Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds.

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach.

Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge's health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.

Decoding emotion with phase-amplitude fusion features of EEG functional connectivity network.

Decoding emotional neural representations from the electroencephalographic (EEG)-based functional connectivity network (FCN) is of great scientific importance for uncovering emotional cognition mechanisms and developing harmonious human-computer interactions. However, existing methods mainly rely on phase-based FCN measures (e.g., phase locking value [PLV]) to capture dynamic interactions between brain oscillations in emotional states, which fail to reflect the energy fluctuation of cortical oscillations over time. In this study, we initially examined the efficacy of amplitude-based functional networks (e.g., amplitude envelope correlation [AEC]) in representing emotional states. Subsequently, we proposed an efficient phase-amplitude fusion framework (PAF) to fuse PLV and AEC and used common spatial pattern (CSP) to extract fused spatial topological features from PAF for multi-class emotion recognition. We conducted extensive experiments on the DEAP and MAHNOB-HCI datasets. The results showed that: (1) AEC-derived discriminative spatial network topological features possess the ability to characterize emotional states, and the differential network patterns of AEC reflect dynamic interactions in brain regions associated with emotional cognition. (2) The proposed fusion features outperformed other state-of-the-art methods in terms of classification accuracy for both datasets. Moreover, the spatial filter learned from PAF is separable and interpretable, enabling a description of affective activation patterns from both phase and amplitude perspectives.

Accelerated Pure Shift NMR Spectroscopy with Deep Learning.

Pure shift nuclear magnetic resonance (NMR) spectroscopy presents a promising solution to provide sufficient spectral resolution and has been increasingly applied in various branches of chemistry, but the optimal resolution is generally accompanied by long experimental times. We present a proof of concept of deep learning for fast, high-quality, and reliable pure shift NMR reconstruction. The deep learning (DL) protocol allows one to eliminate undersampling artifacts, distinguish peaks with close chemical shifts, and reconstruct high-resolution pure shift NMR spectroscopy along with accelerated acquisition. More meaningfully, the lightweight neural network delivers satisfactory reconstruction performance on personal computers by several hundred simulated data learning, which somewhat lifts the prohibiting demand for a large volume of real training samples and advanced computing hardware generally required in DL projects. Additionally, an M-to-S strategy applicable to common DL cases is further exploited to boost the network generalization capability. As a result, this study takes a meaningful step toward deep learning protocols for broad chemical applications.

Research progress on plant functional traits in agroecosystems.

Plant functional traits have an essential role in community formation and ecosystem function. Unlike natu-ral ecosystems, agroecosystems are affected by natural conditions and human management. Plant (i.e., crop)functional traits in the agroecosystems are thus shaped by natural and human selection. We reviewed the development of functional trait research, focusing on crop functional traits, research methods of agroecosystems based on traits, and the application of functional traits in agriculture. We then elaborated on the effect of domestication on crop trait trade-offs, the relationship between community functional structure and agroecosystem services, and the application of functional traits in weed management, cover cropping, and intercropping systems. Future research directions of crop functional traits include enriching the types of agroecosystems and crop species in studies, buil-ding up a crop functional trait database, and focusing on agroecosystem multifunctionality.

Music-emotion EEG coupling effects based on representational similarity.

BACKGROUND: Music can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli. NEW METHOD: We used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands. RESULTS: The results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

A 5.5-kb LTR-retrotransposon insertion inside phytochrome B gene (CsPHYB) results in long hypocotyl and early flowering in cucumber (Cucumis sativus L.).

