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Record W4406097465 · doi:10.1016/j.scr.2025.103653

Testing organ-specific responses to therapies in tissues differentiated from Cystic Fibrosis patient derived iPSCs

2025· article· en· W4406097465 on OpenAlexafffund
Abdelkader Daoud, Sunny Xia, Onofrio Laselva, Janet Jiang, Christine E. Bear

Bibliographic record

VenueStem Cell Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids Foundation
FundersCystic Fibrosis Canada
KeywordsBiologyCystic fibrosisInduced pluripotent stem cellFibrosisImmunologyPathologyBioinformaticsGeneticsGeneEmbryonic stem cellMedicine

Abstract

fetched live from OpenAlex

Cystic Fibrosis (CF) is a life-shortening disease that is caused by mutations in the CFTR gene, a gene that is expressed in multiple organs. There are several primary tissue models of CF disease, including nasal epithelial cultures and rectal organoids, that are effective in reporting the potential efficacy of mutation-targeted therapies called CFTR modulators. However, there is the well-documented variation in tissue dependent, therapeutic response amongst CF patients, even those with the same CF-causing mutation. Hence, there is an interest in developing strategies for comparing therapeutic efficacy in different organs relative to isogenic controls. In this study, we evaluated the CFTR chloride channel response to the highly effective CFTR modulator: Trikafta, in CF patient specific, iPSC-derived colonic and airway cultures relative to mutation-corrected (non-CF) tissues from that same individual. We measured pharmacological rescue in both tissues. This proof-of-concept study provides a roadmap for future comparisons of patient-specific CF therapeutic responses in both pulmonary and extra-pulmonary systems.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.343
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.365
Teacher spread0.298 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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