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Mesenchymal Stromal Cell Therapy in Lung Transplantation

2023· review· en· W4381191520 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioengineering · 2023
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchUniversity of TorontoUniversity Health Network
KeywordsMesenchymal stem cellMedicineTransplantationLung transplantationLungParacrine signallingEx vivoCell therapyPathologyCellIn vivoSurgeryInternal medicineBiologyReceptor

Abstract

fetched live from OpenAlex

Lung transplantation is often the only viable treatment option for a patient with end-stage lung disease. Lung transplant results have improved substantially over time, but ischemia-reperfusion injury, primary graft dysfunction, acute rejection, and chronic lung allograft dysfunction (CLAD) continue to be significant problems. Mesenchymal stromal cells (MSC) are pluripotent cells that have anti-inflammatory and protective paracrine effects and may be beneficial in solid organ transplantation. Here, we review the experimental studies where MSCs have been used to protect the donor lung against ischemia-reperfusion injury and alloimmune responses, as well as the experimental and clinical studies using MSCs to prevent or treat CLAD. In addition, we outline ex vivo lung perfusion (EVLP) as an optimal platform for donor lung MSC delivery, as well as how the therapeutic potential of MSCs could be further leveraged with genetic engineering.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.858
Threshold uncertainty score1.000

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 head0.086
GPT teacher head0.381
Teacher spread0.296 · 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