The clock is ticking on schizophrenia: a study protocol for a translational study integrating phenotypic, genomic, microbiome and biomolecular data to overcome disability
Bibliographic record
Abstract
Background: Shared biological factors may play a role in both the cognitive deficits and the increased prevalence of metabolic syndrome observed in individuals with Schizophrenia (SCZ). These factors could entail disturbances in tryptophan (Trp) to both melatonin (MLT) and kynurenine (Kyn) metabolic pathways, as well as inflammation and alterations in the gut microbiome composition. Methods: The present research project aims to investigate this hypothesis by recruiting 170 SCZ patients from two different recruitment sites, assessing their cognitive functions and screening for the presence of metabolic syndrome. Additionally, we plan to assess the impact of a 3-month cognitive remediation therapy on 30 of these patients. We will analyze clinical data alongside serum biomarkers and gene expression related to the Trp- to MLT and Kyn metabolic pathways, markers of inflammatory and composition of the gut microbiome. The association between Trp-MLT-Kyn levels, expression levels of selected genes, inflammatory markers and clinical phenotypes will be analyses in the context of general linear models. Discussion: This project has the potential to identify some typical SCZ symptomatic clusters that will be more stringently associated with variations in the Trp-MLT-Kyn/inflammatory system and with a better response to cognitive remediation therapy. Moreover, in a future perspective, it may highlight a group of patients who may benefit from a pharmacological treatment aiming at reinstating the physiological Trp to MLT and Kyn system. Therefore, it has the potential to move research toward a personalized approach for SCZ management.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.026 |
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".