Skull metastasis is a poor prognostic factor for prostate cancer patients with bone metastasis: a retrospective study based on a Chinese population.

BACKGROUND: Skull is a relatively rare metastasis site for prostate cancer (PCa). There is no evidence regarding the prognostic indication of skull metastasis (SM) in PCa patients. In this study, we analyzed the prognostic value of SM for metastatic PCa patients receiving androgen deprivation therapy (ADT). METHODS: 107 consecutive patients were included from September 2008 to August 2021. All patients were administered with standard ADT. Abiraterone plus glucocorticoid and/or docetaxel chemotherapy were given after failure to castration-resistant prostate cancer. Clinical parameters and follow-up prognostic data were retrospectively analyzed. The association of clinical and pathological parameters with SM were analyzed. The progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier analysis and Cox regression analyses. RESULTS: Patients with SM (n = 26) had significantly higher biopsy Gleason scores, higher clinical T stage, higher prostate-specific antigen level at diagnosis, and were more likely to have high-burden metastasis and lymph node metastasis, compared with those without SM (n = 81). They also showed significantly lower level of hemoglobin, albumin and serum calcium, along with higher level of alkaline phosphatase. SM was significantly associated with shorter medium PFS (9.4 vs. 18.3 months, p < 0.001) and OS (22.2 vs. 58.2 months, p < 0.001). Cox analysis demonstrated that SM was an independent risk factor for shorter PFS (hazard ratio 2.327 [1.429-3.789], p = 0.001) and shorter OS (hazard ratio 2.810 [1.615-4.899], p < 0.001). CONCLUSION: In this study, we found that SM was significantly correlated with more aggressive disease and indicated poor prognosis in PCa patients with bone metastasis. Our study may provide useful reference for the risk stratification of PCa patients.

Upregulation of mir-1199-5p is associated with reduced type 2 5-α reductase expression in benign prostatic hyperplasia.

BACKGROUND: 5-α reductase inhibitors (5-ARIs) are first-line drugs for managing benign prostatic hyperplasia (BPH). Unfortunately, some patients do not respond to 5-ARI therapy and may even show worsening symptoms. The decreased expression of steroid 5-α reductase type 2(SRD5A2) in BPH tissues may explain the failure of 5-ARI therapy, however, the mechanisms underlying SRD5A2 decreased remained unelucidated. OBJECTIVES: To investigate microRNA-mediated regulation of the expression of SRD5A2 resulting in 5-ARI therapy failure. MATERIALS AND METHODS: The expression of SRD5A2 and microRNAs in BPH tissues and prostate cells were detected by immunohistochemistry, western blotting, and quantitative real-time PCR. Dual-luciferase reporter assay was performed to confirm that microRNA directly combine to SRD5A2 mRNA. The apoptosis of prostatic cells was detected by flow cytometry. RESULTS: SRD5A2 expression was variable; it was negative, weak, and strong in 13.6%, 28.8%, and 57.6% of BPH tissues respectively. The normal human prostatic epithelial cell line RWPE-1 strongly expressed SRD5A2, whereas the immortalized human prostatic epithelial cell line BPH-1 weakly expressed SRD5A2. miR-1199-5p expression was remarkably higher in BPH-1 than in RWPE-1 cells(P<0.001), and miR-1199-5p expression was significantly upregulated in BPH tissues with negative SRD5A2 expression than those with positive SRD5A2 expression. Transfection of miR-1199-5p mimics in RWPE-1 cells led to a marked decrease in SRD5A2 expression, whereas miR-1199-5p inhibitor increased SRD5A2 expression in BPH-1 cells. Dual-luciferase reporter assay showed that miR-1199-5p could bind the 3'untranslated region of SRD5A2 mRNA. miR-1199-5p also decreased the RWPE-1 sensibility to finasteride, an inhibitor of SRD5A2. CONCLUSION: Our results show that SRD5A2 expression varies in BPH tissues and miR-1199-5p might be one of the several factors contributing to differential SRD5A2 expression in BPH patients.

Visual Object Tracking Based on Cross-Modality Gaussian-Bernoulli Deep Boltzmann Machines with RGB-D Sensors.

Visual object tracking technology is one of the key issues in computer vision. In this paper, we propose a visual object tracking algorithm based on cross-modality featuredeep learning using Gaussian-Bernoulli deep Boltzmann machines (DBM) with RGB-D sensors. First, a cross-modality featurelearning network based on aGaussian-Bernoulli DBM is constructed, which can extract cross-modality features of the samples in RGB-D video data. Second, the cross-modality features of the samples are input into the logistic regression classifier, andthe observation likelihood model is established according to the confidence score of the classifier. Finally, the object tracking results over RGB-D data are obtained using aBayesian maximum a posteriori (MAP) probability estimation algorithm. The experimental results show that the proposed method has strong robustness to abnormal changes (e.g., occlusion, rotation, illumination change, etc.). The algorithm can steadily track multiple targets and has higher accuracy.

