UPEI-100, a conjugate of lipoic acid and apocynin, mediates neuroprotection in a rat model of ischemia/reperfusion
Bibliographic record
Abstract
Previous work in our laboratory has provided evidence that preadministration of apocynin and lipoic acid at subthreshold levels for neuroprotection enhanced the neuroprotective capacity when injected in combination. Therefore, the present investigation was designed to determine whether a co-drug consisting of lipoic acid and apocynin functional groups bound by a covalent bond, named UPEI-100, is capable of similar efficacy using a rodent model of stroke. Male rats were anesthetized with Inactin (100 mg/kg iv), and the middle cerebral artery was occluded for 6 h or allowed to reperfuse for 5.5 h following a 30-min occlusion (ischemia/reperfusion, I/R). Preadministration of UPEI-100 dose-dependently decreased infarct volume in the I/R model (P < 0.05), but not in the middle cerebral artery occlusion model of stroke. Using the optimal dose, we then injected UPEI-100 during the stroke or at several time points during reperfusion, and significant neuroprotection was observed when UPEI-100 was administered up to 90 min following the start of reperfusion (P < 0.05). A time course for this neuroprotective effect showed that UPEI-100 resulted in a decrease in infarct volume following 2 h of reperfusion compared with vehicle. The time course of this neuroprotective effect was also used to study several mediators along the antioxidant pathway and showed that UPEI-100 increased the level of mitochondrial superoxide dismutase and oxidized glutathione and decreased a marker of lipid peroxidation due to oxidative stress (HNE-His adduct formation). Taken together, the data suggest that UPEI-100 may utilize similar pathways to those observed for the two parent compounds; however, it may also act through a different mechanism of action.
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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) | 0.001 | 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".