have been used in treatment of overactive bladder (OAB) and neurogenic detrusor overactivity (NDO) in adults. However, their use in children has only recently been approved by the U.S.

Improved prediction of smoking status via isoform-aware RNA-seq deep learning models.

Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, we developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study.

Machine learning-based biomarkers identification from toxicogenomics - Bridging to regulatory relevant phenotypic endpoints.

One of the major challenges in realization and implementations of the Tox21 vision is the urgent need to establish quantitative link between in-vitro assay molecular endpoint and in-vivo regulatory-relevant phenotypic toxicity endpoint. Current toxicomics approach still mostly rely on large number of redundant markers without pre-selection or ranking, therefore, selection of relevant biomarkers with minimal redundancy would reduce the number of markers to be monitored and reduce the cost, time, and complexity of the toxicity screening and risk monitoring. Here, we demonstrated that, using time series toxicomics in-vitro assay along with machine learning-based feature selection (maximum relevance and minimum redundancy (MRMR)) and classification method (support vector machine (SVM)), an "optimal" number of biomarkers with minimum redundancy can be identified for prediction of phenotypic toxicity endpoints with good accuracy.