RapidAIM: A culture- and metaproteomics-based Rapid Assay of Individual Microbiome responses to drugs
Bibliographic record
Abstract
Abstract The gut microbiome has been associated with a growing list of diseases. Drugs and other compounds can affect the microbiome, but our understanding of drug-induced changes in individual microbiomes is limited due to a lack of rapid and effective high-throughput assay methods. We developed an approach named Rapid Assay of Individual Microbiome (RapidAIM) to screen xenobiotics against individual microbiomes. RapidAIM was evaluated by testing 43 compounds against five individual microbiomes using a metaproteomic approach. We show that our workflow enables quantitative profiling of the microbiome. The tested compounds significantly affected overall microbiome abundance, microbiome composition and functional pathways at multiple taxonomic levels. The microbiome responses to berberine, metformin, diclofenac, fructooligosaccharide and most antibiotics were consistent among most individuals. Interestingly, most of our tested NSAIDs, statins, and histamine-2 blockers induced strong and individually distinct responses. Our workflow offers an effective solution to systematically study the effects of many different compounds on individual microbiomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".