Recently, the group led by Chuan-Feng Li and Zong-Quan Zhou at University of Science and Technology of China has successfully demonstrated an integrated spin-wave quantum memory, by implementing spin-wave quantum storage protocols using a specially developed device.

In a review published in Molecular Biomedicine, a team from Datta Meghe Institute of Higher Education and Research investigates the transformative role of Artificial Intelligence (AI) in pandemic response, highlighting its contributions to epidemiological modelling, vaccine development, and disease surveillance. The study examines how AI-driven models like SIR and SIS have advanced disease spread prediction and resource optimization. Drawing from multidisciplinary research, the review underscores the potential of AI in anticipating and mitigating health crises while addressing ethical challenges and data privacy concerns. This comprehensive assessment offers valuable insights for future applications of AI in global health initiatives and pandemic preparedness.

In recent years, the advancement of multimodal large language models (MLLMs) has increasingly demonstrated their potential in medical data mining. However, the diversity and heterogeneity nature of medical images and radiology reports can pose significant challenges to the universality of data mining methods.

To address these challenges, a team led by Dr. Xin Zhang from the Institute of Medical Research, Northwestern Polytechnical University in Xi’an, China, systematically evaluated the performance of Gemini and GPT-series models across various medical tasks. Their findings validate the application potential of multimodal large models in the medical domain.

They proposed a program logic that can formally verify obstruction-freedom of practical implementations, as well as verify linearizability(a safety property), at the same time.

Although many susceptibility loci for IgA nephropathy (IgAN) have been identified, they only account for 11.0% of the overall IgAN variance. We performed a large genome-wide meta-analysis of IgAN in Han Chinese with 3616 cases and 10 417 controls to identify additional genetic loci of IgAN. Considering that inflammatory bowel disease (IBD) and asthma might share an etiology of dysregulated mucosal immunity with IgAN, we performed cross-trait integrative analysis by leveraging functional annotations of relevant cell type and the pleiotropic information from IBD and asthma. Among 8 669 456 imputed variants, we identified a novel locus at 4p14 containing the long noncoding RNA LOC101060498. Cell type enrichment analysis based on annotations suggested that PMA-I-stimulated CD4⁺CD25^–IL17⁺ Th17 cell was the most relevant cell type for IgAN, which highlights the essential role of Th17 pathway in the pathogenesis of IgAN. Furthermore, we identified six more novel loci associated with IgAN, which included three loci showing pleiotropic effects with IBD or asthma (2q35/PNKD, 6q25.2/SCAF8, and 22q11.21/UBE2L3) and three loci specific to IgAN (14q32.32/TRAF3, 16q22.2/TXNL4B, and 21q21.3/LINC00113) in the pleiotropic analysis. Our findings support the involvement of mucosal immunity, especially T cell immune response and IL-17 signal pathway, in the development of IgAN and shed light on further investigation of IgAN.

A recent study has introduced an innovative, label-free imaging approach that enhances the precision of Achilles tendon injury recovery evaluation. This method, using Mueller matrix polarimetry, allows researchers to capture detailed images of tendon tissue, providing key insights into the healing process and effectiveness of various treatments.

A groundbreaking study reveals the environmental and public health advantages of reed wetlands for sludge treatment, demonstrating their potential as an eco-friendly alternative to traditional wastewater management systems.

The authors collected a total of 2,430 inbred lines derived from elite commercial hybrids and 503 inbred lines from natural populations. Such a panel holds a broadly sourced and genetically diverse inbred population. Combining resequencing technology, population genetics analysis, and deep learning algorithms, they conducted a genetic diversity analysis and predicted key breeding traits for 2,933 maize inbred lines. This work offers novel insights into maize genetic improvement and lays a foundation for intelligent breeding design.