MétaCan
Menu
Back to cohort
Record W3000659933 · doi:10.1016/j.aohep.2019.12.006

The effects of hyperthermia on human hepatocellular carcinoma stem and mature cancer cells

2020· article· en· W3000659933 on OpenAlexaff
David Sontag, David A. Miles, Julia Uhanova, Micah Grubert Van Iderstine, Jiaqi Yang, Gerald Y. Minuk

Bibliographic record

VenueAnnals of Hepatology · 2020
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCancer stem cellMedicineHepatocellular carcinomaEpithelial cell adhesion moleculeCD44Stem cellCell sortingCancer researchHyperthermiaLiver cancerCancerPopulationCancer cellCellHeat shock proteinPathologyOncologyImmunologyInternal medicineFlow cytometryBiologyCell biologyGene

Abstract

fetched live from OpenAlex

INTRODUCTION AND OBJECTIVES: Hepatocellular carcinoma (HCC) can recur following radiofrequency ablation and other hyperthermic treatment modalities. Cancer stem cells (CSCs) are a subpopulation of HCC cells that are difficult to eradicate and largely responsible for tumor recurrences. Thus, the principal objective of this study was to determine whether human HCC CSCs are relatively thermal-resistant compared to non-stem or mature cancer cells (MCCs). MATERIALS AND METHODS: MCCs were derived from a human HCC cell line using fluorescence activated cell sorting. Each cell population was exposed to 65°C heat for 0-16min and survival documented at various time points. RESULTS: Cell survival curves were similar in CSC and MCCs throughout the 16min heat exposure period. Maximum killing was obtained after 12-14min of heat exposure. Cytoprotective, heat shock proteins-70 (HSP70) and -90 (HSP90) mRNA expression were not disproportionately increased in CSCs. CONCLUSIONS: These results suggest that human HCC CSCs are not more thermal resistant than MCCs and therefore, do not support the hypothesis that HCC recurrences following hyperthermic treatment reflect CSC thermal-resistance.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.035
GPT teacher head0.260
Teacher spread0.225 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of HepatologySame topicUltrasound and Hyperthermia ApplicationsFrench-language works237,207