Effect of 4-hydroxychalcone as preventive and curative treatment in Wistar rats with liver injury
Bibliographic record
Abstract
The increasing prevalence and complications related to liver diseases (caused by infection, toxic agents, or metabolic syndrome), together with insufficient existence of treatments, make evident the need for better therapeutic alternatives. Therefore, the aim of this study was to determine the effect of 4-hydroxychalcone (4-HC) as preventive and curative treatment in acute and chronic liver injury, respectively. Liver damage was induced with carbon tetrachloride (CCl4) in Wistar rats. Rats were divided into two groups: (1) acute liver injury and (2) chronic liver injury. In turn, each group was divided into four subgroups: (i) control (water); (ii) dimethyl sulfoxide 10%; (iii) CCl4; and (iv) 4-HC. The pre-treatment with 4-HC decreased transaminases, IL-6 serum levels, and hepatic malondialdehyde, increased IL-10 serum levels and hepatic glutathione, and decreased liver damage (necrosis, steatosis, and inflammatory infiltrate). In contrast, treatment with 4-HC after the induction of chronic liver injury decreased IL-6 serum levels and liver damage (steatosis, inflammatory infiltrate, ballooning cells, steatofibrosis, and fibrosis degree). Thus, the 4-HC treatment is proposed as a preventive treatment against acute liver injury; moreover, these results suggested the potential of 4-HC as a curative treatment against chronic liver injury, but other scheme treatments must be evaluated in future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".