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Record W2313776419 · doi:10.5732/cjc.014.10175

MicroRNAs in nasopharyngeal carcinoma

2014· review· en· W2313776419 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChinese Journal of Cancer · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsNasopharyngeal carcinomamicroRNABiologyContext (archaeology)Computational biologyGeneBioinformaticsCancer researchMedicineGeneticsInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) provide insight into both the biology and clinical behavior of many human cancers, including nasopharyngeal carcinoma (NPC). The dysregulation of miRNAs in NPC results in a variety of tumor-promoting effects. Furthermore, several miRNAs are prognostic markers for NPC. In addition to cellular miRNAs, NPC samples also often contain miRNAs encoded by Epstein-Barr virus, and these miRNAs may impact NPC biology by targeting both cellular and viral genes. Given their numerous putative roles in NPC development and progression, a thorough understanding of the impact of miRNA dysregulation in NPC is expected to shed light on useful biomarkers and therapeutic targets for the clinical management of this disease. In this review, we describe the efforts to date to identify and characterize such miRNAs in the context of NPC.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.014
GPT teacher head0.335
Teacher spread0.321 · 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