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Record W4319981931 · doi:10.3390/ijms24043518

Pre-Treatment of Transplant Donors with Hydrogen Sulfide to Protect against Warm and Cold Ischemia-Reperfusion Injury in Kidney and Other Transplantable Solid Organs

2023· review· en· W4319981931 on OpenAlexaff
Liam McFarlane, Pierce Nelson, George J. Dugbartey, Alp Şener

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsTransplantationReperfusion injuryMedicineIschemiaKidneyPathophysiologyKidney transplantationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Ischemia-reperfusion injury (IRI), a pathological condition resulting from prolonged cessation and subsequent restoration of blood flow to a tissue, is an inevitable consequence of solid organ transplantation. Current organ preservation strategies, such as static cold storage (SCS), are aimed at reducing IRI. However, prolonged SCS exacerbates IRI. Recent research has examined pre-treatment approaches to more effectively attenuate IRI. Hydrogen sulfide (H2S), the third established member of a family of gaseous signaling molecules, has been shown to target the pathophysiology of IRI and thus appears to be a viable candidate that can overcome the transplant surgeon’s enemy. This review discusses pre-treatment of renal grafts and other transplantable organs with H2S to mitigate transplantation-induced IRI in animal models of transplantation. In addition, ethical principles of pre-treatment and potential applications of H2S pre-treatment in the prevention of other IRI-associated conditions are discussed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Review · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.020
GPT teacher head0.317
Teacher spread0.297 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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