Age- and Gender-Related Femoral Bowing Analysis in the Korean Population and Features for Clinical Applications.

: The anterolateral bowing of the femur shows differences between races and has recently caused many clinical problems. Asians tend to have increased femoral bowing, but there is a lack of large-scale studies. We aim to identify the patterns of femoral bowing in the Korean population through comprehensive analysis and address its clinical implications.

Solvent-free upcycling of agricultural plastic waste using in situ self-assembly metal nanoparticles co-doped microporous carbocatalyst for advanced transportation fuels.

Plastic mulching film is ubiquitous in modern agriculture for its heat preservation and moisture retention functions. However, plastic mulching film waste accumulated on land results in microplastic pollution, posing potential hazards as these micro and nanoplastics can enter the food chain. Chemical upcycling of waste mulching film is an emerging strategy to realize sustainable development and circular economy.

Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.

Rationale & Objective: Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases.

Study Design: A systematic review following PRISMA guidelines.

Mechanical Force-Induced cGAS Activation in Carcinoma Cells Facilitates Splenocytes into Liver to Drive Metastasis.

Liver metastasis is the main cause of cancer-related mortality. During the metastasis process, circulating carcinoma cells hardly pass through narrow capillaries, leading to nuclear deformation. However, the effects of nuclear deformation and its underlying mechanisms on metastasis need further study.

Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data.

This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection.