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Record W2566391499 · doi:10.5539/ijb.v9n1p41

Tumor-suppressive and tumor-promoting role of Tgf-Beta in Hepatocellular Carcinoma

2016· article· en· W2566391499 on OpenAlexvenueno aff
Somyoth Sridurongrit

Bibliographic record

VenueInternational Journal of Biology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsnot available
Fundersnot available
KeywordsHepatocellular carcinomaTGF beta signaling pathwayCancer researchTransforming growth factor betaLiver cancerBETA (programming language)CytokineTransforming growth factorPathogenesisLiver diseaseCancerMedicineFibrosisApoptosisBiologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Tgf-Beta is a pleiotropic cytokine with diverse functions on hepatic cells. The well-known function of Tgf-Beta in pathogenesis of liver disease is to stimulate liver fibrosis that often precedes the onset of liver cancer. While Tgf-Beta-mediated fibrosis seems to make liver more prone to the development of liver cancer, Tgf-Beta suppresses initial malignant transformation of hepatic cells thru regulation of proliferation and apoptosis. On the other hand, Tgf-Beta has shown to act as an inducer of tumor development thru enhancement of metastatic process. Additionally, it has been shown that Tgf-Beta signaling in hepatocytes promotes hepatocarcinogenesis caused by certain genetic conditions. This review highlights observations that have improved an understanding of how Tgf-Beta contributes to the development of hepatocellular carcinoma.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.000
Threshold uncertainty score0.002

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)0.0000.000

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 head0.008
GPT teacher head0.236
Teacher spread0.228 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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