Swine pseudorabies virus attenuated vaccine reprograms the kidney cancer tumor microenvironment and synergizes with PD-1 blockade.

The global incidence rate of kidney cancer (KC) has been steadily increasing over the past 30 years. With the aging global population, kidney cancer has become an escalating concern that necessitates vigilant surveillance. Nowadays, surgical intervention remains the optimal therapeutic approach for kidney cancer, while the availability of efficacious treatments for advanced tumors remains limited. Oncolytic viruses, an emerging form of immunotherapy, have demonstrated encouraging anti-neoplastic properties and are progressively garnering public acceptance. However, research on oncolytic viruses in kidney cancer is relatively limited. Furthermore, given the high complexity and heterogeneity of kidney cancer, it is crucial to identify an optimal oncolytic virus agent that is better suited for its treatment. The present study investigates the oncolytic activity of the Pseudorabies virus live attenuated vaccine (PRV-LAV) against KC. The findings clearly demonstrate that PRV-LAV exhibits robust oncolytic activity targeting KC cell lines. Furthermore, the therapeutic efficacy of PRV-LAV was confirmed in both a subcutaneous tumor-bearing nude mouse model and a syngeneic mouse model of KC. Combined RNA-seq analysis and flow cytometry revealed that PRV-LAV treatment substantially enhances the infiltration of a diverse range of lymphocytes, including T cells, B cells, macrophages, and NK cells. Additionally, PRV-LAV treatment enhances T cell activation and exerts antitumor effects. Importantly, the combination of PRV-LAV with anti-PD-1 antibodies, an approved drug for KC treatment, synergistically enhances the efficacy against KC. Overall, the discovery of PRV-LAV as an effective oncolytic virus holds significant importance for improving the treatment efficacy and survival rates of KC patients.

A novel approach for diabetic foot diagnosis: Deep learning-based detection of lower extremity arterial stenosis.

PURPOSE OF THE STUDY: Assessing the lower extremity arterial stenosis scores (LEASS) in patients with diabetic foot ulcer (DFU) is a challenging task that requires considerable time and efforts from physicians, and it may yield varying results. The presence of vascular wall calcification and other irrelevant tissue information surrounding the vessel can further compound the difficulties of this evaluation. Automatic detection of lower extremity arterial stenosis (LEAS) is expected to help doctors develop treatment plans for patients faster. METHODS: In this paper, we first reconstructed the 3D model of blood vessels by medical digital image processing and then utilized it as the training data for deep learning (DL) in conjunction with the non-calcified part of blood vessels in the original data. We proposed an improved model of vascular stenosis small target detection based on YOLOv5. We added Convolutional Block Attention Module (CBAM) in backbone, replaced Path Aggregation Network (PANET) with Bidirectional Feature Pyramid Network (BiFPN) and replaced C3 with GhostC3 in neck to improve the recognition of three types of stenosis targets (I: <50 %, II: 51 % - 99 %, III: completely occluded). Additionally, we utilized K-Means++ instead of K-Means for better algorithm convergence performance, and enhanced the Complete-IoU (CIoU) loss function to Alpha-Scylla-IoU (ASIoU) loss for faster reasoning and convergence. Lastly, we conducted comparisons between our approach and five other prominent models. RESULT: Our method had the best average ability to detect three types of stenosis with 85.40% mean Average Precision (mAP) and 74.60 Frames Per Second (FPS) and explored the possibility of applying DL to the detection of LEAS in diabetic foot. The code is available at github.com/wuchongxin/yolov5_LEAS.git.

Microglial Piezo1 senses Aß fibril stiffness to restrict Alzheimer's disease.

The pathology of Alzheimer's disease (AD) is featured with extracellular amyloid-ß (Aß) plaques, whose impact on the mechanical properties of the surrounding brain tissues is unclear. Microglia sense and integrate biochemical cues of the microenvironment. However, whether the microglial mechanosensing pathways influence AD pathogenesis is unknown. Here, we surveyed the elevated stiffness of Aß-plaque-associated tissues and observed the selective upregulation of the mechanosensitive ion channel Piezo1 in Aß-plaque-associated microglia. Piezo1 sensed the stiffness stimuli of Aß fibrils and subsequently induced Ca2+ influx for microglial clustering, phagocytosis, and compacting of Aß plaques. Microglia lacking Piezo1 led to the exacerbation of Aß pathology and cognitive decline, whereas pharmacological activation of microglial Piezo1 ameliorated brain Aß burden and cognitive impairment in 5 × FAD mice. Together, our results reveal that Piezo1, a mechanosensor of Aß fibril stiffness in microglia, represents a potential therapeutic target for AD.