Risk factors for islet loss during culture prior to transplantation
Bibliographic record
Abstract
Culturing islets can add great flexibility to a clinical islet transplant program. However, a reduction in the islet mass has been frequently observed during culture and its degree varies. The aim of this study was to identify the risk factors associated with a significant islet loss during culture. One-hundred and four islet preparations cultured in an attempt to use for transplantation constituted this study. After culture for 20 h (median), islet yield significantly decreased from 363 309 +/- 12 647 to 313 035 +/- 10 862 islet equivalent yield (IE) (mean +/- SE), accompanied by a reduction in packed tissue volume from 3.9 +/- 0.1 to 3.0 +/- 0.1 ml and islet index (IE/islet particle count) from 1.20 +/- 0.04 to 1.05 +/- 0.04. Culture did not markedly alter islet purity or percent of trapped islet. Morphology score and viability were significantly improved after culture. Of 104 islet preparations, 37 suffered a substantial islet loss (> 20%) over culture. Factors significantly associated with risk of islet loss identified by univariate analysis were longer cold ischemia time, two-layer method (TLM) preservation, lower islet purity, and higher islet index. Multivariate analysis revealed that independent predictors of islet loss were higher islet index and the use of TLM. This study provides novel information on the link between donor- isolation factors and islet loss during culture.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".