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Record W2800585043 · doi:10.3791/57559

Intraportal Transplantation of Pancreatic Islets in Mouse Model

2018· article· en· W2800585043 on OpenAlexaff
Rahul Khatri, Birte Hußmann, Divya Rawat, Ali Osman Gürol, Thomas Linn

Bibliographic record

VenueJournal of Visualized Experiments · 2018
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsIsletTransplantationRenal capsuleMedicinePancreatic isletsPancreasDiabetes mellitusInternal medicineEndocrinologyStreptozotocinCollagenaseIslet cell transplantationPancreas transplantationBiologyKidney transplantation

Abstract

fetched live from OpenAlex

Pancreatic islet transplantation to reduce hyperglycemia is highly successful in rodents with chemically-induced diabetes. The most common transplantation site in experimental islet transplantation is the kidney capsule. However, as less is known about the interaction of pancreatic islets with blood constituents, it also makes sense to utilize the portal vein approach in experimental islet transplantation. This protocol demonstrates an intraportal islet transplantation technique in NMRI nude mice. Streptozotocin (180 mg/kg) is injected intraperitoneally to induce hyperglycemia in recipient mice. They are considered as diabetic at a non-fasting blood glucose level greater than 20 mmol/L. One day prior to transplantation, mouse pancreatic islets are isolated from the donor pancreas by collagenase digestion; a minimum of 350 islets are utilized per diabetic recipient. Depending upon the islet isolation yield, two or more donor mice are utilized per recipient. After overnight culture at 37 °C, islets are administered into the recipient liver via the portal vein. After surgery, the mice are protected in red Makrolon houses and observed until are awake. This protocol maintains glycemic control for 120 days in syngeneic mice and 15 days in allogeneic mice.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.035
GPT teacher head0.407
Teacher spread0.373 · 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 teacher head, 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

Citations9
Published2018
Admission routes1
Has abstractyes

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