Antibiotic resistance is worsening worldwide, and plasmid conjugation is a major way resistance genes spread. A new study in Engineering finds that cinnamic acid, a safe, common food additive, can block this transfer process. It works by disrupting bacterial energy metabolism and reducing ATP supply. Tests in lab conditions, gut bacteria, and mice all show positive effects without harming health or gut flora, offering a promising natural way to fight drug-resistant infections.

Discover how obakulactone, a natural compound from Phellodendri cortex, eases rheumatoid arthritis symptoms! A new Engineering study uncovers it targets the ACOT1 protein, regulates fatty acid balance, curbs inflammation, and joint damage. It also rebalances immune cells, offering fresh hope for better rheumatoid arthritis treatments with a clear molecular mechanism.

Kidney diseases often develop silently, with the body compensating so effectively that patients may remain unaware of the problem for years. Symptoms typically appear only at advanced stages and are often nonspecific, such as fatigue or swelling. This is why modern nephrology is increasingly focused not only on diagnosis, but also on predicting disease progression.

Artificial intelligence is playing a growing role in this shift. By analyzing complex clinical data, AI models can estimate the risk of specific outcomes—such as whether a patient’s condition may go into remission—allowing clinicians to view disease as a dynamic, predictable process rather than a set of isolated parameters.

Different types of models are used depending on the data. Classical approaches such as logistic regression, random forests, and XGBoost perform well with structured clinical data, while neural networks are better suited to more complex inputs like medical images. At the same time, experts emphasize that the clinical usefulness and interpretability of models remain more important than their complexity.

A particularly promising direction is the integration of AI with advanced biological analyses, such as proteomics and metabolomics. This combination makes it possible to detect very early molecular changes—before symptoms appear or standard tests show abnormalities—opening the door to earlier diagnosis and more accurate prediction of disease progression.

For patients, these advances mean earlier detection, better prognoses, and more personalized treatment. However, artificial intelligence remains a support tool, with final clinical decisions still made by physicians.

Scientists at Zhejiang University have created a self-powered implantable sensor that tracks hydrogen peroxide levels in plants in real time, a key signal of plant stress. Powered by light, this high-precision sensor monitors how plants respond to osmotic, mechanical and UV stress, offering a new tool for crop health monitoring and stress-resistance breeding research.

The ability to edit 3D scenes—whether through altering appearances, reshaping geometry, or transforming objects—has been a cornerstone of digital content creation.

Pangenomes, which capture the genetic diversity of populations more comprehensively than traditional linear genomes, are foundational to understanding genetic variation in species. While calculating statistical metrics for linear genomes can often be achieved with basic scripts, analyzing graph-based genomes requires efficient algorithms due to their complexity.

Body height and weight estimation from a single non-frontal face image suffers from poor performance due to large face pose variance and lack of labeled data. To solve the problems, a research team led by Shiguang SHAN published their new research in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.