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Background: Endometriosis (EM) is a hormone-dependent condition marked by progressively severe secondary dysmenorrhea, significantly impacting patients' quality of life and overall health. Wen Jing Tang (WJT), a traditional Chinese medicinal formulation derived from the Synopsis of the Golden Chamber, has proven to be an effective therapeutic agent for EM. However, the precise molecular mechanisms underlying its efficacy remain unclear. Objective: This study aims to elucidate the underlying mechanisms of WJT in the treatment of EM by integrating network pharmacology analysis with experimental validation. Methods: The chemical constituents and target sites of WJT were obtained from the TCMSP database, while EM-related target genes were sourced from OMIM, TTD, GeneCards, and the DrugBank databases. A ''herbs-components-targets'' network was constructed using Cytoscape 3.9.1. The intersecting target genes of WJT and EM were then uploaded to the STRING database for protein-protein interaction (PPI) analysis. Subsequently, the common target genes were subjected to GO and KEGG enrichment analysis via the DAVID database. Molecular docking were employed to analyze the binding affinities between the top five core components and their respective targets. Additionally, ELISA were used to quantify the serum levels of IL-6, IL-1β, E2, and P in EM model rats. The expression levels of TNF-α, HIF1A, STAT3, and EGFR mRNA and proteins in ectopic endometrial tissue were assessed using q-PCR and Western blotting. Results: A total of 250 chemical components and 553 targets were identified in WJT, while 3491 EM-related targets were screened from multiple databases. Among these, 187 common targets between WJT and EM were found, with quercetin, kaempferol, and beta-sitosterol emerging as the core chemical components, and AKT1, IL6, TNF, and IL1B identified as the key targets. These core components demonstrated strong binding affinities to the targets. GO and KEGG enrichment analyses revealed that the shared targets were primarily involved in the HIF1 signaling pathway. Furthermore, compared to the control group, the EM model rats exhibited an increased ectopic endometrial area, disordered glandular and stromal cells, and notable inflammatory infiltration. Serum levels of IL-6, IL-1β, E2, and P were significantly elevated (P

Objective: Traumatic brain injury (TBI) is among the leading causes of death and disability globally. Identifying and assessing the risk of in-hospital mortality in traumatic brain injury patients at an early stage is challenging. This study aimed to develop a model for predicting in-hospital mortality in TBI patients using prehospital data from China. Methods: We retrospectively included traumatic brain injury patients who sustained injuries due to external forces and were treated by pre-hospital emergency medical services (EMS) at a tertiary hospital. Data from the pre-hospital emergency database were analyzed, including demographics, trauma mechanisms, comorbidities, vital signs, clinical symptoms, and trauma scores. Eligible patients were randomly divided into a training set (241 cases) and a validation set (104 cases) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were employed to identify independent risk factors. Analyzed the discrimination, calibration, and net benefit of the nomogram across both groups. Results: 17.40 % (42/241) of TBI patients died in the hospital in the training set, while 18.30 % (19/104) in the validation set. After analysis, chest trauma (odds ratio [OR] = 4.556, 95 % confidence interval [CI] = 1.861–11.152, P = 0.001), vomiting (OR = 2.944, 95%CI = 1.194–7.258, P = 0.019), systolic blood pressure (OR = 0.939, 95%CI = 0.913–0.966, P

Objective: Grifolin is a natural secondary metabolite isolated from edible fruiting bodies of the mushroom Albatrellus confluens. Grifolin has antitumor activities in several types of cancer. We aimed to determine the effects of grifolin on lung cancer. Methods: We determined the proliferation, migration, invasion, and apoptosis of lung cancer cells using 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, Ethynyl deoxyuridine, colony formation, wound scratch, transwell, flow cytometry, and xenograft mouse assays. Molecular docking evaluated the binding relation between grifolin and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA). The levels of PIK3CA, AKT, and p-AKT were measured by western blot. Results: Grifolin (10, 20, or 40 μM) inhibited the proliferation, migration, and invasion of lung cancer cells, and induced cell cycle arrest and apoptosis. Grifolin also decreased CDK4, CDK6, and CyclinD1 expression and significantly decreased PIK3CA and p-AKT expression in lung cancer cells. These anticancer effects were abolished by 740Y-P. Conclusions: Grifolin regulates the PI3K/AKT pathway, thus inhibiting lung cancer progression.

