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Record W4362669807 · doi:10.26508/lsa.202201857

Validating organoid-derived human intestinal monolayers for personalized therapy in cystic fibrosis

2023· article· en· W4362669807 on OpenAlexafffund
Liron Birimberg‐Schwartz, Wan Ip, Claire Bartlett, Julie Avolio, Annelotte M. Vonk, T. Gunawardena, Kai Du, Mohsen Esmaeili, Jeffrey M. Beekman, Johanna M. Rommens, Lisa J. Strug, Christine E. Bear, Theo J. Moraes, Tanja Gonska

Bibliographic record

VenueLife Science Alliance · 2023
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersHospital for Sick ChildrenVertex Pharmaceuticals
KeywordsCystic fibrosisOrganoidDrugMedicinePersonalized medicineIvacaftorCancer researchInternal medicineCystic fibrosis transmembrane conductance regulatorPharmacologyBiologyBioinformaticsCell biology

Abstract

fetched live from OpenAlex

Highly effective drugs modulating the defective protein encoded by the CFTR gene have revolutionized cystic fibrosis (CF) therapy. Preclinical drug-testing on human nasal epithelial (HNE) cell cultures and 3-dimensional human intestinal organoids (3D HIO) are used to address patient-specific variation in drug response and to optimize individual treatment for people with CF. This study is the first to report comparable CFTR functional responses to CFTR modulator treatment among patients with different classes of CFTR gene variants using the three methods of 2D HIO, 3D HIO, and HNE. Furthermore, 2D HIO showed good correlation to clinical outcome markers. A larger measurable CFTR functional range and access to the apical membrane were identified as advantages of 2D HIO over HNE and 3D HIO, respectively. Our study thus expands the utility of 2D intestinal monolayers as a preclinical drug testing tool for CF.

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)0.006
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 score0.461
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.384
Teacher spread0.317 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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