Cytokines, stress and depressive illness: brain‐immune interactions
Bibliographic record
Abstract
Cytokines, signaling molecules of the immune system, have been implicated as a contributing factor for mood disorders such as depression. Several lines of evidence supporting this contention are briefly reviewed and caveats are introduced. Essentially, a relationship between cytokines and depression is based on the findings that: 1) proinflammatory cytokines (interleukin-1, 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, 2) cytokines induce neuroendocrine and central neurotransmitter changes reminiscent of those implicated in depression, and these effects are exacerbated by stressors, 3) severe depressive illness is accompanied by signs of immune activation and by elevations of cytokine production or levels, and 4) immunotherapy, using interleukin-2 or interferon-alpha, promotes depressive symptoms that are attenuated by antidepressant treatment. It is argued that cytokine synthesis and release, elicited upon activation of the inflammatory response system, provoke neuroendocrine and brain neurotransmitter changes that are interpreted by the brain as being stressors, and contribute to the development of depression. Furthermore, such effects are subject to a sensitization effect so that a history of stressful experiences or cytokine activation augment the response to later challenges and hence the evolution of 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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".