Plasma Cytokine Level and Influencing Factors of Depression in Stable Schizophrenia Patients
Bibliographic record
Abstract
Background A large body of literature suggests that plasma cytokine levels are associated with symptoms in patients with schizophrenia, but the relationship of plasma cytokines with depressive symptoms, which often occur in the late stage of schizophrenia, still needs to be explored. Objective To explore the relationship between plasma cytokine levels and depressive symptoms in schizophrenic patients. Methods Patients with stable schizophrenia were selected from Department of Psychiatry of three hospitals (Chaohu Hospital Affiliated to Anhui Medical University, Hefei Fourth People's Hospital, and Maanshan Fourth People's Hospital) from May to December 2018. The Calgary Depression Scale for Schizophrenia (CDSS) was used to evaluate the depressive symptoms, and the total score of CDSS≥5 was defined as depression. The plasma levels of interleukin (IL) -1β, IL-2, IL-6 and IL-17A were detected by flowcytometry. Spearman rank correlation analysis and multiple linear regression analysis were used to analyze the relationship between plasma cytokine and depression in stable schizophrenia. Results A total of 111 patients with stable schizophrenia were included, with depression prevalence of 28.83% (32/111). Spearman rank correlation analysis showed that the total score of CDSS was positively correlated with the level of plasma IL-1β, IL-2 or IL-17A (rs=0.507, 0.466, 0.374, P<0.05). Multiple linear regression analysis showed that higher level of plasma IL-17A was associated with decreased of risk of depression in stable schizophrenia (B=-0.125, P<0.05), and longer duration of schizophrenia was associated with increased of risk of depression in stable schizophrenia (B=0.343, P<0.05) . Conclusion Higher level of IL-17A and longer duration of schizophrenia may be correlated with depression in stable schizophrenia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".