Life Sciences

This review systematically evaluates the methodological quality, clinical validity, and translational evidence of cutting-edge technologies in the precision management of gastrointestinal tumor-associated osteoporosis (GTO), summarizing their current applications in AI-assisted early screening and risk prediction, nano-enabled targeted bone protection, and multiomics-based exploration of the tumor-bone-gut axis, and discussing barriers to clinical translation such as limited AI generalizability, nanomedicine safety and manufacturing challenges, difficulties in multidimensional data integration and standardization, imperfect multidisciplinary collaboration, and ethical concerns, concluding that these technologies are expected to move GTO management from empirical practice toward precision m

This MIT Technology Review article surveys concerns voiced by AI company leaders and researchers that AI could aid the design, creation, and release of bioweapons, citing the 2022 case in which Collaborations Pharmaceuticals' AI "molecule generator" produced 40,000 molecules with potential as chemical warfare agents in under six hours and Anthropic's report acknowledging attempts to use its models to explore making chikungunya virus more transmissible and creating a more dangerous bird flu, while also presenting dissenting views from some Imperial College London biologists that AI tools are not yet good enough to fully develop bioweapons and that the greatest pandemic risk comes from already-circulating pathogens such as H5N1, and summarizing existing safeguards—DNA order screening, red-te

The work presents scHPGT, a single-cell Heterogeneous Prior-Guided Transformer that integrates unpaired RNA and chromatin accessibility profiles in a shared latent space using modality-specific encoders, a prior-guided cross-modal Transformer that constrains gene-peak attention with regulatory links, and a domain-adversarial objective, improving clustering agreement, label transfer and biological structure preservation across PBMC3k, mouse spleen, CITE-seq/ASAP-seq PBMC and PBMC10k benchmarks while avoiding forced correspondence of unmatched or condition-specific states in partial-overlap and condition-shift settings, with attention-derived links recovering regulatory relationships, marker-gene regulatory regions, transcription factor programs and regulatory activity profiles.

The study introduces OpenLipid, a large language model (LLM)-based workflow for targeted DIA lipidomics that builds assay libraries from DDA results and, in a zero-shot setting, directly evaluates extracted ion chromatograms (XICs) from DIA data to select target lipid peaks and produce human-readable rationales; across four human plasma and mouse feces datasets in positive and negative ionization modes it identified 55.3%/19.0% (plasma) and 71.8%/31.8% (feces) of library targets at 5% FDR, comparable overall to DIAMetAlyzer (57.8%/19.7%, 89.0%/17.8%) and substantially higher than untargeted MS-DIAL DIA (12.6%/0.0%, 47.5%/0.

This work introduces TRACEDD, a tool-first multi-agent framework in which large language models serve as the reasoning and orchestration layer while domain-validated computational tools supply structural, chemical, pharmacological, and synthetic predictions, and it demonstrates an end-to-end JAK2 workflow spanning target validation, structure retrieval or AlphaFold prediction, druggable pocket identification, reinforcement-learning-based de novo molecular generation, lead optimization, ADMET and bioactivity evaluation, literature evidence integration, and retrosynthesis planning, with each decision linked to explicit tool invocations and intermediate evidence.

Using the tabular foundation model TabPFN informed by a small library of living hydrogels, this work predicts macroscopic material properties of Escherichia coli-produced living hydrogels containing CsgA-based fibres fused to genetically encoded PEG-like biopolymers from genetic and process parameters, achieving the strongest prediction for storage modulus G' (R2 = 85.1%) on an independent validation set with a 48.0% RMSE reduction versus linear regression, and further enabling property-guided design to identify parameters for desired properties.

This work releases curated 2020-2021 Genomes to Fields imagery from 356 drone flights across 19 environments covering 1,180 maize hybrids, and uses functional principal components of vegetation index and weather trajectories together with genomic information for yield prediction and QTL mapping, finding that combined genomic and phenomic kernels raised held-out hybrid prediction to r = 0.501 when environments were represented in training and 0.408 when environments were also withheld, that accumulated growing degree days offered no consistent advantage over days after planting, that weather contributed modest task-dependent gains, and that NGRDI functional principal components repeatedly mapped to quantitative trait loci on chromosomes 3 and 7.

Using the multi-agent system ARES to analyze 1,552 TF binding datasets across 10 cell types and test competing mechanisms of TF-motif dependencies in specific cellular contexts against multi-omic data, the study found that inferred mechanisms converge on three operating routes—direct sequence recognition, protein-mediated recruitment or exclusion, and regulatory context—and that predictive motifs of target TF binding were read by their conventionally 'canonical' TFs in only one third of resolved dependencies, with motif similarity associated with shared regulatory region type but not shared transcriptional outcome, whereas reader identity was associated with both, thereby separating motif identity, reader identity, and regulatory output.

This review surveys the landscape of deep learning-driven protein design pipelines, discusses tailored applications in peptide, small molecule, binder, vaccine, and antibody design, argues that current confidence metrics for filtering and evaluating designs remain optimized for static protein interfaces and can fail on underrepresented or conformationally complex targets, proposes ensemble-based methods as a promising avenue for improving design success rates, and highlights emerging strategies such as fold-switching scaffolds and molecular glues realized through engineered cyclic peptides that expand the functional scope of designed proteins.