[Regenerative medicine for pancreatic beta cells].
Bibliographic record
Abstract
Regenerative medicine involves varying degrees of interaction among many research domains. Regenerative medical therapies based on research into organogenesis and the regeneration of injured or dysfunctional tissue using cell therapy are being developed rapidly. For the treatment of diabetes mellitus (DM), pancreatic transplantation and islet (pancreatic endocrine) cell transplantation are considered to be one form of regenerative medicine to overcome pancreatic tissue dysfunction. Recently, the effective islet cell transplantation Edmonton protocol has been established, ushering in a new era in regenerative therapy for DM. However, unresolved problems remain, including a severe donor shortage and unexpected side effects with the longterm use of some immunosuppressive agents. With continuing advances and the clinical application of fundamental therapy for DM, a pancreatic islet cell transplantation or bioartificial pancreatic transplantation system, consisting of islet (pancreatic endocrine) cell purification, pancreatic cell proliferation techniques, immunoisolative membrane technology, and an appropriate transplantation procedure, will be effective. This paper focuses on applied research on human and/or porcine pancreatic cell purification, embryonic stem cell differentiation, and pancreatic stem cell differentiation into functional insulin-producing cells.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.025 |
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".