Bibliographic record
Abstract
INTRODUCTION: Depression is associated with inflammation, Th1 and Th17 responses, oxidative and nitrosative stress (O&NS), autoimmune responses against neoantigenic determinants, and neuroprogression (i.e., neurodegeneration, impaired plasticity and reduced neurogenesis). These pathways involve increased monocytic activation and interleukin-1 (IL-1) levels. AREAS COVERED: This review will highlight the putative role of IL-1 in depression and the potential use of IL-1 signaling blockade as a treatment of depression. Electronic databases, i.e., Scopus, PUBMED and Google Scholar were employed using keywords: depression, depressive-like, interleukin-1, and interleukin-1 receptor antagonist (IL-1RA). EXPERT OPINION: Ample studies show that depression is accompanied by increased levels of IL-1 and IL-1RA, which attenuates the pro-inflammatory activities of IL-1. In some, but not all studies, antidepressant treatment decreased IL-1β levels. In translational models, IL-1β administration elicits depressive-like behaviors, neuroinflammation and neuroprogression, whereas treatment with IL-1RA yields antidepressant-like effects and attenuates neuroprogression. Anakinra, an IL-1RA, targets not only IL-1 signaling, but also Th1, Th17, O&NS and neuroprogressive pathways and therefore may be advanced to clinical Phase-II trials in depression due to medical conditions associated with an elevated IL-1/IL-1RA ratio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".