Characterization of an Important Enzymatic Component in Collagenase that is Essential for the Effective Digestion of the Human and Porcine Pancreas
Bibliographic record
Abstract
Recent clinical results from Edmonton have demonstrated the feasibility of achieving normoglycemia in type I diabetic patients by islet transplantation. One of the key issues in obtaining this success was transplanting sufficient numbers of islets by sequential transplants. Although the development of semipurified endotoxin-free Clostridium histolyticum-derived collagenase (Liberase) has improved islet yields from the human pancreas, batch-to-batch variation and loss of activity with time still hampers progress in obtaining consistent islet preparations. In order to define key components of crude collagenase, a panel of monoclonal antibodies (McAbs) was raised against crude collagenase. Monoclonal antibodies were generated by fusions between splenocytes of BALB/c mice immunized with Boheringer P collagenase and the myeloma cell line NS-0. These monoclonal antibodies were used as probes to study molecular differences between effective and ineffective collagenase batches using Western blotting. Two monoclonal antibodies (LDS71 and LDS81) were raised and characterized as recognizing separate epitopes on a 125-kDa component. Western blotting indicated that the 125-kDa band was rapidly broken down by storage or by dialysis in the presence of dithiothreitol. However, this breakdown could be prevented by the addition of leupeptin (a protease inhibitor) to the dialysis buffer. On testing fractions at 5-min intervals from the "Ricordi" digestion circuit during porcine and human pancreas digestion, the 125-kDa component was rapidly broken down in relatively ineffective collagenase batches but in effective batches was present throughout the digestion process. The correlation between the presence of the 125-kDa band and effectiveness of pancreas digestion suggests that this may be a key component in the formulation of C. histolyticum collagenase.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".