Search

IntroductionBullous pemphigoid (BP) and prurigo nodularis (PN) are chronic pruritic skin diseases that severely impact patients’ quality of life. Despite the widespread attention these two diseases have garnered within the dermatological field, the specific pathogenesis, particularly the molecular mechanisms underlying the pruritus, remains largely unclear. Limited clinical sequencing studies focusing on BP and PN have hindered the identification of pathological mechanisms and the exploration of effective treatment strategies.MethodsTo address this gap, we collected a total of 23 peripheral blood mononuclear cell samples from BP and PN patients, as well as healthy controls, and performed RNA sequencing analysis. By integrating bioinformatics and machine learning techniques, we aimed to uncover the shared immune regulatory networks and pruritus-related mechanisms between BP and PN.ResultsOur study identified 161 differentially expressed genes shared between BP and PN, which were primarily enriched in immune activation and neural pathways, providing crucial molecular insights into the pruritus-related mechanisms of both diseases. Furthermore, using the machine learning algorithms of support vector machines and random forest, we pinpoint 7 crucial genes shared between the BP and PN databases. Among these, IL-27 emerged as a potential pivotal gene, as its mRNA expression levels strongly correlated with clinical parameters including pruritus scores, immunoglobulin E levels, and eosinophil counts. Validation experiments conducted on clinical samples from an additional 22 participants confirmed the upregulation of IL-27 expression in both BP and PN lesions.DiscussionThis study is the first to unveil the shared inflammatory and immune pathways common to BP and PN, highlighting the critical role of IL-27 in the pathogenesis of these conditions. Our findings not only enhance the understanding of the intricate relationship between BP and PN, but also provide a foundation for the development of novel therapeutic strategies targeting these two dermatological conditions.

Introduction: Bullous pemphigoid (BP) and prurigo nodularis (PN) are chronic pruritic skin diseases that severely impact patients' quality of life. Despite the widespread attention these two diseases have garnered within the dermatological field, the specific pathogenesis, particularly the molecular mechanisms underlying the pruritus, remains largely unclear. Limited clinical sequencing studies focusing on BP and PN have hindered the identification of pathological mechanisms and the exploration of effective treatment strategies. Methods: To address this gap, we collected a total of 23 peripheral blood mononuclear cell samples from BP and PN patients, as well as healthy controls, and performed RNA sequencing analysis. By integrating bioinformatics and machine learning techniques, we aimed to uncover the shared immune regulatory networks and pruritus-related mechanisms between BP and PN. Results: Our study identified 161 differentially expressed genes shared between BP and PN, which were primarily enriched in immune activation and neural pathways, providing crucial molecular insights into the pruritus-related mechanisms of both diseases. Furthermore, using the machine learning algorithms of support vector machines and random forest, we pinpoint 7 crucial genes shared between the BP and PN databases. Among these, IL-27 emerged as a potential pivotal gene, as its mRNA expression levels strongly correlated with clinical parameters including pruritus scores, immunoglobulin E levels, and eosinophil counts. Validation experiments conducted on clinical samples from an additional 22 participants confirmed the upregulation of IL-27 expression in both BP and PN lesions. Discussion: This study is the first to unveil the shared inflammatory and immune pathways common to BP and PN, highlighting the critical role of IL-27 in the pathogenesis of these conditions. Our findings not only enhance the understanding of the intricate relationship between BP and PN, but also provide a foundation for the development of novel therapeutic strategies targeting these two dermatological conditions. [ABSTRACT FROM AUTHOR]

Abstract To address the power supply-demand imbalance caused by the uncertainty in wind turbine and photovoltaic power generation in the regional integrated energy system, this study proposes a bi-level optimization strategy that considers the uncertainties in photovoltaic and wind turbine power generation as well as demand response. The upper-level model analyzes these uncertainties by modeling short-term and long-term output errors using robust optimization theory, applies an improved stepwise carbon trading model to control carbon emissions, and finally constructs an electricity-hydrogen-carbon cooperative scheduling optimization model to reduce wind and carbon emissions. The lower-level model incentivizes users to participate in integrated demand response through dynamic energy pricing, thereby reducing the annual consumption cost of load aggregator. The Karush-Kuhn-Tucker conditions and the Big-M method are used to solve the bi-level optimization model. Simulation results indicate that the improved carbon trading model reduces carbon emissions by approximately 40.12 tons per year, a decrease of 1.1%; the prediction accuracy of the short-term error model improves by 6.77%, and the prediction accuracy of the long-term error model improves by 15.16%; the electricity-hydrogen-carbon synergistic dispatch optimization model enhances the total profit of integrated energy system operator by 14.07% and reduces the total cost of load aggregator by 10.06%.

