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Conventional methods of house dust mite control often involve chemical pesticides, raising concerns about their potential hazards. Mint essential oil presents a natural and eco-friendly alternative for managing house dust mite infestations. In this study, ten varieties of mint plants were cultivated, and their essential oils were extracted through steam distillation. The toxicity of these mint essential oils and their main compounds on adult house dust mites was assessed using contact+fumigant mortality bioassays and vapour-phase mortality bioassays. A repellent bioassay was also conducted to evaluate the repellent effects of mint oils and main compound on house dust mites. The toxicity of mint essential oils varied among the different varieties, with some demonstrating higher potency in eradicating house dust mites. Mint oils showed both acaricidal action and repellent effects on house dust mites, with certain varieties exhibiting stronger efficacy. Linalool as active compounds was identified as key contributors to the acaricidal properties of mint essential oil. Mint essential oil, particularly certain varieties rich in active compounds, shows promise as a natural and effective agent for controlling house dust mites. Its dual functionality in killing and repelling house dust mites, along with its environmentally friendly nature, make it a viable alternative to chemical pesticides for house dust mite management. Further research on the specific mechanisms of action and long-term effectiveness of mint essential oil in house dust mite control is warranted to explore its full potential as a sustainable pest management solution.

Abstract The terminal mining is an essential part of fully mechanized mining, and the reasonable length of the flap roof during the terminal mining is an important basis for the smooth operation of the terminal mining. With the research background of the 213 working face of the Longde Coal Mine, the contribution of this study is the influence law of hydraulic fracturing on the terminal mining mechanical behavior of the working face. Four different numerical calculation models of terminal mining hydraulic fracturing are established, and the optimal hydraulic fracturing site plan is determined according to the length of the terminal mining flap top and the working resistance of hydraulic support. The effect of hydraulic fracturing construction and the law of mechanical behavior in the terminal mining stage in the field test are analyzed. The results show that through hydraulic fracturing technology, the control of the length of the flap roof of the working face during the terminal mining period has been realized.

Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.

ABSTRACTTidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89–0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.

The use of seawater and sea sand in concrete is becoming more prominent, particularly for marine structures, as a solution to the shortage of freshwater and natural aggregates. This research evaluates the durability of concrete made with seawater, fly ash, and recycled sea aggregates. Various techniques, including SEM-EDS, FTIR, M-Ct, and TG/DTA, were employed to analyze the microstructure of this concrete. Mechanical properties were assessed by measuring the compressive strength after 28 days. The findings indicated that higher proportions of sea-based materials increased porosity, while FTIR showed the absence of portlandite due to the use of sulfate-resisting cement. SEM-EDS confirmed XRD results quantitatively. Compressive strength improved with up to 50 % replacement of the original composition. The study demonstrates that incorporating seawater enhances the pozzolanic properties of cement, increasing the formation of Calcium-Silicate-Hydrate (C-S-H) gel, which contributes to a dense microstructure and good mechanical strength comparable to conventional freshwater concrete. These results support the use of seawater, sea sand, and recycled aggregates as sustainable alternatives, balancing structural performance and environmental sustainability in construction materials.

In order to study the deformation and failure law of bolt-mesh support roadway under roof impact load, taking 250104 transport roadway of Huating Coal Mine as background, FLAC3D was used to analyze the variation characteristics of stress, deformation and plastic zone of surrounding rock of bolt-mesh support roadway under different impact load wave velocities, angles and offset distances. The results show that: under the impact of roof load, the plastic zone of roadway roof and rock mass on both sides will gradually expand to upward and both sides, and its maximum displacement will increase exponentially; with the increase of the wave velocity of the roof impact load, the maximum displacement of the roadway roof and two sides will gradually increases exponentially; with the increase of impact angle of roof impact load, the maximum displacement of the roadway roof and two sides presents an “S-shaped” growth curve, and the impact angle of 90° has the most adverse impact on the stability of the surrounding rock of the roadway with bolt-mesh support; with the increase of the impact offset distance of roof impact load, the maximum displacement of roadway roof and impact side rock will increase first and then decrease. When the impact offset distances is equal to 5.0 m, it will pose the greatest threat to roadway safety.

