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Record W4289111352 · doi:10.3390/curroncol29080427

Exosomal Non-Coding RNAs: New Insights into the Biology of Hepatocellular Carcinoma

2022· review· en· W4289111352 on OpenAlexvenueno aff
Qian Zhang, Hanlin Li, Yang Liu, Jian Li, Chunling Wu, Hua Tang

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMicrovesiclesmicroRNAHepatocellular carcinomaExtracellular vesiclesExosomeBiologyCircular RNAComputational biologyCancer researchBioinformaticsRNACancerMedicineGeneCell biologyGenetics

Abstract

fetched live from OpenAlex

Exosomes, extracellular vesicles with a diameter of 40 to 160 nm, are among the smallest extracellular vesicles released by cells. They deliver different cargoes, including proteins, DNAs, and RNAs, and facilitate communication between cells to coordinate a variety of physiological and pathological functions. Hepatocellular carcinoma (HCC) is the sixth common malignant tumor and the fourth leading cause of cancer-related death worldwide. Its molecular mechanism remains largely unknown, and there is a lack of reliable and noninvasive biomarkers for early diagnosis and prognosis prediction. Mounting evidence has shown that exosomes carry a variety of ncRNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), which play critical roles in the occurrence and progression of HCC. In this review, we summarize the recent findings of exosomal miRNAs, lncRNAs, and circRNAs in HCC from their impact on the development of HCC to their potential applications in the diagnosis and treatment of HCC.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.101
GPT teacher head0.383
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2022
Admission routes1
Has abstractyes

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