Abstract Objective Cervical cancer is a common malignancy among women, and radiotherapy remains a primary treatment modality across all disease stages. However, resistance to radiotherapy frequently results in treatment failure, highlighting the need to identify novel therapeutic targets to improve clinical outcomes. Methods The expression of molecule interacting with CasL-2 (MICAL2) was confirmed in cervical cancer tissues and cell lines through western blotting (WB) and immunohistochemistry (IHC). Siha and Hela cells were used to examine the regulatory and biological functions of MICAL2 via knockdown and overexpression experiments. Assays including MTT, colony formation, wound healing, transwell migration, and sphere formation were employed, along with WB analysis. DNA damage in irradiated cells with MICAL2 knockdown or overexpression was evaluated using the comet assay, while γ-H2AX and Rad51 protein levels were detected by WB. In vivo experiments validated the tumorigenic and radioresistance functions of MICAL2. Additionally, the relationship between MICAL2 expression and radiotherapy response was analyzed in 62 patients with cervical cancer by assessing tumor regression and MICAL2 levels six months post-treatment. Results MICAL2 expression was significantly elevated in cervical cancer tissues and cells. Functional analyses demonstrated that MICAL2 promotes cell proliferation, migration, and invasion by activating the MAPK and PI3K/AKT pathways, as confirmed through both in vitro and in vivo experiments. Silencing MICAL2 increased DNA damage, impeded DNA repair, and enhanced radiosensitivity. Among the 62 patients with cervical cancer, elevated MICAL2 expression was associated with a lower complete response rate to radiotherapy (25.6% vs. 60.9% in those with low expression), reduced progression-free survival, and advanced cancer stage (*p

Protein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT family, PRMT1 is the most abundant and ubiquitously expressed in the human body. Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. Through a classification model and molecular docking, a series of potential PRMT1 inhibitors were identified. The representative compound (compound 156) demonstrates stable binding to the PRMT1 protein by molecular hybridization, molecular dynamics simulations, and binding free energy analyses. The study discovered novel scaffolds for potential PRMT1 inhibitors.

In the realm of the development of a Smart City Library, the integration of robust edge computing is vital. The research suggests a novel task-scheduling model for edge computing, leveraging user’s social relationships. Analyzing these connections involves constructing a user’s social relationship graph by implementing mathematical convolution and the Jaccard similarity ratio. This precise quantification of social ties ensures secure and reliable task scheduling. An equipment connection graph of a user equipment service is also crafted based on Euclidean distance, aligning task scheduling with device-to-device (D2D) communication conditions. Combining a user’s social relationship graph and a user’s device-service device connection graph creates a task-device bipartite graph. On the other hand, the calculation of a task execution cost and edge weight determination finalize a scheduling model. Implementing the proposed method for constructing a model for edge computing task scheduling based on utilizing the Kuhn–Munkres (KM) algorithm demonstrates positive impacts, which are few delays and less energy consumption, on edge computing task scheduling. For instance, when the social threshold score changes from 02. To 0.6, the total task execution delay time increases from 23 to 32, which is the best when compared with other algorithms. The approach strengthens security and reliability while decreasing task execution delays and energy consumption. This research advances edge computing for Smart City Libraries, promising transformative implications.

Abstract Essential amino acids (EAA) and microRNAs (miRs) control biological activity of a cell. Whether EAA regulates the activity of miR has never been demonstrated. Here, as proof‐of‐concept, a tryptophan (Trp, an EAA) complex containing Argonaute 2 (Ago2) and miRs including miR‐193a (Trp/Ago2/miR‐193a) is identified. Trp binds miR‐193a‐3p and interacts with Ago2. Trp/Ago2/miR‐193a increases miR‐193a‐3p activity via enhancing Argonaute 2 (Ago2) RNase activity. Other miRs including miR‐103 and miR‐107 in the Trp complex enhance miR‐193a activity by targeting the same genes. Mechanistically, the Trp/Ago2/miR‐193a complex interacts with Trp‐binding pockets of the PIWI domain of Ago2 to enhance Ago2 mediated miR activity. This newly formed Ago2/Trp/miR‐193a‐3p complex is more efficient than miR‐193a‐3p alone in inhibiting the expression of targeted genes and inhibiting colon cancer liver metastasis. The findings show that Trp regulates miR activity through communication with the RNA‐induced silencing complexes (RISC), which provides the basis for tryptophan based miR therapy.

