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448.4: Development of an automated multi-sensor normothermic kidney perfusion device for ex-vivo preservation and assessment.

2024· article· en· W4402800828 on OpenAlexaff
Franka Messner, Marlene Pühringer, Michael Mengel, Andrew K. Capulli, Scott Coull, Piper Curtis, Joshua Filgate, Steven W. Henning, Molly Holmes, Bradley Hovan, Stuart L. Jacobson, Sara Mills, Tim Moreau, Andrew Nelson, Connor Stadnicki, Alec Woods, Stefan Schneeberger

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEx vivoPerfusionIn vivoMedicineBiomedical engineeringKidneyInternal medicineBiologyBiotechnology

Abstract

fetched live from OpenAlex

Background: Static cold storage and hypothermic machine perfusion remain the current standard preservation strategy for kidney grafts. Normothermic machine perfusion (NMP) has the potential to improve organ preservation, extend preservation times and allow for assessment of organ quality. We herein present a next-generation automated kidney perfusion device that allows for stable normothermic machine perfusion while providing a platform for extensive functional assessment of organ quality. Methods:The prototype NMP device consists of a perfusion module, disposable set, and transport module. The device includes a low-hemolysis pump, an array of perfusate sensors, auto-regulated pressure and temperature, and oxygen delivery and glucose infusion that automatically adapts to the metabolic needs of the graft in real-time. These features result in a device that provides unprecedented control, monitoring, and assessment of donor kidneys. Perfusate and urine samples can be easily collected for further assessment. A large touchscreen serves as the user interface and displays all perfusion parameters in real-time or as a history, with cloud connectivity. Urine flow and color are measured, and the device can be configured to either recirculate or divert urine. A live video recording preserves the sterile environment while offering macroscopic assessment and retrospective analysis of the kidney appearance during NMP. Results: 162 porcine and 69 discarded human kidney grafts were perfused resulting in over 8,100 hours of kidney perfusion. Preservation times for human kidney grafts were extended to 48 hours in 11 kidneys and further to a maximum of 118 hours in one human kidney. In this kidney, perfusion flow was stable (median 230 ml/min) at 90 mmHg, but eventually declining after day 4 to 150 ml/min. Stable oxygen and glucose consumption (median 1.62 mL/min, 5.99 mg/min), lactate clearance, pH regulation and uninterrupted urine flow (0.82 mL/min, GFR 1.78 mL/min) were found. Over the course of perfusion, a moderate rise in injury markers was noted. Macroscopic assessment as well as histology show preserved tissue architecture (Banff score after 118 hours of NMP: i0, t0, g0, ptc0, v0, ti0, i-IFTA0, t-IFTA0, ci0, ct0, cg0, mm1, cv0, ah2) with few glomerular microthrombi after multi-day perfusion. Conclusion: The unique design and features of this novel NMP device enable next-generation automated kidney perfusions with minimal need for post-reperfusion interventions and provide a platform for comprehensive ex-vivo graft assessment.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.037
GPT teacher head0.374
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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