Camel milk and bee honey regulate profibrotic cytokine gene transcripts in liver cirrhosis induced by carbon tetrachloride
Bibliographic record
Abstract
The lack of studies regarding the mechanism of the protective effects of camel milk and bee honey against hepatotoxic compounds led us to perform this study. Thirty-six male rats were divided into two main groups. The first group (n = 9) comprised control non-cirrhotic rats. The rats of the second group (n = 27) were administered carbon tetrachloride (CCl4) by intraperitoneal injection to induce liver cirrhosis. The cirrhotic rats were then divided into three equal subgroups, each comprising nine animals, as follows: (i) cirrhotic rats, (ii) cirrhotic rats treated with camel milk, and (iii) cirrhotic rats treated with camel milk and bee honey. The present findings revealed that CCl4 elevated the activities of liver enzymes, blood glucose levels, non-esterified fatty acids (NEFA) in the serum and glycogen content in the liver. On the other hand, CCl4 significantly decreased phosphorylase activity in the liver tissue and significantly increased carbohydrate intolerance and insulin resistance index (HOMA-IR). Moreover, CCl4 induced a significant increase in oxidative stress, along with increased expression of the profibrotic cytokine genes TNF-α and TGF-β. However, camel milk either alone or in combination with bee honey ameliorated these toxic actions. The antioxidant properties of these protective agents and their effects of downregulating certain procirrhotic cytokine gene transcripts underlie this protection.
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) | 0.000 | 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".