Anterior cingulate cortex (ACC) is a critical brain center for chronic pain processing. Dopamine signaling in the brain has been demonstrated to contribute to descending pain modulation. However, the ...role of ACC dopamine receptors in chronic neuropathic pain remains unclear. In this study, we investigated the effect of optogenetic activation of ACC dopamine receptors D1- and D2-expressing neurons on trigeminal neuropathic pain. Chronic constriction injury of infraorbital nerve (CCI-ION) was carried out to induce trigeminal neuropathic pain in mice. We conducted optogenetic stimulation to specifically activate D1- and D2-expressing neurons in the ACC. Western blotting and immunofluorescence staining were used to examine ACC D1 and D2 expression and localization. The von Frey and real-time place preference tests were performed to measure evoked mechanical pain and nonreflexive emotional pain behaviors, respectively. We observed that dopamine receptors D1 and D2 in the ACC are primarily expressed in excitatory neurons and that the D2 receptor is differentially regulated in the early and late phases of trigeminal neuropathic pain. Optogenetic activation of D1-expressing neurons in the ACC markedly exacerbates CCI-ION-induced trigeminal neuropathic pain in both early and late phases, but optogenetic activation of D2-expressing neurons in the ACC robustly ameliorates such pain in its late phase. Our results suggest that dopamine receptors D1 and D2 in the ACC play different roles in the modulation of trigeminal neuropathic pain.
Segmentation methods based on convolutional neural networks (CNN) have achieved remarkable results in the field of medical image segmentation due to their powerful representation capabilities. ...However, for brain-tumor segmentation, owing to the significant variations in shape, texture, and location, traditional convolutional neural networks (CNNs) with limited convolutional kernel-receptive fields struggle to model explicit long-range (global) dependencies, thereby restricting segmentation accuracy and making it difficult to accurately identify tumor boundaries in medical imaging. As a result, researchers have introduced the Swin Transformer, which has the capability to model long-distance dependencies, into the field of brain-tumor segmentation, offering unique advantages in the global modeling and semantic interaction of remote information. However, due to the high computational complexity of the Swin Transformer and its reliance on large-scale pretraining, it faces constraints when processing large-scale medical images. Therefore, this study addresses this issue by proposing a smaller network, consisting of a dual-encoder network, which also resolves the instability issue that arises in the training process of large-scale visual models with the Swin Transformer, where activation values of residual units accumulate layer by layer, leading to a significant increase in differences in activation amplitudes across layers and causing model instability. The results of the experimental validation using real data show that our dual-encoder network has achieved significant performance improvements, and it also demonstrates a strong appeal in reducing computational complexity.
This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems (CPSs) subject to cyber attacks. Under the ...attack circumstance, the output and state information of CPSs is unavailable for the feedback design, and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking task. To solve this, a new coordinate conversion is proposed by considering the attack gains and the reference signal simultaneously. By employing the transformed variables, a modified fractional-order command-filtered signal is incorporated to overcome the complexity explosion issue, and the Nussbaum function is used to tackle the varying attack gains. By systematically constructing the Lyapunov–Krasovskii functional, an adaptive event-triggered mechanism is presented in detail, with which the communication resources are greatly saved, and the finite-time tracking of CPSs under cyber attacks is guaranteed. Finally, an example demonstrates the effectiveness.
The safety and efficacy of on-label use of pipeline embolization devices (PEDs) are well established; however, there is much controversy over their off-label use. This study aimed to investigate the ...safety and efficacy of the off-label use of PEDs for treating intracranial aneurysms.
This single-center study retrospectively included patients with digital subtraction angiography, computed tomographic angiography, or magnetic resonance angiography confirmed intracranial aneurysms treated with PEDs who were admitted to our institution between 1 January 2018 and 1 July 2022. Patients were divided into on- and off-label groups according to the Food and Drug Administration criteria published in 2021. Propensity score matching (PSM) was used to balance disparities in baseline information between the two groups. Safety outcomes included postoperative mortality and complication rates, whereas effectiveness outcomes included aneurysm occlusion rate (O'Kelly-Marotta grading system C + D grades), retreatment rate within 12 months, and postoperative functional score modified Rankin scale (mRS) score. The study was approved by the Ethics Committee of Scientific Research and Clinical Trial of the First Affiliated Hospital of Zhengzhou University (Ethics number: KY 2018-098-02). All patients provided informed consent.
A total of 242 patients with 261 aneurysms (160 on-label and 101 off-label aneurysms) were included in this study. PSM yielded 81 pairs of patients matched for baseline information. Postoperative hemorrhagic, ischemic, and procedure-related complication rates did not reach statistical significance. In addition, no statistically significant differences in the aneurysm occlusion rate, retreatment rate within 12 months, postoperative functional score (mRS score), or mRS score deterioration rate were observed between the two groups. A higher incidence of in-stent stenosis was observed in the off-label (4.9% vs. 21%,
= 0.002) group than in the on-label group; however, all patients were asymptomatic.
Compared with on-label use, off-label use of PEDs for treating intracranial aneurysms did not increase the risk of complications, and the occlusion rates were comparable. Therefore, decisions regarding clinical management should not rely solely on on- or off-label indications.
