GDNF and miRNA-29a as biomarkers in the first episode of psychosis: uncovering associations with psychosocial factors
Bibliographic record
Abstract
Aim: Schizophrenia involves complex interactions between biological and environmental factors, including childhood trauma, cognitive impairments, and premorbid adjustment. Predicting its severity and progression remains challenging. Biomarkers like glial cell line-derived neurotrophic factor (GDNF) and miRNA-29a may bridge biological and environmental aspects. The goal was to explore the connections between miRNAs and neural proteins and cognitive functioning, childhood trauma, and premorbid adjustment in the first episode of psychosis (FEP). Method: This study included 19 FEP patients who underwent clinical evaluation with: the Childhood Trauma Questionnaire (CTQ), the Premorbid Adjustment Scale (PAS), the Positive and Negative Syndrome Scale (PANSS), and the Montreal Cognitive Assessment Scale (MoCA). Multiplex assays for plasma proteins were conducted with Luminex xMAP technology. Additionally, miRNA levels were quantitatively determined through RNA extraction, cDNA synthesis, and RT-qPCR on a 7500 Fast Real-Time PCR System. Results: Among miRNAs, only miR-29a-3p exhibited a significant correlation with PAS-C scores (r = -0.513, p = 0.025) and cognitive improvement (r = -0.505, p = 0.033). Among the analyzed proteins, only GDNF showed correlations with MoCA scores at the baseline and after 3 months (r = 0.533, p = 0.0189 and r = 0.598, p = 0.007), cognitive improvement (r = 0.511, p = 0.025), and CTQ subtests. MIF concentrations correlated with the PAS-C subscale (r = -0.5670, p = 0.011). Conclusion: GDNF and miR-29a-3p are promising as biomarkers for understanding and addressing cognitive deficits in psychosis. This study links miRNA and MIF to premorbid adjustment and reveals GDNF's unique role in connection with childhood trauma.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".