Silencing of ZNF610 suppresses cell proliferation and migration in lung adenocarcinoma.

Zinc finger proteins (ZNFs) play a significant role in the initiation and progression of tumors. Nevertheless, the specific contribution of ZNF610 to lung adenocarcinoma (LUAD) remains poorly understood. This study sought is to elucidate the role of ZNF610 in LUAD. Transcript data of LUAD were obtained from The Cancer Genome Atlas Program (TCGA) database and processed via R program. The expression of ZNF610 was assessed in various cell lines. To compare the proliferative capacity of cells with or without ZNF610 silencing, CCK8, cell colony formation assay, and Celigo label-free cell counting assay were employed. Furthermore, transwell migration and invasion assays were conducted to evaluate the migratory and invasive abilities of the cells. The expression levels of genes and proteins were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot techniques. In different LUAD cells, the expression level of ZNF610 was found to be significantly higher in LUAD cells compared to MRC-5 and BASE-2B cells. Moreover, the silencing of ZNF610 resulted in a decrease in cell proliferation and migration abilities. Additionally, the apoptosis rate of cells increased upon silencing ZNF610. Notably, the proportion of cells in the G0/G1 phase increased, while the proportion of cells in the S phase decreased following ZNF610 silencing. Finally, ß-catenin and snail were identified as downstream targets of ZNF610 in cells. Our findings suggest that silencing ZNF610 could inhibit LUAD cell proliferation and migration, possibly through the downregulation of ß-catenin and snail.

Effects of moderate static magnetic fields on the lipogenesis and lipolysis in different genders of Caenorhabditis elegans.

With the rapid development of magnetic technology, the biological effects of moderate static magnetic fields (SMFs) have attracted increasing research interest due to their potential medical diagnosis and treatment application. The present study explored the effects of moderate SMFs on the lipid metabolism of Caenorhabditis elegans (C. elegans) in different genders including male, female, and hermaphrodite. We found that the fat content was significantly decreased by moderate SMFs in wild-type N2 worms, which was associated with their development stages. The diameters of lipid droplets in N2 worms, him-5 worms, and fog-2 worms were greatly decreased by 19.23%, 15.38%, and 23.07% at young adult stage under 0.5 T SMF, respectively. The mRNA levels of lipolysis related genes atgl-1 and nhr-76 were significantly up-regulated by SMF exposure, while the mRNA levels of the lipogenesis related genes fat-6, fat-7, and sbp-1 were down-regulated by SMF, whereas the concentration of ß-oxidase was increased. There was a slight effect of SMF on the mRNA levels of ß-oxidation related genes. Moreover, the insulin and serotonin pathway were regulated by SMF, instead of the TOR pathway. In wild-type worms, we found that their lifespan was prolonged by exposure to 0.5 T SMF. Our data suggested that moderate SMFs could significantly modify the lipogenesis and lipolysis process in C. elegans in a gender and development stage-dependent manner, which could provide a novel insight into understanding the function of moderate SMFs in living organisms.

Integrated Smart Gas Tracking Device with Artificially Tailored Selectivity for Real-Time Monitoring Food Freshness.

The real-time monitoring of food freshness in refrigerators is of significant importance in detecting potential food spoiling and preventing serious health issues. One method that is commonly reported and has received substantial attention is the discrimination of food freshness via the tracking of volatile molecules. Nevertheless, the ambient environment of low temperature (normally below 4 °C) and high humidity (90% R.H.), as well as poor selectivity in sensing gas species remain the challenge. In this research, an integrated smart gas-tracking device is designed and fabricated. By applying pump voltage on the yttria-stabilized zirconia (YSZ) membrane, the oxygen concentration in the testing chamber can be manually tailored. Due to the working principle of the sensor following the mixed potential behavior, distinct differences in sensitivity and selectivity are observed for the sensor that operated at different oxygen concentrations. Typically, the sensor gives satisfactory selectivity to H2S, NH3, and C2H5OH at the oxygen concentrations of 10%, 30%, and 40%, respectively. In addition, an acceptable response/recovery rate (within 24 s) is also confirmed. Finally, a refrigerator prototype that includes the smart gas sensor is built, and satisfactory performance in discriminating food freshness status of fresh or semi-fresh is verified for the proposed refrigerator prototype. In conclusion, these aforementioned promising results suggest that the proposed integrated smart gas sensor could be a potential candidate for alarming food spoilage.

