Neuroinflammation and kynurenines in schizophrenia: Impact on cognition depending on cognitive functioning and modulatory properties in relation to cognitive remediation and aerobic exercise
Bibliographic record
Abstract
Background: In the last decade, the kynurenine pathway (KP) has gained attention in the pathogenesis of cognitive impairment in schizophrenia being at the croassroad between neuroinflammation and glutamatergic and cholinergic neurotransmission. However, clinical findings are scarse and conflicting, and the specific contributions of these two systems to the neurobiology of cognitive symptoms are far from being elucidated. Furthermore, little is known about the molecular underpinnings of non-pharmacological interventions for cognitive improvement, including rehabilitation strategies. Methods: The current study examined 72 patients with schizophrenia, divided in two clusters depending on the severity of the cognitive impairment, with the aim to evaluate the impact of inflammatory biomarkers and KP metabolites depending on cognitive functioning. Moreover, we studied their possible link to the cognitive outcome in relation to sessions of cognitive remediation therapy (CRT) and aerobic exercise (AE) in a longitudinal arm of 42 patients. Results: Neuroinflammation appeared to exert a more pronounced influence on cognition in patients exhibiting a higher cognitive functioning, contrasting with the activation of the KP, which had a greater impact on individuals with a lower cognitive profile. Cognitive improvements after the treatments were negatively predicted by levels of TNF-α and positively predicted by the 3-hydroxykynurenine (3-HK)/kynurenine (KYN) ratio, an index of the kynurenine-3-monooxygenase (KMO) enzyme activity. Conclusion: Overall, these findings add novel evidence on the biological underpinnings of cognitive impairment in schizophrenia pointing at a differential role of neuroinflammation and KP metabolites in inducing cognitive deficits depending on the cognitive reserve and predicting outcomes after rehabilitation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".