Characterization of the Nile Grass Rat as a Unique Model for Type 2 Diabetic Polyneuropathy
Bibliographic record
Abstract
Type 2 diabetes (T2D) has reached pandemic proportions worldwide. Almost half of T2D patients suffer from polyneuropathy that can present as paresthesia, hyperalgesia, allodynia, or hypoesthesia. Therapeutic treatment options are largely incomplete, suggesting new avenues of research are needed. Herein, we introduce the African Nile Grass rat (NGR), which develops T2D solely by diet manipulation, as a novel T2D polyneuropathy model. The purpose of this study was to first characterize T2D-induced polyneuropathy in the NGRs before highlighting their strength as a potential prediabetic model of T2D. NGRs with long-term T2D exhibit hallmark features of polyneuropathy such as decreased motor nerve conduction velocity, intraepidermal denervation, and hyposensitivity to noxious mechanical and thermal stimulation. At the dorsal root ganglia, T2D neurons have altered sodium channel expression, specifically increased Nav1.7 and Nav1.9, and their surrounding satellite glial cells express glial fibrillary acidic protein. Now that these T2D NGRs have been characterized and shown to have a similar presentation to human and other animal models of T2D, the strength of this diet-induced model can be exploited. The prediabetic changes can be observed over their long progression to develop T2D which may allow for a therapeutic window to prevent T2D before permanent damage occurs.
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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) | 0.001 | 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".