Cancer-associated fibroblast exosomes promote prostate cancer metastasis through miR-500a-3p/FBXW7/HSF1 axis under hypoxic microenvironment.

Metastasis is the main cause of deaths in prostate cancer (PCa). However, the exact mechanisms underlying PCa metastasis are not fully understood. In this study, we discovered pronounced hypoxia in primary lesions of metastatic PCa(mPCa). The exosomes secreted by cancer-associated fibroblasts (CAFs) under hypoxic conditions significantly enhance PCa metastasis both in vitro and in vivo. Through miRNA sequencing and reverse transcription quantitative PCR (RT-qPCR), we found that hypoxia elevated miR-500a-3p levels in CAFs exosomes. Subsequent RT-qPCR, western blotting, and dual luciferase reporter assays identified F-box and WD repeat domain-containing 7(FBXW7) as a target of miR-500a-3p. In addition, immunohistochemistry revealed that FBXW7 expression decreased with the progression of PCa, while heat shock transcription factor 1(HSF1) expression increased. Introducing an FBXW7 plasmid into PCa cells reduced their metastatic potential and significantly lowered HSF1 expression. These findings suggest that CAFs exosomes drive PCa metastasis via the miR-500a-3p/FBXW7/HSF1 axis in a hypoxic microenvironment. Targeting either hypoxia or exosomal miR-500a-3p could be a promising strategy for PCa management.

Regularity and variability of functional brain connectivity characteristics between gyri and sulci under naturalistic stimulus.

The human cerebral cortex is folded into two fundamentally anatomical units: gyri and sulci. Previous studies have demonstrated the genetical, structural, and functional differences between gyri and sulci, providing a unique perspective for revealing the relationship among brain function, cognition, and behavior. While previous studies mainly focus on the functional differences between gyri and sulci under resting or task-evoked state, such characteristics under naturalistic stimulus (NS) which reflects real-world dynamic environments are largely unknown. To address this question, this study systematically investigates spatio-temporal functional connectivity (FC) characteristics between gyri and sulci under NS using a spatio-temporal graph convolutional network model. Based on the public Human Connectome Project dataset of 174 subjects with four different runs of both movie-watching NS and resting state 7T functional MRI data, we successfully identify unique FC features under NS, which are mainly involved in visual, auditory, emotional and cognitive control, and achieve high discriminative accuracy 93.06 % to resting state. Moreover, gyral regions as well as gyro-gyral connections consistently participate more as functional information exchange hubs than sulcal ones among these networks. This study provides novel insights into the functional brain mechanism under NS and lays a solid foundation for accurately mapping the brain anatomy-function relationship.

Characterizing functional brain networks via Spatio-Temporal Attention 4D Convolutional Neural Networks (STA-4DCNNs).

Characterizing individualized spatio-temporal patterns of functional brain networks (FBNs) via functional magnetic resonance imaging (fMRI) provides a foundation for understanding complex brain function. Although previous studies have achieved promising performances based on either shallow or deep learning models, there is still much space to improve the accuracy of spatio-temporal pattern characterization of FBNs by optimally integrating the four-dimensional (4D) features of fMRI. In this study, we introduce a novel Spatio-Temporal Attention 4D Convolutional Neural Network (STA-4DCNN) model to characterize individualized spatio-temporal patterns of FBNs. Particularly, STA-4DCNN is composed of two subnetworks, in which the first Spatial Attention 4D CNN (SA-4DCNN) models the spatio-temporal features of 4D fMRI data and then characterizes the spatial pattern of FBNs, and the second Temporal Guided Attention Network (T-GANet) further characterizes the temporal pattern of FBNs under the guidance of the spatial pattern together with 4D fMRI data. We evaluate the proposed STA-4DCNN on seven different task fMRI and one resting state fMRI datasets from the publicly released Human Connectome Project. The experimental results demonstrate that STA-4DCNN has superior ability and generalizability in characterizing individualized spatio-temporal patterns of FBNs when compared to other state-of-the-art models. We further apply STA-4DCNN on another independent ABIDE I resting state fMRI dataset including both autism spectrum disorder (ASD) and typical developing (TD) subjects, and successfully identify abnormal spatio-temporal patterns of FBNs in ASD compared to TD. In general, STA-4DCNN provides a powerful tool for FBN characterization and for clinical applications on brain disease characterization at the individual level.

