Engineers and scientists, as well as artists, have long been inspired by the beauty and functionality of nature’s designs. Japan designed high-speed trains to cut through the air as smoothly as the kingfisher cuts through water, for example, but useful designs can also be found at a microscopic level. The study of biology in combination with materials science is called biomateriomics. An Italian research team sees great potential in the application of generative artificial intelligence to this already interdisciplinary field. They have described this potential, and the associated limitations and challenges, in an open access review article titled “Generative Artificial Intelligence for Advancing Discovery and Design in Biomateriomics,” published May 1 in Intelligent Computing, a Science Partner Journal.

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

The dairy industry has been plagued by a persistent global problem for decades – bacterial infection of cow udders that significantly reduces milk production. Antibiotics have been used to treat the infection, called bovine mastitis, but there is rising antibiotic resistance and concerns around milk contamination from antibiotic residues. Now, a team of international researchers has developed alternatives to antibiotics that prevent infection through a novel mechanism they discovered. These alternatives have attracted interest from several agricultural companies in Australia, Belgium, Malaysia and New Zealand seeking substitutes that are safer and more environmentally friendly than existing compounds in preventing bovine mastitis. The scientists were led by Nanyang Technological University, Singapore (NTU Singapore), in collaboration with the Antimicrobial Resistance Interdisciplinary Research Group at the Singapore-MIT Alliance for Research and Technology (SMART), Massachusetts Institute of Technology’s (MIT) research enterprise in Singapore.