Animal models in xenotransplantation
Bibliographic record
Abstract
The severe shortage of donor organs has provided a strong impetus to push the investigation into the use of animal organs for humans. Xenotransplantation will not only benefit patients, but also represents a unique and potentially profitable business opportunity. However, there are many barriers to successful clinical xenotransplantation, including immunological barriers, physiological incompatibility, zoonosis and ethical concerns. This overview will focus on currently available animal models used in attempts to break through the immunological barriers to xenotransplantation. There are many advantages to using small animal, namely rodent, models in xenotransplantation research. For example, the use of the mouse model allows the use of knockout mice and careful dissection of rejection mechanisms at the molecular level. The following models can be used to study hyperacute rejection (HAR): guinea-pig-to-rat, mouse-to-rabbit, guinea-pig-to-mouse, rat-to-presensitised mouse and rat-to-alpha-Gal knockout mouse. The hamster-to-rat, mouse-to-rat and rat-to-mouse models are commonly used to study acute vascular rejection. Large animal models are complex and expensive, but they are more relevant to clinical xenotransplantation. Based on experiments using transgenic pig-to-primate models, HAR can be overcome. However, acute vascular rejection remains a major barrier at the present time. A pig cartilage-to-monkey model has been developed to study chronic rejection. Other novel models such as pig venous segment-to-monkey model and rat-to-primate model may represent viable options to study immunological barriers following xenotransplantation. Like many other medical breakthroughs, animal research will continue to make enormous contributions towards the eventual success of xenotransplantation.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".