Hyperglycemia Enhances DNA Fragmentation after Transient Cerebral Ischemia
Bibliographic record
Abstract
Previous histopathologic results have suggested that one mechanism whereby hyperglycemia (HG) leads to exaggerated ischemic damage involves fragmentation of DNA. DNA fragmentation in normoglycemia (NG) and HG rats subjected to 30 minutes of forebrain ischemia was studied by terminal deoxynucleotidyl transferase mediated DNA nick-labeling (TUNEL) staining, by pulse-field gel electrophoresis (PFGE), and by ligation-mediated polymerase chain reaction (LM-PCR). High molecular weight DNA fragments were detected by PFGE, whereas low molecular weight DNA fragments were detected using LM-PCR techniques. The LM-PCR procedure was performed on DNA from test samples with blunt (without Klenow polymerase) and 3'-recessed ends (with Klenow polymerase). In addition, cytochrome c release and caspase-3 activation were studied by immunocytochemistry. Results show that HG causes cytochrome c release, activates caspase-3, and exacerbates DNA fragments induced by ischemia. Thus, in HG rats, but not in control or NGs, TUNEL-stained cells were found in the cingulate cortex, neocortex, thalamus, and dorsolateral crest of the striatum, where neuronal death was observed by conventional histopathology, and where both cytosolic cytochrome c and active caspase-3 were detected by confocal microscopy. In the neocortex, both blunt-ended and stagger-ended fragments were detected in HG, but not in NG rats. Electron microscopy (EM) analysis was performed in the cingulate cortex, where numerous TUNEL-positive neurons were observed. Although DNA fragmentation was detected by TUNEL staining and electrophoresis techniques, EM analysis failed to indicate apoptotic cell death. It is concluded that HG triggers a cell death pathway and exacerbates DNA fragmentation induced by ischemia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".