The Changes of Expression Levels in Mir-181a and Mir-30d, and a Significant Correlation between Clinical Patient Data
Bibliographic record
Abstract
Purpose: Colorectal cancer is known as the most common gastrointestinal cancers. As the age increases, the risk for this cancer also increases, so the only way to improve and hope for life in these patients is early diagnosis of the disease. So far, numerous molecular studies have been carried out on microRNAs in colorectal cancer. In addition, since some of them can be identified as cancer biomarkers. Therefore, in this study we have investigated the expression level of Mir-30d and Mir-181a as cancer biomarkers. Method: The changes of Mir-30d and Mir-181a expression levels in 60 colorectal tumor tissues and 60 adjacent tumor tissues, after RNA extraction and cDNA synthesis were surveyed using the Real Time-PCR method. Results: The results have reported a considerable reduction in the expression level of Mir-30d in tumor tissues, as well as a significant increase in the expression level of Mir-181a tumor expression in tumor tissues (P<0.05). In addition, the correlation between Mir-30d and Mir181a showed that there was a significant difference between the level of expression of mir-30d with age and TNM stage of CRC (P<0.05), whilst these correlations were not observed for Mir-181a (P>0.05).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".