Validating candidate biomarkers for different stages of non-alcoholic fatty liver disease
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a common chronic condition caused by the accumulation of fat in the liver. NAFLD may range from simple steatosis to advanced cirrhosis, and affects more than 1 billion people around the world. To date, there has been no effective treatment for NAFLD. In this study, we evaluated the expression of 4 candidate NAFLD biomarkers to assess their possible applicability in the classification and treatment of the disease.Twenty-six obese subjects, who underwent bariatric surgery, were recruited and their liver biopsies obtained. Expression of 4 candidate biomarker genes, PNPLA3, COL1A1, PPP1R3B, and KLF6 were evaluated at gene and protein levels by RT-qPCR and enzyme-linked immunosorbent assay (ELISA), respectively.A significant increase in the levels of COL1A1 protein (P = .03) and PNPLA3 protein (P = .03) were observed in patients with fibrosis-stage NAFLD compared to that in patients with steatosis-stage NAFLD. However, no significant differences were found in abundance of PPP1R3B and KLF6 proteins or at the gene level for any of the candidate.This is the first study, to our knowledge, to report on the expression levels of candidate biomarker genes for NAFLD in the Saudi population. Although PNPLA3 and PPP1R3B had been previously suggested as biomarkers for steatosis and KLF6 as a possible marker for the fibrosis stage of NAFLD, our results did not support these findings. However, other studies that had linked PNPLA3 to fibrosis in advanced NAFLD supported our current finding of high PNPLA3 protein in patients with fibrosis. Additionally, our results support COL1A1 protein as a potential biomarker for the fibrosis stage of NAFLD, and indicate its use in the screening of patients with NAFLD. Further studies are required to validate the use of COL1A1 as a biomarker for advanced NAFLD in a larger cohort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".