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A machine learning-based transcriptomic signature for predicting tumor recurrence after curative resection in T1 colorectal cancer: a retrospective multicenter cohort study (The Tw1CE trial).

BACKGROUND: T1 colorectal cancer (T1 CRC) is increasingly treated with curative-intent endoscopic resection, but tumor recurrence remains a critical factor influencing patient prognosis. However there is no validated biomarker exists to reliably predict post-resection recurrence, limiting risk-adapted follow-up and adjuvant therapy decisions. MATERIALS AND METHODS: In this multicenter retrospectiv...

Comparison of Pragmatic Post-Thrombectomy Prognostic Scores Not Based on Advanced Imaging in a Large National Stroke Registry.

BACKGROUND: Nearly half of the patients who received endovascular thrombectomy (EVT) for large vessel occlusion experience poor functional outcomes. Reliable tools for early post-procedural prognostication are needed. We aimed to assess and compare the performance of existing, pragmatic post-EVT prognostic scores in a large national multicenter cohort. METHODS: We conducted a systematic literature...

Enhancing classification accuracy in medical datasets using a hybrid distance and cluster refinement-based K-means clustering method.

Machine learning methods, especially the K_Means clustering method, have demonstrated potential in analyzing medical data by facilitating pattern dete...

The escalating threat of multidrug-resistant organisms: COVID-19 impact, global burden, and the Taiwanese experience.

INTRODUCTION: The COVID-19 pandemic has intensified global health challenges, including a silent but escalating crisis: multidrug-resistant organisms ...

Extracellular matrix stiffness drives post-mitotic nuclear pore complex assembly to promote neuroblastoma pathogenesis.

Nuclear pore complex (NPC) undergoes dynamic changes in physiology and pathology, yet its roles in neuroblastoma (NB) remain unclear. We demonstrated ...

Using Artificial Intelligence Methods to Evaluate the Effect of the National Cytomegalovirus Awareness Month on the Content and Sentiment of Social Media Posts: Infodemiology Study.

BACKGROUND: The month of June has been recognized as the National Cytomegalovirus (CMV) Awareness Month since 2011 in the United States. Established b...

Machine learning-enabled prediction of hemorrhagic transformation post-thrombectomy using quantitative DSA.

This study aims to construct a predictive model for post-thrombectomy hemorrhagic transformation (HT) by integrating hemodynamic features derived from...

The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.

AIMS: Focused cardiac ultrasound (FoCUS) can yield valuable information for decision-making. However, it is limited by the skills required to acquire ...

Leveraging an AI-Driven Voice Companion to Enhance Mental Health in Older Adults in Post-Acute and Long-Term Care Settings: A Feasibility Study.

Loneliness, depression, and anxiety are prevalent among older adults in post-acute and long-term care settings and are associated with adverse health ...

Investigating the relationship between teacher efficacy, job satisfaction, and digital resource utilization in assessment practices: Insights from PISA 2018 and 2022.

The COVID-19 pandemic disrupted global education systems, forcing rapid shifts in teaching practices, technology integration, and assessment methods. ...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

OCT-Derived Virtual Fractional Flow Reserve Associated With 1-Year Outcomes After PCI in ACS Patients.

BACKGROUND: Despite PCI, many acute coronary syndrome (ACS) patients experience major adverse cardiovascular events (MACE). Angiography is limited, an...

Physical neural networks using sharpness-aware training.

Recent advances in AI are pushing the limits of traditional hardware, making physical neural networks (PNNs) a promising alternative. However, trainin...

Deep learning-based automatic adenoid segmentation and a novel volume-based index for adenoid hypertrophy assessment.

BACKGROUND: Adenoid hypertrophy is a common cause of pediatric obstructive sleep apnea (OSA), which can impair cognitive development and affect cranio...

Predicting functional results of percutaneous coronary intervention using machine learning modelling.

BACKGROUND: Post-percutaneous coronary intervention (PCI) Murray's law-based quantitative flow ratio (μFR) is associated with long-term clinical outco...

Biometric Data in Post-Traumatic Stress Disorder Detection: A Scoping Review of Digital Health Applications.

CONTEXT: Post-traumatic stress disorder (PTSD) is mainly assessed through self-reports and clinician interviews, which can delay recognition and limit...

Exploration of Mental Health and Stress Among Nursing Interns Through a Chatbot: A Single-Group Pilot Study.

BACKGROUND: Nursing interns face early clinical distress; chatbot-based mental health tools show promise, although evidence of their feasibility and e...

The balance between artificial and human intelligence in clinical practice.

INTRODUCTION: Artificial intelligence (AI) is becoming increasingly integrated into clinical care in hand surgery. Its applications extend across diag...

Flux dynamics and scenario simulation of PFAS under extreme hydrological perturbations in urban tributary flowing into the Yangtze River: A focus on floods.

Floods considerably amplify pollutant transfer into the Yangtze River (YZR) via tributaries. We explored flood-induced changes in PFAS pollution patte...

An adaptive machine learning framework integrating large language models to assess and enhance emotional intelligence in adolescents.

BACKGROUND: Emotional intelligence (EI) is a critical determinant of children's socio-emotional and neurocognitive development, and deficits in EI dim...