Novel hybrid modified krill herd algorithm and fuzzy controller based MPPT to optimally tune the member functions for PV system in the three-phase grid-connected mode.

For the technical, economic and environmental benefits, using solar energy has increased worldwide, especially with the help of photovoltaic (PV) systems. The amount of energy produced in PV systems is dependent on environmental circumstances such as temperature and solar irradiance. In order to extract the maximum possible power in PV systems and increase the efficiency under various environmental conditions, maximum power point tracking (MPPT) controllers have been proposed. To fine-tune the control parameters of the proposed MPPT approach, the fuzzy controller and modified krill herd (MKH) algorithm are jointly employed. Rule base and membership functions (MFs) are two important parameters for implementing the FLC and need to be fine-tune appropriately. However, in the condition where precise information concerning the system is not available, the fine-tuning of these parameters cannot be accurate. To cope with this problem, the MKH algorithm is used to optimize the scaling factors of MFs. To improve the stability of the system under study, the PV system is used with the storage system at the same time. This hybrid system can deal with the stochastic nature of the PV system and provide more stability in all atmospheric conditions. The proposed MPPT method is confirmed by comparing it with other well-known techniques.

Recent progress of catalytic methane combustion over transition metal oxide catalysts.

Methane (CH4) is one of the cleanest fossil fuel resources and is playing an increasingly indispensable role in our way to carbon neutrality, by providing less carbon-intensive heat and electricity worldwide. On the other hand, the atmospheric concentration of CH4 has raced past 1,900 ppb in 2021, almost triple its pre-industrial levels. As a greenhouse gas at least 86 times as potent as carbon dioxide (CO2) over 20 years, CH4 is becoming a major threat to the global goal of deviating Earth temperature from the +2°C scenario. Consequently, all CH4-powered facilities must be strictly coupled with remediation plans for unburned CH4 in the exhaust to avoid further exacerbating the environmental stress, among which catalytic CH4 combustion (CMC) is one of the most effective strategies to solve this issue. Most current CMC catalysts are noble-metal-based owing to their outstanding C-H bond activation capability, while their high cost and poor thermal stability have driven the search for alternative options, among which transition metal oxide (TMO) catalysts have attracted extensive attention due to their Earth abundance, high thermal stability, variable oxidation states, rich acidic and basic sites, etc. To date, many TMO catalysts have shown comparable catalytic performance with that of noble metals, while their fundamental reaction mechanisms are explored to a much less extent and remain to be controversial, which hinders the further optimization of the TMO catalytic systems. Therefore, in this review, we provide a systematic compilation of the recent research advances in TMO-based CMC reactions, together with their detailed reaction mechanisms. We start with introducing the scientific fundamentals of the CMC reaction itself as well as the unique and desirable features of TMOs applied in CMC, followed by a detailed introduction of four different kinetic reaction models proposed for the reactions. Next, we categorize the TMOs of interests into single and hybrid systems, summarizing their specific morphology characterization, catalytic performance, kinetic properties, with special emphasis on the reaction mechanisms and interfacial properties. Finally, we conclude the review with a summary and outlook on the TMOs for practical CMC applications. In addition, we also further prospect the enormous potentials of TMOs in producing value-added chemicals beyond combustion, such as direct partial oxidation to methanol.

Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.

Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. However, previous studies are largely node-centralized and ignore edge features for graph classification tasks, resulting in moderate performance of graph classification accuracy. Moreover, the generalizability of GNN model is still far from satisfactory in brain disorder [e.g., autism spectrum disorder (ASD)] identification due to considerable individual differences in symptoms among patients as well as data heterogeneity among different sites. In order to address the above limitations, this study proposes a novel adversarial learning-based node-edge graph attention network (AL-NEGAT) for ASD identification based on multimodal MRI data. First, both node and edge features are modeled based on structural and functional MRI data to leverage complementary brain information and preserved in the constructed weighted adjacent matrix for individuals through the attention mechanism in the proposed NEGAT. Second, two AL methods are employed to improve the generalizability of NEGAT. Finally, a gradient-based saliency map strategy is utilized for model interpretation to identify important brain regions and connections contributing to the classification. Experimental results based on the public Autism Brain Imaging Data Exchange I (ABIDE I) data demonstrate that the proposed framework achieves a classification accuracy of 74.7% between ASD and typical developing (TD) groups based on 1007 subjects across 17 different sites and outperforms the state-of-the-art methods, indicating satisfying classification ability and generalizability of the proposed AL-NEGAT model. Our work provides a powerful tool for brain disorder identification.

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs).

