Cytokines as a stressor: implications for depressive illness
Bibliographic record
Abstract
Stressful events have been implicated in the provocation of depressive illness. Inasmuch as immunological challenge, and particularly cytokine administration, engender neuroendocrine and central neurochemical changes reminiscent of those provoked by psychogenic stressors, it was suggested that immune activation may also contribute to affective illness. The present report provides a brief overview of the neurochemical sequelae of acute and repeated interleukin-1beta (IL-1beta), tumour necrosis factor-alpha (TNF-alpha) and IL-2 treatment, describes some of the synergisms associated with these treatments, as well as their potential interactions with psychogenic stressors. In addition, a discussion is provided concerning the fact that cytokines, like stressors, may have time-dependent proactive effects, so that re-exposure to the treatments provoke greatly augmented neurochemical changes (sensitization). Given that the effects of cytokines are evident within hypothalamic, as well as extrahypothalamic sites, including various limbic regions, it is suggested that cytokines may impact on emotional changes, including depression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".