KEY MESSAGE: The novel spontaneous long hypocotyl and early flowering (lhef) mutation in cucumber is due to a 5551-bp LTR-retrotransposon insertion in CsPHYB gene encoding PHYTOCHROME B, which plays a major role in regulating photomorphogenic hypocotyl growth and flowering. Hypocotyl length and flowering time are important for establishing high-quality seedlings in modern cucumber production, but little is known for the underlying molecular mechanisms of these two traits. In this study, a spontaneous cucumber long hypocotyl and early flowering mutant was identified and characterized. Based on multiple lines of evidence, we show that cucumber phytochrome B (CsPHYB) is the candidate gene for this mutation, and a 5551-bp LTR-retrotransposon insertion in the first exon of CsPHYB was responsible for the mutant phenotypes. Uniqueness of the mutant allele at CsPHYB was verified in 114 natural cucumber lines. Ectopic expression of the CsPHYB in Arabidopsis phyB mutant rescued the long hypocotyl and early flowering phenotype of phyB-9 mutant. The wild-type CsPHYB protein was localized on the membrane and cytoplasm under white light condition, whereas in the nucleus under red light, it is consistent with its roles as a red-light photoreceptor in Arabidopsis. However, the mutant csphyb protein was localized on the membrane and cytoplasm under both white and red-light conditions. Expression dynamics of CsPHYB and several cell elongation-related genes were positively correlated with hypocotyl elongation; the transcription levels of key positive and negative regulators for flowering time were also consistent with the anthesis dates in the mutant and wild-type plants. Yeast two hybrid and bimolecular fluorescence complementation assays identified physical interactions between CsPHYB and phytochrome interacting factor 3/4 (CsPIF3/4). These findings will provide new insights into the roles of the CsPHYB in cucumber hypocotyl growth and flowering time.

Association of depressive symptoms, Internet addiction and insomnia among medical students in Anhui Province / 中国学校卫生

Objective@#To investigate the status of insomnia, Internet addiction, and depressive symptoms among medical students and to analyze the effect of Internet addiction on insomnia and the mediating role of depressive symptoms, in order to provide a basis for the development of targeted interventions and measurements for medical students.@*Methods@#A stratified whole group sampling method was used to select full-time college students from three medical universities in Anhui Province. The Chinese version of Insomnia Severity Index (ISI), Internet Addiction Test (IAT) scale and 9-item Patient Health Questionnaire (PHQ-9) were used to evaluate the symptoms of insomnia, Internet addiction and depressive in students. A multivariate Logistic regression analysis was used to explore the factors influencing insomnia among medical students and to analyze the relationship between insomnia with Internet addiction and depressive symptoms, respectively.@*Results@#The overall rate of Internet addiction was 49.5%, depressive symptoms was 39.5%, insomnia was 18.6%. High academic stress, and the presence of surrounding people diagnosed with COVID-19 were associated with a higher risk of insomnia ( P <0.05). The higher the level of Internet addiction (mild, OR =2.60; moderate/severe, OR =4.21) and depression. (mild, OR =6.35; moderate/severe, OR =19.32), the higher the risk of insomnia. Mediated effect analysis showed that Internet addiction had a direct predictive effect ( β =0.02, P <0.05) on insomnia and also indirectly affected insomnia through depression (indirect effect=0.07，95% CI =0.06-0.08).@*Conclusion@#The detected rates of insomnia, Internet addiction and depressive symptoms are high among medical students in Anhui Province, and Internet addiction and depressive symptoms are risk factors for insomnia, which should be given more attention and appropriate interventions when necessary to improve their physical and mental health.

Global attitudes on and the status of enteral nutrition therapy for pediatric inflammatory bowel disease.

Enteral nutrition (EN) is a diet-remission therapy for inflammatory bowel disease (IBD) that plays a more important role in children than adults. EN includes exclusive enteral nutrition (EEN), partial enteral nutrition (PEN), and maintenance enteral nutrition (MEN). However, EEN remains an unstandardized treatment for pediatric IBD. The types and methods of EN differ around the world. The current study reviewed the EN literature on children with IBD. A total of 12 survey studies were identified that analyzed the current state of EN use, including clinical opinions, implementation methods, treatment course, EEN formula, IBD classification, progress, dietary reintroduction, and patient feedback. The findings revealed that EEN has a strong effect on mild to moderate Crohn's disease (CD). The usage rates of this treatment in different sites were ileum/colon (Paris classification L3) > ileum (L1) > upper digestive tract (L4) > colon (L2) > perianal disease (P) > ulcerative colitis (UC) > extraintestinal lesions. The polymeric formula was the most used EN formulation. New EN diets include a CD exclusion diet (CDED), a specific carbohydrate diet (SCD), and a CD treatment-with-eating (CD-TREAT) diet. Children with IBD responded similarly to EEN administered orally or using a feeding tube. Most guidelines recommended 6-8 weeks of EEN treatment to induce remission. Many clinicians preferred to combine drug medications during EEN and recommended that MEN accounts for at least 25-35% of daily caloric intake. EN remains an unstandardized therapy that requires teamwork across disciplines.

