B cell immune repertoire sequencing in tobacco cigarette smoking, vaping, and chronic obstructive pulmonary disease in the COPDGene cohort.

Rationale: Cigarette smoking (CS) impairs B cell function and antibody production, increasing infection risk. The impact of e-cigarette use ('vaping') and combined CS and vaping ('dual-use') on B cell activity is unclear.

Objective: To examine B cell receptor sequencing (BCR-seq) profiles associated with CS, vaping, dual-use, COPD-related outcomes, and demographic factors.

Temporal Exploration of COPD Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts.

Background: Chronic obstructive pulmonary disease (COPD) exhibits considerable progression heterogeneity. We hypothesized that elastic principal graph analysis (EPGA) would identify distinct clinical phenotypes and their longitudinal relationships.

Methods: Cross-sectional data from 8,972 tobacco-exposed COPDGene participants, with and without COPD, were used to train a model with EPGA, using thirty clinical, physiologic and CT features.

Lung Transcriptomics Links Emphysema to Barrier Dysfunction and Macrophage Subpopulations.

Rationale: While many studies have examined gene expression in lung tissue, the gene regulatory processes underlying emphysema are still not well understood. Finding efficient non-imaging screening methods and disease-modifying therapies has been challenging, but knowledge of the transcriptomic features of emphysema may help in this effort.

Objectives: Our goals were to identify emphysema-associated biological pathways through transcriptomic analysis of bulk lung tissue, to determine the lung cell types in which these emphysema-associated pathways are altered, and to detect unique and overlapping transcriptomic signatures in blood and lung samples.

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.

Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD.

Rationale: Chronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and blood transcriptome patterns have not been fully described.

Objections: The objective of this study was to identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing (RNA-seq).

Methods: CT scan images and blood RNA-seq gene expression from 1223 participants in the COPD Genetic Epidemiology (COPDGene) study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we labeled image-expression axes (IEAs).

Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers.

Introduction: Chronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: 'relatively resistant smokers (RRS)', 'mild upper lobe-predominant emphysema (ULE)', 'airway-predominant disease (AD)' and 'severe emphysema (SE)'. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers.

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.

Measuring outcomes in healthcare economics using Artificial Intelligence: With application to resource management.

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e., Covid-19) and natural disasters (such as hurricanes and earthquakes).

Cigarette smoking-associated isoform switching and 3' UTR lengthening via alternative polyadenylation.

Cigarette smoking induces a profound transcriptomic and systemic inflammatory response. Previous studies have focused on gene level differential expression of smoking, but the genome-wide effects of smoking on alternative isoform regulation have not yet been described. We conducted RNA sequencing in whole-blood samples of 454 current and 767 former smokers in the COPDGene Study, and we analyzed the effects of smoking on differential usage of isoforms and exons.

Alpha-1 Antitrypsin MZ Heterozygosity Is an Endotype of Chronic Obstructive Pulmonary Disease.

Multiple studies have demonstrated an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygous carriers of the AAT (alpha-1 antitrypsin) Z allele. However, it is not known if MZ subjects with COPD are phenotypically different from noncarriers (MM genotype) with COPD. To assess if MZ subjects with COPD have different clinical features compared with MM subjects with COPD.

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 Characterization of COPD Subtypes: Insights From the COPDGene Study.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms.

Integrative Genomics Analysis Identifies ACVR1B as a Candidate Causal Gene of Emphysema Distribution.

Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation.

Deep Learning Methods for Predicting Disease Status Using Genomic Data.

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process.

Lobar Emphysema Distribution Is Associated With 5-Year Radiological Disease Progression.

Background: Emphysema has considerable variability in its regional distribution. Craniocaudal emphysema distribution is an important predictor of the response to lung volume reduction. However, there is little consensus regarding how to define upper lobe-predominant and lower lobe-predominant emphysema subtypes.

Genome-Wide Association Study of the Genetic Determinants of Emphysema Distribution.

Rationale: Emphysema has considerable variability in the severity and distribution of parenchymal destruction throughout the lungs. Upper lobe-predominant emphysema has emerged as an important predictor of response to lung volume reduction surgery. Yet, aside from alpha-1 antitrypsin deficiency, the genetic determinants of emphysema distribution remain largely unknown.

Saudi Guidelines on the Diagnosis and Treatment of Pulmonary Hypertension: Pulmonary arterial hypertension associated with connective tissue diseases.

The explosive growth of medical literature on pulmonary hypertension (PH) has led to a steady increase in awareness of this disease within the medical community during the past decade. The recent revision of the classification of PH is presented in in the main guidelines. Group 1 PH or pulmonary arterial hypertension (PAH) is a heterogeneous group and includes PH due to inheritable, drug-induced, and toxin-induced causes and to such underlying systemic causes as connective tissue diseases, human immunodeficiency viral infection, portal hypertension, congenital heart disease, and schistosomiasis.

Saudi guidelines on the diagnosis and treatment of pulmonary hypertension: 2014 updates.

The Saudi Association for Pulmonary Hypertension (previously called Saudi Advisory Group for Pulmonary Hypertension) has published the first Saudi Guidelines on Diagnosis and Treatment of Pulmonary Arterial Hypertension back in 2008.[1] That guideline was very detailed and extensive and reviewed most aspects of pulmonary hypertension (PH). One of the disadvantages of such detailed guidelines is the difficulty that some of the readers who just want to get a quick guidance or looking for a specific piece of information might face.

Antioxidant status and its association with elevated depressive symptoms among US adults: National Health and Nutrition Examination Surveys 2005-6.

We examined the relationship of elevated depressive symptoms with antioxidant status. Cross-sectional data from the National Health and Nutrition Examination Surveys 2005–6 on US adults aged 20–85 years were analysed. Depressive symptoms were measured using the Patient Health Questionnaire with a score cut-off point of 10 to define ‘elevated depressive symptoms’.

Patterns and predictors of proton pump inhibitor overuse among academic and non-academic hospitalists.

Objective: In the hospital setting, several studies have reported proton pump inhibitor (PPI) overuse, a majority of which is continued after discharge. In addition to being expensive, PPIs are associated with an increased risk of infections, osteoporosis and serious drug interactions. We examined the trends and predictors of PPI guidelines non-compliance among academic and non-academic hospitalists in USA.

Cardiac complications of systemic sclerosis: recent progress in diagnosis.

Purpose Of Review: Systemic sclerosis (SSc), a collagen vascular disease characterized by diverse organ system involvement, is associated with serious cardiac complications. Cardiac symptoms are much less frequent than autopsy-demonstrated cardiac involvement. Although frequent calls for early diagnosis have been made, validated strategies for assessment of scleroderma cardiac disease are not yet well established, mainly because the right ventricle, a common target of the disease, presents considerable obstacles to simple and reliable appraisal of its structure and function.