Bibliographic record
Abstract
Aging affects every species and tissue but not in equal ways. Human pancreatic β-cells lose their ability to replicate, regenerate, and secrete insulin as one gets older. This natural process increases risk of developing diabetes as you age and is a concern for donor islets and stem cells obtained from older subjects destined for transplantation or emerging regenerative therapies. Using fluidic sorting and RNA sequencing on single cells, Xin et al describe a transcriptional signature of mouse β-cell aging between adulthood and a very old age. Amazingly, expression levels of more than 99% of genes do not change over time, despite the long lifespan of this specialized tissue. They identify a novel set of transcription factors that can explain decreases in cell survival and proliferation genes and potentially drive age-associated decline in regenerative capacity. Yet somehow, mouse β-cells maintain pathways regulating glucose metabolism and β-cell function despite experiencing challenges commonly associated with old age, including increased weight and fat mass. The authors conclude that β-cells of old mice are overall strikingly similar to young β-cells, implying that mechanisms may exist to resist aging and maintain their 'youth'. These new discoveries have interesting implications for efforts to preserve or improve function of human β-cells, providing potential clues toward prolonging the life and health of donor tissues or islets of people with diabetes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".