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Record W3094412139 · doi:10.3389/fcell.2020.585879

RAD-Deficient Human Cardiomyocytes Develop Hypertrophic Cardiomyopathy Phenotypes Due to Calcium Dysregulation

2020· article· en· W3094412139 on OpenAlexaff
Yanan Li, Yun Chang, Xiaolei Li, Xiaowei Li, Jian Gao, Yafei Zhou, Fujian Wu, Rui Bai, Tao Dong, Shuhong Ma, Siyao Zhang, Wenjing Lu, Xiaoqiu Tan, Yongming Wang, Feng Lan

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsUniversity of British Columbia
FundersBeijing Municipal Natural Science FoundationChinese Academy of Medical SciencesNational Natural Science Foundation of China
KeywordsHypertrophic cardiomyopathyPhenotypeCalciumCardiomyopathyCell biologyMedicineInternal medicineBiologyHeart failureGeneticsGene

Abstract

fetched live from OpenAlex

RAD (Ras associated with diabetes) is a membrane protein that acts as a calcium channel regulator by interacting with cardiac L-type Ca 2+ channels (LTCC). RAD defects can disrupt intracellular calcium dynamics and lead to cardiac hypertrophy. However, due to the lack of reliable human disease models, the pathological mechanism of RAD deficiency leading to cardiac hypertrophy is not well understood. In this study, we created a RRAD -/-H9 cell line using CRISPR/Cas9 technology. RAD disruption did not affect the ability and efficiency of myocardial differentiation. However, RAD deficient hESC-CMs recapitulate HCM phenotype in vitro. Further studies have shown that elevated intracellular calcium level and abnormal calcium regulation are the core mechanisms by which RAD deficiency leads to HCM. More importantly, management of calcium dysregulation has been found to be an effective way to prevent the development of HCM in vitro.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Cell and Developmental BiologySame topicCardiomyopathy and Myosin StudiesFrench-language works237,207