Bugs improve nerve regeneration: fasting-induced, microbiome-derived metabolite enhances peripheral nerve regeneration
Bibliographic record
Abstract
In a recent study published in Nature , Serger et al. connected intermittent fasting (IF) to gut microbiome alterations and enhanced peripheral nerve regeneration following injury. 1 Fasting has been purported to have neuroregenerative effects, but the underlying mechanisms remained unclear. The authors found that IF-induced elevation of IPA (a microbiome-derived metabolite) promotes neutrophil infiltration into the dorsal root ganglia (DRG), which enhances the regeneration of sciatic nerve fibers (Fig. 1 ). Fig. 1 Mice receiving food ad libitum (AL) or as an intermittent fasting (IF) diet were subjected to sciatic nerve crush injury. IF led to enhanced nerve regeneration compared to AL feeding. IF was also associated with elevated serum levels of indole-3-propionic acid (IPA) and IPA-producing gram-positive bacteria in the gut. The IF-induced enhanced nerve regeneration was recapitulated with fecal matter transplants from IF mice to AL mice, gut recolonization with Clostridium sporogenes , an IPA-producing bacteria, and systemic IPA delivery. Systemic IPA delivery induced dorsal root ganglia neutrophil infiltration, which was required for IPA enhancement of nerve regeneration. The figure was created with biorender Full size image
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".