Limitation of pro- and anti-inflammatory cytokine analysis to discriminate biological stress effects in patients suffering from chronic psychological distress
Bibliographic record
Abstract
BACKGROUND: Research endeavors aiming to evaluate the effect of prolonged psychological distress on the immune system have been pursed over the past decades. Due to the complexity of the two systems involved, the mental and immune status, a large number of questions still remains to be addressed. AIM: In the present study, we aimed to test if chronic distress is associated with pro- and anti-inflammatory cytokine levels in a well-defined study cohort. METHODS: We recruited 42 inpatients suffering from post-traumatic embitterment disorder (PTED), a condition that has been demonstrated to cause intense and persistent psychological distress. Study participants completed established questionnaires to evaluate stress perception, depression and quality of life before and after psychotherapy, aiming to improve stress coping. Venous blood samples to detect serum levels of pro- and anti-inflammatory cytokines [interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ] were obtained pre- and post-treatment. RESULTS: The psychological assessments showed an increase of quality of life, a decrease of perceived stress and depressive symptoms, between the two groups. These findings are not associated with significant alterations of the cytokine levels before and after treatment. CONCLUSIONS: In our study, the psychological treatment of inpatients suffering from chronic psychological distress does not result in changes in cytokine levels. Further research with a broader analysis of immune markers and enhanced detection methods may be required to unveil psycho-immunological association in PTED patients.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".