N-acetyl-L-cysteine treatment reduces beta-cell oxidative stress and pancreatic stellate cell activity in a high fat diet-induced diabetic mouse model
Bibliographic record
Abstract
Obesity plays a major role in type II diabetes (T2DM) progression because it applies metabolic and oxidative stress resulting in dysfunctional beta-cells and activation of intra-islet pancreatic stellate cells (PaSCs) which cause islet fibrosis. Administration of antioxidant N-acetyl-L-cysteine (NAC) in vivo improves metabolic outcomes in diet-induced obese diabetic mice, and in vitro inhibits PaSCs activation. However, the effects of NAC on diabetic islets in vivo are unknown. This study examined if dosage and length of NAC treatment in HFD-induced diabetic mice effect metabolic outcomes associated with maintaining healthy beta-cells and quiescent PaSCs, in vivo. Male C57BL/6N mice were fed normal chow (ND) or high-fat (HFD) diet up to 30 weeks. NAC was administered in drinking water to HFD mice in preventative treatment (HFDpNAC) for 23 weeks or intervention treatment for 10 (HFDiNAC) or 18 (HFDiNAC+) weeks, respectively. HFDpNAC and HFDiNAC+, but not HFDiNAC, mice showed significantly improved glucose tolerance and insulin sensitivity. Hyperinsulinemia led by beta-cell overcompensation in HFD mice was significantly rescued in NAC treated mice. A reduction of beta-cell nuclear Pdx-1 localization in HFD mice was significantly improved in NAC treated islets along with significantly reduced beta-cell oxidative stress. HFD-induced intra-islet PaSCs activation, labeled by αSMA, was significantly diminished in NAC treated mice along with lesser intra-islet collagen deposition. This study determined that efficiency of NAC treatment is beneficial at maintaining healthy beta-cells and quiescent intra-islet PaSCs in HFD-induced obese T2DM mouse model. These findings highlight an adjuvant therapeutic potential in NAC for controlling T2DM progression in humans.
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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 | 0.001 | 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".