ABSTRACT Background The study aimed to assess the efficacy and safety of 125I seed implantation in the treatment of pelvic recurrent cervical cancer following radiotherapy. This meta‐analysis was registered in PROSPERO. We looked up relevant studies in the databases of CNKI, Wanfang, CBM, PubMed, Embase, Cochrane Library, and Web of Science. The endpoint measures include the objective response rate, disease control rate, progression‐free survival, overall survival, and adverse events. Recent Fidings The meta‐analysis included six studies and a total of 246 patients. The pooled ORR of tumor response was 63%, and the DCR was 87%. The median PFS was 9.09 months, and the median OS was 13.46 months. The incidence of adverse events of Grade ≥III was 6%. Conclusion In conclusion, this meta‐analysis confirmed that 125I seed implantation has a good local control rate and high safety in the treatment of pelvic recurrent cervical cancer following radiotherapy, and can be used as a remedial treatment for pelvic recurrent cervical cancer following radiotherapy to prolong the survival time of patients. Trial Registration PROSPERO: CRD42023423857

Highlights A percolating network of distorted 2D Pt nanomembranes was synthesized by polymer surface buckling-enabled exfoliation for hydrogen evolution reaction. The 2D Pt nanomembrane enabled important technological applications for its high efficiency, low costs, and good stability, making it potential alternative to commercial Pt/C. Our 2D Pt nanomembranes offer insights into a new mechanism for efficient catalyst design strategy: lattice distortion-induced heterogeneous strain.

This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges.

In this study, a coupla risk combinations and coping strategies have been developed for confronting conflicts between population–economy development and water resource management (PEWM) due to population–industry transformation (PIT) into a floodplain of the economic belt under climate change. A location-entropy-based PVAR (Panel Vector Autoregression) model coupla-risk analysis (LPCR) can be introduced into PEWM to reflect the adverse effects of population–industry transformation on a special function area (e.g., floodplain) of the economic belt, where the coupla risk map has been addressed. Meanwhile, an adaptive scenario analysis-based stochastic–fuzzy method (ASSF) can be joined to deal with multiple uncertainties and their interactions due to subjective and artificial factors. The proposed LPCR and ASSF can be integrated into a risk-based stochastic–fuzzy scenario method framework (RASF) to apply for a practical PEWM case study of the Yongding River floodplain in the context of the coordinative development of the Beijing–Tianjin–Hebei economic belt, China. The results can be presented as follows: 1) the PIT was accelerated into the floodplain due to the fact that it can require more water resources, where each additional unit can promote the regional gross domestic product (GDP) by 0.019 units, increasing the water demand by 0.046 units. It can strengthen various risks such as water shortage, soil loss, and flood control investment in a floodplain of the economic belt; 2) the farmland returning to wetland (CFW) can decrease the water demand and shortage ratio (would be 0% at highest) for irrigation in the long run, but this would reduce the direct economic income of irrigation in the short run; 3) backward irrigative schemes and low water utilization efficiency require a cleaner production mode, which could reduce water shortages by 77.23 × 10^3 m3 at the highest; and 4) the combined policy (S12) can reduce coupla risks (including the risk of water shortage, soil loss, and flood) compared to other individual policies (S2, S4, S6, and S8). The findings can assist local decision-makers to gain insights into adjusting interegional strategies not only for remitting population–resource stress in core cities but also for improving the resilience against drought and flood in a floodplain area, which is conducive of the special function of the floodplain to support an integrated sustainable development of the economic belt.

Herein, burdock polysaccharide (BP) and modified burdock polysaccharide (MBP) were prepared, followed by the fabrication of chlorogenic acid (CA)-BP, CA-MBP, isochlorogenic acid A (ICA)-BP, and ICA-MBP nanoparticles. Afterward, the structural characteristics, physical stability, digestive characteristics, and antioxidant activity of hydrogen peroxide (H2O2)-damaged HepaRG cells were evaluated. The result indicated that the loading capacities of CA in BP-CA and MBP-CA were 0.14 and 0.53 μg/mg, respectively. Conversely, the loading capacities of ICA in BP-ICA and MBP-ICA were 0.36 and 0.60 μg/mg, respectively. Four complex nanoparticles exhibited excellent physical stability under different pH values, temperatures, and ionic concentrations, especially MBP-CA and MBP-ICA. Moreover, four complex nanoparticles could protect caffeoylquinic acid from being released in gastric fluid. All six samples exhibited high antioxidant activity in H2O2-induced HepaRG cells, especially BP and MBP-CA. These findings indicated that caffeoylquinic acid–polysaccharide complexes were successfully prepared and highlighted the potential of polysaccharides as natural carriers for hydrophobic bioactive molecules.

