MétaCan
Menu
Back to cohort
Record W4293029334 · doi:10.3390/curroncol29090475

Nasopharyngeal Carcinoma Progression: Accumulating Genomic Instability and Persistent Epstein–Barr Virus Infection

2022· review· en· W4293029334 on OpenAlexvenueno aff
Xue Liu, Yayan Deng, Yujuan Huang, Jiaxiang Ye, Sifang Xie, Qian He, Yong Chen, Yan Lin, Rong Liang, Jiazhang Wei, Yongqiang Li, Jinyan Zhang

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsnot available
FundersNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsNasopharyngeal carcinomaGenome instabilityEpstein–Barr virusVirusHead and neck squamous-cell carcinomaBiologyChromosome instabilityCancer researchVirologyMedicineHead and neck cancerCancerDNAGeneticsGeneInternal medicineDNA damageRadiation therapy

Abstract

fetched live from OpenAlex

Genomic instability facilitates the evolution of cells, tissues, organs, and species. The progression of human malignancies can be regarded as the accumulation of genomic instability, which confers a high evolutionary potential for tumor cells to adapt to continuous changes in the tumor microenvironment. Nasopharyngeal carcinoma (NPC) is a head-and-neck squamous-cell carcinoma closely associated with Epstein-Barr virus (EBV) infection. NPC progression is driven by a combination of accumulated genomic instability and persistent EBV infection. Here, we present a review of the key characteristics of genomic instability in NPC and the profound implications of EBV infection. We further discuss the significance of profiling genomic instability for the assessment of disease progression and treatment efficacy, as well as the opportunities and challenges of targeted therapies for NPC based on its unique genomic instability.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.180
GPT teacher head0.441
Teacher spread0.261 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCurrent OncologySame topicViral-associated cancers and disordersFrench-language works237,207