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Serine-Protease Inhibition during Islet Isolation Increases Islet Yield from Human Pancreases with Prolonged Ischemia. Transplantation 2001; 72: 565.

2001· letter· en· W2410430144 on OpenAlexaboutno aff
Thomas G. Markees

Bibliographic record

VenueTransplantation · 2001
Typeletter
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
Fundersnot available
KeywordsIsletTransplantationImmunosuppressionMedicinePancreas transplantationCollagenaseDiabetes mellitusInternal medicineBiologyEndocrinologyBiochemistryEnzymeKidney transplantation

Abstract

fetched live from OpenAlex

ISLET ISOLATION: SIMPLE MODIFICATIONS CAN STILL YIELD BIG IMPROVEMENTS In this issue, J.R.T. Lakey et al. describe increased islet yields from human pancreata achieved by the incorporation of Pefabloc, a serine-protease inhibitor, into the standard Liberase digestion protocol. Improved yield, together with recent advances in transplantation biology, may enable increased numbers of diabetic patients to receive a curative islet graft. Transplantation of insulin-producing tissue as the definitive cure for diabetes was first proposed more than 100 years ago (1). During the last decade, segmental pancreas transplantation has become a standard form of therapy for longstanding diabetes. Success rates have improved steadily and until recently remained far superior to those achieved with isolated islet of Langerhans transplantation (2). Advances in immunosuppression (the “Edmonton protocol”) during the last 2 years, coupled with increased islet graft mass, have now made islet transplantation a realistic therapeutic alternative (3). The reduced antigen load of isolated islets, coupled with the relative simplicity and low morbidity of transcutaneous intrahepatic injection make the procedure increasingly attractive. However, human islet transplantation continues to be hindered by limited availability of islets. The isolation of human islets of Langerhans has always been expensive and labor intensive. The search for ways to improve efficiency and yield has spanned decades. The earliest results with enzymatic digestion of the pancreas (4) led to increasingly pure collagenase preparations and finally to the current standard, Liberase (5). Incubation of minced tissue in enzyme has been replaced by ductal perfusion with the enzyme. Newer media have been used to purify the isolated islets away from unwanted exocrine tissue. Ingenious apparatus has been designed that separates islets from the undigested exocrine mass and then removes them from the enzyme solution (6). The report by Lakey et al. may be the next significant advance in this area. To summarize the results of Lakey et al., the addition of Pefabloc to the isolation protocol doubled the islet yield without detectable detriment to the tissue. In vitro, the islets appeared to be functional. If these results can be confirmed and extended to document function in vivo, they should make it possible to treat many more persons with diabetes. Four years ago the enzyme mixture Liberase was reported to increase markedly the yield of islets (5). Subsequently, this enzyme preparation has become the “gold standard” for islet isolation. Based on the magnitude of the increase in isolated islets presented in the current paper, the addition of Pefabloc could have a similar effect on the islet isolation procedure. Although the authors do not discuss them, their results suggest that Pefabloc may also have other uses, possibly as a preservative for solid organs after harvest and perhaps for pancreata intended for subsequent islet isolation. In any case, this report appears to be good news for diabetic patients awaiting curative transplantation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.019
GPT teacher head0.235
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2001
Admission routes1
Has abstractyes

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