Bibliographic record
Abstract
Over the last two decades, advancements in sequencing technologies have significantly deepened our understanding of the human microbiome's complexity, leading to increased concerns about the detrimental effects of antibiotics on these intricate microbial ecosystems. Concurrently, the rise in antimicrobial resistance has intensified the focus on how beneficial microbes can be harnessed to treat diseases and improve health and offer potentially promising alternatives to traditional antibiotic treatments. Here, we provide a comprehensive overview of both established and emerging microbe-centric therapies, from probiotics to advanced microbial ecosystem therapeutics, examine the sophisticated ways in which microbes are used medicinally, and consider their impacts on microbiome homeostasis and health outcomes through a microbial ecology lens. In addition, we explore the concept of rewilding the human microbiome by reintroducing "missing microbes" from nonindustrialized societies and personalizing microbiome modulation to fit individual microbial profiles-highlighting several promising directions for future research. Ultimately, the advancements in sequencing technologies combined with innovative microbial therapies and personalized approaches herald a new era in medicine poised to address antibiotic resistance and improve health outcomes through targeted microbiome management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".