Cytokines as a Precipitant of Depressive Illness: Animal and Human Studies
Bibliographic record
Abstract
Cytokines whose primary function is that of acting as signaling molecules of the immune system, have been implicated in the provocation or exacerbation of mood disorders such as depression. This position has been supported by several lines of evidence; (1) proinflammatory cytokines (interleukin-1beta, interleukin-6, tumor necrosis factor-alpha) and bacterial endotoxins elicit sickness behaviors (e.g., fatigue, soporific effects) and symptoms of anxiety/depression that may be attenuated by chronic antidepressant treatment. Interleukin-2 (IL-2) induces less profound sickness, but elicits anhedonia, a key symptom of depression; (2) neuroendocrine and central neurotransmitter changes, reminiscent of those implicated in depression, may be elicited by some of these cytokines, and these effects are exacerbated by stressors; (3) severe depressive illness is accompanied by elevations of cytokine production or levels, although these effects are not necessarily attenuated with antidepressant medication; and (4) immunotherapy, using IL-2 or IFN-alpha, promote depressive symptoms that are attenuated by antidepressant treatment. It is proposed that chronic cytokine elevations engender neuroendocrine and brain neurotransmitter changes that are interpreted by the brain as being stressors, and contribute to the development of depression. Further, the effects of the cytokine treatments may act synergistically with stressors, and cytokines may provoke a sensitization effect so that the effects of later stressor experiences are exacerbated.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".