Ischemic stroke increases levels of the folate receptor and one-carbon enzymes in male and female brain tissue
Bibliographic record
Abstract
Stroke is the second most common cause of death worldwide and predominantly affects individuals over 65 years old. Its prevalence is projected to increase in parallel with the aging global population. Nutrition is a modifiable risk factor for ischemic stroke. Folates, B-vitamins and choline play a central role in one-carbon metabolism (1C), which is a key metabolic network that integrates nutritional signals with biosynthesis, redox homeostasis, epigenetics, regulation of cell proliferation, and stress resistance. Our research group has previously shown that deficiencies in 1C lead to worsened stroke outcomes using preclinical models. However, the impact of ischemic stroke on 1C enzymes in affected brain tissue remains unknown. The objective of this study is to investigate whether ischemic stroke contributes to a change in the levels of 1C enzymes after ischemic stroke in male and female patients. Cortical brain tissue sections from ischemic stroke patients and controls were stained for enzymes involved in 1C. All tissue was co-stained with neuronal nuclei (NeuN) and DAPI (4',6-diamidino-2-phenylindole). The colocalization of all three markers was evaluated by two individuals who were blinded to the experimental groups. Ischemic stroke increased neuronal levels of the folate receptor and 1C enzymes, methylenetetrahydrofolate reductase (MTHFR), thymidylate synthase (TS) and serine hydroxy methyltransferase (SHMT). In male stroke brain tissue was observed to have increased levels of MTHFR, TS, and SHMT. Female brain tissue had increases in the folate receptor and TS. The results suggest that ischemic stroke leads to increased demand of 1C and that there are some differences between males and females.
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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 | 0.001 | 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) | 0.004 | 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".