Bioterrorism

A translational IoT healthcare framework integrating secure sensing, energy-aware intelligence, and adaptive emergency response.

The Internet of Things (IoT) is transforming the healthcare industry by enabling real-time patient monitoring, predictive analytics and smart decision making across interconnected medical environments. It's still hard to do things like timely response in an emergency, energy efficient scheduling, secure data collection, and accurate anomaly detection, particularly in large hospital networks. This ...

AI-based histopathology analysis predicts checkpoint inhibitor response in advanced melanoma and identifies patterns associated with response.

INTRODUCTION: Readily available predictive biomarkers for immune checkpoint inhibitor (ICI) response in advanced melanoma are limited. This study evaluates the predictive value of AI-based histopathology analysis. METHODS: Patients with advanced cutaneous melanoma treated with first-line anti-PD1 ±anti-CTLA4 between 2016 and 2023 across 11 Dutch centers were retrospectively identified using prospe...

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predi...

Spatial synergy in the digital era: a geo-analytics framework for detecting regional health patterns in urban smart city development.

BACKGROUND: With enhanced digitization of large-scale health and socio-economic data, it has generated new opportunities and challenges in the process...

Explainable machine learning identifies features and thresholds predictive of immunotherapy response.

The identification of reliable predictors for immunotherapy response remains a critical challenge in precision oncology. Here, we integrated patient c...

Future Pharmacists in the Age of Artificial Intelligence: A Mixed-Methods Study of First-Year Students in Vietnam.

OBJECTIVES: To assess AI readiness, perceptions, and usage patterns among first-year pharmacy students, and to identify factors associated with AI eng...

A Mechanistic Framework Integrating Renal QSP-PK-PD and Machine Learning for Baseline-Informed Stratification of Diuretic Resistance.

Diuretic resistance represents a major source of heterogeneity in loop diuretic response and remains a key barrier to effective decongestion in heart ...

Domain adaptive uplift modeling across heterogeneous mental health cohorts.

Predicting who will deteriorate under stress is important for targeting mental-health support; yet, treatment-effect models are rarely tested across p...

Influencing factors and mediating effects of advanced medical care plan readiness in young and middle-aged patients with advanced cancer based on machine learning.

OBJECTIVE: To examine the factors influencing advance care planning (ACP) readiness and its correlation with disease benefit perception and social sup...

Automated deep learning model for predicting pathological complete response in rectal cancer: A tool to organ-preserving strategies.

BACKGROUND: Accurate identification of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal canc...

Microbiome in Gastrointestinal Tumors: Implications in Oncogenesis and Therapeutic Response : Microbiome in Gastrointestinal Tumors.

PURPOSE OF REVIEW: To provide an updated overview of the role of the human microbiome in the initiation, progression, and therapeutic response of gast...

Leveraging Retrieval-Augmented Generation (RAG) Technology for Enhanced Accreditation Preparedness and Compliance in Physical Therapy Education.

BACKGROUND AND PURPOSE: Accreditation ensures educational program quality, public accountability, and professional readiness. Compliance demands subst...

Enhancing drought forecasting with CNN-TQWT and metaheuristic hybrids: evidence from Norway.

Droughts are among the most damaging natural hazards, exerting severe impacts on ecosystems, economies, and societies. While typically catastrophic, t...

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rec...

Machine Learning Prediction of Growth Hormone Response in Children Non-Growth Hormone-Deficient Short Stature.

Height gain under recombinant human growth hormone (rhGH) varies widely in children with short stature, making early, reliable response prediction ess...

Who's Thinking About AI? Patterns of Artificial Intelligence Reference Across the Australian Specialist Digital Health Workforce.

Artificial intelligence (AI) is reshaping healthcare delivery, management, and education worldwide, creating new opportunities and challenges for the ...

Defining the artificial intelligence knowledge gap in surgery: experience and perspectives from surgical resident and postgraduate.

Despite the growing prominence of Artificial Intelligence (AI) in surgical practice, surgical residents and postgraduates receive limited formal train...

New learnings from the molecular pathology of the synovial tissue in rheumatoid arthritis: from pathogenesis to therapeutic targeting toward precision medicine.

Despite the success of targeted therapies in rheumatoid arthritis, the lack of predictive biomarkers of response leads to an empirical treatment appro...

Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromi...

Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.

BACKGROUND: Dengue fever remains a persistent public health threat in Dhaka, Bangladesh, necessitating effective early warning systems to enable timel...