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Record W3155151917 · doi:10.1016/j.omto.2021.04.002

Angiogenesis-related non-coding RNAs and gastrointestinal cancer

2021· review· en· W3155151917 on OpenAlexaff
Zahra Razavi, Kasra Asgarpour, Maryam Mahjoubin‐Tehran, Susan Rasouli, Haroon Khan, Mohammad Karim Shahrzad, Michael R. Hamblin, Hamed Mirzaei

Bibliographic record

VenueMolecular Therapy — Oncolytics · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsWestern University
FundersMerck KGaA
KeywordsAngiogenesisGastrointestinal cancerCoding (social sciences)Computational biologyCancer researchCancerBiologyMedicineColorectal cancerGeneticsMathematics

Abstract

fetched live from OpenAlex

Gastrointestinal (GI) cancers are among the main reasons for cancer death globally. The deadliest types of GI cancer include colon, stomach, and liver cancers. Multiple lines of evidence have shown that angiogenesis has a key role in the growth and metastasis of all GI tumors. Abnormal angiogenesis also has a critical role in many non-malignant diseases. Therefore, angiogenesis is considered to be an important target for improved cancer treatment. Despite much research, the mechanisms governing angiogenesis are not completely understood. Recently, it has been shown that angiogenesis-related non-coding RNAs (ncRNAs) could affect the development of angiogenesis in cancer cells and tumors. The broad family of ncRNAs, which include long non-coding RNAs, microRNAs, and circular RNAs, are related to the development, promotion, and metastasis of GI cancers, especially in angiogenesis. This review discusses the role of ncRNAs in mediating angiogenesis in various types of GI cancers and looks forward to the introduction of mimetics and antagonists as possible therapeutic agents.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.360
Teacher spread0.325 · 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 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

Citations55
Published2021
Admission routes1
Has abstractyes

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