Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

Aldosterone-producing adenomas (APAs) vary in phenotype and genotype. Zona glomerulosa (ZG)-like APAs frequently have mutations of an L-type calcium channel (LTCC) CaV1.3. Using a novel antagonist of CaV1.3, compound 8, we investigated the role of CaV1.3 on steroidogenesis in the human adrenocortical cell line, H295R, and in primary human adrenal cells. This investigational drug was compared with the common antihypertensive drug nifedipine, which has 4.5-fold selectivity for the vascular LTCC, CaV1.2, over CaV1.3. In H295R cells transfected with wild-type or mutant CaV1.3 channels, the latter produced more aldosterone than wild-type, which was ameliorated by 100 μM of compound 8. In primary adrenal and non-transfected H295R cells, compound 8 decreased aldosterone production similar to high concentration of nifedipine (100 μM). Selective CaV1.3 blockade may offer a novel way of treating primary hyperaldosteronism, which avoids the vascular side effects of CaV1.2-blockade, and provides targeted treatment for ZG-like APAs with mutations of CaV1.3.

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