STDs

The Translational Gap in AI for Oropharyngeal Squamous Cell Carcinoma: A TRIPOD+AI Scoping Review of Methodological Barriers to Treatment Deintensification.

RATIONALE AND OBJECTIVES: To map artificial intelligence (AI) and radiomics applications in computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography/CT (FDG-PET/CT) for oropharyngeal squamous cell carcinoma (OPSCC) in the context of human papillomavirus (HPV) status and treatment deintensification, evaluate reporting quality using TRIPOD+AI (T...

Comparative analysis of human papilloma virus detection methods and prognostic significance of transcriptionally active viral load in oropharyngeal squamous cell carcinoma.

BACKGROUND: Human papilloma virus (HPV)-related head and neck squamous cell carcinoma (HNSCC) incidence is rising. Despite the prognostic and therapeutic implication of HPV status, standardised detection methods are lacking. This study compared multiple HPV detection methods and evaluated the prognostic value of viral transcriptional activity in archival HNSCC tissues. METHODS: HPV-DNA status and ...

Temporal validation of machine-learning models for shingles vaccine nonvaccination among U.S. adults aged 50 years and older.

BACKGROUND: Herpes zoster vaccination is recommended for U.S. adults aged 50 years and older, yet many remain unvaccinated. Prior National Health Inte...

De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.

Deep learning has greatly advanced de novo protein design, yet its application to rational short peptide design remains underexplored. Here, we develo...

The Rise of Regulatogens: A Crisis of Insouciance and Dilettantism in the Regulatory DART World-And How We Must Fix It.

Developmental and reproductive toxicity testing (DART) safety tests are expensive and time-consuming. The future of DART testing is trending toward in...

Natural language processing to develop a standardized lexicon for precision medicine in oncology.

Precision medicine terminology is increasingly used across clinical, laboratory, payer, regulatory, and policy settings; however, distinct terminology...

Assessment of atypical glandular cell interpretation in Pap tests using the Hologic Genius Digital Diagnostics System.

Atypical glandular cells (AGC) are a diagnostic challenge. The aim of this study was to evaluate the efficacy and diagnostic performance of AGC detect...

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morph...

The Minimum Frequency of Well-Day Capillary Blood Ketone Testing Needed to Predict 1-Month Diabetic Ketoacidosis Risk in Type 1 Diabetes.

OBJECTIVE: Independent of clinical risk factors, performing well-day capillary ketone monitoring over a month predicts near-term diabetic ketoacidosis...

When multimodal fusion helps: An ablation study of EEG-ECG fusion strategies for emotion recognition.

Multimodal physiological signal fusion-particularly electroencephalography (EEG) and electrocardiography (ECG)-is widely assumed to improve emotion re...

Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.

OBJECTIVES: Preoperative prediction of perineural invasion (PNI) in rectal cancer (RC) is challenging due to limited MRI resolution and the neglect of...

Distinguishing common respiratory pathogens using machine learning of symptom profiles to prioritize diagnostic testing.

Clinical symptoms are critical for diagnosing and managing upper respiratory tract infections, yet systematic comparisons across common pathogens rema...

Integrated evaluation of rapid diagnostic testing, genotypic-phenotypic resistance profiling, and AI-Assisted prediction models for antimicrobial stewardship and clinical outcomes in a resource-limited setting.

INTRODUCTION: Antimicrobial resistance (AMR) remains a major global health concern, necessitating timely and accurate diagnostic approaches to guide a...

Uncovering the determinants of high-risk age at childbirth in Bangladesh: A machine learning analysis of the BDHS 2022 data.

Childbirth occurring at high-risk maternal ages (≤18 years or ≥40 years) remains an important public health concern in Bangladesh and is influenced by...

External validation of a machine learning-based web application for personalized testing of objective functioning using the five-repetition sit-to-stand test.

PURPOSE: Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific ...

Graph-theoretic active learning for the closed-loop discovery of stochastic heterogeneous composites.

The inverse design of resilient infrastructure materials is hindered by the combinatorial complexity inherent in optimizing stochastic, heterogeneous ...

Radiomics Integrated with Deep Learning Based on Contrast-Enhanced MRI for Predicting Short-Term Efficacy of Drug-Eluting Beads Transarterial Chemoembolization in Hepatocellular Carcinoma from Multicenter Retrospective Study.

We aimed to develop and validate a predictive model combining radiomics, deep learning, and clinical features for the preoperative prediction of the s...

Genomic characterization of an unusual bloodstream Weissella viridescens isolate obtained from a Bulgarian intensive care unit patient.

BACKGROUND: The present study aimed to explore the genomic characteristics of an extremely rare clinical Weissella viridescens isolate (designated Wvi...

Antibiotic Mixture Exposure in Meconium Affects Child Neurodevelopment via Altering Early-Life Gut Microbiome and Metabolome.

Previous evidence has established associations of antibiotic exposure in early life with neurodevelopmental disorders. However, previous studies have ...

Ultra-low dose chest-abdomen-pelvis CT with deep-learning image reconstruction for cancer follow-up: Impact on image quality and lesion detection.

PURPOSE: The purpose of this study was to compare the image quality and lesion detection between ultra-low dose (ULD) chest-abdomen-pelvis computed to...