Abstract Background Continuous and noninvasive hemoglobin (Hb) monitoring during surgery is essential for anesthesiologists to make transfusions decisions. The aim of this study was to investigate the feasibility and accuracy of noninvasive and continuous Hb monitoring using transesophageal descending aortic photoplethysmography (dPPG) in porcine model. Methods Nineteen landrace pigs, aged 3 to 5 months and weighing 30 to 50 kg, were enrolled in this study. A homemade oximetry sensor, including red (660 nm) and infrared (940 nm) lights, was placed in the esophagus for dPPG signal detection to pair with the corresponding reference Hb values (Hbi−STAT) measured by blood gas analysis. The decrease and increase changes in Hb concentration were achieved by hemodilution and transfusion. Metrics, including alternating current (AC), direct current (DC), and AC/DC for both red and infrared light were extracted from the dPPG signal. A receiver operating characteristic (ROC) curve was built to evaluate the performance of dPPG metrics in predicting the Hb “trigger threshold” of transfusion (Hb 100 g/L). Agreement and trending ability between Hb measured by dPPG (HbdPPG) and by blood gas analysis were analyzed by Bland-Altman method and polar plot graph. Error grid analysis was also performed to evaluate clinical significance of HbdPPG measurement. Results The dPPG signal was successfully detected in all of the enrolled experimental pigs, without the occurrence of a continuous loss of dPPG signal for 2 min during the entire measurement. A total of 376 pairs of dPPG signal and Hbi−STAT were acquired. ACred/DCred and ACinf/DCinf had moderate correlations with Hbi−STAT, and the correlation coefficients were 0.790 and 0.782, respectively. The areas under the ROC curve for ACred/DCred and ACinf/DCinf in predicting Hbi−STAT < 60 g/L were 0.85 and 0.75, in predicting Hbi−STAT > 100 g/L were 0.90 and 0.83, respectively. Bland-Altman analysis and polar plot showed a small bias (1.69 g/L) but a wide limit of agreement (-26.02–29.40 g/L) and a poor trend ability between HbdPPG and Hbi−STAT. Clinical significance analysis showed that 82% of the data lay within the Zone A, 18% within the Zone B, and 0% within the Zone C. Conclusion It is feasible to establish a noninvasive and continuous Hb monitoring by transesophageal dPPG signal. The ACred/DCred extracted from the dPPG signal could provide a sensitive prediction of the Hb threshold for transfusion. The Hb concentration measured by dPPG signal has a moderate correlation with that measured by blood gas analysis. This animal study may provide an experimental basis for the development of bedside HbdPPG monitoring in the future.

Abstract Nonalcoholic fatty liver disease (NAFLD) is the most common liver disorder worldwide. Recent studies show that innate immunity-related signaling pathways fuel NAFLD progression. This study aims to identify potent regulators of innate immunity during NAFLD progression. To this end, a phenotype-based high-content screening is performed, and RING finger protein 13 (RNF13) is identified as an effective inhibitor of lipid accumulation in vitro. In vivo gain- and loss-of-function assays are conducted to investigate the role of RNF13 in NAFLD. Transcriptome sequencing and immunoprecipitation-mass spectrometry are performed to explore the underlying mechanisms. We reveal that RNF13 protein is upregulated in the liver of individuals with NASH. Rnf13 knockout in hepatocytes exacerbate insulin resistance, steatosis, inflammation, cell injury and fibrosis in the liver of diet-induced mice, which can be alleviated by Rnf13 overexpression. Mechanically, RNF13 facilitates the proteasomal degradation of stimulator of interferon genes protein (STING) in a ubiquitination-dependent way. This study provides a promising innate immunity-related target for NAFLD treatment.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) has been traditionally treated using glucocorticoids and immunosuppressants. However, these treatment modes are associated with high recurrence AAV rates and adverse reactions. Therefore, treatment strategies for AAV need to be urgently optimized. The efficacy and safety of biological agents in the treatment of vasculitis have been clinically validated. This review comprehensively summarizes the evidence-based support for the clinical use of existing biological agents in AAV. The findings reveal that multiple biological agents not only effectively reduce the adverse reactions associated with glucocorticoids and immunosuppressants but also demonstrate significant therapeutic efficacy. Notably, rituximab, an anti-CD20 antibody, has emerged as a first-line treatment option for AAV. Mepolizumab has shown promising results in relapsed and refractory eosinophilic granulomatosis with polyangiitis. Other biological agents targeting cytokines, complement, and other pathways have also demonstrated clinical benefits in recent studies. The widespread application of biological agents provides new insights into the treatment of AAV and is expected to drive further clinical research. These advancements not only improve patient outcomes but also offer more possibilities and hope in the field of AAV treatment.