Construction of High-Density Genetic Map and Identification of a Bruchid Resistance Locus in Mung Bean (Vigna radiata L.).

Mung bean (Vigna radiata L.) is an economically important grain legume cultivated in Asian countries. High-density genetic linkage is a valuable and effective tool for mapping quantitative trait loci (QTL). In the current study, a high-resolution genetic map containing 4,180 single-nucleotide polymorphisms (SNPs) was assigned to 11 linkage groups (LGs) and spanning 1,751.39 cM in length was constructed for mung bean, and the average distance between adjacent markers was 0.42 cM. Bruchids (Callosobruchus spp.) cause significant damage to and loss of legume seeds. A locus for bruchid resistance was detected. The gene Vradi05g03810, encoding a probable resistance-specific protein, was found to be the most likely key candidate gene in mung beans. A 69-bp sequence deletion was identified in the coding region by comparing the cDNA sequences of bruchid-resistant and bruchid-susceptible lines. This SNP-based high-density linkage map is one of the first to be constructed across the mung bean genome. This map will not only facilitate the genetic mapping of genes or complex loci that control important agronomic traits but also offer a tool for promoting future genetics and comparative genomic studies in Vigna.

Genetic Diversity and Environmental Influence on Yield and Yield-Related Traits of Adzuki Bean (Vigna angularis L.).

Adzuki beans are an important food legume crop in East Asia. A large number of adzuki bean accessions are maintained in the Chinese national seed genebank. A collection of 59 elite cultivars, 389 landraces, and 27 wild adzuki beans were selected and phenotyped extensively for yield and yield-related traits at two different locations (Nanning and Nanyang, China). Ten agronomic and yield-related traits were scored, and the data were subjected to analysis of variance (ANOVA), principal component analysis (PCA), correlation, and cluster analysis. Significant variation was observed for genotypes, locations, and genotype x environment interaction for most traits. Also, there were significant differences in the phenotypes among accessions of different germplasm types. The broad-sense heritability of traits studied ranged from 4.4% to 77.8%. The number of seeds per pod (77.8%), 100-seed weight (68.0%), and number of plant branches (63.9%) had a high heritability. A total of 10 traits were transformed into 3 comprehensive factors by principal component analysis, and the first three principal component factors contributed 72.31% of the total variability. Cluster analysis categorized the 475 adzuki bean accessions into five distinct groups. The results described in this study will be useful for adzuki bean breeders for the development of varieties with high end-use quality.

Using aquatic animals as partners to increase yield and maintain soil nitrogen in the paddy ecosystems.

Whether species coculture can overcome the shortcomings of crop monoculture requires additional study. Here, we show how aquatic animals (i.e. carp, crabs, and softshell turtles) benefit paddy ecosystems when cocultured with rice. Three separate field experiments and three separate mesocosm experiments were conducted. Each experiment included a rice monoculture (RM) treatment and a rice-aquatic animal (RA) coculture treatment; RA included feed addition for aquatic animals. In the field experiments, rice yield was higher with RA than with RM, and RA also produced aquatic animal yields that averaged 0.52-2.57 t ha-1. Compared to their corresponding RMs, the three RAs had significantly higher apparent nitrogen (N)-use efficiency and lower weed infestation, while soil N contents were stable over time. Dietary reconstruction analysis based on 13C and 15N showed that 16.0-50.2% of aquatic animal foods were from naturally occurring organisms in the rice fields. Stable-isotope-labeling (13C) in the field experiments indicated that the organic matter decomposition rate was greater with RA than with RM. Isotope 15N labeling in the mesocosm experiments indicated that rice used 13.0-35.1% of the aquatic animal feed-N. All these results suggest that rice-aquatic animal coculture increases food production, increases N-use efficiency, and maintains soil N content by reducing weeds and promoting decomposition and complementary N use. Our study supports the view that adding species to monocultures may enhance agroecosystem functions.

