Detection of the level of micro RNA 224 in patients with hepatocellular carcinoma.
Bibliographic record
Abstract
Background: Liver cancer, predominantly hepatocellular carcinoma (HCC), is the second most deadly cancer worldwide. HCC is the most rapidly increasing cause of cancer-related mortality in the U.S. In Canada, total health care costs associated with HCV are expected to increase by 60% until they peak in 2032. Given the extremely frequent tumour recurrence even after aggressive treatment (70% after 5 years of surgical resection) and limited treatment options available for advanced-stage liver disease, including liver transplantation, a costly proposition, prevention of HCC development in patients with advanced liver fibrosis may be the most effective strategy to substantially impact patient survival. Aim of study: to detect the level of miR224 in different stages of hepatocellular carcinoma. Methods: An observational study, in Tropical Medicine Department, El-Minia University Hospital, ElMinia,-Egypt. Patients with hepatocellular carcinoma on top of HCV induced Liver cirrhosiscollected among the patients of tropical medicine department from January 2017 to January2018. Patients were divided into 3 groups according to Barcelona classification of liver cancer (BCLC) into, group 1 with BCLC A, group 2 with BCLC B, group C with BCLC C, and control group of LC without HCC, for all groups: history, examination and routine investigations, abdominal ultrasound, Multislice CT scan and miR-224 assay were done.Results: there was a significant difference between the level of miR 224 in different stages of hepatocellular carcinoma with the lowest level in BCLC A and the highest level in BCLC C with P value 0.001 indicating its role in predicting aggressiveness of hepatocellular carcinoma. Conclusion: miR-224 could serve as a good prognostic biomarker for HCC, and can be used as a marker predicting aggressiveness of HCC.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".