Objective • The objective of this study was to enhance the efficiency of pre-check and triage procedures in gynecology and obstetrics, while ensuring the safety of maternal and infant patients during the COVID-19 pandemic. Methods • Between March 2020 and July 2022, the workflow in gynecology and obstetrics was optimized, and the management of medical staff working in outpatient, ward, and obstetric ward settings was strengthened. Special protocols were developed and implemented for pregnant women, parturients, and neonates. Detailed procedures and routes were established for patient movement from outpatient areas to wards, with strict adherence to pandemic prevention and control measures. Information-based methods were employed to track and monitor the health status of high-risk pregnant women, parturients, and their families, facilitating accurate and efficient pre-check and triage processes. Results • The implementation of these measures yielded favorable outcomes. No cases of COVID-19 infection were reported among pregnant women and parturients admitted to Liangzhou Hospital. The source of infection was effectively controlled, ensuring the safety of the patients. Conclusion • This study demonstrates the significance of improving pre-check and triage efficiency, strengthening the management of medical staff, and implementing specialized measures for pregnant women, parturients, and neonates to ensure their safety during the COVID-19 pandemic. The established protocols and procedures can serve as a valuable reference for other healthcare facilities seeking to enhance their pandemic prevention and control strategies in gynecology and obstetrics settings. [ABSTRACT FROM AUTHOR]

Objective: Methylmalonic acid (MMA) buildup has recently been suggested to contribute to the onset of both age-related conditions and cardiovascular disorders. This research was aimed at examining the link between MMA and abdominal aortic calcification (AAC). Methods: Data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES) were analyzed. Serum MMA levels were determined through LC-MS/MS, and MMA levels 250 nmol/L or above were considered high. Dual-energy X-ray absorptiometry was used to assess the presence of AAC. Logistic regression analysis was performed after propensity score matching (PSM) to study the relationship between MMA and AAC. Results: A total of 2483 participants were involved in this study. To eliminate large differences between the AAC and non-AAC groups, 1:1 PSM was performed. Logistic regression analysis indicated that participants with high MMA levels had a significantly greater likelihood of experiencing AAC than those with low MMA levels (OR: 1.38, 95% CI: 1.01–1.90, P = 0.046). No statistically interaction effects between AAC and BMI or the estimated glomerular filtration rate (eGFR) were observed in subgroup analyses. Conclusion: Our study indicated a significant association between high serum MMA levels and AAC incidence.

Abstract Corrosion-induced bond degradation of deformed steel rebar in concrete is experimentally investigated with acoustic emission (AE) and 3D laser scan technique. Concrete specimens were fabricated and subjected to direct pullout test after being corroded to different levels. The number and width of cracks present during the corrosion tests and the pull-out tests were recorded. The energy released during the pullout tests were captured with AE probes, and the frequency characteristics was analyzed. After pullout tests, the surface morphology of corroded steel rebars was determined with a 3D laser scanner. A modified bond deterioration model was proposed and the parameters associated with the model were analyzed. Results indicated that two types of AE signals were acquired during pullout tests: concrete cracking in high frequency range of 35 ~ 41 kHz and steel-concrete friction in low frequency range of 3 ~ 15 kHz. The bond strength and the bond-slip characteristics depend upon the level of corrosion as well as the number and width of cracks. The reduction factor of the bond-slip model exponentially decreases as a function of the average cross-sectional area loss and linearly decreases with an increase of the rib area loss.

Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels.

Taking a new type of two speed transmission without power interruption in electric vehicles as research object， in order to improve its stability and economy， the housing is designed and optimized. Firstly， the housing structure is designed and its finite element model is established. On this basis， the stiffness and strength of the housing are calculated and the constrained modal calculation is more in line with the engineering practice. The accuracy of the finite element model is verified by the constrained modal test. The simulation and test results show that the stiffness of the housing is low， and the first two natural frequencies are very close to the excitation frequency of gear meshing at the common speed. According to the results of static and dynamic multi-objective topology optimization， the housing structure is improved. The calculation results of the improved housing show that the strength is basically unchanged， the maximum displacement of the bearing hole area is reduced from 0.085 mm to 0.07 mm， the natural frequencies of the first two orders are increased by 24.8 Hz and 50.2 Hz respectively， which reduces the risk of resonance and reduces the mass by 7.5%.

The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise.

The study of flow behaviour of water-sand mixtures in fractured rocks is of great necessity to understand the producing mechanism and prevention of water inrush and sand gushing accidents. A self-developed seepage test system is used in this paper to conduct laboratory experiments in order to study the influence of the particle size distribution, the void ratio, and the initial mass of Aeolian sand on the flow behavior. It is concluded that the water flow velocity is insensitive to the initial mass of the Aeolian sand but increases with the power exponent in the Talbot formula and the specimen height. The outflow of the Aeolian sand increases with the power exponent in the Talbot formula, the specimen height, and the initial mass of the Aeolian sand. Besides, the outflow of the Aeolian sand changes exponentially with the water flow velocity. Finally, it is found that the fractured specimen has a maximum sand filtration capacity beyond which the outflow of the Aeolian sand significantly increases with the initial mass of the Aeolian sand.

Hepatocellular carcinoma(HCC) is the sixth most common cancer in the world and is usually caused by viral hepatitis (HBV and HCV), alcoholic, and non-alcoholic fatty liver disease(NAFLD). Viral hepatitis accounts for 80% of HCC cases worldwide. In addition, With the increasing incidence of metabolic diseases, NAFLD is now the most common liver disease and a major risk factor for HCC in most developed countries. This review mainly described the specificity and similarity between the pathogenesis of viral hepatitis(HBV and HCV)-induced HCC and NAFLD-induced HCC. In general, viral hepatitis promotes HCC development mainly through specific encoded viral proteins. HBV can also exert its tumor-promoting mechanism by integrating into the host chromosome, while HCV cannot. Viral hepatitis-related HCC and NASH-related HCC differ in terms of genetic factors, and epigenetic modifications (DNA methylation, histone modifications, and microRNA effects). In addition, both of them can lead to HCC progression through abnormal lipid metabolism, persistent inflammatory response, immune and intestinal microbiome dysregulation.

Abstract Background Accumulating works show that lncRNAs play critical roles in the development of gastric cancer (GC). LncRNA HLA complex group 18 (HCG18) was implicated in the progression of bladder cancer and glioma, but its role in GC is unknown. Methods RT‐PCR was used to detect HCG18 and miR‐141‐3p expression in GC specimen. GC cell lines (AGS and MKN‐28) were exploited as cell model. The biological effect of HCG18 on cancer cells was probed by CCK‐8, colony formation, flow cytometry, Transwell and wound‐healing experiments in vitro, and subcutaneous xenotransplanted tumor model and tail vein injection model in vivo. Interaction between HCG18 and miR‐141‐3p was determined by bioinformatics analysis, RT‐PCR, and luciferase reporter experiments. Downstream gene expression of miR‐141‐3p, including Wiskott–Aldrich syndrome protein interacting protein family member 1 (WIPF1), Yes associated protein 1 (YAP), and tafazzin (TAZ) were detected using Western blot. Results HCG18 was markedly up‐regulated in GC specimens, while miR‐141‐3p was markedly down‐regulated. Down‐regulation of HCG18 inhibited viability, migration, and invasion of GC cells, while miR‐141‐3p transfection led to opposite effect. HCG18 could down‐regulate miR‐141‐3p through adsorbing it, and a negative association between HCG18 and miR‐141‐3p was found in GC specimens. HCG18 promoted WIPF1, YAP and TAZ expression, nonetheless, such influence was reversed by co‐transfecting with miR‐141‐3p. Conclusion HCG18 was aberrantly up‐regulated in GC tissues, and it indirectly regulated the activity of Hippo signaling through counteracting miR‐141‐3p expression.

