Objectives The aim of this study was to use deep learning (DL) of intraoperative images of urinary stones to predict the composition of urinary stones. In this way, the laser frequency and intensity can be adjusted in real time to reduce operation time and surgical trauma. Materials and methods A total of 490 patients who underwent holmium laser surgery during the two-year period from March 2021 to March 2023 and had stone analysis results were collected by the stone laboratory. A total of 1658 intraoperative stone images were obtained. The eight stone categories with the highest number of stones were selected by sorting. Single component stones include calcium oxalate monohydrate (W1), calcium oxalate dihydrate (W2), magnesium ammonium phosphate hexahydrate, apatite carbonate (CH) and anhydrous uric acid (U). Mixed stones include W2 + U, W1 + W2 and W1 + CH. All stones have intraoperative videos. More than 20 intraoperative high-resolution images of the stones, including the surface and core of the stones, were available for each patient via FFmpeg command screenshots. The deep convolutional neural network (CNN) ResNet-101 (ResNet, Microsoft) was applied to each image as a multiclass classification model. Results The composition prediction rates for each component were as follows: calcium oxalate monohydrate 99% (n = 142), calcium oxalate dihydrate 100% (n = 29), apatite carbonate 100% (n = 131), anhydrous uric acid 98% (n = 57), W1 + W2 100% (n = 82), W1 + CH 100% ( n = 20) and W2 + U 100% (n = 24). The overall weighted recall of the cellular neural network component analysis for the entire cohort was 99%. Conclusion This preliminary study suggests that DL is a promising method for identifying urinary stone components from intraoperative endoscopic images. Compared to intraoperative identification of stone components by the human eye, DL can discriminate single and mixed stone components more accurately and quickly. At the same time, based on the training of stone images in vitro, it is closer to the clinical application of stone images in vivo. This technology can be used to identify the composition of stones in real time and to adjust the frequency and energy intensity of the holmium laser in time. The prediction of stone composition can significantly shorten the operation time, improve the efficiency of stone surgery and prevent the risk of postoperative infection.

This retrospective study aims to examine the correlation between calcium oxalate (CaOx) stones and common clinical tests, as well as urine ionic composition. Additionally, we aim to develop and implement a personalized model to assess the accuracy and feasibility of using charts to predict calcium oxalate stones in patients with urinary tract stones. A retrospective analysis was conducted on data from 960 patients who underwent surgery for urinary stones at the First Affiliated Hospital of Soochow University from January 1, 2010, to December 31, 2022. Among these patients, 447 were selected for further analysis based on screening criteria. Multivariate logistic regression analysis was then performed to identify the best predictive features for calcium oxalate stones from the clinical data of the selected patients. A prediction model was developed using these features and presented in the form of a nomogram graph. The performance of the prediction model was assessed using the C-index, calibration curve, and decision curve, which evaluated its discriminative power, calibration, and clinical utility, respectively. The nomogram diagram prediction model developed in this study is effective in predicting calcium oxalate stones which is helpful in screening and early identification of high-risk patients with calcium oxalate urinary tract stones, and may be a guide for urologists in making clinical treatment decisions.

INTRODUCTION:Bladder cancer (BC) is a major health concern that poses a significant threat to the population, with an increasing incidence rate and a high risk of recurrence and progression. The primary clinical method for diagnosing BC is cystoscopy, but due to the limitations of traditional white light cystoscopy and inadequate clinical experience among junior physicians, its detection rate for bladder tumor, especially small and flat lesions, is relatively low. However, recent years have seen remarkable advancements in the application of artificial intelligence (AI) technology in the field of medicine. This has led to the development of numerous AI algorithms that have been successfully integrated into medical practices, providing valuable assistance to clinicians. The purpose of this study is to develop a cystoscopy algorithm that is real time, cost effective, high performing, and accurate, with the aim of enhancing the detection rate of bladder tumors during cystoscopy.MATERIALS AND METHODS:For this study, a dataset of 3,500 cystoscopic images obtained from 100 patients diagnosed with BC was collected, and a deep learning model was developed utilizing the U-Net algorithm within a convolutional neural network for training purposes.RESULTS:This study randomly divided 3,500 images from 100 BC patients into training and validation groups, and each patient's pathology result was confirmed. In the validation group, the accuracy of tumor recognition by the U-Net algorithm reached 98% compared to primary urologists, with greater accuracy and faster detection speed.CONCLUSION:This study highlights the potential of U-Net-based deep learning techniques in the detection of bladder tumors. The establishment and optimization of the U-Net model is a significant breakthrough and it provides a valuable reference for future research in the field of medical image processing.

