Abnormal protein and mRNA expression of inflammatory cytokines in the prefrontal cortex of depressed individuals who died by suicide
Bibliographic record
Abstract
Background: Depression and stress are major risk factors for suicidal behaviour, and some studies show abnormalities of proinflammatory cytokines in the serum and cerebrospinal fluid (CSF) of depressed and suicidal patients. However, it is not clear if similar abnormalities of cytokines are present in the brain of suicidal and depressed patients. Methods: We therefore determined the mRNA (using realtime polymerase chain reaction) and protein (using enzyme-linked immunosorbent assay and Western Blot) expression levels of interleukin (IL)-1β, IL-6, tumour necrosis factor (TNF)-α, lymphotoxin A, lymphotoxin B, IL-8, IL-10 and IL-13 in the prefrontal cortex (PFC) obtained from 24 depressed individuals who died by suicide and 24 nonpsychiatric controls. Results: We observed that the mRNA and protein levels of IL-1β, IL-6, TNF-α, and lymphotoxin A were significantly increased, and levels of anti-inflammatory cytokine IL-10, and of IL-1 receptor antagonist (IL-1RA) were significantly decreased in the PFC of depressed individuals who died by suicide compared with controls. There were no significant differences in the protein and mRNA levels of IL-8 and IL-13 in the PFC. Limitations: The main limitation of this study is that some of the suicide group had been taking antidepressant medication at the time of death. Conclusion: Our results suggest that alterations of cytokines may be associated with the pathophysiology of depressed suicide and there may be an imbalance between pro- and anti-inflammatory cytokines in people who die by suicide. The causes of these increases in the brain of people who die by suicide, therefore, need to be investigated further.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".