Biomarkers of Oxidative Stress, Proliferation, Inflammation and Invasivity in Saliva from Oral Cancer Patients
Bibliographic record
Abstract
Cancer represents the main cause of death in the economically developed countries and the second cause of death in developing ones. Head and neck squamous cell carcinomas are the sixth most common malignancies worldwide with oral cavity and pharynx cancers being the most common. Saliva qualifies as one of the most suitable diagnostic fluids due to the non-invasivity nature, simple handling procedures, easy collection and storage and good cooperation with patient groups such as children or persons with disabilities. The aim of the present study is to assess the presence in saliva of several cancer biomarkers such as: tumor cells proliferation - Ki-67 Antigen and Squamous Cell Carcinoma Antigen (SCCA), inflammation - Interleukin-6 (IL-6), extracellular matrix collagen degradation - Matrix Metallo-proteinase-9 (MMP-9) and Tissue Inhibitor of Metalloproteinases 2 (TIMP-2), oxidative stress - total antioxidant capacity and uric acid. Both uric acid and total antioxidant capacity showed decreased levelsin the saliva of oral cancer patients. IL-6, Ki-67, SCCA and MMP-9 showed increased levels in the saliva of oral patients compared to the control group. Salivary TIMP-2 levels were also decreased in the patients group. We can conclude that salivary diagnosis has the potential of becoming a powerful tool in detecting and monitoring oral cancer 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".