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Abstract The existing diagnostic methods of epilepsy have great limitations, and more reliable and less difficult diagnostic methods are needed. We collected serum samples of adult patients with first-diagnosed epilepsy (EPs) and seizure control patients (EPRs) for non-targeted metabolomics detection and found that they were both significantly altered, with increased expression of nicotine addiction, linoleic acid metabolism, purine metabolism, and other metabolic pathways. The diagnostic model based on 4 differential metabolites achieved a diagnostic efficiency of 99.4% in the training cohort and 100% in the validation cohort. In addition, the association analysis of oral flora, serum metabolism, and clinical indicators also provided a new angle to analyze the mechanism of epilepsy. In conclusion, this study characterized the serum metabolic characteristics of EPs and EPRs and the changes before and after epilepsy control based on a large cohort, demonstrating the potential of metabolites as non-invasive diagnostic tools for epilepsy.

During the installation of a cable oil terminal, it is easy to leave scratches on the main insulation owing to uneven forces when removing the semi-conductive layer. Scratch defects cause field intensity distortion, which leads to partial discharge and insulation failure. This study attempts to establish a simulation model of a 220 kV cable terminal to determine the effect of the length, depth, and position of the scratch on the maximum field strength at the defect. The simulation and experiment demonstrate that the maximum field strength at the defect increases with greater length and decreases as the depth increases. Therefore, a prediction method for the terminal defect field strength based on a multivariate nonlinear regression model was proposed in this study. When the defect is located at 20 mm from the root of the stress cone, the maximum field strength is 14.5 MV/m when the length and depth are 2 mm and 1 mm, respectively. The maximum field strength at the defect was predicted based on the length, depth, and position of the scratch defect to evaluate the severity of the defect.

Abstract Background SARS-CoV-2 infections usually cause immune dysregulation in the human body. Studies of immunological changes resulting from coinfections with Mycobacterium tuberculosis (Mtb) or HIV are limited. Methods We conducted a retrospective study focusing on patients with COVID-19. A total of 550 patients infected with SARS-CoV-2 were enrolled in our study and categorized into four groups based on the presence of coinfections; 166 Delta-infected patients, among whom 103 patients had no coinfections, 52 who were coinfected with Mtb, 11 who were coinfected with HIV, and 384 Omicron-infected patients. By collecting data on epidemiologic information, laboratory findings, treatments, and clinical outcomes, we analyzed and compared clinical and immunological characteristics. Results Compared with those in the Delta group, the median white blood cell, CD4 + T-cell and B-cell counts were lower in the Mtb group and the HIV group. Except for those in the Omicron group, more than half of the patients in the three groups had abnormal chest CT findings. Among the three groups, there were no significant differences in any of the cytokines. Compared with those in the Delta group, the disease duration and LOS were longer in the Mtb group and the HIV group. For unvaccinated Delta-infected patients, in the Mtb and HIV groups, the number of B cells and CD4 + T cells was lower than that in the Delta group, with no significant difference in the LOS or disease duration. In the Mtb group, three (6%) patients presented with a disease duration greater than four months and had decreased lymphocyte and IL17A counts, possibly due to double infections in the lungs caused by SARS-CoV-2 and M. tuberculosis. Conclusions We found that SARS-CoV-2 patients coinfected with Mtb or HIV exhibited a longer disease duration and longer LOS, with a decrease in B cells and CD4 + T cells, suggesting that these cells are related to immune function. Changes in cytokine levels suggest that coinfection with Mtb or HIV does not result in dysregulation of the immune response. Importantly, we discovered a chronic course of coinfection involving more than four months of Mtb and SARS-CoV-2 infection.

This study aimed to identify biomarkers for chronic kidney disease (CKD) by studying serum metabolomics. Serum samples were collected from 194 non-dialysis CKD patients and 317 healthy controls (HC). Using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS), untargeted metabolomics analysis was conducted. A random forest model was developed and validated in separate sets of HC and CKD patients. The serum metabolomic profiles of patients with chronic kidney disease (CKD) exhibited significant differences compared to healthy controls (HC). A total of 314 metabolites were identified as significantly different, with 179 being upregulated and 135 being downregulated in CKD patients. KEGG enrichment analysis revealed several key pathways, including arginine biosynthesis, phenylalanine metabolism, linoleic acid metabolism, and purine metabolism. The diagnostic efficacy of the classifier was high, with an area under the curve of 1 in the training and validation sets and 0.9435 in the cross-validation set. This study provides comprehensive insights into serum metabolism in non-dialysis CKD patients, highlighting the potential involvement of abnormal biological metabolism in CKD pathogenesis. Exploring metabolites may offer new possibilities for the management of CKD.

