Plasma Proteomic Analysis Reveals Complement System Changes in Irradiated Female BALB/c Mice during Mammary Carcinogenesis.

Unlabelled: The risk of breast cancer increases decades after ionizing radiation exposure, thereby linking aging intrinsically to the evolution of cancer. We hypothesized that radiation accelerates aging and carcinogenesis through similar pathways, specifically low-grade systemic inflammation. In this study, we used the radiation-genetic mammary chimera model to examine the differential expression of 532 plasma proteins in BALB/c female mice between radiation exposure and experiment termination at 18 months.

COUPLED SWIN TRANSFORMERS AND MULTI-APERTURES NETWORK(CSTA-NET) IMPROVES MEDICAL IMAGE SEGMENTATION.

Vision Transformers have outperformed traditional convolution-based frameworks across various visual tasks, including, but not limited to, the segmentation of 3D medical images. To further advance this area, this study introduces the Coupled Swin Transformers and Multi-Apertures Networks (CSTA-Net), which integrates the outputs of each Swin Transformer with an Aperture Network. Each aperture network consists of a convolution and a fusion block for combining global and local feature maps.

Multi-Aperture Transformers for 3D (MAT3D) Segmentation of Clinical and Microscopic Images.

3D segmentation of biological structures is critical in biomedical imaging, offering significant insights into structures and functions. This paper introduces a novel segmentation of biological images that couples Multi-Aperture representation with Transformers for 3D (MAT3D) segmentation. Our method integrates the global context-awareness of Transformer networks with the local feature extraction capabilities of Convolutional Neural Networks (CNNs), providing a comprehensive solution for accurately delineating complex biological structures.

3D-Organoid-SwinNet: High-Content Profiling of 3D Organoids.

Profiling of Patient-Derived organoids is necessary for drug screening and precision medicine. This step requires accurate segmentation of three-dimensional cellular structures followed by protein readouts. While fully Convolutional Neural Networks are widely used in medical image segmentation, they struggle to capture long-range dependencies necessary for accurate segmentation.

Self-supervised region-aware segmentation of COVID-19 CT images using 3D GAN and contrastive learning.

Medical image segmentation is a key initial step in several therapeutic applications. While most of the automatic segmentation models are supervised, which require a well-annotated paired dataset, we introduce a novel annotation-free pipeline to perform segmentation of COVID-19 CT images. Our pipeline consists of three main subtasks: automatically generating a 3D pseudo-mask in self-supervised mode using a generative adversarial network (GAN), leveraging the quality of the pseudo-mask, and building a multi-objective segmentation model to predict lesions.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning.

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis.