Cascading effects of stressors and inflammatory immune system activation: implications for major depressive disorder
Bibliographic record
Abstract
Activation of the inflammatory immune system provokes numerous neuroendocrine and neurotransmitter changes, many of which are similar to those provoked by physical or psychological stressors. These findings, among others, have led to the suggestion that the brain translates immune activation much as if it were a stressor. In this review, I provide synopses of the effects of traditional stressors on the release of corticotropin-releasing hormones at hypothalamic and extrahypothalamic sites, variations of serotonin and its receptors and changes of brain-derived neurotrophic factor (BDNF). These effects are similar to those elicited by activation of the inflammatory immune system, particularly the impact of the immune-signalling molecules interleukin-1 beta, interleukin-6, tumour necrosis factor-alpha and interferon-alpha on neuroendocrine, neurotransmitter and BDNF function. In addition, it is reported that stressors and cytokines may synergistically influence biological and behavioural processes and that these treatments may have long-term ramifications through the sensitization of processes associated with stress responses. Finally, I present an overview of the depressogenic actions of these cytokines in rodent models and in humans, and I provide provisional suggestions (and caveats) about the mechanisms by which cytokines and stressors might culminate in major depressive disorder.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".