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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".