Preparation and Evaluation of Physicochemical Properties of the Doxepin Mucoadhesive Gel
Bibliographic record
Abstract
Background: Oral mucositis is a common debilitating complication of cancer chemotherapy and radiotherapy that can reduce the quality of patient’s lives. Hence, treating this condition plays an important role in increasing the patient’s tolerance. Objectives: Doxepin mucoadhesive gel is useful for treating oral mucosa inflammation caused by long-term effects of chemotherapy, which has low adverse effects. Methods: Doxepin gel’s formulation was prepared with various concentrations of poloxamer 407 and hydroxypropyl methylcellulose in deionized water. The prepared gels were evaluated for pH, appearance, viscosity, spreadability, stability, and drug release. Results: After providing gels containing doxepin, formulations 1, 2, 8, and 9 had low quality and, thus, were removed from the study. Based on qualitative evaluations, formulations 3 and 4 did not meet the criteria for mucoadhesive gel and were removed from the study. The best formulation contained 17% w/w poloxamer 407, 10% w/w hydroxypropyl methylcellulose, and 5% w/w doxepin. Conclusions: Suitable physicochemical properties of prepared doxepin mucoadhesive gel enable it to well cover inflamed and damaged oral mucosa. On the other hand, doxepin’s slow release from formulation (8 hours) can increase therapeutic effects and reduce side effects, which can heal and soothe inflammations of the oral mucosa and be useful in cancer patient’s treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".