Bibliographic record
Abstract
Methylglyoxal (MG) is a reactive metabolite presents in all biological systems.The accumulation of MG in diabetic patients and animals has been long recognized.Recently, studies have shown that MG levels are elevated in hypertensive rats.However, the pathological effects of MG in diabetes and related insulin resistance syndrome such as obesity are currently unknown.In the present study, the role of MG in the pathogenesis of insulin resistance was investigated.First, it was observed that MG induced structural and functional changes of insulin.Incubation of human insulin with MG in vitro yielded MG-insulin adducts, as evidenced by additional peaks observed upon mass spectrometric (MS) analysis.Tandem MS analysis of insulin B-chain adducts confirmed attachment of MG at an arginine residue.[ 3 H]-2-deoxyglucose uptake ([ 3 H]-2-DOG) by 3T3-L1 adipocytes was significantly and concentration-dependently decreased after treatment with MG-insulin adducts, in comparison with the effect of native insulin at the same concentration.A significant decrease of glucose uptake induced by MG-insulin adducts was also observed in L8 skeletal muscle cells.Unlike native insulin, MG-insulin adducts did not inhibit insulin release from pancreatic β-cells.The degradation of MG-insulin by cultured liver cells was also decreased.In conclusion, MG modifies insulin by attaching to internal arginine residue in the β-chain of insulin.The formation of this MG-insulin adduct decreases insulin-mediated glucose uptake, impairs autocrine control of insulin secretion, and decreases insulin clearance.These structural and functional abnormalities of the insulin molecule may contribute to the pathogenesis of insulin resistance.Wu for providing me with a great research opportunity.Her invaluable guidance is a great help not only in the completion of this project but also in my future academic pursuits.I greatly appreciate the wonderful time of working with her.Her mastery of science and devotion to research set a role model to me, and I will be conscious of her example in my whole life.
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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 | 0.001 | 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".