ABSTRACTVaccination has emerged as the primar approach for managing the COVID-19 pandemic. Despite certain clinical trials reporting the safety and immunogenicity of CoronaVac, additional multicenter real-world studies are still necessary. In this study, we recruited 506 healthy volunteers who were not infected with COVID-19 or vaccinated. Each participant provided peripheral blood samples three times: prior to the first dose of vaccine, prior to the second dose, and 8 weeks following the second dose. Ultimately, 388 participants completed the entire follow-up process. No serious adverse events were observed among any of the participants. Within 1 week of vaccination, 13.4% of participants experienced systemic adverse reactions, with fatigue (5.93%) and dizziness (3.35%) being the most frequent. Although some clinical indicators, including creatinine, significantly changed after vaccination (p

This study investigates the axial capacity of round-cornered square high-strength steel tube confined UHPC (HSSTCU) columns with the load primarily borne by the inner concrete. 9 round-corned square HSSTCU specimens with different steel strength grades and thicknesses were tested under axial compression, and compared with the same cross-sectional concrete-filled steel tube (CFST). Additionally, a finite element model (FEM) and parametric analysis were employed to examine the axial compression behavior, load bearing analysis, material utilization, and stress distribution. The results show that the round-cornered HSSTCU columns usually exhibit buckling damage failure at both ends, and the three typical load-displacement curve types are closely linked to the confinement index. The HSSTCU columns generally show smaller ultimate bearing load compared to the CFST columns with same cross-sectional parameters, but more superior ductility with higher residual bearing capacity after peak-load and better deformation capacity. The confinement index shows almost linear correlations on axial bearing capacity and material efficiency, up to 84.7% and 141% improvements with increasing confinement index from 0.717 to 2.386, respectively. The confined stress on the core concrete diffuses from the centre of the section towards the corners, intensifying near the corners. To accurately predict the bearing capacity of HSSTCU columns, a simplified formula was derived and validated using experimental results, literature data, and FEM simulations.

Abstract The modification and recognition of 5‐methylcytosine (m5C) are involved in the initiation and progression of various tumor types. However, the precise role and potential mechanism of Y‐box‐binding protein 1 (YBX1) in esophageal squamous cell carcinoma (ESCC) remains unclear. Here, it is found that YBX1 is frequently upregulated in ESCC compared with matched nontumor tissues. Gain‐ and loss‐of‐function assays show that YBX1 promoted the proliferation and metastasis of ESCC cells both in vitro and in vivo. Functional studies revealed that NOP2/Sun RNA methyltransferase family member 2 (NSUN2) is a critical RNA methyltransferase that facilitates YBX1‐mediated ESCC progression. Mechanistically, integrated analysis based on RNA immunoprecipitation sequencing (RIP‐seq) and m5C methylated RNA immunoprecipitation and sequencing (MeRIP‐seq) assays identified spermine oxidase (SMOX) as a target gene containing an m5C site in its coding sequence (CDS) region, which coincided well with the binding site of YBX1. Overexpression of SMOX‐WT but not SMOX‐Mut partially restored the proliferation and invasion ability of ESCC cells curbed by YBX1 knockdown. Moreover, YBX1 activated the mTORC1 signaling pathway by stabilizing SMOX mRNA. The study reveals that YBX1 promotes ESCC development by stabilizing SMOX mRNA in an m5C‐dependent manner, thus providing a valuable therapeutic target for ESCC.

Abstract Chronic hepatitis B (CHB) remains a major world's most serious public health issues. Despite the remarkable effect of nucleos(t)ide analogues (NAs) in inhibiting hepatitis B virus (HBV) deoxyribonucleic acid (DNA) as the first-line drug, there are several limitations still, such as poor antigen inhibition, drug resistance, low-level viremia, restricting patients' functional cure. Due to the constraints of NAs, traditional medicines, such as traditional Chinese medicine (TCM), have become more prevalently used and researched in the clinical treatment of CHB as complementary alternative therapies. As a consequence, the review focuses on the background based on HBV’s life cycle as well as the NAs’ limitations, progress based on direct and indirect pathway of targeting HBV of TCM, and challenges of TCM. We found TCMs play an increasingly important role in anti-HBV. In a direct antiviral way, they regulate HBV infection, replication, assembly, and other aspects of the HBV life cycle. As for indirect way, TCMs can exert anti-HBV effects through targeting the host, including immune regulation, apoptosis, autophagy, oxidative stress, etc. Especially, TCMs have the advantages of strong antigenic inhibition compared to NAs. Specifically, we can combine the benefits of TCMs in strong HBV antigen inhibition with the benefits of NAs in targeted antiviral effects, in order to find a suitable combination of "TCM + NAs" to contribute to Chinese knowledge of the realisation of the “global elimination of HBV by 2030” goal of the World Health Organization.

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality.

Oropharyngeal microbiomes play a significant role in the susceptibility and severity of COVID-19, yet the role of these microbiomes play for the development of COVID-19 Omicron variant have not been reported. A total of 791 pharyngeal swab samples were prospectively included in this study, including 297 confirmed cases of Omicron variant (CCO), 222 confirmed case of Omicron who recovered (CCOR), 73 confirmed cases of original strain (CCOS) and 199 healthy controls (HC). All samples completed MiSeq sequencing. The results showed that compared with HC, conditional pathogens increased in CCO, while acid-producing bacteria decreased. Based on six optimal oropharyngeal operational taxonomy units (OTUs), we constructed a marker microbial classifier to distinguish between patients with Omicron variant and healthy people, and achieved high diagnostic efficiency in both the discovery queue and the verification queue. At same time, we introduced a group of cross-age infection verification cohort and Omicron variant subtype XBB.1.5 branch, which can be accurately distinguished by this diagnostic model. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group-severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. In addition, we found that some genera with significant differences gradually increased or decreased with the recovery of Omicron variant infection. The results of Spearman analysis showed that 27 oropharyngeal OTUs were closely related to 6 clinical indexes in CCO and HC. Finally, we found that the Omicron variant had different characterization of oropharyngeal microbiomes from the original strain. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant, described the correlation between microbial OTUs and clinical indexes. It was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes.

