Bibliographic record
Abstract
The interplay between the brain and the immune system is crucial in maintaining psychological health. The disruptions in this bidirectional communication can contribute to chronic neuroinflammation. This paper reviews the literature on the crucial role of neuroinflammation in psychiatric disorders, particularly depression and anxiety. It highlights the association between proinflammatory cytokines and psychiatric disorders, advocating for anti-inflammatory treatments, including COX-2 inhibitors and hydroxytyrosol. This review explores the impact of early-life immune activation on psychiatric outcomes and identifies cytokines such as IFN-γ, IL-1β, and TNF-α as key contributors to the progression of depression. Furthermore, early exposure to inflammatory conditions has been linked to an increased risk of developing disorders like schizophrenia, emphasizing the long-term mental health implications of childhood trauma and early life stress (Nettis et al., 2019). The analysis underscores the importance of addressing early-life immune dysregulation and the development of integrated interventions that combine traditional therapies with anti-inflammatory treatments. Therefore, this paper asserts a fundamental relationship between neuroinflammatory factors and the development of psychiatric illnesses, proposing that exploring this relationship could revolutionize the treatment methods for these conditions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".