Unraveiling the correlation among neurodevelopmental and inflammatory biomarkers in patients with chronic schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Nuclear distribution element like-1 (Ndel1) is a cytosolic oligopeptidase, which was suggested as a potential biomarker of aberrant neurodevelopment and early stage of schizophrenia (SCZ). The involvement of Ndel1 in neurite outgrowth, neuronal migration and neurodevelopment was demonstrated. Moreover, Ndel1 cleaves neuropeptides, including the endogenous antipsychotic peptide neurotensin, and lower Ndel1 activity was reported in SCZ patients compared with healthy controls (HCs). Changes in brain-derived neurotrophic factor (BDNF) and inflammatory cytokines levels were also implicated in SCZ. OBJECTIVE: This preliminary study aimed to investigate the interactions between these immune and neurodevelopmental/neurotrophic biomarkers, namely BDNF and the recently identified SCZ biomarker Ndel1. RESULTS: = 29). Interestingly, significant correlation between Ndel1 activity and IL-4 levels was observed in SCZ, while no correlation with any other evaluated interleukins (namely IL-2, IL-8, IL-10 and IL-17A) or BDNF levels was noticed. CONCLUSION: Although this hypothesis needs to be further explored for a better understanding of the mechanisms by which these altered pathways are associated to each other in SCZ, we suggest that Ndel1 and the inflammatory marker IL-4 are directly correlated.
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) | 0.002 | 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".