No abstract

Prostate cancer (Pca) is the second most common cancer type worldwide. Microorganisms colonized in different body parts may affect the development/progression and treatment of Pca through direct or indirect interactions. The composition of microorganisms in different colonization sites and their effects on Pca may differ. In recent years, several studies have focused on the differences in the microbiota of patients with Pca, and dysbiosis may affect the inflammatory status, hormone levels and microbial metabolites leading to Pca progression. However, little is known about the interaction between Pca treatment and microorganisms; for example, how androgen deprivation therapy and androgen receptor axis-targeting therapeutics for Pca affect microbiota composition and metabolism, and how the microbiota affects treatment response in patients with Pca remain to be understood. The present review explored the current studies on the relevance of microbiota to Pca progression and treatment to provide direction for future microbiome-Pca research. Due to the complexity of the potential interconnections between Pca and the microbiota, further investigation is critical. Contents 1. Introduction 2. Microbiota and Pca 3. Conclusions and future directions

Background Inflammatory myofibroblastic tumor (IMT) is a rare disease that mostly occurs in younger people and is located in the lungs in the general population. We report a rare case of adrenal IMT in a patient with HIV infection, which is believed to be the first of its kind worldwide. Case presentation We present a rare case of a 44-year-old man with HIV infection who was diagnosed with adrenal IMT. The patient refused regular highly active antiretroviral therapy 13 years ago until he was admitted to hospital after an adrenal mass was found. The patient underwent successful computed-tomography-guided needle biopsy, and pathological analysis showed fibroblastic–myofibroblastic proliferation with inflammatory infiltration, which confirmed a diagnosis of IMT. We failed to perform complete resection of the tumor because of its diffuse invasion. The patient was complicated with severe multiple pulmonary infections postoperatively because of immunodeficiency, which eventually caused his death 2 months later. Conclusion Differential diagnosis of IMT is difficult, and tumor biopsy is an essential means of diagnosis. Surgical resection is preferred for both adrenal and HIV-related IMTs. Conservative treatment should be considered when there are technical difficulties with complete resection, and most patients have achieved good outcomes. However, more cases and longer follow-up are warranted to confirm long-term outcomes of HIV-related IMT.

This study was to explore the diagnostic value of magnetic resonance imaging (MRI) optimized by residual segmentation attention dual channel network (DRSA-U-Net) in the diagnosis of complications after renal transplantation and to provide a more effective examination method for clinic. 89 patients with renal transplantation were selected retrospectively, and all underwent MRI. The patients were divided into control group (conventional MRI image diagnosis) and observation group (MRI image diagnosis based on DRSA-U-Net). The accuracy of MRI images in the two groups was evaluated according to the comprehensive diagnostic results. The root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) of DRSA-U-Net on T1WI and T2WI sequences were better than those of U-Net and dense U-Net P < 0.05 ; comprehensive examination showed that 39 patients had obstruction between ureter and bladder anastomosis, 13 cases had rejection, 10 cases had perirenal hematoma, 5 cases had renal infarction, and 22 cases had no complications; the diagnostic sensitivity, specificity, accuracy, and consistency of the observation group were higher than those of the control group P < 0.05 . In the control group, the sensitivity, specificity, and accuracy in the diagnosis of complications after renal transplantation were 66.5%, 84.1%, and 78.32%, respectively; in the observation group, the sensitivity, specificity, and accuracy in the diagnosis were 67.8%, 86.7%, and 80.6%, respectively. DRSA-U-Net denoising algorithm can clearly display the information of MRI images on the kidney, ureter, and surrounding tissues, improve its diagnostic accuracy in complications after renal transplantation, and has good clinical application value.