Bioterrorism

Can Electroencephalography and Machine Learning Predict Treatment Response to Transcranial Magnetic Stimulation in Depression?

Deep learning model for predicting immunotherapy response in patients with advanced NSCLC: Study findings demonstrate a strong and independent deep learning-based feature associated with an immune checkpoint inhibitor response in patients with NSCLC across cohorts.

Identification and validation of inflammatory response genes linking chronic kidney disease with coronary artery disease based on bioinformatics and machine learning.

Coronary artery disease (CAD) commonly occurs and elevates the risk of cardiovascular events and mor...

Addressing Workforce and Ethical Gaps in AI-Driven Mental Health Care: A Response to Higgins and Wilson.

Artificial intelligence (AI)-based clinical decision support systems (CDSS) hold great promise for m...

Non-Invasive Tumor Budding Evaluation and Correlation with Treatment Response in Bladder Cancer: A Multi-Center Cohort Study.

The clinical benefits of neoadjuvant chemoimmunotherapy (NACI) are demonstrated in patients with bla...

Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms.

PURPOSE: This study aimed to establish and validate a prediction model based on machine learning met...

A mean field theory for pulse-coupled neural oscillators based on the spike time response curve.

A mean field method for pulse-coupled oscillators with delays used a self-connected oscillator to re...

A novel artificial intelligence-based methodology to predict non-specific response to treatment.

Non-specific response to treatment (NSRT) is the primary contributor to the failure of randomized cl...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method...

Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, de...

Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.

Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its a...

Chaperonin containing TCP1 subunit 5 as a novel pan-cancer prognostic biomarker for tumor stemness and immunotherapy response: insights from multi-omics data, integrated machine learning, and experimental validation.

BACKGROUND: Chaperonin containing TCP1 subunit 5 (CCT5), a vital component of the molecular chaperon...

Groundwater drought and anthropogenic amplifiers: A review of assessment and response strategies in arid and semi-arid areas.

Groundwater drought, a prolonged period of abnormally low groundwater levels, poses a significant th...

Clinical prediction of pathological complete response in breast cancer: a machine learning study.

BACKGROUND: This study aimed to develop and validate machine learning models to predict pathological...

Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification.

Clinical Document Classification (CDC) is crucial in healthcare for organizing and categorizing larg...

Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.

Identifying patients suitable for conversion therapy through early non-invasive screening is crucial...

Pros, Cons and Limits of AI in Public Health.

This paper explores the role of Artificial Intelligence (AI) in Public Health (PH), examining its be...

Annexin A2 Contributes to Release of Extracellular Vimentin in Response to Inflammation.

Vimentin, an abundant intracellular cytoskeletal protein, is secreted into the extracellular space, ...

Exploring the Opportunities and Challenges of Healthcare Innovation in UK Higher Education: A Narrative Review.

: The healthcare sector is under increasing pressure due to an ageing population, rising multimorbid...

A computational framework for IoT security integrating deep learning-based semantic algorithms for real-time threat response.

The growth of IoT networks has led to significant security issues, especially in areas of real-time ...

Optimization process of coffee pulp wines combined with the artificial neural network and response surface methodology.

Coffee pulp wine was made from coffee pulp. The level range of fermentation factors was determined b...