Bioterrorism

Advancing IBD Management: A Literature Review on the Role of Non-Invasive Blood-Based Biomarkers in Predicting and Assessing Pharmacodynamic Response to Treatment.

Reliable biomarkers that enable noninvasive, longitudinal assessment of disease activity and therapeutic response remain a major unmet need in inflammatory bowel disease (IBD). While colonic biopsies are the gold standard for evaluating mucosal inflammation, their invasive nature and limited spatial and temporal resolution constrain their utility in routine monitoring and clinical trials. Blood-ba...

Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation.

BACKGROUND/AIM: Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response rates vary significantly among patients. Predicting treatment response is crucial to avoid unnecessary toxicity and optimize therapeutic strategies. This study aims to develop a deep learning model leveraging RNA sequencing data to predict chemothera...

Predicting the viability of pharmaceutical formulations for continuous direct compression using machine learning approaches.

Pharmaceutical formulation is the activity in which the chemical substances that form a final medicinal product are combined, including the active pha...

Core competencies of clinical nursing educators in the AI era: a qualitative study.

BACKGROUND: As artificial intelligence (AI) becomes increasingly integrated into healthcare and nursing education, existing competency standards and t...

Identification and verification of worst-case radiological transport scenarios for Ireland: A simulation-based approach to nuclear emergency preparedness (2011-2024).

This study presents a comprehensive simulation-based assessment of potential transboundary radiological transport to Ireland from six nuclear faciliti...

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.

OBJECTIVES: Oligometastatic disease represents an intermediate stage of cancer, often treated with surgery or ablative radiotherapy (ART). This scopin...

Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.

Ewing sarcoma is a highly aggressive small round cell sarcoma primarily affecting children and adolescents. Imaging plays a central role from diagnosi...

Radiomics-based outcome prediction for irinotecan-TACE in colorectal liver metastases: advanced analysis from the prospective CIREL trial.

OBJECTIVES: Transarterial chemoembolization (TACE) is a promising locoregional therapy for unresectable colorectal liver metastases, but patient selec...

Dual framework for rainfall prediction: a multi-seed machine and deep learning evaluation across Pakistan's climatic regimes.

Accurate prediction of rainfall events is vital for agriculture, hydrology, flood preparedness, and climate-adaptive strategies in regions of Pakistan...

OCTN1 variants shape innate immunity and predict individual response to anti-TNFα in ulcerative colitis patients: An exploratory study.

BACKGROUND: Ulcerative colitis (UC) exhibits substantial heterogeneity in inflammatory pathways and therapeutic response. The organic cation transport...

Does Sequential Use of Multiple Chatbots Influence Emergency Guidance for Dental Avulsion?

BACKGROUND/AIM: Artificial intelligence-based chatbot systems are increasingly used for dental emergency guidance, yet the clinical value of sequentia...

Integrating AI Into Governmental Public Health Decision Making: Challenges, Considerations, and a Path Forward.

Public health emergencies such as pandemics, natural disasters, and epidemics may require rapid, high-stakes decisions often made by elected officials...

Algometric assessment of sensitization and habituation as predictive markers for OnabotulinumtoxinA response in chronic migraine.

BACKGROUND: Chronic migraine is a debilitating disorder characterized by central sensitization and impaired habituation. Although OnabotulinumtoxinA (...

Evaluating the Utility and Limitations of Machine Learning Tumor Segmentation for Automated Longitudinal RANO Treatment Response Classification.

PURPOSE: Machine learning segmentation has emerged in tumor assessment with high performance in volumetric evaluation of brain tumors. It is unclear, ...

Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types.

Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has...

Advances in molecular diagnostic strategies during the SARS-CoV-2 pandemic.

INTRODUCTION: The SARS-CoV-2 pandemic provided critical insights into pandemic preparedness. The community spread can be slowed down or contained thro...

Monotherapy cancer drug-blind response prediction is limited to intraclass generalization.

Monotherapy cancer drug response prediction (DRP) models predict the response of a cell line to a given drug. Analyzing these models' performance incl...

Key factors of the deranged antiviral response in elderly patients with COVID-19: a machine-learning analysis.

Age is a well-known risk factor to develop severe viral respiratory infections, including severe COVID-19. This study aimed to identify the biological...

Deep learning-based early prediction of carotid plaque response to lipid-lowering therapy using longitudinal multimodal ultrasound imaging.

OBJECTIVE: This study aimed to develop and validate a deep learning prediction model using longitudinal multimodal ultrasound imaging for early identi...

Artificial Intelligence for Public Health Surveillance: Promise, Inequities, and the Need for Transparent Global Adoption.

Artificial intelligence (AI) has demonstrated a strong technical potential to enhance public health surveillance, but routine implementation remains l...