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Record W2099126555 · doi:10.3892/etm_00000035

Reduced expression of mir15a in the blood of patients with oral squamous cell carcinoma is associated with tumor staging

2009· article· en· W2099126555 on OpenAlexaff
João Artur Ricieri Brito, Carolina Cavaliéri Gomes, Flávio Juliano Pimenta, Alvimar Afonso Barbosa, Marco A. M. Prado, Vânia F. Prado, Marcus V. Gomez, Ricardo Santiago Gomez

Bibliographic record

VenueExperimental and Therapeutic Medicine · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsWestern University
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsOncogeneMolecular medicinemicroRNAImmunohistochemistryMetastasisBasal cellStage (stratigraphy)Lymph nodePathologyCancerMedicineCellLymph node metastasisCell cycleCancer researchBiologyInternal medicineGene

Abstract

fetched live from OpenAlex

MicroRNAs (miRNAs) mir15a and let7a are important regulators of bcl-2, ras and c-myc proteins. Considering that these miRNAs are commonly altered in many human cancers and that these proteins are reported to be altered in oral squamous cell carcinoma (OSCC), we investigated them in a set of OSCC cases. The miRNAs as well as the proteins were evaluated in the tumor and blood of 20 patients by real-time quantitative PCR and immunohistochemistry, respectively. The expression of mir15a and bcl-2 proteins in the tumors was not associated with each other or with tumor staging. On the other hand, we found reduced expression of this miRNA in the blood of patients with an advanced stage of OSCC and with lymph node metastasis. The expression of let7a in the tumor and blood was not associated with tumor size, lymph node metastasis, tumor staging and immunoexpression of ras and c-myc proteins. In conclusion, the present study shows that reduced expression of mir15a is associated with OSCC staging.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations15
Published2009
Admission routes1
Has abstractyes

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