Bioterrorism

Cross-sectional Study on Medical Attitude Towards Artificial Intelligence Use in Fibromyalgia: Insights From the Annual Thinking Lab on Fibromyalgia Syndrome (ATLAS 2024).

BACKGROUND: The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize clinical practice, particularly in the management of complex conditions such as fibromyalgia (FM). Despite its promise, the adoption of this technology in practice faces several challenges, including limited knowledge and preparedness among healthcare professionals.

[The future of medicine: an informed look into the "crystal ball"].

This article explores potential future scenarios for the medical field based on current trends, technological advancements, and social dynamics. By examining advances in artificial intelligence, immersive technologies, genomics, and digital health infrastructure, the paper envisions a healthcare system poised for transformative change. The anticipated role of AI as a digital assistant in diagnosti...

Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network.

Ammonia is widely acknowledged to be a stressor and one of the most detrimental gases in animal enclosures. In livestock- and poultry-breeding facilit...

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determ...

Artificial intelligence in respiratory pandemics-ready for disease X? A scoping review.

OBJECTIVES: This study aims to identify repeated previous shortcomings in medical imaging data collection, curation, and AI-based analysis during the ...

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resoluti...

Role of artificial intelligence in early diagnosis and treatment of infectious diseases.

Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificia...

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potentia...

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods...

The Role of Artificial Intelligence in Accelerating Vaccine Development: Challenges and Opportunities in Pandemic Preparedness.

Artificial intelligence (AI) has been studied and applied to medicines and vaccine development. However, there is still a need to understand the proce...

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. ...

Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a ...

Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques.

OBJECTIVES: The aim of the study was to analyze the data of diabetic patients regarding warning signs of hypoglycemia to predict it at an early stage ...

Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.

OBJECTIVES: This study was designed to assess computed tomography (CT)-based radiomics of colorectal liver metastases (CRLM), extracted from posttreat...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighte...

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning.

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability....

Large-scale deep learning identifies the antiviral potential of PKI-179 and MTI-31 against coronaviruses.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to the global pandemic of Coronavirus Disease (2019) (COVID-19), underscoring the...

Towards regulatory generative AI in ophthalmology healthcare: a security and privacy perspective.

As the healthcare community increasingly harnesses the power of generative artificial intelligence (AI), critical issues of security, privacy and regu...

Sampled-data synchronization for fuzzy inertial cellular neural networks and its application in secure communication.

This paper designs the sampled-data control (SDC) scheme to delve into the synchronization problem of fuzzy inertial cellular neural networks (FICNNs)...

Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm.

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS...