Xenotransplantation: The next quarter century
Bibliographic record
Abstract
Transplantation has become a preferred therapy for the treatment of end stage organ failure, improving the quality and duration of recipients lives. The major limitation of organ transplantation is the shortage of suitable donor organs available for clinical use. Xenotransplantation using genetically modified pig organs could provide an unlimited source of organs, allowing all patients in need to receive a transplant in a timely fashion. Xenotransplantation was limited to the experimental realm because of the presence of anti-pig antibodies that are present in the blood of every human (1, 2). The development of genetic engineering tools, especially CRISPR/Cas9 and somatic cell nuclear transfer made it possible to create pigs missing key glycan pig antigens so that the antibodies did not bind to the new pig (3-5). Preclinical results using kidneys from new donor pigs has improved to the point where nonhuman primate recipients are living for more than 4 years (Andrew Adams personal communication). The improvements in survival seen in preclinical models has led to clinical attempts at heart and kidney xenotransplantation (6, 7). Thus far in the first 5 clinical xenotransplant cases success has been modest, with only one graft (kidney) functioning past 60 days to date. The other patients receiving pig xenografts (2 hearts and 2 kidneys) succumbed to early antibody mediated rejection (AMR) (7-9). Nevertheless, the developments in preclinical and compassionate use xenotransplantation have resulted in the first FDA approved clinical trial with renal xenotransplantation. This article will deal with the issues that are likely to be the focus of the next 25 years with regards to development of clinical xenotransplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".