Monoculture, where only one type of crop is grown to the exclusion of any other organism, is a pillar of modern agriculture. Yet this narrow focus disregards how complex inter-species interactions can increase crop yield and biodiversity while decreasing the need for fertilizers or pesticides. For example, many farmers across Asia introduce carps, crabs, turtles or other freshwater grazers into their rice paddies. This coculture approach yields promising results but remains poorly understood. In particular, it is unclear how these animals' behaviours and biological processes benefit the ecosystem. To examine these questions, Guo, Zhao et al. conducted three separate four-year field experiments; they compared rice plots inhabited by either carp, mitten crabs or Chinese softshell turtles with fields where these organisms were not present. With animals, the rice paddies had less weeds, better crop yields and steady levels of nitrogen (a natural fertiliser) in their soil. These ecosystems could breakdown organic matter faster, use it better and had a reduced need for added fertilizer. While animal feed was provided in the areas that were studied, carp, crabs and turtles obtained up to half their food from the field itself, eating weeds, algae and pests and therefore reducing competition for the crops. This work helps to understand the importance of species interactions, showing that diversifying monocultures may boost yields and make agriculture more sustainable.

Deep neural networks for magnetic resonance elastography acceleration in thermal-ablation monitoring.

PURPOSE: To develop a deep neural network for accelerating magnetic resonance elastography (MRE) acquisition, to validate the ability to generate reliable MRE results with the down-sampled k-space data, and to demonstrate the feasibility of the proposed method in monitoring the stiffness changes during thermal ablation in a phantom study. MATERIALS AND METHODS: MRE scans were performed with 60 Hz excitation on porcine ex-vivo liver gel phantoms in a 0.36T MRI scanner to generate the training dataset. The acquisition protocol was based on a spin-echo MRE pulse sequence with tailored motion-sensitive gradients to reduce echo time (TE). A U-Net based deep neural network was developed and trained to interpolate the missing data from down-sampled k-space. We calculated the errors of 80 sets magnitude/phase images reconstructed from the zero-filled, compressive sensing (CS) and deep learning (DL) method for comparison. The peak signal-to-noise rate (PSNR) and structural similarity index (SSIM) of the magnitude/phase images were also calculated for comparison. The stiffness changes were recorded before, during, and after ablation. The mean stiffness values over the region of interest (ROI) were compared between the elastograms reconstructed from the fully sampled k-space and interpolated k-space after thermal ablation. RESULTS: The mean absolute error (MAE), PSNR, and SSIM of the proposed DL approach were significantly better than the results from the zero-filled method (p < 0.0001) and CS (p < 0.0001). The stiffness changes before and after thermal ablation assessed by the proposed approach (before: 7.7±1.1 kPa, after: 11.9±4.0 kPa, 4.2-kPa increase) gave close agreement with the values calculated from the fully sampled data (before: 8.0±1.0 kPa, after: 12.6±4.2 kPa, 4.6-kPa increase). In contrast, the stiffness changes computed from the zero-filled method (before: 4.9±1.4 kPa, after: 5.6±2.8 kPa, 0.7-kPa increase) were substantially underestimated. CONCLUSION: This study demonstrated the capability of the proposed DL method for rapid MRE acquisition and provided a promising solution for monitoring the MRI-guided thermal ablation.

Genome-wide identification and expression profiles of AP2/ERF transcription factor family in mung bean (Vigna radiata L.).

