Clockwork intruders: Do parasites manipulate their hostsʼ circadian rhythms?
Bibliographic record
Abstract
Most organisms have developed circadian clocks to adapt to 24-hour cycles in the environment. These clocks have become crucial for modulating and synchronizing complex behavioral and biological processes. A number of parasites seem to have evolved to take advantage of their hosts' circadian rhythms to favor their own infection and survival. Some species, such as Microphallus sp. and Trypanosoma cruzi, can alter the patterns of locomotor behavior of infected intermediate hosts, which can promote transmission to a subsequent primary host. Some fungi of the genera Ophiocordyceps and Entomophthora, as well as hairworms (Nematomorpha), elicit complex behaviors that promote their host's death at a time and place that optimizes continuation of the parasite's life-cycle. At least in some cases, a proposed mechanism might involve a change in the expression of clock-controlled genes. Lastly, some disease-causing protozoan parasites of the genera Trypanosoma, Plasmodium, and Leishmania induce changes in the circadian rhythms of their primary hosts upon infection. Some of these changes may be attributed to circadian alterations resulting from the host's inflammatory response to the infection or other unexplored responses or adaptations to the illness. Thus, a distinction must be made between manipulation of the parasite and response of the host when studying these alterations in the future. Parasitic manipulation of circadian rhythms, which vastly modulates behavior and physiology, is an essential issue that has been relatively understudied. A deeper understanding of this phenomenon could lead to the development of novel therapeutic approaches for the diseases that these parasites convey.
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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".