In allusion to solve the issue of fault diagnosis for bearing and other rotatory machinery, a technique based on fined-grained multi-scale Kolmogorov entropy and whale optimized multi-class support vector machine (abbreviated as FGMKE-WOA-MSVM) is proposed. Firstly, vibration signals are decomposed by fine-grained multi-scale decomposition, and the Kolmogorov entropy of the sub-signals at different analysis scales is calculated as the multi-dimension feature vector, which quantitatively characterize the complexity of the signal at multi-scales. Aiming at the problem of sensitive parameters selection for multi-class support vector machine model (abbreviated as MSVM), the whale optimization algorithm (abbreviated as WOA) is introduced to optimize the penalty factor and kernel function parameter, and constructing optimal WOA-MSVM model. Finally, an instance analysis is carried out with Jiangnan University bearing datasets to verify the effectiveness and superiority of this technique. The results show that compared with different feature vectors and models such as K nearest neighbors (abbreviated as KNN) and Decision Tree (abbreviated as RF), the proposed technique is superior with fast computation speed and high diagnostic efficiency.

A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed. Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Deviation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector machine model, the Whale Optimization Algorithm is used to optimize the c and g parameters. Finally, both Recursive Quantitative Analysis and standard deviation are combined with the WOA-SVM model to perform fault diagnosis of rolling bearings. The rolling bearing datasets from Case Western Reserve University and Jiangnan University were used for example analysis, and the fault identification accuracy reached 100% and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced through accurate fault diagnosis, which is also important for safety and environmental policies. This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagnosis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mechanical fault diagnosis.

Previous works had successfully demonstrated the clinical effectiveness of unilateral external fixator in treating various types of fracture, ranging from the simple type, such as oblique and transverse fractures, to complex fractures. However, literature that investigated its biomechanical analyses to further justify its efficacy is limited. Therefore, this paper aimed to analyse the stability of unilateral external fixator for treating different types of fracture, including the simple oblique, AO32C3 comminuted, and 20 mm gap transverse fracture. These fractures were reconstructed at the distal diaphysis of the femoral bone and computationally analysed through the finite element method under the stance phase condition. Findings showed a decrease in the fixation stiffness in large gap fracture (645.2 Nmm-1 for oblique and comminuted, while 23.4 Nmm-1 for the gap fracture), which resulted in higher displacement, IFM and stress distribution at the pin bone interface. These unfavourable conditions could consequently increase the risk of delayed union, pin loosening and infection, as well as implant failure. Nevertheless, the stress observed on the fracture surfaces was relatively low and in controlled amount, indicating that bone unity is still allowable in all models. Briefly, the unilateral fixation may provide desirable results in smaller fracture gap, but its usage in larger gap fracture might be alarming. These findings could serve as a guide and insight for surgeons and researchers, especially on the biomechanical stability of fixation in different fracture types and how will it affect bone unity.

Sports image classification using image processing and machine vision is a growing area of research that involves the use of algorithms and techniques to identify and analyze objects in sports images and videos. This technology has a wide range of applications, including detecting illegal plays, analyzing team performance, and creating highlight reels. Additionally, it can provide valuable visual feedback during training and competition. In this paper, we propose a novel deep learning and optimization hybrid framework for sports image classification. Specifically, we use a modified version of the Battle Royal optimization algorithm as a feature selector to reduce the dimensionality of the images and achieve higher accuracy with only the essential features. We evaluate the proposed framework using sports images and demonstrate that our WOA-based framework outperforms other methods in terms of both classification accuracy and dimensionality reduction. Our results highlight the effectiveness of the proposed approach and its potential to improve sports image classification.

