Cytokines, stress and depressive illness: brain‐immune interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it