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Record W4293384775 · doi:10.1016/j.xpro.2022.101638

Protocol to derive human trophoblast stem cells directly from primed pluripotent stem cells

2022· article· en· W4293384775 on OpenAlexaff
Yanxing Wei, Xiao Lu, Lishi Ma, Zhijian Wang, Liping Huang, Huiying Li, Guangjin Pan, Stephen J. Lye, Yongli Shan

Bibliographic record

VenueSTAR Protocols · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsUniversity of TorontoSinai Health SystemLunenfeld-Tanenbaum Research Institute
FundersYouth Innovation Promotion Association of the Chinese Academy of SciencesNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsInduced pluripotent stem cellReprogrammingTrophoblastStem cellBiologyHuman placentaCell biologyComputational biologyEmbryonic stem cellPlacentaCellFetusGeneticsPregnancyGene

Abstract

fetched live from OpenAlex

Human trophoblast stem cells (hTSCs) are useful for studying human placenta development and diseases, but primed human pluripotent stem cells (hPSCs) routinely cultured in most laboratories do not support hTSC derivation. Here, we present a protocol to derive hTSCs directly from primed hPSCs. This approach, containing two strategies either with or without bone morphogenetic protein 4 (BMP4), provides a simple and accessible tool for deriving hTSCs to study placenta development and disease modeling without ethical limitations or reprogramming process. For complete details on the use and execution of this protocol, please refer to Wei et al. (2021).

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)0.001
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: Protocol · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.014

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 head0.015
GPT teacher head0.273
Teacher spread0.257 · 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
GenreProtocol

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

Citations4
Published2022
Admission routes1
Has abstractyes

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