The common inflammatory etiology of depression and cognitive impairment: a therapeutic target
Bibliographic record
Abstract
Chronic inflammation has been shown to contribute to the development of a wide variety of disorders by means of a number of proposed mechanisms. Depression and cognitive impairment are two such disorders which may share a closely linked inflammatory etiology. The ability of inflammatory mediators to alter the activity of enzymes, from key metabolic pathways, may help explain the connection between these disorders. The chronic up-regulation of the kynurenine pathway results in an imbalance in critical neuroactive compounds involving the reduction of tryptophan and elevation of tryptophan metabolites. Such imbalances have established implications in both depression and cognitive impairment. This may implicate the immune system as a potential therapeutic target in the treatment of these disorders. The most common treatment modalities currently utilized, involve drug interventions which act on downstream targets. Such treatments help to reestablish protein balances, but fail to treat the inflammatory basis of the disorder. The use of anti-inflammatory interventions, such as regular exercise, may therefore, contribute to the effectiveness of current drug interventions in the treatment of both depression and cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".