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Culture Medium Modulates Proinflammatory Conditions of Human Pancreatic Islets Before Transplantation

2006· article· en· W2139685116 on OpenAlexaff
Simona Marzorati, Barbara Antonioli, Rita Nano, Paola Maffi, Lorenzo Piemonti, C. Giliola, Antonio Secchi, Jonathan R.T. Lakey, Federico Bertuzzi

Bibliographic record

VenueAmerican Journal of Transplantation · 2006
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProinflammatory cytokineIsletTransplantationMedicineCCL2MonocyteInflammationPancreatic isletsEndocrinologyInternal medicineImmunologyChemokineDiabetes mellitus

Abstract

fetched live from OpenAlex

A portion of transplanted islets is lost during engraftment as a result of stressful events, involving hypoxia and production of proinflammatory molecules by islets. Two of these molecules (monocyte chemoattractant protein-1, CCL2/MCP-1 and tissue factor, TF) are directly correlated with reduced graft function. We evaluated which factors reduce islet proinflammatory conditions. In particular the effects of different culture media supplemented with proteins or antioxidant agents on CCL2/MCP-1 and TF human islet release were evaluated. We observed that human islets after culture in final wash culture medium (FW) significantly decreased CCL2/MCP-1 release and TF production compared with CMRL and M199. These effects were independent from the type of protein added to the media (human serum, human albumin, fetal calf serum). Glutathione in FW further decreased CCL2/MCP-1 in a dose-dependent manner. Culture conditions can modulate the proinflammatory state of islets, and could be used in clinical islet transplantation to reduce inflammation during engraftment.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.249
Teacher spread0.243 · 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

Citations54
Published2006
Admission routes1
Has abstractyes

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