AIMC Topic: SARS-CoV-2

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Predicting Antibody-Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV-2 Variants.

Journal of chemical information and modeling
Predicting antibody-antigen interactions is a critical step in developing new therapeutics to defend against viral infections. However, measuring the extent of these interactions is costly and time-consuming. With the increased availability of exper...

Pulmonary diseases accurate recognition using adaptive multiscale feature fusion in chest radiography.

Scientific reports
Pulmonary disease can severely impair respiratory function and be life-threatening. Accurately recognizing pulmonary diseases in chest X-ray images is challenging due to overlapping body structures and the complex anatomy of the chest. We propose an ...

Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods.

Scientific reports
COVID-19 has posed a significant global health challenge, affecting individuals across all age groups. While extensive research has focused on adults, pediatric patients exhibit distinct clinical characteristics that necessitate specialized predictiv...

Effects of a Theory- and Evidence-Based, Motivational Interviewing-Oriented Artificial Intelligence Digital Assistant on Vaccine Attitudes: A Randomized Controlled Trial.

Journal of medical Internet research
BACKGROUND: Attitude-targeted interventions are important approaches for promoting vaccination. Educational approaches alone cannot effectively cultivate positive vaccine attitudes. Artificial intelligence (AI)-driven chatbots and motivational interv...

LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.

PloS one
Lung diseases (LGDs) are related to an extensive range of lung disorders, including pneumonia (PNEUM), lung cancer (LC), tuberculosis (TB), and COVID-19 etc. The diagnosis of LGDs is performed by using different medical imaging such as X-rays, CT sca...

Integrating Physics-Based Simulations with Data-Driven Deep Learning Represents a Robust Strategy for Developing Inhibitors Targeting the Main Protease.

Journal of chemical information and modeling
The coronavirus main protease, essential for viral replication, is a well-validated antiviral target. Here, we present Deep-CovBoost, a computational pipeline integrating deep learning with free energy perturbation (FEP) simulations to guide the stru...

A lightweight hybrid DL model for multi-class chest x-ray classification for pulmonary diseases.

Biomedical physics & engineering express
Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and effi...

Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.

Journal of global health
BACKGROUND: Respiratory viral infections pose a substantial disease burden worldwide. Spatiotemporal techniques help identify transmission patterns of these infections, thereby supporting timely control and prevention efforts. We aimed to synthesise ...

Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis.

Human vaccines & immunotherapeutics
Exploring the influencing factors of COVID-19 vaccine hesitancy and summarizing countermeasures is of great significance for effectively addressing potential public health crises. Based on survey data from China, we employed a Gradient Boosting Decis...

Point-of-need one-pot multiplexed RT-LAMP test for detecting three common respiratory viruses in saliva.

Biosensors & bioelectronics
Respiratory viral infections pose a significant global public health challenge, partly due to the difficulty in rapidly and accurately distinguishing between viruses with similar symptoms at the point of care, hindering timely and appropriate treatme...