Epigenetic biomarkers indicate islet cell death in xenotransplantation
Bibliographic record
Abstract
BACKGROUND: Xenotransplantation of porcine islets has emerged in recent decades as a potential treatment for type 1 diabetes (T1D). Current methods of detection, indicative of successful engraftment, occur downstream of actual islet death. Epigenetic biomarkers can be detected in circulating cell-free DNA (cfDNA) to provide an earlier indication of graft dysfunction. AIMS: The present study identified a biomarker of islet death using differential methylation of the insulin gene, INS, originating from β-cells in porcine islets. MATERIALS & METHODS: Pyrosequencing primers specific for porcine INS were designed to quantify hypomethylation along 12 cysteine-guanine dinucleotide (CpG) sites, including three sites in the cyclic adenosine monophosphate (cAMP) response element (CRE) binding protein 2 (CRE2) binding region of the 5' untranslated region (UTR) and nine sites within intron 2. RESULTS: PCR amplification of bisulfite-converted DNA combined with pyrosequencing data support the conclusion that hypomethylated porcine INS is specific to islet origin. CONCLUSION: Moreover, the results of this study indicate a highly specific epigenetic biomarker, capable of detecting a single islet, supporting the measurement of cfDNA as a biomarker for transplanted islet death. Defining the epigenetic characteristics of porcine-derived islets within cfDNA will be crucial to develop a better understanding of graft survival immunology for 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 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) | 0.002 | 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".