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

In vitro co-metabolism of epigallocatechin-3-gallate (EGCG) by the mucin-degrading bacterium Akkermansia muciniphila.

Akkermansia muciniphila is a Gram-negative bacterium that resides within the gut mucus layer, and plays an important role in promoting gut barrier integrity, modulating the immune response and inhibiting gut inflammation. Growth stimulation of A. muciniphila by polyphenols including epigallocatechin-3-gallate (EGCG) from difference sources is well-documented. However, no published in vitro culture data on utilization of polyphenols by A. muciniphila are available, and the mechanism of growth-stimulating prebiotic effect of polyphenols on it remains unclear. Here in vitro culture studies have been carried out on the metabolism of EGCG by A. muciniphila in the presence of either mucin or glucose. We found that A. muciniphila did not metabolize EGCG alone but could co-metabolize it together with both these substrates in the presence of mineral salts and amino acids for mucin and protein sources for glucose. Our metabolomic data show that A. muciniphila converts EGCG to gallic acid, epigallocatechin, and (-)-epicatechin through ester hydrolysis. The (-)-epicatechin formed is then further converted to hydroxyhydroquinone. Co-metabolism of A. muciniphila of EGCG together with either mucin or glucose promoted substantially its growth, which serves as a further demonstration of the growth-promoting effect of polyphenols on A. muciniphila and provides an important addition to the currently available proposed mechanisms of polyphenolic prebiotic effects on A. muciniphila.

Knockdown of FAK inhibits the invasion and metastasis of Tca­8113 cells in vitro.

Tongue cancer originating on the surface of the tongue is most commonly squamous cell carcinoma, which has a higher invasive ability and a lower survival rate compared with other forms of tongue cancer. Notably, tongue squamous cell carcinomas metastasize into lymph nodes at early stages. Focal adhesion kinase (FAK) is an important protein tyrosine kinase involved in invasion and metastasis of cancer cells. In the present study, the role of FAK in the invasion and metastasis of tongue cancer was evaluated and the underlying mechanisms involved in this process were explored. FAK knockdown was performed using shRNA in the tongue cancer cell line, Tca­8113, and the invasion and metastasis potentials were analyzed using wound healing and transwell assays, respectively. Cytoskeletal arrangement was detected by fluorescence using TRITC­conjugated phalloidin staining. The activity of matrix metalloproteinase (MMP)­2 and ­9 was examined by gelatin zymography. Paxillin distribution was observed by immunofluorescence. The levels of E­cadherin, N­cadherin, MMP­2 and ­9, and c­Jun N­terminal kinase (JNK) was detected by western blot analysis. Wound healing and transwell assays demonstrated that FAK knockdown inhibited the invasion and metastasis of Tca­8113 cells. Further analysis revealed that FAK knockdown caused the rearrangement of the cytoskeleton and decreased the activity of MMP­2 and ­9. Immunofluorescence analysis revealed that downregulation of FAK induced the relocalization of paxillin. Paxillin accumulated as dots and patches at the cell membrane in control cells. By contrast, in FAK knockdown cells, paxillin was distributed homogeneously in the cytoplasm. Western blot analysis revealed that FAK knockdown inhibited epithelial-mesenchymal transition (EMT) and decreased levels of MMP­2 and ­9, and p­JNK. Knockdown of FAK inhibits the invasion and metastasis of Tca­8113 by decreasing MMP­2 and ­9 activities and led to the rearrangement of the cytoskeleton and inhibited the EMT.

[A comparative study of marginal microleakage using different cements in porcelain-fused-to-metal crown].

OBJECTIVE: To evaluate the marginal microleakage of porcelain-fused-to-metal crown using four different cements. METHODS: Sixteen porcelain-fused-to-metal crowns were built and randomly divided into 4 group, luted onto standard prepared human forward molars using four different cements (glass ionomer cement, resin-modified glass ionomer cement, PanaviaF, Super-Bond C&B adhesive luting system). After temperature cycling test, all the crowns were then submerged in 2% fuchsin for 24 h. The marginal microleakage at tooth cement interfaces was observed using light stereomicroscopy and evaluated in classification index. The marginal microleakage grade of 4 groups were analyzed by SPSS 13.0. RESULTS: The PanaviaF demonstrated the least marginal microleakage, Super-Bond C&B adhesive luting system, resin-modified glass ionomer cement showed an intermediate level of marginal microleakage, glass ionomer cement was associated with severe marginal microleakage (total, Chi2 = 157.60, P < 0.01; among the different groups, P<0.05). CONCLUSION: Adhesive resin luting system which is the first selection in clinical is better than glass ionomer cement and is good at porcelain-fused-to-metal crown.