Gut microbes, ageing & organ function: a chameleon in modern biology?
Bibliographic record
Abstract
All species, including humans, are cohabited by a myriad of microbial species, which massively influences body function in a diet-, exercise- and age-dependent manner. The microbiome composition differs between individuals, partly due to the polymorphic immune system, as well as the environment, making the microbe-host interplay unique in each one of us. Ageing is a gradual loss of function in part due to reduced repair mechanisms and accumulation of tissue damage through mechanisms largely unknown. Accumulating evidence suggests that our indigenous microbes, a known major regulator of human physiology, are also connected to regulate the ageing process through signalling pathways and metabolites though the biological mechanisms are unknown. At an ageing meeting in Singapore in 2018, investigators discussed the current understanding of microbe regulation and its impact on healthy ageing. This review summarizes the highlights from the meeting and conveys some of the new ideas that emerged around gut microbes and the biology of ageing. While highly speculative, an idea emerged in which gut microbes constantly respond and evolve to environmental cues, as part of an ageing process, thus serving as a second messenger to support and attenuate organ decline in a diet-, gender- and age-dependent manner.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".