Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis.

Background: Many studies have explored the value of the systemic inflammation response index (SIRI) in predicting the prognosis of patients with breast cancer (BC); however, their findings remain controversial. Consequently, we performed the present meta-analysis to accurately identify the role of SIRI in predicting BC prognosis.

Methods: PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched between their inception and February 10, 2024.

PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation.

Skin lesion segmentation is a crucial step in the process of skin cancer diagnosis and treatment. The variation in position, shape, size and edges of skin lesion areas poses a challenge for accurate segmentation of skin lesion areas through dermoscopic images. To meet these challenges, in this paper, using UNet as the baseline model, a convolutional neural network based on position and context information fusion attention is proposed, called PCF-Net.

iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation.

In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and skip connections. However, limited by the inherent limitations of convolution, CNNs-based methods usually exhibit limitations in modeling long-range dependencies and are unable to extract large amounts of global contextual information, which deprives neural networks of the ability to adapt to different visual modalities. In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U.

Dermoscopic image segmentation based on Pyramid Residual Attention Module.

We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA.

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.

Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited.

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer.

Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder-decoder structure and skip-connection have been frequently used.

Effect of Hcp Iron Ion Regulation on the Interaction Between With Human Pulmonary Alveolar Epithelial Cells and Biofilm Formation.

is a type of bacterial nosocomial infection with severe drug resistance. Hemolysin co-regulated protein (Hcp) is a marker of activated type VI secretion system (T6SS), a key secretory system that promotes Gram-negative bacteria colonization, adhesion, and invasion of host cells. Hcp is also regulated by iron ions (Fe).

Dynamic Monitoring of sTREM-1 and Other Biomarkers in Acute Cholangitis.

Background: Sepsis is a common complication of acute cholangitis (AC), which is associated with a high mortality rate. Our study is aimed at exploring the significance of white blood cell (WBC), C-reactive protein (CRP), procalcitonin (PCT), soluble triggering receptor expressed on myeloid cells 1 (sTREM-1), and temperature (T) alone or combined together in early identification and curative effect monitoring of AC with or without sepsis.

Methods: 65 consecutive cases with AC and 76 control cases were enrolled.

Effect of calcium on the interaction of Acinetobacter baumannii with human respiratory epithelial cells.

Background: Investigating the factors that influence Acinetobacter baumannii(Ab) adhesion/invasion of host cells is important to understand its pathogenicity. Metal cations have been shown to play an important role in regulating the biofilm formation and increasing the virulence of Ab; however, the effect of calcium on host-bacterial interaction has yet to be clarified. Here, the dynamic process of the interaction between Ab and human respiratory epithelial cells and the effect of calcium on host-bacterial interaction were explored using microscopic imaging, quantitative PCR and real time cellular analysis (RTCA).