Perfusion decellularization for vascularized composite allotransplantation
Bibliographic record
Abstract
Vascularized composite allotransplantation is becoming the emerging standard for reconstructive surgery treatment for patients with limb trauma and facial injuries involving soft tissue loss. Due to the complex immunogenicity of composite grafts, patients who undergo vascularized composite allotransplantation are reliant on lifelong immunosuppressive therapy. Decellularization of donor grafts to create an extracellular matrix bio-scaffold provides an immunomodulatory graft that preserves the structural and bioactive function of the extracellular matrix. Retention of extracellular matrix proteins, growth factors, and signaling cascades allow for cell adhesion, migration, proliferation, and tissue regeneration. Perfusion decellularization of detergents through the graft vasculature allows for increased regent access to all tissue layers, and removal of cellular debris through the venous system. Grafts can subsequently be repopulated with appropriate cells through the vasculature to facilitate tissue regeneration. The present work reviews methods of decellularization, process parameters, evaluation of adequate cellular and nuclear removal, successful applications of perfusion decellularization for use in vascularized composite allotransplantation, and current limitations.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".