Regenerative medicine, organ bioengineering and transplantation
Bibliographic record
Abstract
BACKGROUND: Organ transplantation is predicted to increase as life expectancy and the incidence of chronic diseases rises. Regenerative medicine-inspired technologies challenge the efficacy of the current allograft transplantation model. METHODS: A literature review was conducted using the PubMed interface of MEDLINE from the National Library of Medicine. Results were examined for relevance to innovations of organ bioengineering to inform analysis of advances in regenerative medicine affecting organ transplantation. Data reports from the Scientific Registry of Transplant Recipient and Organ Procurement Transplantation Network from 2008 to 2019 of kidney, pancreas, liver, heart, lung and intestine transplants performed, and patients currently on waiting lists for respective organs, were reviewed to demonstrate the shortage and need for transplantable organs. RESULTS: Regenerative medicine technologies aim to repair and regenerate poorly functioning organs. One goal is to achieve an immunosuppression-free state to improve quality of life, reduce complications and toxicities, and eliminate the cost of lifelong antirejection therapy. Innovative strategies include decellularization to fabricate acellular scaffolds that will be used as a template for organ manufacturing, three-dimensional printing and interspecies blastocyst complementation. Induced pluripotent stem cells are an innovation in stem cell technology which mitigate both the ethical concerns associated with embryonic stem cells and the limitation of other progenitor cells, which lack pluripotency. Regenerative medicine technologies hold promise in a wide array of fields and applications, such as promoting regeneration of native cell lines, growth of new tissue or organs, modelling of disease states, and augmenting the viability of existing ex vivo transplanted organs. CONCLUSION: The future of organ bioengineering relies on furthering understanding of organogenesis, in vivo regeneration, regenerative immunology and long-term monitoring of implanted bioengineered organs.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".