Method for Predicting RUL of Rolling Bearings under Different Operating Conditions Based on Transfer Learning and Few Labeled Data.

As industrial development increases, electric machine systems are more widely used in industrial production. Rolling bearings play a key role in machine systems and so the prevention of faults in rolling bearings is more important than ever before. Recently, with the development of artificial intelligence, neural networks have been used to monitor the remaining useful life of rolling bearings. However, there are two problems with this technique. First, a network trained by data for a single operating condition (source domain) cannot predict the remaining useful life of bearings under a different operating condition (target domain), such as a different load or speed. Second, a large number of labeled data are needed for network training, but the acquisition of labeled data for different operating conditions is a challenging task. To address these problems, this paper proposes a domain-adaptive adversarial network, in which a transfer learning strategy and maximum mean discrepancy algorithm are used for network optimization, so that remaining useful life can be predicted without labeled data in target domain training. Our results confirm that a model trained by source domain data alone cannot predict the remaining useful life of bearings under different conditions, but the domain-adaptive adversarial network can accurately predict remaining useful life for varying operating conditions. The method proposed also exhibits good performance even if there are noises in the signals.

Click Synthesis Enabled Sulfur Atom Strategy for Polymerization-Enhanced and Two-Photon Photosensitization.

Facile tailoring of photosensitizers (PSs) with advanced and synergetic properties is highly expected to broaden and deepen photodynamic therapy (PDT) applications. Herein, a catalyst-free thiol-yne click reaction was employed to develop the sulfur atom-based PSs by using the in situ formed sulfur "heavy atom effect" to enhance the intersystem crossing (ISC), while such an effect can be remarkably magnified by the polymerization. The introduction of a tetraphenylpyrazine-based aggregation-induced emission (AIE) unit was also advantageous in PS design by suppressing their non-radiative decay to facilitate the ISC in the aggregated state. Besides, the resulting sulfur atom electron donor, together with a double-bond π bridge and AIE electron acceptor, created a donor-π-acceptor (D-π-A) molecular system with good two-photon excitation properties. Combined with the high singlet oxygen generation efficiency, the fabricated polymer nanoparticles exhibited an excellent in vitro two-photon-excited PDT towards cancer cells, therefore possessing a huge potential for the deep-tissue disease therapy.

Silicon substrate significantly alters dipole-dipole resolution in coherent microscope.

Considering a coherent microscopy setup, influences of the substrate below the sample in the imaging performances are studied, with a focus on high refractive index substrate such as silicon. Analytical expression of 3D full-wave vectorial point spread function, i.e. the dyadic Green's function is derived for the optical setup together with the substrate. Numerical analysis are performed in order to understand and compare magnification, depth of field, and resolution when using silicon substrate versus the conventional glass substrate or usually modelled condition of no substrate. Novel insights are generated about the scope of resolution improvement due to near field effect of the silicon substrate. Importantly, we show that the expected resolution varies greatly with the position of the sources and the substrate interface relative to the focal plane. Both better and worse resolution as compared to glass substrate may be expected with small changes in their positions. Therefore, our studies show that deriving a single indicative number of expected resolution is neither possible nor judicious for the case of silicon substrate.

Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species.

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including genome sequence, gene models, transcript sequence, genetic variation, and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments and expansions. These include the incorporation of almost 20 000 additional genome sequences and over 35 000 tracks of RNA-Seq data, which have been aligned to genomic sequence and made available for visualization. Other advances since 2015 include the release of the database in Resource Description Framework (RDF) format, a large increase in community-derived curation, a new high-performance protein sequence search, additional cross-references, improved annotation of non-protein-coding genes, and the launch of pre-release and archival sites. Collectively, these changes are part of a continuing response to the increasing quantity of publicly-available genome-scale data, and the consequent need to archive, integrate, annotate and disseminate these using automated, scalable methods.

Comparative study on registration application of proprietary Chinese medicine in the Guangdong-Hong Kong-Macau Greater Bay Area of China.

