The Healing Effect of Aged Garlic Extract on Acetic Acid and 5-Fluorouracil-Induced Oral Mucositis in Mice
Bibliographic record
Abstract
Background: This preliminary study investigated the effect of aged garlic extract (AGE) on acetic acid (AA) and/or 5-fluorouracil (5-FU)-induced oral mucositis in tumor-bearing mice, and whether AGE affects the antitumor activity of 5-FU. Methods: There were four mouse groups: control, AA, AA + 5-FU, and AA + 5-FU + AGE. Mouse squamous cell carcinoma cells (SCCVII) were used to develop tumors in mice, except for the control group. Oral mucositis was induced in tumor-bearing mice by intraperitoneal injection with 5-FU (18 mg/kg) for 9 days and/or topical application of 50% AA to the dorsal tongue for 1 day. Mucositis was treated with AGE (2.0 g/kg/day) for 10 days in AA + 5-FU + AGE group, while the other groups received saline (0.2 mL/day). The wound healing and antitumor effects of AGE were examined. Whole transcriptome analysis and ingenuity pathways analysis (IPA) of the tongue and tumor samples were used to investigate the mechanisms behind the wound healing and antitumor effects of AGE. Results: Body weight was increased significantly in AA + 5-FU + AGE group compared to AA + 5-FU group. Moreover, tumor volume was significantly decreased in AA + 5-FU + AGE group than that in the other groups. In AA + 5-FU group, toluidine blue-positive area (wound area) in the tongue was the largest, and the size and weight of the salivary glands were decreased compared to other groups. In contrast, wound area was significantly reduced, and the size and weight of the salivary glands were increased in AA + 5-FU + AGE group compared to AA + 5-FU group. Therefore, AGE treatment could heal tongue ulcers and salivary gland damage in AA + 5-FU + AGE group. Whole transcriptome analysis and IPA data suggested that AGE could heal 5-FU-induced oral mucositis by promoting normal cell differentiation and keratinization, and it may also enhance the antitumor effects of 5-FU through the activation of B cells in mouse tumors. Conclusion: AGE could alleviate AA and 5-FU-induced oral mucositis in mice while potentially enhancing the antitumor activity of 5-FU. Therefore, AGE might be useful in the treatment of oral mucositis in cancer patients receiving 5-FU-based therapies.
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) | 0.000 | 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".