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Record W2972014922

Unraveling Hepatitis C virus-induced biological circuits contributing to the development of hepatocellular carcinoma

2016· preprint· en· W2972014922 on OpenAlexfundno aff
Nicolaas Van Renne

Bibliographic record

Venuetheses.fr (ABES) · 2016
Typepreprint
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
FundersInterregUniversität HeidelbergMedical Research CouncilNational Institutes of HealthUniversité de StrasbourgInstitut National de la Santé et de la Recherche MédicaleAgence Nationale de la RechercheBroad InstituteEuropean CommissionWilhelm Sander-StiftungMonique Weill-Caulier TrustMcGill University
KeywordsHepatocellular carcinomaTranscriptomeHepatitis C virusHepatocyteDownregulation and upregulationIn silicoCancer researchLiver cancerSuppressorIn vivoChronic liver diseaseVirusMedicineBiologyCancerIn vitroInternal medicineVirologyGene expressionCirrhosisGene
DOInot available

Abstract

fetched live from OpenAlex

En combinant un nouveau système de culture cellulaire à partir d'hépatocytes différenciés avec du virus de l’hépatite C (VHC) purifié, nous pouvons induire un profil transcriptomique caractéristique des patients à risque élevé de développer un carcinome hépatocellulaire (CHC). En utilisant ce modèle, nous avons découvert le rôle fonctionnel de l'EGFR comme élément moteur de la signature du risque de développement d'un CHC. De plus, nous avons identifié des gènes candidats impliqués dans le développement du CHC. Pour étudier les maladies du foie in vivo, nous avons caractérisé l'expression des protéines phosphatases dans des biopsies hépatiques de patients infectés par le VHC. Nous avons observé une régulation négative de PTPRD, un suppresseur de tumeur, causé par une augmentation de miR-135a-5p qui cible l'ARNm de PTPRD. Par ailleurs, l'analyse in silico montre que l'expression de PTPRD dans le tissu hépatique est corrélée à la survie chez les patients atteints de CHC.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.122
GPT teacher head0.338
Teacher spread0.216 · 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.

Study designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venuetheses.fr (ABES)Same topicHepatitis C virus researchFrench-language works237,207