Increased telomerase activity in major depressive disorder with melancholic features: Possible role of pro-inflammatory cytokines and the brain-derived neurotrophic factor
Bibliographic record
Abstract
The biological mechanisms responsible for depression symptoms are not yet understood. For this reason, it is important to reveal the etiopathogenetic mechanisms in this disease. This study aims to compare the levels of pro-inflammatory cytokines, Brain-Derived Neurotrophic Factor (BDNF), and telomerase activity in patients with major depressive disorder (MDD) and healthy controls. Plasma BDNF, interleukin-6 (IL-6), IL-1beta, and Tumor Necrosis Factor-alpha (TNF-alpha) levels, and telomerase activity were measured in 39 patients with major depression and 39 healthy controls matched with patients in terms of age, gender, and education year. Plasma concentration of BDNF, IL-6 levels, and telomerase activity was significantly different between patients with MDD and healthy controls. Correlation analysis showed a positive trend between plasma BDNF levels and plasma IL-6 levels in patients with MDD with melancholic features. Furthermore, the path analysis results showed that the telomerase activity was indirectly affected by gender, IL-1β, IL-6, BDNF, and BMI, via the severity of depression and anxiety and MDD status as the mediators. Further studies are needed to examine the molecular mechanism of the telomerase activity and the role of BDNF and pro-inflammatory cytokines in the telomerase activation in MDD.
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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".