Recently, increasing numbers of studies have demonstrated that transient receptor potential ankyrin 1 (TRPA1) can be used as a potential target for the treatment of inflammatory diseases. TRPA1 is ...expressed in both neuronal and non-neuronal cells and is involved in diverse physiological activities, such as stabilizing of cell membrane potential, maintaining cellular humoral balance, and regulating intercellular signal transduction. TRPA1 is a multi-modal cell membrane receptor that can sense different stimuli, and generate action potential signals after activation
osmotic pressure, temperature, and inflammatory factors. In this study, we introduced the latest research progress on TRPA1 in inflammatory diseases from three different aspects. First, the inflammatory factors released after inflammation interacts with TRPA1 to promote inflammatory response; second, TRPA1 regulates the function of immune cells such as macrophages and T cells, In addition, it has anti-inflammatory and antioxidant effects in some inflammatory diseases. Third, we have summarized the application of antagonists and agonists targeting TRPA1 in the treatment of some inflammatory diseases.
This paper addresses the problem of secure event-triggered control is to guarantee the stability and security of a general class of industrial cyber-physical systems (CPSs) under limited resource ...budget and deception attacks. Note that the coupled data and the complicated structure of the CPS make it hard for the traditional control algorithms to fulfill the stability and security requirements. To this end, a neural network learning-based approximation algorithm is first proposed to separate the coupling influence, and then estimate the lumped uncertain nonlinearities. This is done by using the separated data as the input of the neural networks. Second, Nussbaum-type functions are presented to settle the unknown sign of the time-varying attack injection signal. Third, an event triggering mechanism is developed to reduce the communication load. Under the developed secure control laws, all the signals of the resulted closed-loop CPS are bounded. Finally, to validate the effectiveness of the proposed secure event-triggered control method, a benchmark control example for the one-link manipulator actuated by a DC motor is exploited.
•Secure event-triggered control is proposed to ensure the security of industrial CPSs.•Neural network learning algorithm is used to estimate the uncertain nonlinearities.•Nussbaum-type functions are presented to handle the unknown sign of the time-varying attack injection signal.•Event triggering mechanism is developed to reduce the communication.
Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disease and clinically manifests with cognitive decline and behavioral disabilities. Over the past years, mounting studies have ...demonstrated that the inflammatory response plays a key role in the onset and development of AD, and neuroinflammation has been proposed as the third major pathological driving factor of AD, ranking after the two well-known core pathologies, amyloid β (Aβ) deposits and neurofibrillary tangles (NFTs). Epigenetic mechanisms, referring to heritable changes in gene expression independent of DNA sequence alterations, are crucial regulators of neuroinflammation which have emerged as potential therapeutic targets for AD. Upon regulation of transcriptional repression or activation, epigenetic modification profiles are closely involved in inflammatory gene expression and signaling pathways of neuronal differentiation and cognitive function in central nervous system disorders. In this review, we summarize the current knowledge about epigenetic control mechanisms with a focus on DNA and histone modifications involved in the regulation of inflammatory genes and signaling pathways in AD, and the inhibitors under clinical assessment are also discussed.
This paper is concerned with the event-triggered cooperative output regulation problem of a switched linear multi-agent system. Firstly, a novel distributed integral-based event-triggered ...communication scheme, which only adopts discrete information of neighboring agents, is developed. The event-triggered scheme promises several advantages such as no continuous communication among the agents, the exclusion of Zeno behavior, and a significant reduction of triggered events. Secondly, an effective agent-dependent switching law, permitting all the switched subsystems of each agent are unstabilizable, is designed to guarantee the feasibility of cooperative output regulation for the switched multi-agent system. Finally, the effectiveness and merits of the proposed event-triggered cooperative output regulation method are validated through an illustrative example.
The unprecedented crisis during the fifth wave of the COVID-19 pandemic in Hong Kong placed a significant burden on the health care system. Therefore, the Hong Kong government advocated that ...individuals with no or mild COVID-19 symptoms should self-care at home. This study aimed to understand intrapersonal and interpersonal level factors that shaped self-care practices among home-quarantined individuals with COVID-19 during the peak of the pandemic.
This study used convenience and snowball sampling whereby a total of 30 semi-structured telephone interviews were conducted between March and April 2022. Inductive content analysis was used to analyze the data.
Factors reported at the intrapersonal level included socioeconomic status and housing conditions, information and knowledge about COVID-19, long COVID, and psychological adjustments brought about by home quarantine. Factors identified at the interpersonal level included caregiving responsibilities, family relationships, and social support.
Findings from this study identified a combination of intra and interpersonal level factors influenced an individual's self-care practices as a result of pandemic-induced quarantine. It was particularly concerning for those individuals in socially and economically deprived groups, where access to services was challenging. This study also raised awareness of the ineffectual and insufficient knowledge individuals held of self-medication and overall COVID-19 management. A key recommendation is developing family-based resilience programmes to support and empower vulnerable families to better cope with the realities of self-quarantine.
Entity disambiguation refers to the accurate inference of the real mention of an entity with the same name according to the context. Most existing studies focused on long texts, for short texts, the ...performance has been unsatisfactory due to sparsity. In this paper, we treat the entity disambiguation task as a classification problem. we propose a novel neural network-based capsule network and convolutional neural network for entity disambiguation, leveraging full semantic information of short text data. In particular, a self-attention mechanism is utilized to further filter the semantic information extracted from the capsule network. On the other hand, a convolutional neural network with combined pooling is established to capture semantics from another channel. In the end, the semantic features obtained by the above models are combined through a fully connected layer to complete the task of entity disambiguation. The experimental results on the CCKS 2019 entity linking dataset showed that the dual-channel hybrid network proposed in this paper achieved an F1-score of 88.04%, which is superior to that of the existing mainstream deep learning model, thereby verifying the effectiveness of the model.
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