Bibliographic record
Abstract
Inflammatory bowel diseases (IBD) is severe inflammation of the gastrointestinal tract. This can lead to a breakdown of mucosal barriers, causing dissemination of commensal bacteria throughout the body. To better understand bacterial translocation during IBD, aim to develop a fluorescent microbiota in mice that we can interrogate using live imaging techniques. Our preliminary experiments depleted commensals using broad-spectrum antibiotics, and replaced these microbiota with a fluorescent E. coli strain. The length of time that E.coli stays in the mice gut were monitored. We show that E. coli can persist in the ‘germ-free’ mouse gut for at least 21 days; control mice lose all added E. coli by 8-14 days. The establishment of the E. coli colony suggests this could be a reasonable model to study bacterial translocation. We are currently going to treat the colonized mice with DSS to induce colitis, and then to study translocation of E. coli by intravital microscopy. Considering E. coli is only a fraction of the normal microbiota and perhaps not a relevant model, future work aims at making a fluorescent microbiota consisting of multiple endogenous murine microbes. This will entail the use of a bacterial conjugation system capable of ubiquitously transforming many microbial species.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".