Characterization of the transcriptome in isolated and transplanted mouse pancreatic islets
Bibliographic record
Abstract
The transplantation of pancreatic islets is an option for therapeutic management of hypoglycemia unawareness in select patients with type 1 diabetes mellitus. Characteristics of the transcriptome of freshly isolated islets, islet allografts, and islet isograft are reported in the literature. However, no single experiment has undertaken a comparison of the islet allograft to isograft. Potential implications of the latter are the use in diagnosis of rejection and to discover the molecular pathways in islet allograft dysfunction after transplant. Here, the mouse model of islet transplant is used to characterize the transcriptome of freshly isolated islets and compare islet graft in an isogeneic vs. allogeneic host using an Affymetrix GeneChip® Array assay. A set of islet associated transcripts (IAT) was developed, and subsequently shown to have high level of expression in islet allografts and isografts harvested either five- or ten-days after transplant. Furthermore, specific analysis of transcriptome differences between islet isografts and pre-rejection allografts (ten-day), reveal a series of islet rejection associated transcripts (IRAT). Nearly half of IRAT show overlap with previously described pathogenesis based transcript sets identified in the setting of mouse kidney allograft rejection. The novel transcripts identified to be associated with islet rejection include those involved in chemotaxis or lymphocyte function. Although use of biopsy based monitoring of humans islet transplants remains difficult at the present time, this study provides proof of principle for a transcriptome based technique for islet graft rejection monitoring and describes the transcripts associated with islet graft dysfunction.
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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".