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235.7: Analysis of collagenase dose on allogeneic islet isolation outcomes and glucose-stimulated insulin release

2023· article· en· W4387881369 on OpenAlexaff
Doug O’Gorman, Tatsuya Kin, Shawn Rosichuk, Wendy Zhai, Jennifer Moriarty, Kyle Park, Advaita Ganguly, Peter Senior, James AM Shapiro

Bibliographic record

VenueTransplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIsletCollagenaseInternal medicineEndocrinologyInsulinThermolysinMicrobial collagenasePancreasMedicineBiologyEnzymeTrypsinBiochemistry

Abstract

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Introduction: The use of a low collagenase dose has been previously reported as potentially being beneficial to allogeneic islet isolation outcomes and specifically the glucose-stimulated insulin response (GSIR). In this study, the effect of the low collagenase dose on islet isolation and insulin secretion is expanded to investigate the role of the enzyme type and non-collagenolytic components. Methods: Clinical islet isolations were performed utilizing standard-of-care protocols between Jul 2008 and Mar 2023. Pancreases were perfused with an enzyme blend of either a non-targeted Liberase MTF high collagenase dose with thermolysin (HD-MTF, n= 441), a targeted (<20 WU/g) Liberase MTF low collagenase dose with thermolysin (LD-MTF, n=41) or a targeted (<20 WU/g) rCollagenase HI with BP Protease low collagenase dose (LD-rHI, n=93). Dynamic GSIR testing was performed at the time of the islet transplant using approximately 100 IE. Islets were exposed to 16 min intervals of low glucose (2.3 mMol) and high glucose (23.0 mMol) following a 60 min stabilization period. Perfusate was collected and analyzed for insulin concentrations. Perifusion samples were collected, and islets were stained with dithizone, and quantified to normalize the raw insulin values. Stimulation indexes are reported as both peak to baseline as well as the area under the curve. Basal and stimulated metabolic rates were calculated to express the final islet product insulin secretion. Results: In comparing some critical donor parameters across all groups (Table 1), HD-MTF had a significantly longer cold ischemia time and smaller pancreas than LD-MTF. The use of LD-rHI had a notably higher purification recovery despite pre-purification trapped proportions being similar between groups. Islet yields at all time points of processing showed comparable results, however, LD-rHI had a higher percentage of islet preparations proceed to transplant than LD-MTF.In assessing GSIR data (Table 2), the low-dose test groups both had higher stimulated responses to glucose than the high-dose control group, however, only the LD-rHI group was statistically higher. There was no observed difference between the 2 low dose groups. Both the raw and normalized stimulated insulin secretion levels were significantly higher in LD-rHI vs the HD-MTF.Conclusions: The use of a low-dose collagenase is associated with improved islet isolation parameters as well as insulin secretion in response to high glucose and can effectively and reliably lead to improved islet isolation outcomes in comparison to the high-dose strategy. The results were similar when comparing the type of enzyme used with a low-dose protocol. However, there was an improved purification recovery and transplant success rate observed. Only the LD-rHI group showed statistical improvement on the standard high-dose approach when assessing insulin secretion. This could be a result of utilizing BP protease instead of Thermolysin as the non-collagenolytic component.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.022
GPT teacher head0.286
Teacher spread0.264 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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