Long‐term blood glucose control via glucose‐activated transcriptional regulation of insulin analogue in type 1 diabetes mice
Bibliographic record
Abstract
AIM: To achieve glucose-activated transcriptional regulation of insulin analogue in skeletal muscle of T1D mice, thereby controlling blood glucose levels and preventing or mitigating diabetes-related complications. MATERIALS AND METHODS: We developed the GANIT (Glucose-Activated NFAT-regulated INSA-F Transcription) system, an innovative platform building upon the previously established intramuscular plasmid DNA (pDNA) delivery and expression system. In the GANIT system, skeletal muscle cells are genetically engineered to endogenously produce the insulin analogue INSA-F (Insulin Aspart with Furin cleavage sites). The transcription of INSA-F is precisely controlled by a glucose-responsive promoter containing NFAT (Nuclear Factor of Activated T-cells) regulatory motifs, which can be activated in response to changes in extracellular glucose concentrations. This design enables glucose-dependent regulation of insulin analogue expression, mimicking physiological glucose-responsive insulin secretion. RESULTS: T1D mice that received two GANIT treatments over a 2-month experimental period demonstrated significant improvements in glucose homeostasis, glucose tolerance and glycated haemoglobin (HbA1c) levels. Additionally, the treatment effectively reduced oxidative stress and alleviated cardiac and renal fibrosis, while maintaining a favourable biosafety profile. CONCLUSION: The GANIT system provides significant advantages in terms of efficiency, convenience and cost-effectiveness, making it a promising approach for regulating blood glucose levels and alleviating diabetes-related complications in insulin-deficient 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".