Antibiotic and Modulation of Microbiota
Bibliographic record
Abstract
Recently new insights on gut microbiota have revolutionized many concepts of the modern medicine. The alteration of microbiota, which is called dysbiosis, has been associated with an expanding list of diseases and conditions. The development of next-generation sequencing techniques allowed comprehensive analysis of gut microbiota composition without the limitations of classic culture methods. Furthermore, introduction of functional techniques such as metabolomics and proteomics allowed for integrated analysis thus obtaining more robust insights on microbiota functions in health and disease. These tools allow to address the role of factors able to modify the gut microbiota, the so called "microbiota influencers." These data are useful to explain the physiopathology of several disease and thus to identify new potential therapeutic targets. Among microbiota influencers, many studies focused on the impact of antibiotic administration on the gut microbiota, because of their widespread use. Notably, beside the known beneficial effect of antibiotic in treating infectious diseases, these drugs have shown detrimental effects on gut microbiota which, in turn, might have long-term consequences on the host. Finally, therapeutic modulation of gut microbiota, by means of selected antibiotics with eubiotic effects, probiotics and with fecal microbiota transplantation seems of great interest as it might be able to prevent or even revert antibiotic-induced dysbiosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".