Exploring the Role of Oxidative Stress in Metallic Taste During Head and Neck Cancer Treatment: A Study of Salivary Malondialdehyde and Therapeutic Interventions
Bibliographic record
Abstract
Abstract Introduction Head and neck cancers (HNC) treatments often cause a metallic taste (MT), adversely affecting patients’ quality of life. This study aims to investigate the lipoperoxidation hypothesis of MT by examining salivary malondialdehyde (MDA) levels, a marker of oxidative stress, in HNC patients undergoing treatment. Methods This prospective cohort study included 44 newly diagnosed HNC patients. Saliva samples were collected before, during and up to one year after the HNC treatment. Analyses including MDA and other markers were performed. Additionally, a bovine lactoferrin mouthwash was evaluated for its efficacy in alleviating MT. Results Out of the 44 patients, 12 (27.2%) reported MT, primarily during treatment phases. Salivary MDA levels significantly increased during radiotherapy, peaking mid-treatment, before declining post-treatment. Despite this fluctuation, no significant relationship was found between MDA levels and MT. Bovine lactoferrin mouthwash alleviated MT at least partially in 63.2% of the occurrences. Other salivary markers such as protein concentration, antioxidant properties, catalase activity, and superoxide dismutase activity showed no significant link to MT. Discussion The increase in MDA levels during radiotherapy indicated heightened oxidative stress. However, the lack of a significant association between MDA and MT suggests other factors may contribute to MT development. The partial efficacy of lactoferrin mouthwash highlighted a potential benefits. Future research should explore other mechanisms, such as the role of oral microbiota, to better understand and manage MT in HNC patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".