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Modulating endothelial cells with EGFL7 to diminish aGVHD after allogeneic bone marrow transplantation in mice

2021· article· en· W3206027994 on OpenAlexaff
Adrienne M. Dorrance, Moutuaata M. Moutuou, Chinmayee Goda, Natalie E. Sell, Sonu Kalyan, Malith Karunasiri, Rohan Kulkarni, Marie Goulard, Sofia Kolovich, Alexander Rudich, Eric Naumann, Antoine Ackaoui, Charles-Etienne Bigras, Francis Daudelin, Ramiro Garzon, Parvathi Ranganathan, Martin Guimond

Bibliographic record

VenueBlood Advances · 2021
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de Montréal
FundersNational Cancer Institute
KeywordsMedicineHematopoietic stem cell transplantationImmunologyTransplantationLeukemiaBone marrowHaematopoiesisInflammationStem cellGraft-versus-host diseaseDiseaseEndothelial stem cellCancer researchInternal medicineBiology

Abstract

fetched live from OpenAlex

Acute graft-versus-host disease (aGVHD) is the second most common cause of death after allogeneic hematopoietic stem cell transplantation (allo-HSCT), underscoring the need for novel therapies. Based on previous work that endothelial cell dysfunction is present in aGVHD and that epidermal growth factor-like domain 7 (EGFL7) plays a significant role in decreasing inflammation by repressing endothelial cell activation and T-cell migration, we hypothesized that increasing EGFL7 levels after allo-HSCT will diminish the severity of aGVHD. Here, we show that treatment with recombinant EGFL7 (rEGFL7) in 2 different murine models of aGVHD decreases aGVHD severity and improves survival in recipient mice after allogeneic transplantation with respect to controls without affecting graft-versus-leukemia effect. Furthermore, we showed that rEGFL7 treatment results in higher thymocytes, T, B, and dendritic cell counts in recipient mice after allo-HSCT. This study constitutes a proof of concept of the ability of rEGFL7 therapy to reduce GHVD severity and mortality after allo-HSCT.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.678

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)0.0000.000

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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
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

Citations2
Published2021
Admission routes1
Has abstractyes

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