An <i>in vitro</i> model maintaining taxon-specific functional activities of the gut microbiome
Bibliographic record
Abstract
Abstract The gut microbiome is a new target for therapeutics. In vitro high-throughput culture models could provide time-and-cost saving solutions to discover microbiome responses to drugs. Unfortunately, there has been no report of in vitro models capable of maintaining functional and compositional profiles resembling the in vivo gut microbiome. Here, we developed and validated a high-throughput culturing model named Mipro to maintain individuals’ microbiomes. The Mipro model quintupled viable bacteria count while maintained the functional and compositional profiles of individuals’ gut microbiomes. Comparison of taxon-specific functions between pre -and-post culture microbiomes showed Pearson’s correlation coefficient r of 0.83 ± 0.03. Moreover, the Mipro model also exhibited a high degree of in vitro – in vivo correlation (Pearson’s r of 0.68 ± 0.09) in microbial responses to metformin in mice fed a high-fat diet. Mipro provides a highly simulated gut microbiome for high-throughput investigation of drug-microbiome interactions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".