Ensuring equitable access to green spaces in urban built-up areas is not only vital for fostering environmental justice but also aligns with the United Nations Sustainable Development Goals (SDGs). However, there is a noticeable gap in the current body of research regarding the role of small urban green spaces, especially their multifunctionality from an ecosystem services perspective. Taking the urban built-up area of Harbin as an example, this study first applied the Analytic Hierarchy Process to classify the supply and demand of green space into three types. Then, the article further analyzes the potential functional positioning of the newly added green spaces, including ecological and social functions, using Minimum Cumulative Resistance and Point of Interest. Finally, multi-criteria decision models are used to explore the priority and functional positioning of green space and construct a multi-functional and highly-efficient small urban green space network. The results indicate a significant imbalance in green space supply and demand, with severe and medium mismatch areas accounting for 30.17 % and 48.50 %, respectively. By assessing the multifunctionality of small green spaces, we propose guidelines that include five types of areas: Concentrated Development (85.85 km2, 16.94 %), Backup Development (70.74 km2, 14.31 %), Maintenance (304.49 km2, 61.51 %), Protection (14.94 km2, 3.02 %), and Optimization (20.89 km2, 4.22 %). Finally, the article proposes a 277.60 km multifunctional small urban green space network. By examining small urban green spaces, this study crafts a pivotal framework for enhancing green space equity in urban built-up environments, providing valuable insights for policymakers and urban planners. The approach has significant implications for developing multifunctional green networks in varied urban contexts and offers a model for wider application, serving as a reference for achieving green space equity in developing countries globally.

With the development of safety science, the concept of safety capacity (SC) has been proposed. SC has been applied in many fields. Concurrently, there has been an active and robust development of research pertaining to it. However, SC research mainly focuses on practical engineering problems, and SC research is only regarded as the establishment of a quantitative model for system safety. Hence, the theoretical study of SC is ignored. Indeed, the integrated theoretical system and connotation of SC are lacking. In this study, an attempt is made to enrich the theoretical connotation of SC. The following aspects of theoretical exploration are included: (a) The theoretical connotation of SC is redefined so that the lack of theory with respect to SC can be made up. Moreover, research directions of SC are indicated as dimension mining (DM) and dimensionality reduction (DR). (b) The application value of safety similarity theory in the DM of SC is expounded according to the principle of analogism. Core principles of DM are proposed and explained. (c) Common methods of DR are summarized, and the one-sided cognition of DR for the current SC research is corrected. A case study of an explosion accident caused by a boiler water shortage is employed for further discussion. For the investigation of the explosion accident using fault tree analysis (FTA), DR of SC is utilized to provide a more comprehensive explanation. Therefore, the proposed SC is proven to be practical for the analysis of system safety. The results obtained from this study have important implications for research and practice of SC.

The application of packing agents affects the final surgical outcomes in treating otitis media (OM) and introduces the risk of infection. To decrease the infectious risks of packing agents and even introduce positive bacteriostatic functions, a kind of PPDO-grafted Ag-incorporated TiO2 nanoparticles (Ag@TiO2-PPDO NP)-coated gauzes were prepared by a solution immersion method. Morphologies and in vitro Ag+ releasing of Ag@TiO2-PPDO NP coated gauzes were determined by scanning electron microscope (SEM) and inductively coupled plasma-mass spectrum (ICP-Ms). Ag@TiO2-PPDO NP could respond to visible light, which might make Ag@TiO2-PPDO NP inhibit the proliferation of bacteria continually and positively with irradiation of visible light. Then the bacteriostatic effects of these gauzes on OM pathogens were investigated in vitro and in vivo. These gauzes could inhibit the proliferation of pathogenic Staphylococcus aureus (S. aureus) and Streptococcus pneumoniae (S. pneumoniae) in vitro and rat subcutaneous infection models. Specifically, the bacteriostatic effect of these gauzes on S. aureus and S. pneumoniae could be enhanced with irradiation by visible light in vitro. Further, the rat external auditory canal infection model verified the enhanced bacteriostatic effect of Ag@TiO2-PPDO-coated gauzes on S. aureus with irradiation by visible light. The Ag@TiO2-PPDO-coated gauzes are promising for packing materials after OM surgery and could reduce postoperative antibiotic requirements.

