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Record W3205497581 · doi:10.1016/j.stemcr.2021.09.020

A new platform for high-throughput therapy testing on iPSC-derived lung progenitor cells from cystic fibrosis patients

2021· article· en· W3205497581 on OpenAlexaffabout
Jia Xin Jiang, Leigh Wellhauser, Onofrio Laselva, Irina Utkina, Zoltán Bozóky, T. Gunawardena, Zoe Ngan, Sunny Xia, Michelle Di Paola, Paul D. W. Eckford, Félix Ratjen, Theo J. Moraes, John Parkinson, Amy P. Wong, Christine E. Bear

Bibliographic record

VenueStem Cell Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsCystic fibrosisProgenitor cellInduced pluripotent stem cellBiologyLungStem cellBioinformaticsProgenitorCancer researchComputational biologyInternal medicineMedicineEmbryonic stem cellCell biologyGeneticsGene

Abstract

fetched live from OpenAlex

For those people with cystic fibrosis carrying rare CFTR mutations not responding to currently available therapies, there is an unmet need for relevant tissue models for therapy development. Here, we describe a new testing platform that employs patient-specific induced pluripotent stem cells (iPSCs) differentiated to lung progenitor cells that can be studied using a dynamic, high-throughput fluorescence-based assay of CFTR channel activity. Our proof-of-concept studies support the potential use of this platform, together with a Canadian bioresource that contains iPSC lines and matched nasal cultures from people with rare mutations, to advance patient-oriented therapy development. Interventions identified in the high-throughput, stem cell-based model and validated in primary nasal cultures from the same person have the potential to be advanced as therapies.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.026
GPT teacher head0.280
Teacher spread0.254 · 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

Citations40
Published2021
Admission routes2
Has abstractyes

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