Applying Neural Networks to Analyse Inflammatory, Sociodemographic, and Psychological Factors in Non-Melanoma Skin Cancer and Colon Cancer: A Statistical and Artificial Intelligence Approach.

Chronic inflammation and psychosocial factors significantly influence cancer progression and patient behavior in seeking medical care. Understanding their interplay is essential for enhancing early detection and developing personalized treatment strategies. This study aims to develop a comprehensive patient profiling model by comparing non-melanoma skin cancer (NMSC) and colorectal cancer (CRC).

MAPLE-prepared graphene oxide-based coatings for improved orthopedic screws used in knee interventions.

Orthopedic screws are subjected to high mechanical stress, corrosive environment, and microbial colonization, which may cumulatively lead to implant failure and periprosthetic joint infections. To overcome these issues, this study has focused on modifying the surface chemistry and topography of screws utilized in knee intervention toward enhancing their mechanical and biological behaviors. Specifically, this study has explored the optimization of composite coatings made of polycaprolactone (PCL), graphene oxide (GO), and Meropenem (MRP) via the matrix-assisted pulsed laser evaporation (MAPLE) technique.

Enhancing the Understanding of Abdominal Trauma During the COVID-19 Pandemic Through Co-Occurrence Analysis and Machine Learning.

Background: This study examines the impact of the COVID-19 pandemic on abdominal trauma management by comparing pre-pandemic (17 February 2018-26 February 2020) and pandemic periods (27 February 2020-7 March 2022).

Methods: Analyzing data from 118 patients at the Emergency County Clinical Hospital of Craiova, we identified significant shifts in clinical practices affecting patient outcomes.

Results: During the pandemic, a moderate increase in surgical interventions for specific abdominal traumas indicated the effective adaptation of the medical system.