Old residential areas present unique challenges in terms of design, stakeholders, and renovation requirements compared to traditional building projects. However, unreasonable construction plans can lead to delays, cost overruns, poor quality, and conflicts between the construction party and local residents. This article proposes an optimization model that prioritizes progress, quality, and cost as the key control objectives, leveraging the actual conditions of renovating old residential areas. The NSGA-II genetic algorithm is employed to solve the mathematical model. To validate the effectiveness and scientific rigor of the algorithm, a renovation project in an old residential area in Wuhan is used as a case study. The findings of this study offer valuable theoretical support for decision makers in selecting appropriate construction plans.

The rapid development of urbanization makes the phenomenon of urban heat islands even more serious. Predicting the impact of land cover change on urban heat island has become one of the research hotspots. Taking Wuhan, China, as an example, this study simulated the land type change in 2020 through the Cellular Automata-Markov-Chain (CA-Markov) model. The urban heat island in 2020 was simulated and analyzed in conjunction with the Weather Research & Forecasting Model (WRF), and the simulation results of wind velocity and temperature were confirmed using weather station observation data. Based on this, the land cover and urban heat island of Wuhan in 2030 were predicted. The temperature was found to be well-fit by CA-Markov simulated land use data, with an average inaccuracy of about 2.5°C for weather stations. Wind speed had a poor fitting effect; the average error was roughly 2 m/s. The built-up area was the center of the high temperature area both before and after the prediction, the water was the low temperature area, and the peak heat island happened at night. According to the forecast results, there will be more built-up land in 2030, and there will be a greater intensity of heat islands than in 2020.

ABSTRACTThe gut microbiota is involved in the production of numerous metabolites that maintain host wellbeing. The assembly of the gut microbiome is highly dynamic, and influenced by many postnatal factors, moreover, little is known about the development of the gut metabolome. We showed that geography has an important influence on the microbiome dynamics in the first year of life based on two independent cohorts from China and Sweden. Major compositional differences since birth were the high relative abundance of Bacteroides in the Swedish cohort and Streptococcus in the Chinese cohort. We analyzed the development of the fecal metabolome in the first year of life in the Chinese cohort. Lipid metabolism, especially acylcarnitines and bile acids, was the most abundant metabolic pathway in the newborn gut. Delivery mode and feeding induced particular differences in the gut metabolome since birth. In contrast to C-section newborns, medium- and long-chain acylcarnitines were abundant at newborn age only in vaginally delivered infants, associated by the presence of bacteria such as Bacteroides vulgatus and Parabacteroides merdae. Our data provide a basis for understanding the maturation of the fecal metabolome and the metabolic role of gut microbiota in infancy.

ABSTRACTThe length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients’ APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778*Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT).

BackgroundThe existing diagnostic methods of epilepsy such as history collection and electroencephalogram have great limitations in practice, so more reliable and less difficult diagnostic methods are needed.MethodsBy characterizing oral microbiota in patients diagnosed with epilepsy (EPs) and patients whose seizures were under control (EPRs), we sought to discover biomarkers for different disease states. 16S rRNA gene sequencing was performed on 480 tongue swabs [157 EPs, 22 EPRs, and 301 healthy controls (HCs)].ResultsCompared with normal individuals, patients with epilepsy exhibit increased alpha diversity in their oral microbiota, and the oral microbial communities of the two groups demonstrate significant beta diversity differences. EPs exhibit a significant increase in the abundance of 26 genera, including Streptococcus, Granulicatella, and Kluyvera, while the abundance of 14 genera, including Peptostreptococcus, Neisseria, and Schaalia, is significantly reduced. The area under the receiver operating characteristic curve (AUC) of oral microbial markers in the training cohort and validation cohort was 98.85% and 97.23%, respectively. Importantly, the AUC of the biomarker set achieved 92.44% of additional independent validation sets. In addition, EPRs also have their own unique oral community.ConclusionThis study describes the characterization of the oral microbiome in EP and EPR and demonstrates the potential of the specific microbiome as a non-invasive diagnostic tool for epilepsy.

Abstract Background Alterations in oral microbiota in patients with systemic lupus erythematosus (SLE) is less evaluated. The aim of this study was to compare the characteristics of the oral microbiome in SLE patients and healthy controls, and construct an SLE classifier based on the oral microbiota. Methods We sequenced tongue-coating samples of individuals in treatment-naïve SLE (n = 182) and matched healthy controls (n = 280). We characterized the oral microbiome and constructed a microbial classifier in the derivation cohort and validated the results in the validation cohorts. Furthermore, the oral microbiome of posttreatment SLE (n = 73) was characterized. Results The oral microbial diversity of SLE was increased, and the microbial community was different between SLE and healthy controls. The genera Prevotella and Veillonella were enriched, while Streptococcus and Porphyromonas were reduced in SLE. In addition, an increase was noted in 27 predicted microbial functions, while a decrease was noted in 34 other functions. Thirty-nine operational taxonomy units (OTUs) were identified to be related with seven clinical indicators. Two OTUs were identified to construct a classifier, which yielded area under the curve values of 0.9166 (95% CI 0.8848–0.9483, p

With the rapid development of China’s urbanization process and the promotion of the ‘double carbon’ strategy, green buildings will become an inevitable trend in the future development of the construction industry. Among the various building evaluation criteria, it is important to discuss how to promote the development of green buildings more efficiently and adaptively according to the characteristics of personnel needs. This study constructed a questionnaire to assess building use satisfaction based on China’s national standards. Field research was conducted on 23 projects in six cities in Hubei Province, China, and a total of 2251 questionnaires were collected. The survey evaluated satisfaction with the current use of green buildings across different age groups and genders. A new satisfaction evaluation model is constructed through fuzzy comprehensive evaluation to provide guidance for the differentiated development of green buildings in different cities.

