Characterization of Antibodies to Products of Proinsulin Processing Using Immunofluorescence Staining of Pancreas in Multiple Species
Bibliographic record
Abstract
The efficient processing of proinsulin into mature insulin and C-peptide is often compromised under conditions of beta cell stress, including diabetes. Impaired proinsulin processing has been challenging to examine by immunofluorescence staining in pancreas tissue because the characterization of antibodies specific for proinsulin, proinsulin intermediates, processed insulin and C-peptide has been limited. This study aimed to identify and characterize antibodies that can be used to detect products of proinsulin processing by immunofluorescence staining in pancreata from different species (mice, rats, dog, pig and human). We took advantage of several knockout mouse lines that lack either an enzyme involved in proinsulin processing or an insulin gene. Briefly, we report antibodies that are specific for several proinsulin processing products, including: a) insulin or proinsulin that has been appropriately processed at the B-C junction; b) proinsulin with a non-processed B-C junction; c) proinsulin with a non-processed A-C junction; d) rodent-specific C-peptide 1; e) rodent-specific C-peptide 2; and f) human-specific C-peptide or proinsulin. In addition, we also describe two 'pan-insulin' antibodies that react with all forms of insulin and proinsulin intermediates, regardless of the species. These antibodies are valuable tools for studying proinsulin processing by immunofluorescence staining and distinguishing between proinsulin products in different species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".