Abstract B028: Expression of NOTCH1 in head and neck tumours at selected hospitals in Ghana
Bibliographic record
Abstract
Abstract Head and neck tumors (HNT) are of paranasal sinuses origin (the salivary glands and the upper aerodigestive tract). It constitutes the sixth most common tumors. Studies have shown that, factors such as tobacco and alcohol intake and viral infections may increase the relative risk to the tumor. Notch1 pathways are frequently altered in many tumors, however, the clinical significance of NOTCH1dysregulation in head and neck tumors is poorly understood. METHOD: The aim of this study was to investigate the expression NOTCH1 in head and neck tumor and also to elucidate the various anatomical pattern of the head and neck tumors in selected hospitals in Ghana. NOTCH1 expression were analyzed using quantitative real-time PCR (qRT-PCR) and immunohistochemical staining on tissue samples from a consecutive series of head and neck tumor patients resections over a four-year period. RESULTS: The percentage of low expression of Notch1 were 71.4% in the malignant tissue (n=98) and 76.9% (n=52) in the benign tissue. The anatomical site with high of low expression of the NOTCH1 was found in oral cavity (37.1%) followed by respiratory (27.1%) followed by mandible (14.3%). CONCLUSION: There were low expression of NOTCH1 in head and neck tumour tissues suggesting that NOTCH1may be a tumor suppressor gene. We further propose that, NOTCH1 offers a potential tool as a target molecule in the development of therapies for head and neck tumors. Citation Format: Precious Barnes, Roland Osei Saahene. Expression of NOTCH1 in head and neck tumours at selected hospitals in Ghana [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B028.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".