Environmental problems including the depletion of natural resources and energy have drawn a lot of attention from all sectors of society in the context of high-quality global development, and solid waste generated by the construction industry accounts for 36% of the total amount of municipal waste. The generation of large amounts of construction waste not only causes a waste of resources, but also causes great damage to the environment. Reducing the quantity of solid waste produced during a building’s new construction period can be greatly aided by construction site solid waste statistics and forecasts. Based on the statistical data of 61 public construction projects in Hainan Province, China, this study uses the Random Forest algorithm to rank the importance of possible factors affecting the amount of solid waste generated, and linearly fits the data to achieve the prediction of solid waste at construction sites. The findings indicate that building area, building height, concrete usage, steel usage and assembly rate are the main factors affecting solid waste in construction sites. In office buildings and exhibition buildings, an increase in ground area, building height, concrete usage, and steel usage increases the generation of each type of solid waste (inorganic non-metallic solid waste, metallic solid waste), with the exception of an increase in concrete usage, which results in a decrease in the generation of metallic solid waste. Furthermore, a higher assembly rate can substantially lower the production of all waste types. These results offer a theoretical foundation for the implementation of assembly construction to support the high-quality development of the construction industry, as well as partial design inspiration for the architectural design stage.

BackgroundSevere community-acquired pneumonia (sCAP) is life-threatening and characterized by intensive care unit (ICU) admission and high mortality. And they are vulnerable to hospital-acquired infection. In such a severe condition, metagenomic next-generation sequencing (mNGS) outperforms for short turnaround time and broad detection spectrum.Case presentationA 15-year-old male with severe influenza and methicillin-resistant Staphylococcus aureus (MRSA) pneumonia progressed rapidly, initially misdiagnosed as influenza co-infected with Aspergillus for misleading bronchoscopy manifestations. The turnaround time of mNGS is 13 h, which has the potential to expedite the clinical medication process. With the powerful support of mNGS and extracorporeal membrane oxygenation (ECMO), anti-infective therapy was adjusted accordingly, and vital signs gradually stabilized. After tortuous treatment and unremitting efforts, the patient recovered well.ConclusionsRapid mNGS applications, timely medication adjustments, strong ECMO support and active family compliance contribute to this miracle of life. False-negative or false-positive results are alarming, anti-infective medications should be adjusted after a comprehensive review of physical status and other indicators.

Abstract This paper studies a distributed formation problem for non‐holonomic mobile robots. Consideration of the leader dynamics of the robots as non‐ideal, that is, subject to disturbances/unmodelled variables, is the distinguishing feature of this work. The issue is resolved by a distributed combined disturbance‐and‐leader estimator, allowing for the distributed reconstruction of the leader's signals. The estimator needs to detect the leader's information and disturbance. In order to reject such disturbance and achieve the formation asymptotically, the control law incorporates the smooth estimator's estimate of the leader disturbance. Furthermore, the stability of the total distributed formation control algorithm is also examined using the Lyapunov technique. Finally, to show the viability of the proposed theoretical results, simulations and actual experiments are carried out.

Abstract Manual or conventional image processing algorithms are commonly used to detect surface problems on mobile phone screens. However, inefficiency and inflexibility are disadvantages. Although the semantic segmentation method has high adaptability and accuracy, it also has a low defect detection efficiency due to its excessive parameters. In order to increase defect detection efficiency, a novel efficient encoder–decoder architecture termed MB encoder–decoder architecture based on MBConv blocks, and that it reduces the number of parameters used in semantic segmentation methods i presented. In addition, by applying the MB encoder–decoder design to the U‐Net, the efficient U‐Net (EU‐Net) is proposed. It confirms the MB encoder–decoder architecture's superiority. Then, EU‐Net to mobile phone surface defect detection in real industrial scenarios. Experimental results on a dataset show the superiority of the proposed algorithm and it can meet the real‐time requirement of industrial production.

Abstract The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID‐19). However, the oral fungal microbiota in COVID‐19 remains unclear. This article aims to characterize the oral mycobiome in COVID‐19 and recovered patients. Tongue coating specimens of 71 COVID‐19 patients, 36 suspected cases (SCs), 22 recovered COVID‐19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID‐19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID‐19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID‐19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID‐19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID‐19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID‐19 patients. The correlation between distinct fungi and bacteria in COVID‐19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID‐19.

Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death in the world. Nanosecond pulsed electric fields (nsPEFs) have emerged as a new treatment for cancer. This study aims to identify the effectiveness of nsPEFs in the treatment of HCC and analyze the alterations in the gut microbiome and serum metabonomics after ablation.Methods: C57BL/6 mice were randomly divided into three groups: healthy control mice (n = 10), HCC mice (n = 10), and nsPEF-treated HCC mice (n = 23). Hep1-6 cell lines were used to establish the HCC model in situ. Histopathological staining was performed on tumor tissues. The gut microbiome was analyzed by 16S rRNA sequencing. Serum metabolites were analyzed by liquid chromatography–mass spectrometry (LC-MS) metabolomic analysis. Spearman’s correlation analysis was carried out to analyze the correlation between the gut microbiome and serum metabonomics.Results: The fluorescence image showed that nsPEFs were significantly effective. Histopathological staining identified nuclear pyknosis and cell necrosis in the nsPEF group. The expression of CD34, PCNA, and VEGF decreased significantly in the nsPEF group. Compared with normal mice, the gut microbiome diversity of HCC mice was increased. Eight genera including Alistipes and Muribaculaceae were enriched in the HCC group. Inversely, these genera decreased in the nsPEF group. LC-MS analysis confirmed that there were significant differences in serum metabolism among the three groups. Correlation analysis showed crucial relationships between the gut microbiome and serum metabolites that are involved in nsPEF ablation of HCC.Conclusion: As a new minimally invasive treatment for tumor ablation, nsPEFs have an excellent ablation effect. The alterations in the gut microbiome and serum metabolites may participate in the prognosis of HCC ablation.

