Variation in chemokines plasma concentrations in primary care depressed patients associated with Internet-based cognitive-behavioral therapy
Bibliographic record
Abstract
How the presence of inflammation has repercussions for brain function is a topic of active research into depression. Signals released from immune system-related cells, including chemokines, might be indicative of active depression and can, hypothetically, serve as biomarkers of response to interventions, both pharmacological and psychological. The objective of this study is to analyze the peripheral plasma concentrations of CXCL12, CCL11, CX3CL1 and CCL2 in a cohort of depressed primary-care patients, as well as their evolution after an internet-based cognitive-behavioral intervention. The concentrations of those chemokines were measured in 66 primary-care patients with mild and moderate depression, before and after the intervention, as well as 60 controls, using multiplex immunoassays. Concentrations of CXCL12 and CCL2 were significantly higher in the clinical sample in comparison with controls. A stable multivariate discriminative model between both groups was found. Concentrations of all chemokines decreased after the internet-based psychological intervention. These findings support the implication of chemokines in depression, even in a sample of patients with mild and moderate severity. Furthermore, they demonstrate the need for further multidisciplinary research that confirms how biomarkers such as plasma chemokines can serve as a marker for depression and are sensitive to non-pharmacological interventions.
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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 | 0.000 | 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".