Identifying iNOS and glycogen as biomarkers for degenerated cerebellar purkinje cells in autism spectrum disorder: Protective effects of erythropoietin and zinc sulfate
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a collective neurodevelopmental disorder affecting young children and accounting for 1% of the world's population. The cerebellum is the major part of the human brain affected by ASD and is associated with a substantial reduction in the number of Purkinje cells. An association between ASD and the expression of the nitrosative stress biomarker inducible nitric oxide synthase (iNOS), as well as glycogen deposition in damaged Purkinje cells, has not been previously reported in the medical literature. To explore this correlation, young rats were injected with propionic acid (PPA) (500 mg/kg) for 5 days (model group), while the protection groups were treated with either erythropoietin (EPO, 5,000 U/kg) or 2 mg/kg zinc sulfate immediately after the PPA injections. ASD-like features were developed in the model group, as evidenced by cerebellum damage (degeneration of Purkinje cells) and cerebellar dysfunction (behavioral impairment). This study documented the exclusive expression of iNOS in the degenerated Purkinje cells, along with glycogen deposition in these cells. Additionally, PPA significantly (p < 0.001) modulated cerebellar tissue levels of mammalian target of rapamycin (mTOR), gamma-aminobutyric acid (GABA), GABAA receptor, serotonin, the marker of neuronal loss (calbindin D28K), and social interaction deficit. Some of these parameters were differentially protected by EPO and zinc sulfate, with the former providing greater protection than zinc sulfate. Furthermore, a significant correlation between the iNOS score and these parameters associated with ASD was observed. These findings demonstrate the colocalization of iNOS and glycogen in the damaged Purkinje cells induced by ASD, along with the modulation of ASD parameters, which were protected by EPO and zinc sulfate treatments. Thus, these potential novel biomarkers may offer possible therapeutic targets for the treatment of ASD.
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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".