The importance of ctokeratins in the early detection of oral squamous cell carcinoma
Bibliographic record
Abstract
Background: Oral cancer is usually diagnosed at advanced stages. The pattern of keratin expression in normal epithelia and the change in their expression in premalignant lesions and carcinomas have suggested the possibilities of improving diagnosis. The aim of this study is to determine the use of acidic cytokeratins (CKs) as biomarkers of histopathological progression in oral carcinogenesis. Materials and Methods: A total of 50 paraffin blocks of histological specimens diagnosed as hyperplastic epithelium, dysplastic epithelium, well-differentiated squamous cell carcinoma (SCC) and poorly-differentiated SCC (10 specimens each) were included in this study, in addition to 10 normal oral mucosal samples. All samples were stained immunohistochemically with CKs (10-ab1, 14, 16-ab1, 18-dc10 and 19-abs10) using Ventana Medical Systems (Arizona-USA). The expression of CKs antigen was evaluated as absent, mild, moderate and severe. Results: CK10-ab1 was found to be positive in the suprabasal layers of all specimens in normal and hyperplastic epithelium, while it was moderate in dysplastic epithelium and mild in well-differentiated SCC. CK10-ab1 was negative in all samples with poorly-differentiated SCC ( P < 0.005). CK14 was positive in all specimens of all groups whereas CK16-ab1 was negative in all specimens of all groups. The stain of CKs 18-dc10 and 19-abs10 was restricted to the basal cells only in normal, hyperplastic and dysplastic epithelium, while it was mild in well-differentiated and poorly-differentiated SCC ( P < 0.01). Conclusion: CK10-ab1 disappeared gradually with the progression of malignant changes of squamous cells whereas CKs 18-dc10 and 19-abs10 increased gradually at the same time. Such changes in the protein mapping of squamous cells need more investigation for a better understanding of oral SCC.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".