Background: Spinal cord injury (SCI) has a high disability rate and mortality rate. Recently, LncRNA XIST has been found to be involved in the regulation of inflammatory responses. Therefore, we aimed to investigate the role of XIST in the occurrence and development of SCI and the specific regulation mechanism. Methods: 100 ng/mL lipopolysaccharide (LPS) was used to treat mouse microglia BV2 cells. Hitting spinal cord was performed to C57BL/6 mice for establishing SCI model. Real-time reverse transcriptase-polymerase chain reaction (RT-qPCR), Western blot, Immunofluorescence (IF) and Enzyme linked immunosorbent assay (ELISA) experiments were used to explore the function of XIST, miR-124–3p and IRF1 in LPS-induced BV2 cells. RT-qPCR, Nissl staining, IF, Western blot and ELISA experiment were performed to study the function of XIST in SCI mice. Dual-luciferase reporter assay, RNA immunoprecipitation (RIP), RT-qPCR and Western blot assays were utilized to identify the interaction among XIST, miR-124–3p and IRF1. Results: XIST was upregulated in LPS-induced BV2 cells and spinal cord tissues of SCI mice. Overexpression of XIST promoted the M1 microphages polarization and cytokines concentration in LPS-stimulated BV2 cells, aggravated SCI of mice. Downregulated XIST promoted M1-to-M2 conversion of microglial and relieved the injury of SCI mice. Mechanism verification indicated that XIST acted as a molecular sponge of miR-124–3p and regulated IRF1 expression. Increased miR-124–3p or reduced IRF1 inhibited M1 polarization of microglial and decreased the production of inflammatory cytokines in LPS-induced BV2 cells. Increased XIST or decreased miR-124–3p had an opposite of on LPS-induced BV2 cells. Conclusion: Overexpression of XIST enhanced M1 polarization of microglia and promoted the level of inflammatory cytokines through sponging miR-124–3p and regulating IRF1 expression.

Background: Coronary microcirculation has a fundamental role in the regulation of coronary blood flow in response to cardiac requirements, which has aroused wide concerns in basic science and clinical cardiovascular research. We aimed to analyze coronary microcirculation-associated literatures over 30 years and provide insightful information on the evolutionary path, frontier research hotspots, and future developmental trends. Methods: Publications were retrieved from the Web of Science Core Collection (WoSCC). VOSviewer was used to perform co-occurrence analyses for countries, institutions, authors, and keywords and to generate visualized collaboration maps. CiteSpace was used to visualize the knowledge map derived from reference co-citation analysis, burst references, and keywords detection. Results: This analysis was performed based on 11,702 publications including 9981 articles and 1721 reviews. The United States and Harvard University ranked at the top among all the countries and institutions. The majority of articles were published in Circulation, and it also was the most co-cited journal. Thematic hotspots and frontiers were focused on coronary microvascular dysfunction, magnetic resonance imaging, fractional flow reserve, STEMI, and heart failure. Additionally, keywords burst and co-occurrence cluster analysis showed that management, microvascular dysfunction, microvascular obstruction, prognostic value, outcomes, and guidelines were current knowledge gaps and future directions. Conclusions: Coronary microcirculation presented a research hotspot relevant wide spectrum of cardiovascular diseases. Definite diagnostics and prognostics are particularly valued. The protection of cardiovascular events that influence clinical outcomes should be an insightful concern in the future. Multidisciplinary collaborations will provide significant advances for the development of coronary microcirculation.

