Parasitic Aphrodisiacs: Manipulation of the Hosts' Behavioral Defenses by Sexually Transmitted Parasites
Bibliographic record
Abstract
Animals have a number of behavioral defenses against infection. For example, they typically avoid sick conspecifics, especially during mating. Most animals also alter their behavior after infection and thereby promote recovery (i.e., sickness behavior). For example, sick animals typically reduce the performance of energetically demanding behaviors, such as sexual behavior. Finally, some animals can increase their reproductive output when they face a life-threatening immune challenge (i.e., terminal reproductive investment). All of these behavioral responses probably rely on immune/neural communication signals for their initiation. Unfortunately, this communication channel is prone to manipulation by parasites. In the case of sexually transmitted infections (STIs), these parasites/pathogens must subvert some of these behavioral defenses for successful transmission. There is evidence that STIs suppress systemic signals of immune activation (e.g., pro-inflammatory cytokines). This manipulation is probably important for the suppression of sickness behavior and other behavioral defenses, as well as for the prevention of attack by the host's immune system. For example, the cricket, Gryllus texensis, is infected with an STI, the iridovirus IIV-6/CrIV. The virus attacks the immune system, which suffers a dramatic decline in its ability to make proteins important for immune function. This attack also hampers the ability of the immune system to activate sickness behavior. Infected crickets cannot express sickness behavior, even when challenged with heat-killed bacteria. Understanding how STIs suppress sickness behavior in humans and other animals will significantly advance the field of psychoneuroimmunology and could also provide practical benefits.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".