Smoking & Tobacco

Clinical outcomes and predictors of response to PD-(L)1 blockade in patients with NSCLC without actionable genomic alterations who never used tobacco.

PURPOSE: Predictive biomarkers of response to immune checkpoint inhibitors (ICI) remain poorly defined in patients with non-small cell lung cancer (NSCLC) without a history of tobacco use and lacking actionable genomic alterations (AGA). We aimed to identify clinical and molecular predictors of response to ICI-based regimens in patients who have never smoked and lack AGA. EXPERIMENTAL DESIGN: We r...

Dual-Scale StaphAIR: Predictive Modeling for the Diagnosis of S. aureus Infection via Simultaneous Detection and Quantification of Cytokines and Antibodies.

Staphylococcus aureus causes life-threatening bacterial infections. Current diagnostics for bone infection consist of invasive sample collection and lengthy microbial culture; available blood tests are not effective in the orthopedic context. To address this gap, we report an immune biomarker panel for the simultaneous measurement of cytokines and S. aureus-specific antibodies in serum and evaluat...

Optimising supervised machine learning algorithms predicting cigarette cravings and lapses for a smoking cessation just-in-time adaptive intervention (JITAI).

This study aimed to optimise the balance between participant burden and algorithm performance for predicting high-risk moments in a smoking cessation ...

Machine learning and multi-dimensional transcriptomics reveal the key molecular network of benzo(a)pyrene/NNK in promoting laryngeal cancer and develop prognostic models.

Benzo(a)pyrene (BaP) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), as typical environmental carcinogens, are widely present in tobacco smo...

Clinical and quality of life predictors in patients with schizophrenia with and without substance use disorder.

The comorbidity of schizophrenia (SZ) and substance use disorder (SUD), also known as dual schizophrenia (SZ+), represents a clinical challenge due to...

Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.

BACKGROUND: Oral cancer is a major public health problem among the population of India. In rural setting whereby there is exposure to high-risk factor...

MORGAS (MORtality ORiented Glucose, Age, Smoking, and Stent Score) predicts mortality after percutaneous coronary intervention.

BACKGROUND: Accurate prediction of mortality after percutaneous coronary intervention (PCI) remains a clinical challenge. Existing risk scores often l...

Identifying factors associated with vaping cessation in young adults: A machine learning and XAI approach.

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks, as e‑cigarettes may reduc...

Understanding LUng Cancer risk factors and their Impact Assessment (LUCIA): protocol for multicentre observational cohort study.

INTRODUCTION: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, primarily due to diagnosis at advanced stages. Although low...

Integrating human and nonhuman intelligence to reduce tobacco's public-health burden.

PURPOSE OF REVIEW: Tobacco use remains the leading preventable cause of death worldwide, while the rise of electronic nicotine products has sparked a ...

Environmental Exposures and Neuroimaging in Children with Neurodevelopmental Disorders: A Scoping Review.

PURPOSE OF REVIEW: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have...

CT-based body composition and its change through time in relation to outcomes in participants screened for lung cancer.

BACKGROUND: Computed Tomography (CT) scans allow opportunistic evaluation of body composition. We investigated whether body composition and change thr...

Gene-Lifestyle Synergy in Diabetic Retinopathy Development.

PURPOSE: To develop a machine learning (ML)-driven polygenic risk score (PRS) for diabetic retinopathy (DR) and evaluate the extent to which lifestyle...

Cigarette smoking and long-term incidence of colorectal cancer subclassified by tumor-infiltrating myeloid cells: A prospective cohort study.

Evidence indicates that cigarette smoking affects anti-tumor immunity. We tested a hypothesis that the association of smoking with long-term colorecta...

Understanding online sentiment toward waterpipe tobacco smoking by applying deep-learning language models to Twitter posts (2021-2023) in the United States.

INTRODUCTION: Waterpipe tobacco smoking remains a popular social activity among young adults in the United States. This study aims to understand senti...

EPIMETRIC: A cfDNA Methylation-Based Algorithm for COPD Diagnosis and Stratification.

OBJECTIVE: To develop a blood-based algorithm using cell-free DNA (cfDNA) methylation profiles to diagnose and stratify patients with COPD according t...

Integration of AHRR methylation, heavy metals, and clinical characteristics for urothelial carcinoma risk stratification: An explainable artificial intelligence approach.

The short metabolic half-life of conventional tobacco biomarkers often limits their ability to reflect cumulative toxicological damage, which may comp...

Edge-enabled IoT framework for real-time tobacco quality monitoring.

Tobacco quality inspection plays a vital role in ensuring standardized processing, reducing economic losses, and improving industrial automation. Howe...

HRS scientific statement on artificial intelligence integration framework into clinical electrophysiology workflows.

Artificial intelligence (AI) and digital health technologies (DHTs) are rapidly transforming health care, offering unprecedented opportunities for enh...

Can Algorithms Efficiently Identify Interpretable and Persuasive Message Features? An Agnostic Causal Machine Learning Approach.

Argument strength and message persuasiveness are key constructs in message effects research. Yet, researchers still lack a systematic and efficient ap...