CHIKVdb is a comprehensive genomic database developed to address limitations in existing resources for chikungunya virus (CHIKV) surveillance and outbreak response. It integrates 8,193 nucleotide and 10,637 protein sequences from 99 countries over 40 years, accompanied by standardized metadata. The platform features an interactive web interface with tools for phylogenetic analysis, source tracing, SNP identification, and genotype identification, streamlining workflows for public health and research applications. Global analyses reveal spatiotemporal heterogeneity in CHIKV transmission, highlighting the predominance of ECSA and ECSA-IOL genotypes and the central role of human and mosquito hosts. CHIKVdb enhances genomic surveillance by providing curated data and analytical capabilities, supporting efforts in pandemic preparedness and targeted control strategies. The database is freely accessible at https://nmdc.cn/gcpathogen/chikv.

A review paper by scientists at the University of Oxford highlights recent advancements in SMTE, including innovations in scaffold design, cell sourcing, usage of external physicochemical cues, and bioreactor technologies.

The review paper, published on May. 15, 2025 in the journal Cyborg and Bionic Systems, presented the emerging synergies between SMTE and robotics, focusing on the use of robotic systems to enhance bioreactor performance and the development of biohybrid devices integrating engineered muscle tissue.

A research paper by scientists at Duke University proposed a novel sharp-edge acoustofluidic platform designed for rapid and effective sample preparation, coupled with sensitive detection of specific sEV populations based on their surface markers.

The new research paper, published on July. 17 in the journal Cyborg and Bionic Systems, presented an acoustofluidic technology which enables highly flexible, specific, and efficient capture and detection of circulating extracellular vesicles (sEVs) from small sample volumes. Its portability, low cost, and ease of use make it an ideal tool for point-of-care detection of sEV surface markers, while its modular design allows for one-step, high-throughput capture and detection of diverse sEV populations

An international team of researchers has reviewed the latest advances in multimodal artificial intelligence (AI) for cardiovascular diseases (CVD), highlighting its superior diagnostic accuracy, risk prediction, and therapeutic guidance compared with traditional single-data approaches. The review outlines how integrating imaging, genomics, electronic health records, and wearable data into unified AI models can enable earlier diagnosis, personalized therapy, and continuous remote monitoring, heralding a new era of precision cardiology.