Bioterrorism

The potential roles for artificial intelligence in supporting veterinarians during an emergency response.

This article considers how artificial intelligence (AI) could support, within a New Zealand context, the contribution of private veterinarians to protecting animal health and welfare during emergency responses. These may include veterinary involvement in natural disasters, such as fire and flood, or formal emergency animal disease responses, which are the responsibility of different government dep...

External validation of an explainable electrocardiogram-only deep learning algorithm for the prediction of response after cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) can improve clinical outcomes in patients with dyssynchronous heart failure, but many patients selected according to the current guidelines do not respond. OBJECTIVE: This study aimed to externally validate an explainable deep learning algorithm (the FactorECG algorithm) for predicting response after biventricular pacing. METHODS: We previously t...

Public health surveillance and outbreak preparedness for mpox.

PURPOSE OF REVIEW: The 2022 global mpox outbreak showed that surveillance systems were not ready to quickly detect or adapt to the new dynamic of huma...

Integrated region-specific modeling of H5 avian influenza in Asia using ENSO-based forecasts.

Highly pathogenic avian influenza (HPAI), particularly of the H5 subtype, remains a persistent threat to poultry, wildlife, and public health across A...

Exploring temperamental and clinical predictors of lithium treatment outcomes in bipolar disorder using diverse machine learning approaches.

BACKGROUND: Lithium is a core treatment for bipolar disorder (BD), yet clinical response varies across patients. Testing accessible predictors of lith...

Integration of histopathological characteristics by machine learning improves the prediction of neoadjuvant immunochemotherapy response in triple-negative breast cancer.

Neoadjuvant immunochemotherapy (NAIC) is a standard treatment for triple-negative breast cancer (TNBC), but there is no reliable biomarker to identify...

Spatiotemporal prediction of aeropollen concentration using tree-based machine learning.

Pollen is a major aeroallergen and an important environmental health concern, with its concentrations strongly modulated by climate, air pollution, an...

The global epidemiology of botulism outbreaks from open-source intelligence, 2017-2024.

BACKGROUND: Botulism is a rare but potentially fatal illness caused by botulinum neurotoxin, with outbreaks reported globally in humans, animals, and ...

Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response.

BACKGROUND: Despite advances in treatment approaches for Major Depressive Disorder (MDD), significant challenges persist in predicting individual trea...

Development of an Infectious Disease Vulnerability Index (IDVI) for dairy farms: A data-driven approach to assessing risk and informing biosecurity practices.

Dairy farms are vulnerable to a wide range of infectious diseases, which can have significant economic and public health consequences. However, there ...

Bayesian selection of lipidome dynamics features for a K-nearest neighbors (KNN) classifier model of tumor response in patients with advanced gastrointestinal adenocarcinoma undergoing systemic treatment.

BACKGROUND: Dysregulated lipid metabolism is common in patients with gastrointestinal (GI) cancer. This study investigated the ability of plasma lipid...

Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer.

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological com...

Self-reported periodontitis: A multiethnic community-based validation study.

OBJECTIVE: Self-reported periodontal measures offer a practical alternative to clinical examinations in large-scale studies, but few validation effort...

Health system capacity for infectious disease pandemic response in low- and lower-middle-income countries: Insights from national health facility surveys.

OBJECTIVE: Despite growing recognition of pandemic preparedness gaps in low- and lower-middle-income countries (LLMICs), empirical evidence on facilit...

Machine learning models for predicting response to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Predicting clinical and radiological outcomes of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with ...

Assessing the Readiness of Health Care Organizations for Safe AI Integration: Perspectives From Quality and Safety Leaders.

BACKGROUND: Artificial intelligence (AI) technologies hold great promise for improving patient outcomes, reducing clinician workload, and enhancing pa...

Early prediction of response to ADT plus ARPIs in mHSPC using interpretable machine learning on PSMA PET/CT: a real-world study.

BACKGROUND: Reliable tools for early prediction of treatment response to androgen deprivation therapy (ADT) plus novel androgen receptor pathway inhib...

Combined dual-phase CT radiomics and deep transfer learning model for oxaliplatin response in unresectable colorectal liver metastases.

OBJECTIVES: Accurate prediction of response to first-line oxaliplatin-based chemotherapy in unresectable colorectal liver metastases (CRLM) is critica...

Resting-state functional connectivity of the fronto-limbic and default mode network as neural correlates of antidepressant response in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet antidepressant response remains highly variable, with many...

Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning.

The widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic envi...