Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.

Background: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning.

Suprachoroidal Delivery in Rats and Guinea Pigs Using a High-Precision Microneedle Injector.

Purpose: Methods of injection into the suprachoroidal space (SCS) have been developed for larger animals and humans, but reliable administration to the SCS of rodents remains challenging given their substantially smaller eyes. Here, we developed microneedle (MN)-based injectors for SCS delivery in rats and guinea pigs.

Methods: We optimized key design features, including MN size and tip characteristics, MN hub design, and eye stabilization, to maximize injection reliability.

ETECADx: Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images.

Early detection of breast cancer is an essential procedure to reduce the mortality rate among women. In this paper, a new AI-based computer-aided diagnosis (CAD) framework called ETECADx is proposed by fusing the benefits of both ensemble transfer learning of the convolutional neural networks as well as the self-attention mechanism of vision transformer encoder (ViT). The accurate and precious high-level deep features are generated via the backbone ensemble network, while the transformer encoder is used to diagnose the breast cancer probabilities in two approaches: (i.

Enhanced immune responses to vaccine antigens in the corneal stroma.

To examine the widely accepted dogma that the eye is an immune-privileged organ that can suppress antigen immunogenicity, we explored systemic immune responses to a model vaccine antigen (tetanus toxoid) delivered to six compartments of the rodent eye (ocular surface, corneal stroma, anterior chamber, subconjunctival space, suprachoroidal space, vitreous body). We discovered that antigens delivered to corneal stroma induced enhanced, rather than suppressed, antigen-specific immune responses, which were 18- to 30-fold greater than conventional intramuscular injection and comparable to intramuscular vaccination with alum adjuvant. Systemic immune responses to antigen delivered to the other ocular compartments were much weaker.

A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.

Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism.