Potential of PKM2 as a drug target in mouse models with type 1 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: This study aimed to determine the effect of PKM2 knockout in STZ induced type 1 diabetes mellitus (T1D) mouse models and to explore the possible mechanism. METHOD: PKM2fl/fl C57BL/6 mouse was backcrossed with Ins-1cre C57BL/6 mouse to generate β-cell-specific PKM2 knockout mouse after tamoxifen administration. The expression level of PKM2 in pancreas tissues was detected by quantitative reverse-transcription polymerase chain reaction and western blot analysis. The blood glucose levels in STZ induced T1D mouse models were measured to validate the establishment of T1D models. The pathological changes of T1D mouse were examined by hematoxylin and eosin. The oxidative stress (OS) and inflammatory response in T1D mouse were determined by measuring the expression levels of malondialdehyde, superoxide dismutase, and 8-OHdG in pancreatic tissues and the serum levels of interleukin-6 and tumor necrosis factor-α. The ability to catabolize glucose was assessed through intraperitoneal glucose tolerance test and insulin tolerance test. RESULTS: β-cell-specific PKM2 knockout was successfully achieved in PKM2fl/flcre+ mouse. T1D mouse with PKM2 knockdown had decreased blood glucose level and suppressed cell apoptosis. PKM2 knockout in T1D mouse attenuated β cell injury. OS and inflammatory response in T1D mouse with PKM2 knockout were also suppressed compared with T1D mouse without PKM2 knockout. CONCLUSION: PKM2 knockout in T1D mouse can attenuate OS and inflammatory response as well as decrease blood glucose level, suggesting the potential of PKM2 as a drug target for T1D treatment.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".