Smoking & Tobacco

Predictive Factors of Early Postoperative Complications After Robot-Assisted Radical Cystectomy for Urothelial Bladder Carcinoma.

To identify protective and risk factors of early postoperative complications after robot-assisted radical cystectomy (RARC) for urothelial bladder carcinoma. Data of all robot-assisted cystectomies performed in six French centers between February 2010 and December 2019 were retrospectively reviewed. All RARCs for bladder cancer (muscle-invasive and high-risk or Bacillus Calmette-Guerin-resistant...

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography.

Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like smoking, hypercholesterolemia or hypertension. However, current diagnostic techniques often do not detect early stages of PAD. We theorize that PAD's un...

Acute Effects of Nicotine on Physiological Responses and Sport Performance in Healthy Baseball Players.

There is interest in whether nicotine could enhance attention in sporting performance, but evidence on the acute effect of nicotine on physical respon...

Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.

Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to morta...

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction ...

Predicting obesity and smoking using medication data: A machine-learning approach.

PURPOSE: Administrative health datasets are widely used in public health research but often lack information about common confounders. We aimed to dev...

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 fundame...

Risk factor assessments of temporomandibular disorders via machine learning.

This study aimed to use artificial intelligence to determine whether biological and psychosocial factors, such as stress, socioeconomic status, and wo...

In utero exposure to electronic-cigarette aerosols decreases lung fibrillar collagen content, increases Newtonian resistance and induces sex-specific molecular signatures in neonatal mice.

Approximately 7% of pregnant women in the United States use electronic-cigarette (e-cig) devices during pregnancy. There is, however, no scientific ev...

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort.

This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants w...

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing.

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, whi...

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes i...

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of ...

Risk factors of osteoporosis in soldiers of the Armed Forces: A cross-sectional study from Western India.

BACKGROUND: Osteoporosis may result from risk factors such as smoking, alcohol, low body mass index, less physical exercise, and dietary calcium defic...

A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.

Inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract....

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, a...

Evaluating the Efficacy of Povidone-Iodine Solution Infection Prophylaxis in Immediate Tissue Expander-Based Breast Reconstruction: A Controlled Retrospective Analysis.

There is currently no consensus among plastic surgeons regarding the optimal infection prophylaxis for immediate tissue expander placement following ...

Predicting changes in substance use following psychedelic experiences: natural language processing of psychedelic session narratives.

: Experiences with psychedelic drugs, such as psilocybin or lysergic acid diethylamide (LSD), are sometimes followed by changes in patterns of tobacco...

Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy.

Various volatile aerosols have been associated with adverse health effects; however, characterization of these aerosols is challenging due to their dy...

Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit.

BACKGROUND: Persistent smoking after cancer diagnosis is associated with increased overall mortality (OM) and cancer mortality (CM). According to the ...