Autologous DNA mobilization and multiplication expedite natural products discovery from bacteria.

The transmission of antibiotic-resistance genes, comprising mobilization and relocation events, orchestrates the dissemination of antimicrobial resistance. Inspired by this evolutionarily successful paradigm, we developed ACTIMOT, a CRISPR-Cas9-based approach to unlock the vast chemical diversity concealed within bacterial genomes. ACTIMOT enables the efficient mobilization and relocation of large DNA fragments from the chromosome to replicative plasmids within the same bacterial cell.

Differences in Plasma Extracellular Vesicles of Different Origin in On-Pump Versus Off-Pump Cardiac Surgery.

Coronary artery bypass grafting (CABG) using cardiopulmonary bypass (CPB) causes a systemic inflammatory response that can worsen patient outcomes. Off-pump surgery has been associated with a reduced inflammatory response. The precise mechanisms and the role of extracellular vesicles (EVs) in this context are not fully understood.

Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics.

The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.

Guiding questions to avoid data leakage in biological machine learning applications.

Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons for this is data leakage, which can be seen as the illicit sharing of information between the training data and the test data, resulting in performance estimates that are far better than the performance observed in the intended application scenario.