Self-Condensation Culture Enables Vascularization of Tissue Fragments for Efficient Therapeutic Transplantation
Bibliographic record
Abstract
Clinical transplantation of tissue fragments, including islets, faces a critical challenge because of a lack of effective strategies that ensure efficient engraftment through the timely integration of vascular networks. We recently developed a complex organoid engineering method by "self-condensation" culture based on mesenchymal cell-dependent contraction, thereby enabling dissociated heterotypic lineages including endothelial cells to self-organize in a spatiotemporal manner. Here, we report the successful adaptation of this method for generating complex tissues from diverse tissue fragments derived from various organs, including pancreatic islets. The self-condensation of human and mouse islets with endothelial cells not only promoted functionalization in culture but also massively improved post-transplant engraftment. Therapeutically, fulminant diabetic mice were more efficiently treated by a vascularized islet transplant compared with the conventional approach. Given the general limitations of post-transplant vascularization associated with 3D tissue-based therapy, our approach offers a promising means of enhancing efficacy in the context of therapeutic tissue transplantation.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".