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Record W4214489112 · doi:10.1158/0008-5472.can-06-0561

Patterns of Known and Novel Small RNAs in Human Cervical Cancer

2007· article· en· W4214489112 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

VenueCancer Research · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsImmunovaccine (Canada)
FundersNational Cancer InstituteNational Human Genome Research Institute
KeywordsmicroRNACervical cancerCancerBiologyCervixCervical carcinomaComputational biologyCancer researchBioinformaticsGeneGenetics

Abstract

fetched live from OpenAlex

Recent studies suggest that knowledge of differential expression of microRNAs (miRNA) in cancer may have substantial diagnostic and prognostic value. Here, we use a direct sequencing method to characterize the profiles of miRNAs and other small RNA segments for six human cervical carcinoma cell lines and five normal cervical samples. Of 166 miRNAs expressed in normal cervix and cancer cell lines, we observed significant expression variation of six miRNAs between the two groups. To further show the biological relevance of our findings, we examined the expression level of two significantly varying miRNAs in a panel of 29 matched pairs of human cervical cancer and normal cervical samples. Reduced expression of miR-143 and increased expression of miR-21 were reproducibly displayed in cancer samples, suggesting the potential value of these miRNAs as tumor markers. In addition to the known miRNAs, we found a number of novel miRNAs and an additional set of small RNAs that do not meet miRNA criteria.

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)0.001
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.060
GPT teacher head0.402
Teacher spread0.342 · 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