O042 Enzymatic conversion of human blood group A kidneys to universal blood group O
Bibliographic record
Abstract
Abstract Introduction The ABO blood group restriction on allocation of donor organs leads to waiting time disadvantages for individuals of more restrictive blood types. Here, we outline the first preclinical use of two enzymes from Flavonifractor plautii to convert human blood group A kidneys to universal blood group O. Methods Six pairs of human kidneys rejected for transplantation and offered for research were used to in this study with approval from the National Research Ethics committee and Research and Development Office (NRES: 15/NE/0408). Three pairs were perfused for 6 h using normothermic machine perfusion (NMP), and three pairs were perfused for 24 h using hypothermic machine perfusion (HMP), with one kidney per pair randomised to enzyme treatment. Cortical biopsies collected throughout perfusion were examined for antigen loss using immunofluorescence microscopy. Results After 2hrs of NMP, a significant loss of 83.4±10.2% blood group A antigen expression was observed compared to pre-treatment levels (p=0.012), while no significant changes were observed in control kidneys (p=0.999). For HMP, a maximal loss of 71.2±21.9% was observed after 6 h (p=0.066), with no decrease observed in the contralateral controls (p=0.977). Haemodynamic perfusion parameters were stable in both cohorts, with no significant difference between control vs treated kidneys. Conclusion Our results show a loss of 70-80% of blood group A antigens in as little as 2 h of NMP and 6 h of HMP. These approaches pave the way for the first clinical studies that could herald the start of a new era that transforms donor organ allocation in kidney transplantation.
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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 | 0.000 | 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) | 0.003 | 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".