Background: Proprietary Chinese medicine (PCM) is widely used in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) of China. However, the regulatory frameworks and procedures for PCM registration in the region are not well-established, and there are differences among the three jurisdictions. The study is aimed to compare the legal basis, regulatory guidelines, application requirements, and evaluation criteria in each jurisdiction. Methods: We conducted a comprehensive review of the registration application processes for PCMs in the Chinese mainland, Hong Kong, and Macau based on publicly available information from respective regulators. Results: The study found that the registration application process in the GBA was complex and time-consuming, with differences in requirements and procedures among the three jurisdictions. The study also identified several challenges faced by PCM manufacturers, such as the lack of harmonisation of regulatory requirements and procedures and the requirement of package inserts and labelling for PCM products. The study proposed recommendations for improving the registration process and promoting the development of the PCM industry in the GBA. Conclusion: This study provides a comprehensive understanding of the PCM product license application procedures and requirements in the GBA, coupled with discernment of their similarities and disparities, equips applicants with the knowledge to formulate an appropriate strategy for obtaining product approval. Exploring potential methods for harmonising the regulatory process stands to benefit manufacturers, regulators, and patients by improving efficiency and curtailing costs.

Regional scale terrace mapping in fragmented mountainous areas using multi-source remote sensing data and sample purification strategy.

As a stepped cross section of farmland built along the contour lines, terrace is widely distributed on hill-slopes. It changes the original surface slope and runoff coefficient, reduces soil nutrient loss, and has become the most important soil erosion control measure in China. Accurate terrace mapping at regional scale is crucial for soil conservation, agriculture sustainability and ecological planning. Due to the influence of cloudy and rainy weather, poor data availability makes it difficult to identify terrace distribution only using optical remote sensing images in mountainous areas. In this study, we incorporated multi-spectral optical and SAR data, features of terrain, texture and time sequence information, and proposed a pixel-based supervised classification method based on sample purification strategy to obtain a 10 m resolution terraced map in a plateau mountainous region. With 610 terrace/non-terrace validation sample data, 10-fold cross-validation was used to test the classification results. For identified terrace, the values of Overall Accuracy (OA), Producer's Accuracy (PA) and User's Accuracy (UA) stay stable above 90 %, the F1 score and Kappa coefficient show the smallest fluctuation and is stable in the range of 0.90-0.93 and 0.81-0.87, respectively. The accuracy evaluation of grid units show that the uncertainty of the terrace distribution is mainly concentrated in the north and south of the study area. Slope cultivated land, low-slope terrace and non-agricultural vegetation are easily mixed due to the heterogeneity of terrace features and the spectrum similarity among these land types. It should be noted that the features of time series and texture play a key role in the terrace recognition process, rather than terrain factors, which is different from previous studies. The sample purification strategy proposed provides a more reliable regional scale terrace distribution map compared to the existing product, and is potentially transferable to other mountainous areas as a robust approach for rapid identification of terrace.

Can Double J stent encrustation be predicted by risk analysis and nomogram?: A retrospective case-control study.

To explore the risk factors and develop a nomogram to predict Double J stent encrustation incidence. The general demographic characteristics and underlying risk factors of 248 patients with upper urinary tract calculus who underwent endoscopic lithotripsy and Double J stenting at the Fifth Affiliated Hospital of Sun Yat-Sen University between January 1st, 2018 and January 1st, 2023 were retrospectively analyzed. Among them,173 patients were randomly selected to form the development cohort. A multivariate logistic regression model was employed to identify the independent risk factors associated with Double J stent encrustation, and a nomogram was developed for predicting its occurrence. Additionally, 75 patients were randomly selected to form the validation cohort to validate the nomogram. Multivariate logistic regression analysis revealed that several factors were significantly associated with Double J stent encrustation: indwelling time (odds ratio [OR]1.051; 95% confidence interval [CI] 1.030-1.073, P < .001), urine PH (OR 2.198; 95% CI 1.061-4.539, P = .033), fasting blood glucose (OR 1.590; 95% CI 1.300-1.943, P < .001), and total cholesterol (OR 2.676; 95% CI 1.551-4618, P < .001).Based on these findings, A nomogram was developed to predict the occurrence of Double J stent encrustation. The nomogram demonstrated good performance with an area under the curve of 0.870 and 0.862 in the development and validation cohorts, respectively. Furthermore, the calibration curve indicated a well-fitted model. We constructed and validated an accessible nomogram to assist urologists in evaluating the risk factors associated with Double J stent encrustation and predicting its likelihood.

