Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

Usefulness of salivary sialic acid as a tumor marker in tobacco chewers with oral cancer.

Aim: This study aims to assess the usefulness of salivary sialic acid (SA) as a tumor marker in the detection of oral squamous cell carcinoma (OSCC) among tobacco chewers.

Materials And Methods: After the approval of study protocol by the Institutional Ethics Committee and informed voluntary consent, salivary samples were collected from 96 participants in each group of tobacco chewers with OSCC, tobacco chewers without precancerous or cancerous lesion, and healthy controls. Salivary protein-bound SA (PBSA) and salivary-free SA (FSA) were measured by Yao et al.

Clinical spectrum, diagnostic criteria, and polymerase chain reaction of aqueous humor in viral and toxoplasma detection in Fuchs' uveitis syndrome.

Aim: The aim of this study is to describe the clinical features and diagnostic criteria of Fuchs' uveitis (FU) and to determine whether it has an association with virus and toxoplasma in the aqueous humor during cataract surgery.

Setting And Design: This is a prospective, case-control study.

Materials And Methods: Patients with FU (n = 25), anterior uveitis (n = 15), and no uveitis (normal) (n = 50) were included based on predefined inclusion and exclusion criteria for all three groups.