Omarigliptin inhibits brain cell ferroptosis after intracerebral hemorrhage
Bibliographic record
Abstract
Abstract Intracerebral hemorrhage (ICH) is a disastrous disease without effective treatment. An extensive body of evidence indicate that neuronal ferroptosis is a key contributor to neurological disfunctions after ICH. Omarigliptin, also known as MK3102, is an anti-diabetic drug that inhibits dipeptidyl peptidase (DPP4). Recently, MK3102 is reported to exhibit anti-ferroptosis and anti-oxidative effects in different pathological conditions. However, the anti-ferroptosis ability of MK3102 in ICH injury is unknown. Hemin was administrated to model ICH injury in cultured primary cortical neurons, and collagenase VII was used to induce ICH in C57BL/6 mice. MK3102 was administered after ICH. Cell Counting Kit-8 (CCK-8) was applied to detect cell viability. Neurological functions were assessed through the Focal deficits neurological scores and corner test. HE and TUNEL staining was applied to evaluate brain damage areas and cell death, respectively. Ferroptosis was evaluated in cultured neurons by fluorescent probe DCFH-DA, FerroOrange, Liperfluo and immunofluorescence of GPX4, AIFM2 and FACL4. Perls staining was performed to visualize Fe3+ deposition. Ferroptosis-related proteins in mouse brain were measured by immunohistochemistry and western blotting. MK3102 reduced the neurotoxicity of hemin in cultured primary cortical neurons. It improved neurological functions associated with a decrease in the number of dead neurons and the area of brain damage after ICH in mice. Moreover, MK3102 prominently upregulated glucagon-like peptide-1 receptor (GLP-1R) levels after ICH. In addition, the elevation of iron content, lipid peroxidation and FACL4 after ICH; and reduction of GPX4 and AIFM2; were mitigated by MK3102 in vitro and in vivo. The neuroprotective effect of MK3102 may be related to anti-ferroptosis by regulating GLP-1R after ICH 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) | 0.001 | 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 | 0.001 |
| 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".