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Objective: To assess the diagnostic ability of peripapillary vessel density (pVD) in primary open-angle glaucoma suspect (GS) patients. Methods: Sixteen primary open-angle GS patients (22 eyes) and 20 normal controls (22 eyes) were included. In the GS group, OCTA measurements of pVD (superior, inferior, nasal, temporal, and global), OCT measurements of retinal nerve fiber layer (RNFL) thickness, disc area, rim area and ganglion cell complex (GCC) thickness were examined. In the control group, pVD measurements were performed. The vessel density between the two groups was compared. The correlation between OCTA and OCT parameters was evaluated. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of OCTA measurements. Results: The global (P < 0.001), nasal (P = 0.003), and inferior (P = 0.002) quadrant pVD in GS group was considerably lower than the control group. The global pVD was positively correlated with the inferior RNFL thickness (r = 0.492, P = 0.023) and rim area (r = 0.483, P = 0.027). The inferior pVD was positively correlated with the inferior RNFL thickness (r = 0.648, P = 0.001), the nasal RNFL thickness (r = 0.441, P = 0.045), the rim area (r = 0.439, P = 0.046) and the GCC thickness (r = 0.472, P = 0.048). The global pVD had the best diagnostic value (AUC=0.825, sensitivity 86.36 %, specificity 72.73 %, cutoff value 45 %), followed by the inferior (AUC=0.749) and nasal (AUC=0.748) quadrant pVD. Conclusion: In primary open-angle GS patients, the global and inferior quadrant pVD was lower than that of normal people, and it was positively correlated with the inferior RNFL thickness and rim area. The diagnostic value of pVD for discriminating GS from normal people was excellent with high sensitivity and specificity.

Abstract The complex pathogenesis of castration-resistant prostate cancer (CRPC) makes it challenging to identify effective treatment methods. Matrix metalloproteinase (MMP)-12 can degrade elastin as well as various extracellular matrix (ECM) components, which is associated with cancer progression. However, the relationship between MMP-12 and CRPC progression is poorly understood. In this study, we observed the effect of MMP-12 on the progression of CRPC and further explored its potential mechanism of action. High levels of MMP-12 were observed in patients with CRPC. We therefore developed cell co-culture and mouse models to study the function of MMP-12. Silencing MMP-12 in CRPC cells disrupted lipid utilization and autophagy marker expression via the CD36/CPT1 and P62/LC3 pathways, respectively, leading to reduced CRPC cell migration and invasion. Moreover, animal experiments confirmed that MMP-12-knockdown CRPC xenograft tumors exhibited reduced tumor growth, and the mechanisms involved the promotion of cancer cell autophagy and the inhibition of lipid catabolism. According to our results, MMP-12 played important roles in the progression of CRPC by disrupting adipocyte maturation and regulating cancer migration and invasion via the modulation of autophagy and lipid catabolism pathways.

The transformation of Chengdu Tianfu New Area, a state-level new district, from the first site of constructing the “Park City” to the representative of the “Park City”, contains a unique path of “Chinese Style Governance”. To expand the diverse paths of constructing megacities, the current research references the nudge theory of behavioral science, adopts the extended case method, and takes Chengdu Tianfu New Area as a typical case to summarize the “nudge” path of “Party City” construction. The study found that the government achieves the lock-in effect by the priming mechanism and uses the nudge strategy of improving environment and guiding public opinion, thereby helping the people make free choices. It realizes the framework effect by the salience mechanism and uses the strategies of highlighting key information and optimizing information delivery methods, which directly affects the behavior of the public. It achieves the commitment effect by the comparison mechanism, including the horizontal comparison and vertical comparison strategies, and gradually realizes the policy goals. It realizes the social norm effect by the imitation mechanism and uses the strategies of creating peer pressure and playing celebrity effect, which in turn influence people’s choices. With the help of the “nudging” mechanism, the construction of the park city has realized the interactive cycle of “government output-citizen feedback” and achieved the goal of “making great change through small actions”. As a new behavioral intervention tool, “nudging” provides an alternative explanation for the construction of the park city in Tianfu New Area and opens up new ideas for the construction of megacities. This study concludes that the construction of megacities can achieve the goal of “common return” through different ways and provides practical inspiration for future urban construction.

Interprovincial counterpart support is a cooperative system used by local governments to achieve horizontal flow of resources based on cross-regional cooperation. Existing research has mainly focused on governance efficiency, institutional advantages, and ranking incentives while ignoring the scrambling behavior and operational mechanisms of local governments formed by ranking incentives and territorial responsibilities. This study selected the Wenchuan earthquake, Yushu earthquake, and COVID-19 as three typical cases. We constructed a theoretical framework for competition among provincial local governments and found that competition in interprovincial disaster counterpart support followed a dual behavioral logic of “striving to be first” and “fear of being last”. Specifically, local governments will choose striving to be first under the logic of time coercion, content games, and territorial responsibility; they will choose fear of being last under the logic of responsibility avoidance and moral pressure. This type of scrambling-based horizontal competition reflects the logic of local government competition tournaments. This study further revealed the specific processes, mechanisms, and results of horizontal local government competition, which can provide inspiration for cross-regional and provincial cooperation.

The pandemic of COVID-19 has caused economic and social crisis across the world. Existing studies have shown that the uncertain social context has profoundly affected people’s life, triggering a variety of social psychological phenomena including the deterioration of mental health and the change of political behavioral patterns. However, little has been known about the differences in people’s pre-pandemic political ideology and their influences on people’s mental health and political behaviors after the pandemic. Using the secondary data from the 2018 and 2020 China Family Panel Studies, we measured nationalism tendencies, state performance ratings, social justice evaluation and life satisfaction of 29,629 adults before the pandemic. Using latent profile analysis (LPA), we examined the typologies of respondents’ political ideology and values. Five types emerged to identify respondents with different political ideology and values: (Class-1) High nationalism tendency, country evaluation, social justice perception, and life satisfaction; (Class-2) Low life satisfaction; (Class-3) Moderate ratings; (Class-4) Low nationalism tendency; and (Class-5) Low country evaluation, low social justice perception. We further explored the predicting roles of those typologies on people’s depressive symptoms and political engagement behaviors after the pandemic. We found that, after the pandemic, although the depressive symptoms of people with low life satisfaction (Class-2) and low country and society ratings (Class-5) eased, they still tended to have more severe depressive symptoms than the Moderate ratings group (Class-3). People with low life satisfaction (Class-2) were also less likely to follow political information than the moderate group (Class-3). Our research revealed how the psychology and behaviors of Chinese people with different political views changed when faced with uncertainty in social context. Further research needs to be carried out to depict how these changes occur.

Abstract Background Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Results Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. Conclusions In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance. [ABSTRACT FROM AUTHOR]