Bioterrorism

Multi-response optimization and validation analysis in the detection of acetochlor and butachlor by HPLC based on D-optimal design methodology.

Acetochlor and butachlor, widely used herbicides, pose environmental and health risks through water contamination. This study developed a high-performance liquid chromatography (HPLC) method for analyzing these compounds, employing a multi-response approach and D-optimal design. The D-optimal design reduced the number of required experiments from 36 to 14. The response model was analyzed by varian...

Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.

ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of multi-sequences MR based on machine learning (ML) to assess early response in LA-NPC after CCRT.MethodsThis study retrospectively enrolled 104 LA-NPC pati...

Infodemic Versus Viral Information Spread: Key Differences and Open Challenges.

As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influe...

An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy.

The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance...

Mismatch between warning information and protective behavior: Why experts + AI < 2?

Warning information plays a vital role in encouraging disaster preparedness among residents. Using survey experiment data from 619 respondents in Chin...

A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health.

Pesticides and other synthetic agrochemicals play a critical role in emerging agricultural practices by enhancing crop productivity and protecting aga...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patie...

Neural networks to model COVID-19 dynamics and allocate healthcare resources.

This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves hig...

Predicting the Next Response: Demonstrating the Utility of Integrating Artificial Intelligence-Based Reinforcement Learning with Behavior Science.

The concepts of reinforcement and punishment arose in two disparate scientific domains of psychology and artificial intelligence (AI). Behavior scient...

Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety.

As artificial intelligence (AI) technology quickly grows, college students have new worries and fears. Using Marx's theory of labour alienation, this ...

Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.

AI is emerging as a promising tool for diagnosing COVID-19 based on chest CT scans. The aim of this study was the comparison of AI models for COVID-19...

Prompt architecture induces methodological artifacts in large language models.

We examine how the seemingly arbitrary way a prompt is posed, which we term "prompt architecture," influences responses provided by large language mod...

Predictive modeling of response to repetitive transcranial magnetic stimulation in treatment-resistant depression.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (...

Ambulance route optimization in a mobile ambulance dispatch system using deep neural network (DNN).

The ambulance dispatch system plays a crucial role in emergency medical care by ensuring efficient communication, reducing response times, and ultimat...

Self-supervised network predicting neoadjuvant chemoradiotherapy response to locally advanced rectal cancer patients.

Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data an...

Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods.

Ovarian cancer surgery requires multiple radical resections with a high risk of complications. The aim of this single-centre, retrospective study was...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential gene...

Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy.

Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and pr...

A machine learning computational approach for the mathematical anthrax disease system in animals.

OBJECTIVES: The current research investigations present the numerical solutions of the anthrax disease system in animals by designing a machine learni...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer ...