Bioterrorism

Predicting genetic evolution of viruses to identify suitable vaccines using artificial intelligence.

The evolution of the viruses is rapidly becoming a global challenge to the creation of vaccines since the new variants are often capable of escaping the immune system and decreasing the vaccine efficacy. The traditional methods of genomic epidemiology rely on the retrospective phylogenetic analysis, which can elucidate the previous mutations, but cannot predict the evolutionary trends in the futur...

Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.

IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local hospital capacity. Some risk metrics, such as the Center for Disease Control and Prevention's (CDC's) Community Levels, were developed to predict the impact of COVID-19 on the community-level health care system based on routine surveillance data. Howe...

Transforming clinical toxicology in Singapore: four decades of progress and beyond.

By examining key milestones, challenges and future directions, this review chronicles the evolution of clinical toxicology in Singapore into a recogni...

Students Are Ready for AI-But Is Medical Education?

BACKGROUND: Artificial Intelligence (AI) is increasingly relevant to medical training, yet formal AI instruction remains limited. This study examined ...

Prediction of anti-epileptic drug response of patients based on peripheral blood RNA profiles and machine learning.

OBJECTIVE: In this study, we aimed to develop a method for predicting the response of patients to three commonly used anti-epileptic drugs (AEDs), nam...

Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.

OBJECTIVE: Dosiomics and radiomics elaborate the low-and high-order features extracted from images to predict clinical outcomes. Whole-brain radiother...

A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease.

PURPOSE: To develop and evaluate a deep learning-based framework for quantifying thyroid eye disease (TED) severity before and after teprotumumab trea...

Predicting nonresponse to sexual identity question in youth risk behavior surveillance: A machine learning analysis of complex survey data.

PURPOSE: To compare seven machine learning (ML) models developed to predict non-response to the sexual identity question in the 2023 Youth Risk Behavi...

Neural Network Prediction of Response Factors for Extractables and Leachables in Pharmaceuticals and Medical Devices.

Ensuring safety of patients using pharmaceuticals and medical devices through chemical characterization requires accurate estimation of extractables a...

Response to venom immunotherapy: Exploratory retrospective machine learning clustering analysis.

BACKGROUND: Hymenoptera venom immunotherapy is an established treatment for severe allergic reactions, aiming to modulate the immune response and redu...

Bioinspired triboelectric droplet sensor for ammonia monitoring.

Gas sensors with fast response are in high demand for environmental and health applications. Conventional solid-state sensing materials are inherently...

HRD-Informed Digital Histology Model for Predicting Platinum Chemo-Response and Prognosis in High-Grade Serous Ovarian Cancer.

Homologous recombination deficiency (HRD) is a critical biomarker in high-grade serous ovarian cancer for the clinical benefit from platinum-based che...

Spatial variation in socio-economic vulnerability to Influenza-like Infection for the US population.

This study aims to quantify environmental health impacts and assess risk by understanding the disproportionate burden of infectious diseases, specific...

Machine Learning forecasting of dengue in São Paulo using virtual data augmentation and urban incident predictors: Addressing the exceptional surge of cases in 2024.

Dengue remains one of the most critical vector-borne diseases globally, with Brazil consistently reporting the highest incidence in the Americas. The ...

Trust at risk: Why public health must lead the use of AI in pandemic preparedness.

Artificial intelligence (AI) is poised to play a transformative role in pandemic preparedness, with the potential to enhance surveillance, risk assess...

Charting exposomethics: a roadmap for the ethical foundations of the human exposome project.

BACKGROUND: The Human Exposome Project (HEP) aims to chart lifelong environmental exposures and their biological consequences, furnishing the environm...

Machine learning-based typing of Clostridium botulinum group III by FT-IR spectroscopy.

This study aimed to investigate the utility of Fourier-Transform Infrared Spectroscopy (FT-IRS) for differentiating Clostridium botulinum (C. botulinu...

A Comparative Study of Machine Learning Models for Identification of Antiviral Peptides Using Various Encoded Features.

Viruses are a significant threat to human life, as demonstrated by the global COVID-19 pandemic and the Ebola outbreak. Diseases such as smallpox, AID...

Integrating single-cell and bulk transcriptomes with machine learning reveals a CAF signature for immunotherapy response in dMMR endometrial cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) are key players in the tumor microenvironment (TME), but their roles in prognosis and immunotherapy r...

Cost-effectiveness analysis of artificial intelligence (AI) for response prediction of neoadjuvant radio(chemo)therapy in locally advanced rectal cancer (LARC) in the Netherlands.

BACKGROUND: Given the potential of artificial intelligence (AI) and the increasing importance of understanding AI's economic impact, this study aims t...