Microbes in the Era of Circadian Medicine
Bibliographic record
Abstract
The organisms of most domains of life have adapted to circadian changes of the environment and regulate their behavior and physiology accordingly. A particular case of such paradigm is represented by some types of host-pathogen interaction during infection. Indeed, not only some hosts and pathogens are each endowed with their own circadian clock, but they are also influenced by the circadian changes of the other with profound consequences on the outcome of the infection. It comes that daily fluctuations in the availability of resources and the nature of the immune response, coupled with circadian changes of the pathogen, may influence microbial virulence, level of colonization and damage to the host, and alter the equilibrium between commensal and invading microorganisms. In the present review, we discuss the potential relevance of circadian rhythms in human bacterial and fungal pathogens, and the consequences of circadian changes of the host immune system and microbiome on the onset and development of infection. By looking from the perspective of the interplay between host and microbes circadian rhythms, these concepts are expected to change the way we approach human infections, not only by predicting the outcome of the host-pathogen interaction, but also by indicating the best time for intervention to potentiate the anti-microbial activities of the immune system and to weaken the pathogen when its susceptibility is higher.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".