MétaCan
Menu
Back to cohort
Record W2889680305 · doi:10.1213/ane.0000000000003757

An In Vitro Model for Identifying Cardiac Side Effects of Anesthetics

2018· article· en· W2889680305 on OpenAlexfundno aff
Alex Chia Yu Chang, Andrew Chia Hao Chang, Luka Nicin, Gerhard Weber, Colin Holbrook, M. Frances Davies, Helen M. Blau, Edward Bertaccini

Bibliographic record

VenueAnesthesia & Analgesia · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingCanadian Institutes of Health Research
KeywordsEtomidateInduced pluripotent stem cellPropofolMedicineAnestheticIn vitroContraction (grammar)PharmacologyIn vivoDepressantAnesthetic AgentAnesthesiaInternal medicineEmbryonic stem cellBiochemistry

Abstract

fetched live from OpenAlex

The understanding of anesthetic side effects on the heart has been hindered by the lack of sophisticated clinical models. Using micropatterned human-induced pluripotent stem cell-derived cardiomyocytes, we obtained cardiac muscle depressant profiles for propofol, etomidate, and our newly identified anesthetic compound KSEB01-S2. Propofol was the strongest depressant among the 3 compounds tested, exhibiting the largest decrease in contraction velocity, depression rate, and beating frequency. Interestingly, KSEB01-S2 behaved similarly to etomidate, suggesting a better cardiac safety profile. Our results provide a proof-of-concept for using human-induced pluripotent stem cell-derived cardiomyocytes as an in vitro platform for future drug design.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.298
Teacher spread0.277 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAnesthesia & AnalgesiaSame topic3D Printing in Biomedical ResearchFrench-language works237,207