Glucose-responsive artificial promoter-mediated insulin gene transfer improves glucose control in diabetic mice
Bibliographic record
Abstract
AIM: To investigate the effect of insulin gene therapy using a glucose-responsive synthetic promoter in type 2 diabetic obese mice. METHODS: We employed a recently developed novel insulin gene therapy strategy using a synthetic promoter that regulates insulin gene expression in the liver in response to blood glucose level changes. We intravenously administered a recombinant adenovirus expressing furin-cleavable rat insulin under the control of the synthetic promoter (rAd-SP-rINSfur) into diabetic Lepr(db/db) mice. A recombinant adenovirus expressing β-galactosidase under the cytomegalovirus promoter was used as a control (rAd-CMV-βgal). Blood glucose levels and body weights were monitored for 50 d. Glucose and insulin tolerance tests were performed. Immunohistochemical staining was performed to investigate islet morphology and insulin content. RESULTS: Administration of rAd-SP-rINSfur lowered blood glucose levels and normoglycemia was maintained for 50 d, whereas the rAd-CMV-βgal control virus-injected mice remained hyperglycemic. Glucose tolerance tests showed that rAd-SP-rINSfur-treated mice cleared exogenous glucose from the blood more efficiently than control virus-injected mice at 4 wk [area under the curve (AUC): 21 508.80 ± 2248.18 vs 62 640.00 ± 5014.28, P < 0.01] and at 6 wk (AUC: 29 956.60 ± 1757.33 vs 60 016.60 ± 3794.47, P < 0.01). In addition, insulin sensitivity was also significantly improved in mice treated with rAd-SP-rINSfur compared with rAd-CMV-βgal-treated mice (AUC: 9150.17 ± 1007.78 vs 11 994.20 ± 474.40, P < 0.05). The islets from rAd-SP-rINSfur-injected mice appeared to be smaller and to contain a higher concentration of insulin than those from rAd-CMV-βgal-injected mice. CONCLUSION: Based on these results, we suggest that insulin gene therapy might be one therapeutic option for remission of type 2 diabetes.
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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".