The discharge of warm water from water source heat pump (WSHP) energy stations can cause local changes in the water temperature, leading to changes in the water quality around and downstream of the discharge outlet, resulting in a cumulative effect that alters the aquatic habitat elements and affects the ecosystem stability. This study took the demonstration project of the water source heat pump energy station in the Hankou Binjiang International Business District of Wuhan City as an example. Based on measured data, this study established the control equation for the warm water discharge of the project and used FLUENT 2022 R1 software to establish a numerical model of warm water diffusion. This study analyzed the changes in discharge water temperature during unfavorable conditions in both summer and winter during dry and wet seasons by using four parameters: equal area surface, volume, length, and width of the area of influence. The results suggest that the impact of the project’s warm water discharge on the temperature of the river body will quickly decay and its impact range will be limited. Hence, the project will not have adverse effects on the water and ecological environment in the mid–lower reaches of the Yangtze River.

Background: Patients who survive initial esophagogastric variceal bleeding (EVB) are at an increased risk of recurrent bleeding and death; however, a reliable predictive model is lacking. We aimed to develop a model for rebleeding prediction in patients with EVB after modified percutaneous transhepatic variceal embolization (PTVE) with cyanoacrylate. Methods: A total of 122 patients with EVB who underwent PTVE from January 2015 to November 2020 were enrolled. Multivariate logistic analyses were conducted to determine independent risk factors for nomogram construction. The discrimination, calibration, and clinical utility of the nomogram were compared with the Model for End-stage Liver Disease score (MELD) and the Child–Pugh model. Risk stratification was performed according to the nomogram. Results: Rebleeding within 3 months of PTVE occurred in 32 patients (26.2%). Independent rebleeding indicators included prior history of endoscopic therapy, Child–Pugh score, partial splenic embolization, and creatinine level. The nomogram incorporating these four predictors achieved excellent calibration and discriminatory abilities, with a concordance index of 0.85, which was confirmed to be 0.83 through bootstrapping validation. The nomogram demonstrated superior discrimination and clinical applicability than the MELD and Child–Pugh models. As shown in the Kaplan–Meier curves, high-risk patients had a high probability of rebleeding (P ​

Background: Recent studies highlighted the functional role of protein arginine methyltransferases (PRMTs) catalyzing the methylation of protein arginine in malignant progression of various tumors. Stratification the subtypes of hepatocellular carcinoma (HCC) is fundamental for exploring effective treatment strategies. Here, we aim to conduct a comprehensive analysis of PRMTs with bioinformatic tools to identify novel biomarkers for HCC subtypes classification and prognosis prediction, which may be potential ideal targets for therapeutic intervention.Methods: The expression profiling of PRMTs in HCC tissues was evaluated based on the data of TCGA-LIHC cohort, and further validated in HCC TMA cohort and HCC cell lines. HCC was systematically classified based on PRMT family related genes. Subsequently, the differentially expressed genes (DEGs) between molecular subtypes were identified, and prognostic risk model were constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to evaluate the prognosis, gene mutation, clinical features, immunophenotype, immunotherapeutic effect and antineoplastic drug sensitivity of HCC.Results: PRMTs expression was markedly altered both in HCC tissues and HCC cell lines. Three molecular subtypes with distinct immunophenotype were generated. 11 PRMT-related genes were enrolled to establish prognostic model, which presented with high accuracy in predicting the prognosis of two risk groups in the training, validation, and immunotherapy cohort, respectively. Additionally, the two risk groups showed significant difference in immunotherapeutic efficacy. Further, the sensitivity of 72 anticancer drugs was identified using prognostic risk model.Conclusion: In summary, our findings stratified HCC into three subtypes based on the PRMT-related genes. The prognostic model established in this work provide novel insights into the exploration of related therapeutic approaches in treating HCC.

The condition and health of large oil-immersed power transformers’ insulation have a direct impact on the safety and stability of the power grid. Therefore, it is crucial to investigate the aging characteristics of oil–paper insulation in power transformers. In this study, we developed a computational model for reclosing current calculation and multiphysics coupling models for magnetic-circuit-force, electrostatic field, and temperature field simulations. The calculated aging resulted in a mechanical stress of 8.71 MPa, an electric field strength of 2.26 × 106 V/m, and a temperature of 113.7 °C. We conducted combined electrical–thermal–mechanical aging tests on the oil–paper insulation and measured various insulating paper performance parameters at different aging stages. Our study revealed that both the mechanical and electrical properties of the insulating paper deteriorated in both aging groups. However, the changes were more pronounced in the electrical–thermal–mechanical aging group compared to the electrical–thermal aging group, indicating that mechanical stress accelerated the aging process of the insulating paper. In the early stages of aging, the rate of performance changes in the electrical–thermal aging group was similar to that in the electrical–thermal–mechanical aging group. However, as the aging time increased, the degradation of performance induced by mechanical aging became more significant. This suggests that the insulating paper’s resistance to mechanical damage, specifically short-circuit resistance, noticeably decreased after prolonged aging.