GenURL: A General Framework for Unsupervised Representation Learning.

Unsupervised representation learning (URL) that learns compact embeddings of high-dimensional data without supervision has achieved remarkable progress recently. However, the development of URLs for different requirements is independent, which limits the generalization of the algorithms, especially prohibitive as the number of tasks grows. For example, dimension reduction (DR) methods, t-SNE and UMAP, optimize pairwise data relationships by preserving the global geometric structure, while self-supervised learning, SimCLR and BYOL, focuses on mining the local statistics of instances under specific augmentations. To address this dilemma, we summarize and propose a unified similarity-based URL framework, GenURL, which can adapt to various URL tasks smoothly. In this article, we regard URL tasks as different implicit constraints on the data geometric structure that help to seek optimal low-dimensional representations that boil down to data structural modeling (DSM) and low-dimensional transformation (LDT). Specifically, DSM provides a structure-based submodule to describe the global structures, and LDT learns compact low-dimensional embeddings with given pretext tasks. Moreover, an objective function, general Kullback-Leibler (GKL) divergence, is proposed to connect DSM and LDT naturally. Comprehensive experiments demonstrate that GenURL achieves consistent state-of-the-art performance in self-supervised visual learning, unsupervised knowledge distillation (KD), graph embeddings (GEs), and DR.

Increased LOXL2 expression is related to poor prognosis in lung squamous cell carcinoma.

Background: The lysyl oxidate-like (LOXL) family was reported to be involved in the process of cancer development. However, the prognostic value of LOXL in lung cancer is unknown. We aimed to study the expression pattern and prognostic value of LOXL family members in lung squamous cell carcinoma (LUSC). Methods: The Wilcoxon test and logistic regression analysis were used to study the expression level of LOXLs and its correlation with clinical characteristics. The Kaplan-Meier method and Cox regression analysis were performed to estimate the correlation of LOXsL expression with the survival of LUSC patients. Receiver operator characteristic (ROC) curves were plotted, and areas under the curves (AUCs) were calculated to estimate the diagnostic and prognostic power of LOXL. Cell Counting Kit-8 (CCK-8) assays, wound healing assays and Transwell assays were used to estimate the impact of LOXL2 on LUSC cells. Results: LOXL1 and LOXL2 expression was upregulated in LUSC tissues (P<0.001). LOXL1 and LOXL2 showed high diagnostic power in LUSC patients, with AUCs of 0.784 and 0.751, respectively. Patients with high LOXL2 expression levels showed poor overall survival (OS) (P=0.019) and progression-free survival (PFS) (P=0.015). High LOXL2 expression was an independent prognostic factor for poor survival (P=0.026). Inhibition of LOXL2 suppressed proliferation, migration and invasion in LUSC cell lines. Conclusions: Increased LOXL2 was related to poor survival in LUSC. LOXL2 may be a potential prognostic biomarker and therapeutic target in LUSC.

Dual-targeting of tumor cells and subcellular endoplasmic reticulum via AgPPIX-based Janus nanoparticles for photodynamic/immunotherapy against TNBC.

Triple-negative breast cancer (TNBC) is known for its strong invasiveness, high recurrence rates, and poor prognosis. Heme oxygenase-1 (HO-1) is closely related to tumor invasion, metastasis, recurrence and formation of tumor immunosuppression. The expression of HO-1 is high in TNBC and low in normal tissues. In this study, AgPPIX was synthesized as a heme oxygenase-1 (HO-1) inhibitor and a photosensitizer for TNBC therapy. PDA nanoparticles were synthesized and modified with anti-CD24 and p-toluenesulfonamide (PTSC) on their both sides to obtain PTSC@AgPPIX/PDA@anti-CD24 Janus nanoparticles (PAPC) for AgPPIX-targeted delivery. Anti-CD24 is targeted to CD24 on tumor cells and the PTSC moiety is targeted to endoplasmic reticulum (ER), where HO-1 is located. The results indicated that PAPC Janus nanoparticles exhibited higher cytotoxicity in 4T1 cells than that of the mono-modified nanoparticles. PAPC not only inhibited the expression of HO-1 and VEGF but also reduced TrxR activity significantly. Furthermore, PAPC not only promoted intracellular ROS production under laser irradiation for tumor photodynamic therapy (PDT) but also polarized TAMs from M2-type to M1 for tumor immunotherapy. In vivo experiments confirmed that PAPC could remodel the tumor immune microenvironment and almost completely inhibit the tumor growth in mouse models. Therefore, PAPC Janus nanoparticles are a promising nanoplatform with a dual-targeting capacity for TNBC immune/PDT synergistic therapy.

