A highly sensitive quantitative method of polysialic acid reveals its unique changes in brain aging and neuropsychiatric disorders
Bibliographic record
Abstract
Polysialic acid (polySia), a glycoepitope critical for neural development and plasticity, remains difficult to quantify owing to its structural complexity. Here, we present a highly sensitive sandwich enzyme-linked immunosorbent assay (ELISA) utilizing novel probes to measure polySia expression. Using this method, we quantified polySia levels in mouse brain samples across various developmental and aging stages. Notable age-related changes were observed, particularly in neuroplastic regions such as the hippocampus and olfactory bulb, where polySia levels increased at 12 months, potentially reflecting resilience mechanisms against brain aging. Elevated polySia levels in blood samples were also detected in both a schizophrenia mouse model and human patients, with a notable male preponderance. In contrast, no significant changes were observed in patients with chronic inflammatory demyelinating polyneuropathy. These findings, enabled by the novel probes, highlight a potential role for polySia in brain aging and neuropsychiatric disorders, offering new insights into developmental and disease mechanisms and supporting its utility as a diagnostic biomarker for brain impairments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".