Republicans are trying to scapegoat the infectious disease expert, even as they have gutted healthcare amid outbreaks of measles, cyclosporiasis and Ebola

“Based on simple math, these outbreak responses—which require rapid, timely responses—are going to be greatly diminished,” the former CDC lab director tells WIRED.

Frontline medics describe extreme heat conditions they feel are unsafe and lacking in dignity for patients

Pips Accenture and NEC to bag decade-long deal for cops across England, Wales, and beyond

We would like to inform you about an incident involving UK Biobank data. We apologise to our participants for the concern this will cause, and we hope to provide reassurance by outlining the serious a...

The myth of open data, reproducibility, responsibility, and accountability in science, and your role in it

npj Antimicrobials and Resistance - Changes in antimicrobial resistance profiles of Escherichia coli and the metagenome on Dutch pig farms after antimicrobial usage interventions

Benchmarking metagenomic binning tools with simulated and real datasets reveals factors affecting genome recovery and provides practical guidelines for improving metagenome assembled genome reconstruc...

The human microbiome plays a critical role in health and disease, and its dynamic nature has made longitudinal sampling a key strategy for elucidating microbiome disease relationships. Although the gut microbiome generally stabilizes over time, a subset of samples frequently shows marked deviations from an individual baseline profile. We refer to these as abnormal samples. To analyze these abnormal samples, we developed a three stage workflow to identify and classify these abnormal samples to figure out the underlying causes of these abnormal samples. Moreover, we systematically investigated abnormal samples across 16 publicly available metagenomic datasets, comprising a total of 5,171 metagenomes. Our analysis revealed that abnormal samples are often the result of mislabeling during sample collection, processing, or sequencing. Of which, fecal samples from family are more likely mislabeled. We found evidence of mislabeling in 75% of longitudinal datasets, involving up to dozens of samples per study, and in 25% of randomly selected cross sectional datasets. Additional factors such as disease status (e.g., inflammatory bowel disease), sampling intervals, and sampling density may also contribute to sample abnormalities owing to true biological variations. These findings highlight that mislabeling is a common yet underrecognized issue in microbiome research. Our work underscores the importance of identifying and correcting abnormal samples to ensure data integrity in microbiome studies and provides a practical solution for quality control in large scale metagenomic datasets. ### Competing Interest Statement The authors have declared no competing interest. National Key R&D Program of China, 2024YFC3405800 General Program of the Natural Science Foundation of Fujian Province, 2023Y9116, 2024J01577 Government of Fuqing city, 2019B003 High-level Talents Research Start-up Project of Fujian Medical University, XRCZX2020037, XRCZX2022001, XRCZX2023030, XCRZX2023004, XRCZX2023005

Scientists found an unexpected viral hitchhiker lurking inside a common gut bacterium – and it was twice as prevalent in people with colorectal cancer.