Inflammation: opportunities for treatment stratification among individuals diagnosed with mood disorders
Bibliographic record
Abstract
Mood disorders continue to be a significant burden to those affected, resulting in significant illness-associated disability and premature mortality. In addition to mood disturbance, individuals also suffer from other transdiagnostic impairments (eg, anhedonia and cognitive impairment). Although there have been significant advancements in psychiatric treatment over the last few decades, treatment efficacy (eg, symptom remission, lack of functional recovery, and disease modification) continues to be an important limitation. Consequently, there is an urgent need to identify novel approaches capable of addressing the foregoing needs, providing the basis for the exploration of conceptual models and treatment opportunities that consider inflammation to be a key factor in mood disorder development. In part driven by metabolic comorbidities, a large proportion of individuals with mood disorders also have an imbalance in the inflammatory milieu. The aim of this review is to highlight evidence implicating inflammation in various effector systems in mood disorders, with a particular focus on the intercommunication with glutamatergic signaling, immune system signaling, as well as metabolic parameters (eg, L-methyl folate bioavailability). This article also briefly reviews novel and repurposed agents that are capable of targeting the innate immune inflammatory system and possibly correcting an abnormal immune/inflammatory milieu (eg, infliximab).
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".