MGL: Mutual Graph Learning for Camouflaged Object Detection.

Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps - one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://github.com/fanyang587/MGL.

Global epidemiology and burden of tetanus from 1990 to 2019: A systematic analysis for the Global Burden of Disease Study 2019.

OBJECTIVES: Tetanus is a serious infectious disease. In recent decades, the epidemiology and disease characteristics of tetanus have been reported by many medical workers, but these studies usually have limited sample sizes. METHODS: We retrieved all the epidemiological data related to tetanus from the Global Burden of Disease Study 2019, and a secondary analysis was performed to report the global epidemiology and disease burden of tetanus. RESULTS: From 1990 to 2019, the incidence and death rate of tetanus decreased worldwide. In general, high sociodemographic index (SDI) countries have lower age-standard incidence rates and age-standard death rates than low SDI countries. Moreover, in low SDI regions, newborns were the highest-risk group for tetanus. In high SDI areas, half of the tetanus cases occurred in the 70+ years age group. The disease burden of tetanus was significantly higher in males than in females. CONCLUSION: The disease burden of tetanus decreased significantly worldwide from 1990 to 2019. Neonatal tetanus is serious in low SDI areas, whereas the proportion of elderly tetanus is the highest in high SDI areas. The containment of tetanus in all age groups and sex still requires effort from all sectors.

Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions.

Graph neural networks (GNNs) have recently achieved remarkable success on a variety of graph-related tasks, while such success relies heavily on a given graph structure that may not always be available in real-world applications. To address this problem, graph structure learning (GSL) is emerging as a promising research topic where task-specific graph structure and GNN parameters are jointly learned in an end-to-end unified framework. Despite their great progress, existing approaches mostly focus on the design of similarity metrics or graph construction, but directly default to adopting downstream objectives as supervision, which lacks deep insight into the power of supervision signals. More importantly, these approaches struggle to explain how GSL helps GNNs, and when and why this help fails. In this article, we conduct a systematic experimental evaluation to reveal that GSL and GNNs enjoy consistent optimization goals in terms of improving the graph homophily. Furthermore, we demonstrate theoretically and experimentally that task-specific downstream supervision may be insufficient to support the learning of both graph structure and GNN parameters, especially when the labeled data are extremely limited. Therefore, as a complement to downstream supervision, we propose homophily-enhanced self-supervision for GSL (HES-GSL), a method that provides more supervision for learning an underlying graph structure. A comprehensive experimental study demonstrates that HES-GSL scales well to various datasets and outperforms other leading methods. Our code will be available in https://github.com/LirongWu/Homophily-Enhanced-Self-supervision.

Enhanced synergistic removal of Cu(II) and Cr(VI) with multifunctional biomass hydrogel from strong-acid media.

Simultaneous recovery of heavy metal ions (HMIs) such as Cu(II) and Cr(VI) from strong-acid media was a great challenge due to the inhibition of protons. Herein, a novel biomass hydrogel (CMC/PEI-PD) containing various groups (bis-picolylamine, amino, and hydroxyl groups) was newly prepared by a facile two-step process. The static experiments relating pH, kinetics and isothermal co-adsorption confirmed the synergistic effect towards Cu(II) and Cr(VI) consistently. Specifically, the adsorption capacities of Cu(II) and Cr(VI) at pH 2.0 increased by 23.73% and 40.18% in comparison with the single systems. Moreover, coexistence of inorganic anions and cations could further increase the adsorption of Cu(II) and Cr(VI) by 59.90% and 43.39%, respectively. At the same time, the adsorption and desorption ratios for both HMIs remained stable. The superior performance came from the two dominant mechanisms of co-removal. On the one hand, Cu(II) chelated by bis-picolylamine group attracted Cr(VI) in the form of cation bridge, thus promoting Cr(VI) adsorption. On the other hand, the protonated amine group attracted Cr(VI) by electrostatic interaction and weakened the inter-cationic repulsion by electrostatic shielding, thus promoting Cu(II) adsorption. In addition, the dynamic column experiment towards simulated acidic electroplating wastewater involving Cu(II)-Cr(VI)-Ni(II) certified the high efficiency and feasibility of the co-removal. Therefore, CMC/PEI-PD owned great potential in the separation of typical HMIs even directly from strong-acid media.

Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks.

Graph neural networks (GNNs) have been playing important roles in various graph-related tasks. However, most existing GNNs are based on the assumption of homophily, so they cannot be directly generalized to heterophily settings where connected nodes may have different features and class labels. Moreover, real-world graphs often arise from highly entangled latent factors, but the existing GNNs tend to ignore this and simply denote the heterogeneous relations between nodes as binary-valued homogeneous edges. In this article, we propose a novel relation-based frequency adaptive GNN (RFA-GNN) to handle both heterophily and heterogeneity in a unified framework. RFA-GNN first decomposes an input graph into multiple relation graphs, each representing a latent relation. More importantly, we provide detailed theoretical analysis from the perspective of spectral signal processing. Based on this, we propose a relation-based frequency adaptive mechanism that adaptively picks up signals of different frequencies in each corresponding relation space in the message-passing process. Extensive experiments on synthetic and real-world datasets show qualitatively and quantitatively that RFA-GNN yields truly encouraging results for both the heterophily and heterogeneity settings. Codes are publicly available at: https://github.com/LirongWu/RFA-GNN.

Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study.

Obstructive: To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. Methods: A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the training cohort (n = 189), internal validation cohort (n = 81) and external validation cohort (n = 84). Based on features selected by Wilcoxon test, Spearman Correlation Analysis and least absolute shrinkage and selection operator (LASSO) regression algorithm, six machine learning models based on radiomics features were established with six classifiers (LR, DT, SVM, RF, XGBoost, KNN). After comparison with those models, the most robust model was selected. Considering its feature importance as radscore, the combined model and a nomogram were constructed by incorporating indwelling time. Accuracy, sensitivity, specificity, area under the curve (AUC), decision curve analysis (DCA) and calibration curve were used to evaluate the recognition performance of models. Results: 1,409 radiomics features were extracted from 641 volumes of interest (VOIs) and 20 significant radiomics features were selected. Considering the superior performance (AUC 0.810, 95%CI, 0.722-0.888) in the external validation cohort, feature importance of XGBoost was used as a radscore, constructing a combined model and a nomogram with indwelling time. The accuracy, sensitivity, specificity and AUC of the combined model were 98, 100, 97.3% and 0.999 for the training cohort, 83.3, 80, 84.5% and 0.867 for the internal cohort and 78.2, 76.3, 78.8% and 0.820 for the external cohort, respectively. DCA indicates the favorable clinical utility of models. Conclusion: Machine learning model based on radiomics features enables to identify ureteral stent encrustation with high accuracy.

Chondroitin Polymerizing Factor (CHPF) promotes cell proliferation and tumor growth in human osteosarcoma by inhibiting SKP2's ubiquitination while activating the AKT pathway.

Osteosarcoma is a common malignant tumor occurring in children and young adults. Chondroitin sulfate (CS) participates in cell adhesion, cell division, and the formation of neural networks in the body, the biosynthesis of which requires the participation of glycosyltransferases. CHPF, a glycosyltransferase, plays a role in the extension of CS. Recently, CHPF's biological roles and functional importance in human diseases including malignant tumors have been widely discussed. However, whether CHPF is involved in osteosarcoma development and growth has not been revealed. The present work aimed to investigate the expression levels, functional significance and molecular mechanism of CHPF in osteosarcoma progression. Our results revealed that CHPF is strongly expressed in osteosarcoma tissues and cells. Furthermore, CHPF serves as a tumor promoter in the development and progression of osteosarcoma through enhancing cell proliferation and migration while suppressing apoptosis. Exploration of the mechanism by which CHPF promotes osteosarcoma indicated that CHPF promotes osteosarcoma through counteracting SKP2's ubiquitination and activating the Akt signaling pathway. For the first time, we clarified the roles of CHPF in osteosarcoma, and our results suggested that CHPF might be a novel therapeutic target in the treatment strategies for osteosarcoma.