Mung bean (Vigna radiata L. Wilczek) is an economically important grain legume crop in Asia, with high nutritional quality and potential in other parts of the world particularly arid and semiarid regions. Considering the potential adverse effects of drought, high salt, and other abiotic stresses on crop yield, significant efforts have been made to understand the underlying molecular mechanisms of tolerance to these abiotic stresses in legumes. In this study, a total of 186 putative AP2/ERF genes were identified, which were named VrERF1-186. These VrERF genes were classified into four main subfamilies according to the number of AP2 domains and sequence similarity, including 24 AP2 gene members, 81 ERF gene members, 79 DREB gene members, and 2 RAV members. VrERF genes are scattered across all 11 chromosomes and form small gene clusters on chromosomes due to segmental or tandem duplication. Promoter analysis revealed various cis-acting elements related to light, hormones, and stress responsiveness processes. The expression profiles of the VrERF genes in tissues during development and in response to abiotic stresses were assessed by transcriptome sequencing, and the selected reference genes were validated by qRT-PCR. A total of 174 VrERF genes were expressed in at least one of five tissues, while others showed distinct expression patterns in different tissues or under specific abiotic stress treatments, which indicates that VrERF genes are involved in developmental and environmental stress responses in V. radiata. In conclusion, the genome localization, genome-wide characterization, gene duplication, phylogenetic relationships, and expression pattern of VrERF genes in V. radiata were analyzed, and these results will lay the foundation for further functional analysis of these genes and improve stress tolerance to adverse conditions in plants.

Case report: Partial cystectomy for pheochromocytoma of the urinary bladder: A case report and review of literature.

Pheochromocytomas are neuroendocrine tumors that produce catecholamines and can be difficult to diagnose. Bladder involvement is uncommon with pheochromocytoma. Hypertension (sometimes with hypertensive crisis coinciding with micturition), headache, hematuria and syncope, which are commonly associated with voiding, are the most prevalent symptoms. While transurethral resection may be performed in roughly 20% of patients, 70% require partial cystectomy and 10% require radical cystectomy. We present a case of pheochromocytoma with hypertension and syncope that was often associated with voiding, satisfactorily treated by partial cystectomy.

Retraction note: Effect of probucol on autophagy and apoptosis in the penile tissue of streptozotocin-induced diabetic rats.

[This corrects the article DOI: 10.4103/aja.aja_89_19].

Inhibition of FSTL3 abates the proliferation and metastasis of renal cell carcinoma via the GSK-3ß/ß-catenin signaling pathway.

Renal cell carcinoma (RCC) is a lethal malignancy of the genitourinary system. Follistatin-like 3 (FSTL3), which mediates cell differentiation and growth, acts as a biomarker of tumors and participates in cancer development and progression. Presently, the FSTL3's functions in RCC were investigated. Quantitative reverse transcription PCR (qRT-PCR), Western Blot, and enzyme linked immunosorbent assay (ELISA) were conducted to verify FSTL3 expression in RCC tissues and cell lines. BrdU assay and CCK8 experiment were made to monitor cell proliferation. Transwell was implemented to examine the invasion of the cells. Flow cytometry analyzed cell apoptosis, and Western Blot evaluated the protein levels of E-cadherin, Twist, and Slug. In the meantime, the protein profiles of the GSK-3ß, ß-catenin, and TGF-ß signaling pathways were ascertained. Moreover, the Xenograft tumor model was constructed in nude mice for measuring tumor growth in vivo. The statistics showed that FSTL3 presented an overexpression in RCC, and patients with a lower expression of FSTL3 manifested a better prognosis. Down-regulated FSTL3 hampered the proliferation, invasion, EMT, and tumor growth of RCC cells and caused cell apoptosis. On the contrary, FSTL3 overexpression enhanced the malignant behaviors of RCC cells. Furthermore, FSTL3 knockdown bolstered GSK-3ß, suppressed ß-catenin, and reduced BMP1-SMAD pathway activation. Inhibited ß-catenin substantially mitigated FSTL3-mediated promoting functions in RCC. In short, FSTL3 functions as an oncogene in RCC by modulating the GSK-3ß/ß-catenin signaling pathway.