Explainable Artificial Intelligence in Paediatric: Challenges for the Future.

Background: Explainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences.

Enhanced antibacterial activity of a novel silver-based metal organic framework towards multidrug-resistant .

The growth and spread of multidrug-resistant bacterial species, such as , pose a serious threat to human health and require the development of innovative antibacterial agents. The search for an acceptable, safe, and efficient antibacterial is a matter of significant concern. In the present work, silver-based metal-organic frameworks (Ag-MOFs) showed efficient antibacterial activity against multidrug-resistant (KBP 11) with a minimum inhibitory concentration and minimum bactericidal concentration of 10 μg mL.

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.

Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible.