Combining Crocin and Sorafenib Improves Their Tumor-Inhibiting Effects in a Rat Model of Diethylnitrosamine-Induced Cirrhotic-Hepatocellular Carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies, with continuously increasing cases and fatalities. Diagnosis often occurs in the advanced stages, confining patients to systemic therapies such as sorafenib. Sorafenib (SB), a multi-kinase inhibitor, has not yet demonstrated sufficient efficacy against advanced HCC. There is a strong argument in favor of studying its use in combination with other medications to optimize the therapeutic results. According to our earlier work, crocin (CR), a key bioactive component of saffron, hinders HCC development and liver cancer stemness. In this study, we investigated the therapeutic use of CR or its combination with SB in a cirrhotic rat model of HCC and evaluated how effectively SB and CR inhibited tumor growth in this model. Diethylnitrosamine (DEN) was administered intraperitoneally to rats once a week for 15 weeks, leading to cirrhosis, and then 19 weeks later, leading to multifocal HCC. After 16 weeks of cancer induction, CR (200 mg/kg daily) and SB (10 mg/kg daily) were given orally to rats for three weeks, either separately or in combination. Consistently, the combination treatment considerably decreased the incidence of dyschromatic nodules, nodule multiplicity, and dysplastic nodules when compared to the HCC group of single therapies. Combined therapy also caused the highest degree of apoptosis, along with decreased proliferating and β-catenin levels in the tumor tissues. Additionally, when rats received combined therapy with CR, it showed anti-inflammatory characteristics where nuclear factor kappa B (NF-κB) and cyclooxygenase-2 (Cox-2) were considerably and additively lowered. As a result, CR potentiates the suppressive effects of SB on tumor growth and provides the opportunity to strengthen the therapeutic effects of SB in the treatment of HCC.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".