Bibliographic record
Abstract
Diabetes mellitus is a chronic condition resulting from insufficient β-cell mass, which leads to improper glycemia regulation. Research efforts have focused on expanding islets and β-cells in vitro for use in cell-based therapies to cure diabetes. Collagens are triple-helix extracellular matrix proteins with widespread expression in mammals. With multiple functions, collagen can provide structural integrity in addition to mediating cellular signaling. In the pancreas, collagens I and IV are abundant and support cell structures while also stimulating cell surface receptors to influence pancreatic cell processes. Collagen-based materials and scaffolds have also been used to assist in the maintenance and expansion of islet cells in vitro, primarily through integrin and discoindin domain receptors. Islet transplantation using collagen-based scaffolds may improve long-term glycemic control, but progressive research efforts are required to realize this potential in humans. This review will outline the critical role played by native collagens I and IV and their receptors in maintaining islet function. The advantages of using collagens I and IV as culture gels/scaffolds and islet encapsulation vehicles for transplantation will be described.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".