For three-dimensional (3D) risk assessments, the scalar fields of risk can be clarified so that the risk value at any point in 3D space can be obtained. Notably, the 3D risk function can be used to calculate the surface integral to reveal the overall risk level in a certain area. As a result, a novel field theory-based 3D risk assessment method called the regional overall risk assessment (RORA) is proposed in this study. The regional overall risk (ROR) is introduced to describe the overall risk level of the assessed area. The corresponding definition and algorithm of the ROR are determined. The selection rule of the surfaces, which are used to create the surface integrals and compute the ROR, is also provided and discussed. To demonstrate the effectiveness of the RORA, the 3D risk caused by biomass gasification stations is utilized to conduct a case study. For the assessed area (Huangtukan Village and Yanjia Village), values of ROR are 29.5787 and 39.3858, respectively. The results represent accurate overall risk levels of the assessed areas and can provide effective guidance for risk prevention in the assessed areas, including land-use planning and safety planning. Moreover, the validity and availability of the proposed RORA is verified by a sensitivity analysis. The prospects and limitations of the RORA are also analyzed and discussed in this work.

Rationale and Objectives: It is still a challenge to make confirming diagnosis of malignant pleural mesothelioma (MPM), especially differentiating from metastatic pleural disease (MPD). The aim of this study was to develop a model to distinguish MPM with MPD based on primary CT signs. Materials and methods: We retrospectively recruited 150 MPM patients and 147 MPD patients from two centers and assigned them to training (115 MPM patients and 113 MPD patients) and testing (35 MPM patients and 34 MPD patients) cohorts. The images were analyzed for pleural thickening, hydrothorax, lymphadenopathy, thoracic volume and calcified pleural plaque (CPP). The selected clinical characteristics and primary CT signs comprised the model by multivariate logistic regression in the training cohort. Then the model was tested on the external testing cohort. ROC curve and F1 score were used to validate the capability of the model in both two cohorts. Results: There were significant differences between two groups: (1) carcinoembryonic antigen (CEA); (2) nodular and mass pleural thickening; (3) the enhancement of pleura; (4) focal, diffuse and circumferential pleural thickening; (5) the thickest pleura; (6) thickening of diaphragmatic pleura; (7) multiple nodules and effusion of interlobar pleura; (8) hilar LN and ring enhancement of LN; (9) punctate and stipe CPP. The AUC and F1 score of the model were 0.970 and 0.857 in the training cohort, 0.955 and 0.818 in the testing cohort. Conclusion: The model holds promise for use as a diagnostic tool to distinguish MPM from MPD.

Aim of the Study: The research group proposed that the mechanism of "Feature Identification based Quality Assessment" (FIQA) of Traditional Chinese Medicine (TCM) can be explained according to the relationship between the "Feature" of TCM and the Pharmacodynamic Components representing the holistic effect of TCM. Gastrodiae Rhizoma (GR) was selected as the research object to reveal the close relationship between "Feature" and the quality of TCM., Materials and Methods: In this study, the "Feature" such as "Shape", "Color", "Odour" and "Taste" of GR are quantified by the electronic nose, electronic tongue, and other instruments. Then, the Pharmacodynamic Components Group (PCG) of GR was determined which could reflect the holistic effect of GR by spectrum effect relationship analysis. By analyzing the correlation between the "Feature" and the content of PCG, the mechanism of FIQA of GR was determined., Results: The quantitative results of "Shape", "Color", "Odour" and "Taste" of GR from different sources were significantly different. Six components were selected as the PCG, which can represent the holistic effect of GR in the aspect of the neuroprotective effect. There was a good correlation between the components in the PCG and "Feature"., Conclusions: The quality of GR can be determined quantitatively according to its "Shape", "Color", "Odour" and "Taste". The mechanism of FIQA of TCM can be explained according to the relationship between the Shape of TCM and the Pharmacodynamic Components representing the quality of TCM. The revelation of this mechanism reflects the holistic characteristics of the multi-component synergistic effect of TCM. This study provides a reference research method for revealing the mechanism of FIQA of TCM., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Published by Elsevier Ltd.)