BackgroundKidney renal clear cell carcinoma (KIRC) is a major subtype of renal cell carcinoma with poor prognosis due to its invasive and metastatic nature. Despite advances in understanding the molecular underpinnings of various cancers, the role of branched-chain amino acid transferase 1 (BCAT1) in KIRC remains underexplored. This study aims to fill this gap by investigating the oncogenic role of BCAT1 in KIRC using single-cell RNA-seq data and experimental validation.MethodsSingle-cell transcriptomic data GSE159115 was utilized to investigate potential biomarkers in KIRC. After screening, we used BCAT1 as a target gene and investigated its function and mechanism in KIRC through databases such as TCGA-GTEx, using genome enrichment analysis (GSEA), genome variation analysis (GSVA), gene ontology (GO) and Kyoto Encyclopedia of the Genome (KEGG). BCAT1 expression was detected in clinical tissue samples using Western Blotting (WB) and immunohistochemical (IHC) staining techniques. We established cell lines stably overexpressing and knocking down BCAT1 and performed WB, qRT-PCR, cell scratch assay and transwell assay.ResultsBCAT1 was highly expressed in KIRC and was associated with disease prognosis and TME. Patients with mutations in the BCAT1 gene had shorter overall survival (OS) and disease-free survival (DFS). patients with high BCAT1 expression had shorter OS, progression-free interval (PFI), and disease-specific survival (DSS). GSEA showed that BCAT1 was significantly enriched in epithelial mesenchymal transition (EMT). Bioinformatics analysis and WB and IHC staining showed that BCAT1 expression was higher in KIRC than in paracancerous tissues. In vitro experiments confirmed that BCAT1 in KIRC cells may promote EMT affecting its invasion, migration. We constructed a protein interaction network (PPI) to hypothesize proteins that may interact with BCAT1. Single-sample gene set enrichment analysis (ssGSEA) revealed the immune infiltration environment of BCAT1. Furthermore, hypomethylation of the BCAT1 promoter region in KIRC may contribute to disease progression by promoting BCAT1 expression.ConclusionBCAT1 promotes KIRC invasion and metastasis through EMT and has prognostic predictive value and potential as a biomarker. It may become a novel biomarker.

前列腺尿路上皮型黏液腺癌是一种十分罕见的肿瘤,进展快、预后差,必须与其他黏液性肿瘤进行鉴别.这种区别对诊断和治疗具有重要意义,但仅从形态学和免疫组化特征很难确诊这种罕见的肿瘤.早期的鉴别诊断极为重要.本文报告了1例源自前列腺尿道的尿路上皮型黏液腺癌,对该病临床表现、病理特点/治疗防范及预后等进行分析总结如下.

背景与目的 肾上腺偶发瘤是在腹部影像学检查中偶然发现的病变,由于肿瘤的位置和大小、影像学诊断技术的限制、医疗机构的水平和医生的经验等等,肾上腺偶发瘤难以明确诊断.因此,对肾上腺偶发瘤诊疗过程的研究具有重要的现实意义,可以为临床医生准确诊断肾上腺偶发瘤提供指导,为患者的治疗和康复提供帮助.本研究旨在探讨偶然发现的肾上腺区占位病变与肾上腺肿瘤的鉴别诊断,以及患者入院后的诊断计划.方法 回顾分析我院2021年收治的2例外院误诊为肾上腺肿瘤的肾上腺区占位病变患者,并在PubMed数据库检索2015年以来,国内外报道的其他误诊为肾上腺肿瘤的病变或解剖异常的患者资料,利用统计学软件SPSS进行综合分析.结果 共收集到68例符合要求的患者病例,包括副脾8例、巨大淋巴结病(castleman病)7例、腹膜后支气管囊肿23例、胃肠道间质瘤2例、淋巴管瘤11例、胃憩室2例、腹膜后畸胎瘤13例、肺隔离症2例.女性患者(57.8％)多于男性患者(41.2％);左侧(69.1％)多于右侧(29.4％).结论 肾上腺偶发肿瘤的诊断往往是排他性的,在诊断过程中,完善各种检查,包括影像学检查和相关激素检测具有重要意义.如有必要,还需完善相关的专业检查,消除误诊可能性.

Abstract Background Elevated expression and promotion of metastasis have been reported for branched-chain amino acid transferase 1 (BCAT1) in various malignancies, such as myeloid leukaemia and glioma. The effect of BCAT1 on the incidence and progression of clear cell renal cell carcinoma (KIRC) is currently unknown. Methods Single-cell transcriptomic data GSE159115 was utilized to investigate potential biomarkers in KIRC. After screening, we used BCAT1 as a target gene and investigated its function and mechanism in KIRC through databases such as TCGA-GTEx, using genome enrichment analysis (GSEA), genome variation analysis (GSVA), gene ontology (GO) and Kyoto Encyclopedia of the Genome (KEGG). BCAT1 expression was detected in clinical tissue samples using Western Blotting (WB) and immunohistochemical (IHC) staining techniques. We established cell lines stably overexpressing and knocking down BCAT1 and performed WB, qRT-PCR, cell scratch assay and transwell assay. Results BCAT1 was highly expressed in KIRC and was associated with disease prognosis and TME. Patients with mutations in the BCAT1 gene had shorter overall survival (OS) and disease-free survival (DFS). patients with high BCAT1 expression had shorter OS, progression-free interval (PFI), and disease-specific survival (DSS). GSEA showed that BCAT1 was significantly enriched in epithelial mesenchymal transition (EMT). Bioinformatics analysis and WB and IHC staining showed that BCAT1 expression was higher in KIRC than in paracancerous tissues. In vitro experiments confirmed that BCAT1 in KIRC cells may promote EMT affecting its invasion, migration. We constructed a protein interaction network (PPI) to hypothesize proteins that may interact with BCAT1. Single-sample gene set enrichment analysis (ssGSEA) revealed the immune infiltration environment of BCAT1. Furthermore, hypomethylation of the BCAT1 promoter region in KIRC may contribute to disease progression by promoting BCAT1 expression. Conclusion BCAT1 promotes KIRC invasion and metastasis through EMT and has prognostic predictive value and potential as a biomarker. It may become a novel biomarker.