In the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined forecasting model based on clustering results, which can achieve a short-time prediction of daily electricity based on a campus building’s electricity data over the past 15 days. Considering the diversity of campus buildings in energy consumption and functional aspects, the selected campus buildings are firstly classified into three categories using K-Means clustering in terms of their daily power consumption. Compared with the mainstream building energy consumption prediction models, i.e., LSTM and SVR, the results show that the combined forecast model is superior to other models. Furthermore, an average percentage fluctuation (APF) index is found to be close to the MAPE, which can reflect the prediction accuracy in advance.

ObjectiveDiabetic kidney disease (DKD) is one of the most prevalent complications of diabetes mellitus (DM) and is associated with gut microbial dysbiosis. We aim to build a diagnostic model to aid clinical practice and uncover a crucial harmful microbial community that contributes to DKD pathogenesis and exacerbation.DesignA total of 528 fecal samples from 180 DKD patients and 348 non-DKD populations (138 DM and 210 healthy volunteers) from the First Affiliated Hospital of Zhengzhou University were recruited and randomly divided into a discovery phase and a validation phase. The gut microbial composition was compared using 16S rRNA sequencing. Then, the 180 DKD patients were stratified into four groups based on clinical stages and underwent gut microbiota analysis. We established DKD mouse models and a healthy fecal microbiota transplantation (FMT) model to validate the effects of gut microbiota on DKD and select the potential harmful microbial community. Untargeted metabolome–microbiome combined analysis of mouse models helps decipher the pathogenetic mechanism from a metabolic perspective.ResultsThe diversity of the gut microbiome was significantly decreased in DKD patients when compared with that of the non-DKD population and was increased in the patients with more advanced DKD stages. The DKD severity in mice was relieved after healthy gut microbiota reconstruction. The common harmful microbial community was accumulated in the subjects with more severe DKD phenotypes (i.e., DKD and DKD5 patients and DKD mice). The harmful microbial community was positively associated with the serum injurious metabolites (e.g., cholic acid and hippuric acid).ConclusionThe fecal microbial community was altered markedly in DKD. Combining the fecal analysis of both human and animal models selected the accumulated harmful pathogens. Partially recovering healthy gut microbiota can relieve DKD phenotypes via influencing pathogens’ effect on DKD mice’s metabolism.

This paper aims to utilize converter steel slag aggregates (SSA) to develop sustainable ultra-high performance concrete (UHPC) for multifunctional applications. UHPC mixtures with different particle sizes and contents of SSA are developed by applying a modified packing model. The physical, mechanical, thermal and sustainable properties of the designed UHPC have been explored by analysing bulk density, ultrasound pulse velocity (UPV), water-permeable porosity, mechanical strength, spalling and residual strength after elevated temperature, binder efficiency and heavy metal ions leaching. The results show that the optimized particle distribution of converter SSA up to 5.6 mm could be introduced in sustainable UHPC with high 28-days compressive strength up to 177.8 MPa and low binder content as low as 567.2 kg/m3. The designed UHPC mixtures attain bulk density and UPV up to 3070 kg/m3 and 5236 m/s, respectively, which makes them suitable to be utilized as heavy-weight concrete in the protective shield against nuclear radiation. No obvious explosive spalling and relatively high residual strengths reveal the potential application in heat or fire-resistant concrete. The converter steel slag in form of aggregates in UHPC does not cause negative environmental impacts on CO2 emission and heavy metals pollution, considering the relatively high binder efficiency (0.178–0.313 MPa/(kg/m3)) and non-detectable leaching of B, Cr and V.

Primary liver cancer (PLC), including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), and other rare tumours, is the second leading cause of cancer-related mortality. It has been a major contributor to the cancer burden worldwide. Of all primary liver cancer, HCC is the most common type. Over the past few decades, chemotherapy, immunotherapy and other therapies have been identified as applicable to the treatment of HCC. However, evidence suggests that chemotherapy resistance is associated with higher mortality rates in liver cancer. The tumour microenvironment (TME), which includes molecular, cellular, extracellular matrix(ECM), and vascular signalling pathways, is a complex ecosystem. It is now increasingly recognized that the tumour microenvironment plays a pivotal role in PLC prognosis, progression and treatment response. Cancer cells reprogram the tumour microenvironment to develop resistance to chemotherapy drugs distinct from normal differentiated tissues. Chemotherapy resistance mechanisms are reshaped during TME reprogramming. For this reason, TME reprogramming can provide a powerful tool to understand better both cancer-fate processes and regenerative, with the potential to develop a new treatment. This review discusses the recent progress of tumour drug resistance, particularly tumour microenvironment reprogramming in tumour chemotherapy resistance, and focuses on its potential application prospects.

