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A Novel RORγt Inverse Agonist to Target Th17 in Sensitized Skin Allograft Mouse Model

2025· other· W7094972132 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Language
FieldSocial Sciences
TopicContemporary Sociological Theory and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsTacrolimusColchicineTransplantationAgonistRetinoic acidImmunohistochemistryImmunosuppression

Abstract

fetched live from OpenAlex

🧾 AbstractPurpose: Th17 and IL-17 play a critical role in acute and chronic antibody-mediated allograft rejection and accelerated graft loss. We have recently designed novel inverse agonists against the retinoic acid receptor-related orphan receptor gamma t (RORγt) and demonstrated in-vitro activity against IL-17 expression in a Th17 lymphocyte polarization assay. We sought to determine whether this approach would delay skin allograft rejection in a sensitized mouse model. Methods: C57BL/6 mice were sensitized by administration of 10⁷ Balb/c splenocytes intraperitoneally on days 0, 7, and 14, and transplanted with Balb/c skin grafts on day 15. Mice were treated daily with the RORγt inhibitor TF-S14 (1 mg/kg, IP), tacrolimus (0.5 mg/kg, IP), or their combination. Modified ASEPSIS score was used to assess graft inflammation, and survival was monitored until 100% necrosis. Skin grafts were sampled at day 5 for histology and immunohistochemistry (CD3⁺, CD4⁺, CD8⁺). Results: The RORγt inhibitor (TF-S14) prolonged median graft survival from 6 to 13.5 days and from 7 to 23 days when combined with tacrolimus. Wound scores were reduced fourfold at day 7 compared to controls (p < 0.05). Neutrophilic and lymphocytic infiltrations were markedly reduced in TF-S14 and combination groups compared to vehicle or tacrolimus alone. Conclusions: The novel RORγt inhibitor TF-S14, especially in combination with tacrolimus, significantly prolongs graft survival in sensitized mouse models. These results highlight RORγt as a promising target for Th17-mediated rejection and potential therapy for highly sensitized transplant patients.📁 File DescriptionThe file represents the official published abstract from the American Transplant Congress (ATC 2023) Poster Abstracts section (Abstract B28, AJT Volume 23, Issue 6, Supplement 1).📍 Conference and MetadataPresented at: American Transplant Congress (ATC 2023) Abstract ID: B28 Presentation Date: 7 June 2023 Location: San Diego, CA, USA Abstract Category: Immunosuppression & Tolerance – Preclinical & Translational Studies Presentation Type: Poster Presentation Authors: Ahmed Fouda, Mohamed Taoubane Maallah, Sarita Negi, Steven Paraskevas, Jean Tchervenkov Affiliation: Experimental Surgery and McGill University Health Centre, McGill University, Montreal, QC, Canada 📚 Full Citation📘 Fouda, A.; Maallah, M.; Negi, S.; Paraskevas, S.; Tchervenkov, J. A Novel RORγt Inverse Agonist to Target Th17 in Sensitized Skin Allograft Mouse Model. In Proc. Am. Transplant Congr. 2023; Am. J. Transplant., Vol. 23, Issue 6 (Suppl. 1), Abstract B28. American Transplant Congress (ATC 2023), San Diego, CA, USA, 3–7 June 2023. DOI: 10.6084/m9.figshare.30422875 © 2023, 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.330
Teacher spread0.248 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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