Abstract Under the background of green and low‐carbon transformation of industrial high‐energy‐consuming parks, in order to realize the coordinated development planning of energy and transportation under different peaking capacity requirements, this paper proposes a collaborative optimization model of industrial high‐energy‐consuming parks under green and low‐carbon. Firstly, by studying the energy balance mechanism between industrial load and new energy, an energy topology prior model of energy interaction in the next generation of industrial new energy power grid is constructed. Then, based on the probability characteristics of the equilibrium state of different regions in the high‐energy‐consuming industrial zone, combined with the prior knowledge of industrial load and low‐carbon power system, a collaborative development planning model of high‐energy‐consuming industrial parks is constructed. Finally, the proposed model is simulated. From the simulation results the authors can see the dynamic division model and solution algorithm of energy balance region established in this paper can effectively improve the energy balance ability of power grid, reduce peak shaving demand and use more renewable energy.

In order to accurately assess the risk of the hybrid AC/DC network under fault, a risk assessment method for the hybrid AC/DC system based on the transient energy function is proposed. First, based on the energy transfer relationship of the hybrid AC/DC power system, the transient energy function model of the hybrid AC/DC system is established. Based on the operating data of the power grid, the energy function is used as an efficiency variable, and the efficiency variable is integrated into the prior risk probability calculation of nodes in the network, and a Bayesian network-based risk assessment model of hybrid AC/DC system is established. Considering the dynamic update model of network cascading failures, the clique tree propagation algorithm is used to dynamically calculate the posterior risk probability of the node to realize the dynamic assessment of the network risk. Finally, the improved IEEE-39 node hybrid AC/DC system is used as an example for analysis. The results show that the proposed model can not only effectively evaluate the overall safety of the network, but also has feasibility in predicting faults, which can provide a theoretical basis for the stability control of the hybrid AC/DC system.

To enhance the precision of wind power forecasting and the integration of renewable energy, a wind power prediction model, synthesising recursive Bayesian filtering state estimation with time-series data mining, was developed. Initially, the Autoregressive Integrated Moving Average Model (ARIMA)-Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity (FIGARCH) model was utilised for mining historical wind power data and establishing a model. Subsequently, the double-parameter t-distribution was employed to fit the prior estimation error and observation error, which integrated observational information with prior estimates through a sophisticated recursive Bayesian filtering approach, culminating in the formulation of a robust predictive model. Validation of this model was conducted using a diverse dataset, encompassing wind farms with varying capacities and distinct time intervals. Simulation outcomes reveal that this model's forecasting accuracy markedly surpasses that of conventional methodologies. Notably, an enhanced predictive precision is observed in wind farms with larger capacities, particularly when shorter intervals of observational data are employed. This model demonstrates significant potential for advancing the accuracy and efficiency of wind power forecasting, a critical element in the optimization of renewable energy utilization.

The lung is the first and most frequent organ to fail among sepsis patients. The mortality rate of sepsis‐related acute lung injury (ALI) is high. Despite appropriate antimicrobial therapy, no treatment strategies are available for sepsis‐induced ALI. Stem cell‐mediated paracrine signaling is a potential treatment method for various diseases. This study aimed to examine the effects of induced pluripotent stem cell‐derived conditioned medium (iPSC‐CM) combined with antibiotics on ALI in a rat model of Escherichia coli‐induced sepsis. Rats were administered either iPSC‐CM or the vehicle (saline) with antibiotics (ceftriaxone). After 72 h, liquid biopsy, bronchoalveolar lavage fluid (BALF), and tissues were harvested for analysis. Survival rates were observed for up to 3 days. Furthermore, we examined the effects of iPSC‐CM on cytokine production, metalloproteinase 9 (MMP‐9) expression, and NLRP3–ASC interaction in RAW264.7 cells stimulated with lipopolysaccharide/interferon‐γ (LPS/IFN‐γ). Combined treatment of iPSC‐CM with antibiotics significantly improved survival in E. coli‐infected rats (p = 0.0006). iPSC‐CM ameliorated E. coli‐induced infiltration of macrophages, reducing the number of cells in BALF, and suppressing interleukin (IL)‐1β, MIP‐2, IL‐6, and MMP‐9 messenger RNA in lung sections. iPSC‐CM treatment attenuated NLRP3 expression and inhibited NLRP3 inflammasome activation by disrupting NLRP3‐mediated ASC complex formation in LPS/IFN‐γ‐primed RAW264.7 cells. This study reveals the mechanisms underlying iPSC‐CM‐conferred anti‐inflammatory activity in ALI through the attenuation of macrophage recruitment to the lung, thus inactivating NLRP3 inflammasomes in macrophages. iPSC‐CM therapy may be a useful adjuvant treatment to reduce sepsis‐related mortality by ameliorating ALI. [ABSTRACT FROM AUTHOR]