Infectious Disease

Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.

The global impact of SARS-CoV-2 has highlighted the urgent need for novel antiviral therapies. This study integrates combinatorial chemistry, molecular docking, and deep learning to design, evaluate and synthesize new pyrazole derivatives as potential inhibitors of the SARS-CoV-2 main protease (M). A library of over 60,000 pyrazole-based structures was generated through scaffold decoration to enha...

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of routine diagnostic FFPE biopsy specimens (n = 55) representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohn's-(CD), Herpes simplex (HSV) and C...

Radiomics in glioma: emerging trends and challenges.

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emer...

Contrastive-learning of language embedding and biological features for cross modality encoding and effector prediction.

Identifying and characterizing virulence proteins secreted by Gram-negative bacteria are fundamental for deciphering microbial pathogenicity as well a...

End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.

Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data al...

Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely ben...

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features.

BACKGROUND: Accurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph ne...

The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update.

This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PS...

Synergistic modeling of hemorrhagic dengue fever: Passive immunity dynamics and time-delay neural network analysis.

Dengue fever poses a formidable epidemiological challenge, particularly for vulnerable groups such as infants. This research paper establishes a mathe...

The Emerging Influence of Artificial Intelligence on Traditional Medical Textbooks.

This review provides a comprehensive exploration of how artificial intelligence (AI) is reshaping medical education and the role of traditional textbo...

User-Oriented Requirements for Artificial Intelligence-Based Clinical Decision Support Systems in Sepsis: Protocol for a Multimethod Research Project.

BACKGROUND: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the...

Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.

Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold...

Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunode...

Preliminary exploration of ChatGPT-4 shows the potential of generative artificial intelligence for culturally tailored, multilingual antimicrobial resistance awareness messaging.

OBJECTIVE: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to...

Pathogen genomic surveillance and the AI revolution.

The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the ...

Leveraging machine learning to uncover multi-pathogen infection dynamics across co-distributed frog families.

BACKGROUND: Amphibians are experiencing substantial declines attributed to emerging pathogens. Efforts to understand what drives patterns of pathogen ...

The TB27 Transcriptomic Model for Predicting Culture Conversion.

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of . Recently, host RNA signatures have been used to mon...

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in...

Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study.

BACKGROUND: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determ...

Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.

Antimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of m...