Postmortem evidence of cerebral inflammation in schizophrenia: a systematic review
Bibliographic record
Abstract
Schizophrenia is a psychiatric disorder which has a lifetime prevalence of ~1%. Multiple candidate mechanisms have been proposed in the pathogenesis of schizophrenia. One such mechanism is the involvement of neuroinflammation. Clinical studies, including neuroimaging, peripheral biomarkers and randomized control trials, have suggested the presence of neuroinflammation in schizophrenia. Many studies have also measured markers of neuroinflammation in postmortem brain samples from schizophrenia patients. The objective of this study was to conduct a systematic search of the literature on neuroinflammation in postmortem brains of schizophrenia patients indexed in MEDLINE, Embase and PsycINFO. Databases were searched up until 20th March 2016 for articles published on postmortem brains in schizophrenia evaluating microglia, astrocytes, glia, cytokines, the arachidonic cascade, substance P and other markers of neuroinflammation. Two independent reviewers extracted the data. Out of 5385 articles yielded by the search, 119 articles were identified that measured neuroinflammatory markers in schizophrenic postmortem brains. Glial fibrillary acidic protein expression was elevated, lower or unchanged in 6, 6 and 21 studies, respectively, and similar results were obtained for glial cell densities. On the other hand, microglial markers were increased, lower or unchanged in schizophrenia in 11, 3 and 8 studies, respectively. Results were variable across all other markers, but SERPINA3 and IFITM were consistently increased in 4 and 5 studies, respectively. Despite the variability, some studies evaluating neuroinflammation in postmortem brains in schizophrenia suggest an increase in microglial activity and other markers such as SERPINA3 and IFITM. Variability across studies is partially explained by multiple factors including brain region evaluated, source of the brain, diagnosis, age at time of death, age of onset and the presence of suicide victims in the cohort.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".