Analysis of expression status and relationship between senescence- related genes and pancreatic function-related genes in human islets of various ages
Bibliographic record
Abstract
Abstract Background Although studies on senescence-related genes using human islets have been performed, the expression of senescence-related genes and their association with functional genes in islets remain insufficiently investigated. We aimed to determine whether and what types of senescent related genes are expressed in islets and identify their correlations with pancreatic function-related genes by using islets from individuals of various ages isolated for islet transplantation. Methods Islet from deceased donors of both sexes and different ages were used for analysis. The expression status of senescence-related genes (GLS1, IL-6, IL-8, p16, p21, and SA-β-gal) and pancreatic function-related genes (GCG and INS) was examined by RT-qPCR, and their relationships with age were investigated. Results We obtained isolated human islets from 18 deceased multiorgan donors. There was no correlation between donor age and each senescence-related gene. Regarding correlations between donor age and pancreatic function-related genes, age was positively correlated only with INS (r = 0.49, p = 0.03). Meanwhile, INS expression was not correlated with GLS1 (r = 0.23, p = 0.34), IL-6 (r=-0.06, p = 0.79), or IL-8 (r=-0.1, p = 0.12), but positively related with p16 (r = 0.89, p < 0.0001), p21 (r = 0.51, p = 0.02), and SA-β-gal (r = 0.52, p = 0.02). Conclusion We did show the functional potential of even aged islets, which were originally thought to be functionally impaired. We were unable to identify any senescence-related genes expressed in islets from various ages. Therefore, a new index would need to be established to evaluate not only actual chronological age but also organ- and cell-specific age.
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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 | 0.001 | 0.001 |
| 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".