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Record W7093341892 · doi:10.6084/m9.figshare.30433699

RORγt: A Potential Drug Target in Transplantation

2018· other· W7093341892 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2018
Typeother
Language
FieldArts and Humanities
TopicHistorical and Architectural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPeripheral blood mononuclear cellTransplantationDrugClinical trialRetinoic acidSecretionRAR-related orphan receptor gammaAntibody

Abstract

fetched live from OpenAlex

🧾 AbstractIntroduction: Retinoic acid receptor-related orphan receptor γt (RORγt) regulates the activity of T helper 17 (Th17) cells, which are implicated in many autoimmune diseases and in solid graft rejection. Research from our laboratory and recent data from others show that Th17 contributes to antibody-mediated rejection through the promotion of tertiary lymphoid tissue and the production of donor-specific antibodies. Th17 cells secrete IL-17A, IL-17F, IL-21, IL-22, and several other cytokines that promote both Th1 and Th2 types of inflammation, B cell proliferation, antibody production, macrophage recruitment, and other inflammatory pathways. There are no current trials for a systemic RORγt inhibitor registered in the NIH clinical trials database, since the last two trials were discontinued due to liver toxicity. The objective of the current work is to explore the therapeutic potential of RORγt inhibition in the prevention and treatment of solid graft rejection. Methods: Using a rational drug design approach, virtual screening, and scaffold hopping, we identify a few compounds that can bind to RORγt with high affinity. We test the effects of two compounds (analogues) on peripheral blood mononuclear cells (PBMCs) from healthy volunteers, highly sensitized, and non-sensitized patients in a Th17 polarization assay. Results: Highly sensitized patients show increased propensity to polarize to the Th17 phenotype compared to healthy volunteers or non-sensitized patients (n = 9, p < 0.05). The first compound demonstrates inverse agonist behavior, as it reduces the production of IL-17, IL-21, and IL-22 (p < 0.05, n = 9). Conclusions: Small-molecule inhibition of RORγt can attenuate Th17 activity and confer a therapeutic benefit to solid graft recipients.📁 File Description This file represents a scanned image of the abstract published on page 42 in the Proc. Exp. Surg. Grad. Prog. & IRR Prog. Joint Res. Day 2018 booklet.📍 Conference and MetadataPresented at: Experimental Surgery Graduate Program & IRR Program Joint Research Day 2018 Presentation Date: 2 November 2018 Location: Montreal, QC, Canada Abstract Category: Outcomes and Education Presentation Type: Oral Presentation Authors: Ahmed Fouda, Jean Tchervenkov Affiliation: Department of Experimental Surgery, McGill University, Montreal, QC, Canada 📚 Full CitationFouda, A.; Tchervenkov, J. RORγt: A Potential Drug Target in Transplantation. In Proc. Exp. Surg. Grad. Prog. & IRR Prog. Joint Res. Day 2018; p. 42. Experimental Surgery Graduate Program and Injury, Repair & Recovery (IRR) Program Joint Research Day, Montreal, QC, Canada, 2 November 2018. DOI: 10.6084/m9.figshare.30433699 © 2018, Ahmed Fouda.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.206
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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