Immunohistochemical Expression of VEGF and Microvessel Density (CD 34) in Oral Epithelial Dysplasia and Oral Squamous Cell Carcinoma: Original Research.

Background: Angiogenesis, the formation of new blood vessels from preexisting ones via capillary sprouting, is a crucial process in tumor growth and metastasis. As a tumor's angiogenic capacity increases, its microvasculature, measured by micro vessel density (MVD), also increases. This study aims to evaluate the expression of Vascular Endothelial Growth Factor (VEGF) and CD34 in oral epithelial dysplasia and oral squamous cell carcinoma through immunohistochemical methods.

The nucleolin antagonist N6L and paclitaxel combination treatment could be a new promising therapeutic strategy for pancreatic ductal adenocarcinoma therapy.

Pancreatic cancer (PCa) is one of the most devastating cancers with few clinical signs and no truly effective therapy. In recent years, our team has demonstrated that nucleolin antagonists such as N6L could be a therapeutic alternative for this disease. In order to study a possible clinic development of N6L (multivalent pseudopeptide), we undertook to study the effect of combination of N6L with chemotherapies classically used for PCa on the survival of pancreatic cancer cells.

Estimated Long-Term Dietary Exposure to Cadmium from Consumption of Spinach in the United States: A Probabilistic Assessment.

As part of the U.S. Food and Drug Administration's (FDA's) Closer to Zero initiative, a toxicological reference value (TRV) for oral dietary exposure to Cd was established.

The Life and Work of Prof. Dr. Antapur Venkoba Rao: The Enduring Legacy of an Innovative Mind.

Dr. Antapur Venkoba Rao, born on August 20, 1927, in Andhra Pradesh, was a pioneering figure in Indian psychiatry, often recognized as the "Father of Indian Psychiatry" and the "Father of Geriatric Mental Health." His exceptional academic achievements led him to specialize in psychiatry, where he made substantial contributions, particularly in the study of depressive disorders.

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation.