Ex-vivo mRNA therapy to predict kidney vitality during transplantation
Bibliographic record
Abstract
High-risk kidneys have been used for transplantation due to the big gap between supply and demand for kidney transplantation, which sometimes exhibit unexpected prognosis. Presently, it is still a great challenge to predict the function and metabolic activity of the marginal kidneys for rational preselection in the clinic. Here we utilize non-invasive biodegradable nanoparticles as mRNA carriers to delivery and assess ex-vivo kidney viability and metabolic activity, and finally predict the quality of a donor kidney on a cellular basis for transplantation. The in vitro performance of lipid nanoparticles (LNPs) for mRNA delivery were screened and validated in HKC-8 cells. Particles loaded with self-quenching dye-conjugated oligos or reporter mRNAs were administered through normothermic machine perfusion system and real-time monitored non-invasively by near infrared (NIF) camera. We hypotheses the uptake and translation are active behaviours of live cells, where only metabolically active cells can emit reporter signal. Data have shown the accumulation of nanoparticles in pig kidneys and successfully detection of fluorescent signals predominantly in cortex by real-time imaging during ex-vivo perfusion. The delivery of reporter mRNA through developed lipid nanoparticle have shown significant mRNA expression both in vitro and ex-vivo in pig kidneys. The correlation studies to distinguish healthy vs damaged kidneys will be further investigated. We envision that the strategy can potentiate the application to various diseases and target tissues.
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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 | 0.001 | 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) | 0.002 | 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".