Partridgeberry polyphenols protect rat primary cortical neurons from oxygen–glucose deprivation–reperfusion-induced injury via suppression of inflammatory adipokines and regulation of HIF-1α and PPARγ
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to investigate the neuroprotective ability of partridgeberry polyphenols in rat primary cortical neurons against oxygen-glucose deprivation/reperfusion (OGD/R) injury in vitro and explore the underlying therapeutic mechanism(s). METHODS: The OGD/R injury was induced in rat primary cortical neurons by incubation with deoxygenated glucose-free medium in a hypoxia chamber. RESULTS: The strongest activity in this regard was exhibited by partridgeberry-derived PPF2 and PPF3, i.e. the flavan-3-ol- and flavonol-rich polyphenol fractions of partridgeberry (P ≤ 0.05). Moreover, partridgeberry polyphenol pre-treatment reduced the membrane damage in primary neurons, as measured by the lactose dehydrogenase (LDH) release assay (P ≤ 0.05). Furthermore, PPF2 and PPF3 pre-treatment (100 µg ml(-1)) for 24 hours, before OGD/R, resulted in the strongest suppression of interleukin (IL)-6 and tumor necrosis factor-α induction by OGD/R injury, compared with the control group (P ≤ 0.05). Additionally, the protein levels of hypoxia-inducible factor (HIF-1α) and PPARγ, quantified by ELISA presented a significant modulation following PPFs treatment (100 µg ml(-1)), favorably toward neuroprotection, compared with the respective controls after OGD/R injury in vitro (P ≤ 0.05). CONCLUSION: In summary, partridgeberry polyphenols at concentrations of 1-100 µg ml(-1), significantly induced a decline in OGD/R injury-triggered apoptosis in vitro, suppressed the inflammatory biomarkers in primary neurons, and modulated the activity of HIF-1α and proliferator-activated receptor gamma (PPARγ) following hypoxic injury.
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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) | 0.000 | 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".