Effect of Diet Intake Imbalance in Hepatocellular Carcinoma Progression
Bibliographic record
Abstract
Incidence of hepatocellular carcinoma (HCC) has increased sharply in the last 10 years, with an especially high incidence in Egypt. This study was conducted to evaluate the impact of unbalanced diets on liver tumor through investigation of some biochemical mediators/pathways implicated in the pathogenesis of HCC. Male albino mice were divided into two major groups: Control group and Hepatocellular carcinoma (HCC) group; each group was further divided into four subgroups according to received diet: high fat (HF), low fat (LF), high carbohydrate (HC), and low carbohydrate (LC) groups. The results indicated that induction of HCC in mice showed marked body weight loss. Liver sections of HCC groups showed malignant giant cells and strong expression of p53. HCC mice groups kept on HF and LC diets showed the lowest survival rate, a significant increase in glucose-6-phosphate dehydrogenase (G6PDH), aldolase, and citrate synthase activities, a significant increase in serum E-cadherin as well as a significant decrease in insulin-like growth factor-1 (IGF-1) compared with LF diet. These results suggest that the molecular pathogenesis of HCC in mice correlates reduction of serum IGF-1 and elevated serum E-cadherin accompanied by reprogrammed metabolic profile shifted towards increased glycolysis and lipogenesis. These pathogenic changes were enhanced by over-consumption of carbohydrates, fats, and proteins, whereas dietary fat restriction could have a protective/ameliorative effect against the incidence 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".