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Record W4405107327 · doi:10.1016/j.jhlto.2024.100192

Ex vivo delivery of recombinant IL-10 to human donor lungs

2024· article· en· W4405107327 on OpenAlexafffund
Jonathan Yeung, Terumoto Koike, Dirk Wagnetz, Tiago Machuca, Riccardo Bonato, Mingyao Liu, S. Juvet, Marcelo Cypel, Shaf Keshavjee

Bibliographic record

VenueJHLT Open · 2024
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsEx vivoRecombinant DNAIn vivoChemistryBiologyMedicineBiochemistryGeneBiotechnology

Abstract

fetched live from OpenAlex

Background: The immunoregulatory cytokine interleukin-10 (IL-10) has been shown to be a promising therapy for donor lung injuries before transplantation. However, the very short half-life of IL-10 in vivo (∼2 hours) has necessitated the use of gene therapy in almost all animal models of lung transplantation. Because isolation of the donor lung on the ex vivo lung perfusion (EVLP) circuit removes it from the influence of renal and hepatic clearance mechanisms, a much-prolonged half-life of IL-10 is anticipated. Thus, we hypothesized that delivery of recombinant IL-10 (rIL-10) to injured donor lungs isolated on EVLP could be a clinically relevant and a logistically simpler method of employing IL-10 therapy in lung transplantation. Methods: = 5/group) to receive either saline (control), rIL-10 (5 µg in 2-liter perfusate), or rIL-10 (25 µg) aerosolized into the airways. Results: Perfusate and intratracheal delivery of rIL-10 did not provide the therapeutic anti-inflammatory action that has been traditionally achieved with gene therapy. It appears that intratracheally delivered rIL-10 moves into the perfusate where it seems to be biologically inactive. Conclusions: Gene therapy remains superior as it allows for continued production of IL-10 within the alveoli where it has the potential to continuously act on alveolar macrophages and epithelial cells in a paracrine fashion.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.058
GPT teacher head0.401
Teacher spread0.343 · 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.

Study designNot applicable
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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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