An optimized low-dose STZ model rapidly induces diabetic neuropathy in rats
Bibliographic record
Abstract
Abstract Background Diabetic neuropathy is one of the most common complications of diabetes mellitus and remains difficult to study due to the prolonged experimental periods and high mortality frequently associated with conventional streptozotocin (STZ)-induced models. We aimed to develop and characterize a rapid, reproducible, and high-survival rat model of early diabetic neuropathy and to identify molecular alterations associated with disease development. Methods Male Wistar rats received three intraperitoneal injections of low-dose STZ (30 mg/kg) on alternating days or vehicle control. Blood glucose and body weight were monitored, and a neurophysiological assessment was performed using M-wave and F-wave recordings. To complement functional characterization, dorsal root ganglion (DRG) microarray datasets from STZ-induced diabetic rats and human DRG gene expression samples were analyzed using differential expression, protein-protein interaction, functional enrichment, and receiver operating characteristic (ROC) analyses. Results STZ-treated animals developed sustained hyperglycemia and significant body weight loss compared with controls. Neurophysiological assessment revealed a marked reduction in F-wave occurrence and prolonged F-wave latency, indicating early peripheral nerve dysfunction. High survival throughout the study. Transcriptomic analysis identified 2,693 differentially expressed genes, with the top 500 enriched in inflammatory, calcium signaling, neuropeptide signaling, and myeloid immune pathways. Network analysis highlighted TNF, HTR2A, CXCL10 , and CXCR2 as hub genes. Validation in an independent human DRG dataset demonstrated significant upregulation of TNF and HTR2A , with strong diagnostic ability (AUC = 0.91 and 0.80, respectively). Conclusions An optimized low-dose STZ regimen rapidly induces neurophysiological features of diabetic neuropathy within four weeks while maintaining high animal survival. This model provides a practical platform for investigating early pathogenic mechanisms and evaluating therapeutic interventions. Integrative transcriptomic analyses further identify TNF and HTR2A as candidate biomarkers with translational relevance for diabetic neuropathy.
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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 | 0.001 |
| 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".