Search

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.

Abstract 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.

In response to issues such as the lack of capability for timely early warning and the difficulty in monitoring the status of rolling bearings, a condition-monitoring method for rolling bearings based on the Honey Badger Algorithm (HBA) for optimizing dynamic asynchronous periods is proposed. This method is founded on the peak factor and involves comparing peak factors at different periods to construct a dynamic asynchronous peak-factor-ratio-monitoring index, which is then optimized using the HBA. Simulated experiments were carried out using the XJTU-SY dataset. The results indicate that, compared to the early warning times defined by international standards, the warning times provided using this method are consistently over 33 min in advance within the test dataset. Additionally, an envelope spectrum analysis of the warning data confirms the existence of early faults. This demonstrates that the monitoring indicator developed in this paper is capable of delivering earlier and more accurate early fault warnings and condition monitoring for rolling bearings.

Based on the daily precipitation data from 753 meteorological stations provided by the National Meteorological Information Center (China) and the daily reanalysis data from NCEP/NCAR and ERA5 during the period from 1980 to 2020, the low-frequency (LF) precipitation characteristics of the typical summer flood and drought years in southern China and their relation to the LF atmospheric circulation at different latitudes are compared and analyzed, and extended-range forecasting signals are given. The results show that: (a) In both flood and drought years, summer precipitation in southern China is controlled by 10–20 day oscillation (quasi-biweekly oscillation, QBWO); (b) LF convection is active in southern China in both flood and drought years, but the convective center is southward in flood years, and the vertical meridional circulation is stronger. The key circulation systems of 500 hPa LF height field in flood and drought years include LF “two ridges and one trough” and LF “+”, “−”, “+” East Asia Pacific (EAP) teleconnection wave train in mid-high latitudes of Eurasia. However, the “two ridges and one trough” in flood years are more westward and meridional than in drought years, and the LF Subtropical High is stronger and more extensive, with more significant westward extension; (c) In flood (drought) years, there is northerly and then westerly (central westerly) dry-cold, northeasterly wet-cold, southwesterly (none), and southeasterly (including southerly across the equator) wet-warm water vapor channels. The sources of dry and wet cold air in flood (drought) years are located near Novaya Zemlya (the eastern West Siberian Plain), the Yellow Sea, and the Bohai Sea (Sea of Japan). Additionally, the sources of wet-warm water vapor include the Arabian Sea, the Bay of Bengal, the western Pacific Ocean, and the sea area of northeastern Australia (the western Pacific Ocean and the northern sea area of Australia); and (d) The LF predictive signals of outgoing longwave radiation (OLR) appear on −11 days, while the signals of the 500 hPa height field are on −9 days. There are both westward and eastward propagation predictive signals in flood years, and only westward spread signals in drought years.