In the past few decades, great progress has been achieved in the understanding of microbiome-cancer interactions. However, most of the studies have focused on the gut microbiome, ignoring how other microbiomes interact with tumors. Emerging evidence suggests that in many types of cancers, such as lung cancer, pancreatic cancer, and colorectal cancer, the intratumoral microbiome plays a significant role. In addition, accumulating evidence suggests that intratumoral microbes have multiple effects on the biological behavior of tumors, for example, regulating tumor initiation and progression and altering the tumor response to chemotherapy and immunotherapy. However, to fully understand the role of the intratumoral microbiome in cancer, further investigation of the effects and mechanisms is still needed. This review discusses the role of intratumoral bacteria in tumorigenesis and tumor progression, recurrence and metastasis, as well as their effect on cancer prognosis and treatment outcome, and summarizes the relevant mechanisms.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2 has been prominent around the world since it was first discovered, affecting more than 100 million people. Although the symptoms of most infected patients are not serious, there is still a considerable proportion of patients who need hospitalization and even develop fatal symptoms such as cytokine storms, acute respiratory distress syndrome and so on. Cytokine storm is usually described as a collection of clinical manifestations caused by overactivation of the immune system, which plays an important role in tissue injury and multiorgan failure. The immune system of healthy individuals is composed of two interrelated parts, the innate immune system and the adaptive immune system. Innate immunity is the body’s first line of defense against viruses; it can quickly perceive viruses through pattern recognition receptors and activate related inflammatory pathways to clear pathogens. The adaptive immune system is activated by specific antigens and is mainly composed of CD4+ T cells, CD8+ T cells and B cells, which play different roles in viral infection. Here, we discuss the immune response after SARS-CoV-2 infection. In-depth study of the recognition of and response of innate immunity and adaptive immunity to SARS-CoV-2 will help to prevent the development of critical cases and aid the exploration of more targeted treatments.

BackgroundThe continued ‘evolution’ of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential.MethodsUsing medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021.FindingsCommon initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%).ResultsCompared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.

The healthy growth of infants during early life is associated with lifelong consequences. Breastfeeding has positive impacts on reducing obesity risk, which is likely due to the varied components of breast milk, such as N-acetylneuraminic acid (Neu5Ac). However, the effect of breast milk Neu5Ac on infant growth has not been well studied. In this study, targeted metabolomic and metagenomic analyses were performed to illustrate the association between breast milk Neu5Ac and infant growth. Results demonstrated that Neu5Ac was significantly abundant in breast milk from infants with low obesity risk in two independent Chinese cohorts. Neu5Ac from breast milk altered infant gut microbiota and bile acid metabolism, resulting in a distinct fecal bile acid profile in the high-Neu5Ac group, which was characterized by reduced levels of primary bile acids and elevated levels of secondary bile acids. Taurodeoxycholic acid 3-sulfate and taurochenodeoxycholic acid 3-sulfate were correlated with high breast milk Neu5Ac and low obesity risk in infants, and their associations with healthy growth were reproduced in mice colonized with infant-derived microbiota. Parabacteroides might be linked to bile acid metabolism and act as a mediator between Neu5Ac and infant growth. These results showed the gut microbiota-dependent crosstalk between breast milk Neu5Ac and infant growth.

Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time.

BackgroundHepatocellular carcinoma (HCC) is a highly aggressive malignancy. Irreversible electroporation (IRE) is an ablative modality that uses high-voltage electrical pulses to permeabilize the cell membrane leading to cell necrosis. Unlike traditional thermal ablation, IRE is hardly affected by the “heat-sink” effect and can prevent damage of the adjacent vital structures. Nanosecond pulsed electric field (nsPEF) is a new IRE technique using ultra-short pulses (nanosecond duration), can not only penetrate the cell membranes, but also act on the organelles. Sufficient preclinical researches have shown that nsPEF can eliminate HCC without damaging vital organs, and elicit potent anti-tumor immune response.ObjectiveThis is the first clinical study to evaluate feasibility, efficacy, and safety of nsPEF for the treatment of HCC, where thermal ablation is unsuitable due to proximity to critical structures.Methods and analysisWe will conduct an open-labeled, single-arm, prospective, multicenter, and objective performance criteria trial. One hundred and ninety-two patients with HCC, in which the tumor is located immediately (

Gut microbiota is key to human health and disease. Convincing studies have demonstrated that dysbiosis in the commensal gut microbiota is associated with intestinal and extra-intestinal diseases. Recent explorations have significantly contributed to the understanding of the relationship between gut microbiota and bone diseases (osteoporosis, osteoarthritis, rheumatoid arthritis, and bone cancer). Gut microbiota and its metabolites may become associated with the development and progression of bone disorders owing to their critical role in nutrient absorption, immunomodulation, and the gut–brain–bone axis (regulation hormones). In this work, we review the recent developments addressing the effect of gut microbiota modulation on skeletal diseases and explore a feasible preventive approach and therapy for bone diseases.

BackgroundOsteosarcoma (OS) is the most prevalent primary malignant bone cancer with poor prognosis. The aim of this study was to explore the prognostic factors that influence survival, and build up and validate a simple risk model to predict mortality in OS patients.Materials and MethodsThis was a single-center retrospective cohort study. A total of 153 patients with newly diagnosed OS were enrolled as the training group. We analyzed the clinical data and outcomes of the OS patients. Prognostic risk factors were identified and evaluated by a logistic regression model with Markov Chain Monte Carlo simulation. The risk score was constructed based on the training group and was further validated using each patient.ResultsAmong the 153 patients, the mean (standard deviation) age was 21.6 (14.2) years, and 62 (40.5%) patients were females. The rate of in-hospital mortality of patients was 41.2% (95% CI, 31.6–50.7%). The candidate prognostic factors were selected and evaluated in relation to patient age, sex, tumor site (lower/upper extremity), tumor volume, intramedullary length of lesion, serum levels of alkaline phosphatase (ALP) and primary metastasis. However, only tumor size and primary metastasis were identified as independent prognostic indicators for patients with osteosarcoma. The risk model had a C-statistic of 0.7308 with a predictive range of 21.05–68.42%. Based on the distribution of the risk score, 24.8, 49.7 and 25.5% of patients were stratified into the high-, average- and low-risk groups for in-hospital mortality, with corresponding probabilities of 0.684, 0.329, and 0.210, respectively.ConclusionA simple risk model was developed and validated to predict the prognosis for patients with osteosarcoma of the extremity at primary diagnosis. The simple risk score system could be used to stratify patients into different risk groups of in-hospital mortality and may help clinicians judge the outcomes of prognosis and establish appropriate surveillance strategies.

