The adhesion molecules NCAM, HCAM, PECAM‐1 and ICAM‐1 in normal salivary gland tissues and salivary gland malignancies
Bibliographic record
Abstract
BACKGROUND: Some malignant salivary gland tumors are known for their propensity to exhibit perineural invasion and vascular metastases. It was hypothesized that alterations in the expression of cell adhesion molecules are involved in these processes. METHODS: The expression and distribution of neural cell adhesion molecule (NCAM), HCAM (CD44), platelet-endothelial cell adhesion molecule-1 (PECAM-1), and intercellular cell adhesion molecule-1 (ICAM-1) in normal salivary gland tissues and selected salivary gland malignancies, especially adenoid cystic carcinoma (AdCyCa) and polymorphous low-grade adenocarcinoma (PMLG), were determined immunohistochemically, and their influence on histologically demonstrated perineural invasion, vascular invasion, and tumor recurrence/patient death were investigated. RESULTS: NCAM, HCAM, and ICAM-1 were often found to be expressed by neoplastic cells, but no correlation to perineural invasion, tumor behavior, or patient prognosis was found. PECAM-1 was rarely and only focally expressed in three tumors, all of which were related to tumor metastases and patient death. CONCLUSIONS: Immunohistochemical demonstration of NCAM, HCAM, and ICAM-1 is not related to perineural invasion or tumor behavior. PECAM-1 expression was related to vascular invasion and poor patient prognosis in three cases.
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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 | 0.000 | 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".