Establishment of Native Insulin‐Negative NOD Mice and the Methodology to Distinguish Specific Insulin Knockout Genotypes and a B:16 Alanine Preproinsulin Transgene
Bibliographic record
Abstract
We hypothesize that NOD mice without native insulin, but with an altered insulin B:9-23 sequence, will be completely protected from diabetes/insulitis if insulin B:9-23 is an essential T cell epitope. To investigate this hypothesis, we have established initial insulin 1- and 2-negative NOD mice with a transgene directing production of preproinsulin with alanine at position B:16 rather than the native tyrosine of both insulin 1 and insulin 2. Sets of primers for PCR-based assays have been created and validated. They are able to distinguish the presence or absence of the insulin gene knockouts and of both native insulin 1 and insulin 2 (and thus distinguish heterozygous versus homozygous knockouts), as well as the presence of the altered insulin transgene, B:16 alanine preproinsulin. Four B:16 alanine transgenic founders were produced directly in NOD mice and, by intercrossing, initial live native insulin-negative B:16 alanine transgenic mice have been generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".