A cruise ship, which has large-scale open spaces, has an uneven cabin thermal environment in the cruise public space, leading to overcooling or poor cooling issues. Therefore, optimizing the thermal environment of public spaces during a cruise should be the priority. According to the space functions of the cruise ship, the large public space is divided into three subzones: the entertainment area (Subzone I), the round-table dining area (Subzone II), and the square-table dining area (Subzone III). To create a uniform, stable, and comfortable thermal environment, this study proposes a subzone-based temperature setting approach to independently adjust the thermal environment of each subzone. Coupling simulation of building energy modeling (BEM) and computational fluid dynamics (CFD) was adopted in this study to determine proper temperature setpoints of the subzones under different occupancy rates. The results indicate that, compared with a single-temperature setpoint for the entire public space, the subzone-based temperature setpoints could achieve a uniform thermal environment. The average temperature difference among the three subzones was 0.68 °C. Moreover, the airflow between two adjacent subzones considerably affected the BEM results of energy consumption of the air-conditioning system.

Abstract To identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855–0.928), 0.891 (95% CI 0.848–0.932), and 0.829 (95% CI 0.455–1.000), and predictive ranges of 4.6–98.2%, 2.3–94.9%, and 0–12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning.

The fight against Mycobacterium tuberculosis (MTB) has been going on for thousands of years, while it still poses a threat to human health. In addition to routine detections, metagenomic next-generation sequencing (mNGS) has begun to show presence as a comprehensive and hypothesis-free test. It can not only detect MTB without isolating specific pathogens but also suggest the co-infection pathogens or underlying tumor simultaneously, which is of benefit to assist in comprehensive clinical diagnosis. It also shows the potential to detect multiple drug resistance sites for precise treatment. However, considering the cost performance compared with conventional assays (especially Xpert MTB/RIF), mNGS seems to be overqualified for patients with mild and typical symptoms. Technology optimization of sequencing and analyzing should be conducted to improve the positive rate and broaden the applicable fields.

With the continuous expansion of urban cable scale and the increase in service time, the fire accident risk of cable tunnels also increases. Defects in the configuration of fire prevention measures lead to many fire accidents and seriously affect the reliability of power supply. First, the detection data of a typical tunnel fire accident are analyzed in this study. The results show that the main problems are the unreasonable installation position of fire extinguishers, the lack of fire prevention measures between intermediate joints, and the insufficient fire prevention performance of interlayer fire barriers. Then, the field test of cable tunnel fire is carried out in a cable tunnel to be put into operation. The test data show that the high-temperature area is mainly concentrated on the top of the tunnel, and the insufficient suspension height of the fire extinguisher will affect the trigger time. The energy of a short circuit accident is large, the cable burns and spreads rapidly, and the delayed triggering may lead to large fire to extinguish effectively. Comparing the simulation and test results of abnormal characteristic parameters in fire, the temperature error is less than 4%. The fire power development curve is obtained through simulation, and the burner power is set to test the fire prevention and extinguishing devices. Finally, the optimal configuration scheme of the fire anomaly data detection equipment and fire-extinguishing products is proposed. The temperature drop rate increases by 135%.

Abstract Background Systemic lupus erythematosus (SLE) is associated with increased risk of cancer and the mechanism remains unclear. Here, we examined the level of auto-antibodies and disease activity index scores in SLE patients with cancers and analyzed whether medications for SLE management might contribute to the higher cancer risk in SLE patients. Methods In this retrospective study, we carried out a nested case-control study in a large cohort of SLE patients. We screened 5858 SLE patients to identify the newly diagnosed and yet to be treated cancers. The following clinical features were evaluated: auto-antibodies levels, SLE disease activity index scores, and previous medication used for SLE management. Systemic glucocorticoid, cyclophosphamide, hydroxychloroquine (HCQ), methotrexate, and azathioprine were considered the main medication indices. Results Our analyses identified 51 SLE patients who also had cancer and 204 matched control patients who had SLE but not cancer. Of the 51 SLE patients, thyroid cancer (14/51, 27.45%), cervical cancer (10/51, 19.61%), and lung cancer (7/51, 13.73%) were the most common types. Our analyses did not reveal any significant differences in the levels of auto-antibodies in SLE patients with cancers relative to the control group. Further, we observed that disease activity was significantly lower in SLE patients with cancers relative to the matched control SLE group. There was no statistically significant association between the cancer risk and the use of systemic glucocorticoid, cyclophosphamide, methotrexate, or azathioprine. Importantly, the administration of HCQ was significantly lower in SLE patients suffering cancers relative to the cancer-free matched control group. Conclusions Our analyses indicate that SLE patients with cancers might have a lower disease activity at the time of cancer diagnosis. HCQ was negatively associated with cancer risk in SLE patients. These findings highlight a potential and novel prevention strategy for SLE.