ObjectiveTo explore the association between dietary fiber and heart failure (HF).MethodsData were collected from the 2009–2018 National Health and Nutrition Examination Survey. Dietary fiber intake data were obtained from two 24-h dietary recall interviews. Logistic regression and restricted cubic spline models were used to explore the association of dietary intakes of total, cereal, fruit, and vegetable fiber with HF prevalence.ResultsA total of 21869 adults were included in this study. After adjusting for multiple confounding factors, the odds ratios (OR) and 95% confidence intervals (CI) for HF was 0.49 (0.28 to 0.87, P for trend = 0.016) for the highest tertile versus lowest tertile of total fiber intake. Similar results were observed for cereal but not fruit and vegetable fiber intake. Dose-response analysis indicated that dietary intake of total and cereal fiber were inversely associated with HF in a linear manner.ConclusionIntakes of total and cereal fiber were inversely associated with HF in adults.

The effects of government R&D subsidies can vary across recipient firms, depending on the various characteristics of a firm, potentially including the firm's accounting information quality (AIQ). It has been well recognized that high AIQ helps to reduce information asymmetry between investors and firms and, consequently, improves investment efficiency. However, there is a lack of clear understanding about the specific role of corporate accounting information in the ex-post effectiveness of government R&D subsidies. This study thus examines the main effects of government R&D subsidies on both firms’ R&D inputs and innovation outputs, and the positive moderating role played by the quality of corporate accounting information. The data include 1,561 sample firms listed in the stock markets in China and 11,853 firm-year observations between 2007 and 2015. We find that the moderating effect of AIQ is economically sizable where an improved AIQ (discretionary accruals), by a standard deviation, increases the additionality effect by 16% for corporate R&D investment and 4% for the growth of firms’ R&D inputs. In addition, we find the subsidies have a stronger favorable effect on firms’ R&D outputs (the number of patents) for those firms with a higher AIQ.

Maintaining smooth traffic during disaster evacuation is a lifesaving step. Traffic resilience is often used to define the ability of a roadway during disaster evacuation to withstand and recover its functionality from disturbances in terms of traffic flow caused by a disaster. However, a high level of variances due to system complexity and inherent uncertainty associated with disaster and evacuation risks poses great challenges in predicting traffic resilience during evacuation. To fill this gap, this study aimed to propose a new integrated data-driven predictive resilience framework that enables incorporating traffic uncertainty factors in determining road traffic conditions and predicting traffic performance using machine learning approaches and various space and time (spatiotemporal) data sources. This study employed an augmented Long Short-Term Memory (LSTM)-based approach with correlated spatiotemporal traffic data to predict traffic conditions, then to map those conditions to traffic resilience levels: daily traffic, segment traffic, and overall route traffic. A case study of Hurricane Irma’s evacuation traffic was used to demonstrate the effectiveness of the proposed framework. The results indicated that the proposed method could effectively predict traffic conditions and thus help to determine traffic resilience. The data also confirmed that the traffic infrastructures along the US I-75 route remained resilient despite the disturbances during the disaster evacuation activities. The findings of this study suggest that the proposed framework is applicable to other disaster management scenarios to obtain more robust decisions for the emergency response during disaster evacuation.

Heme, the protoporphyrin IX iron complex is widely present in the human body and it is involved in oxygen storage, electron transfer, and enzymatic reactions. However, free heme can be toxic as it catalyzes the production of reactive oxygen species, oxidizes lipids and proteins, and causes DNA damage, thereby inducing a pro-inflammatory environment. The generation, metabolism, and degradation of heme in the human body are regulated by precise mechanisms to ensure that heme remains non-toxic. However, in several types of cardiovascular diseases, impaired metabolism and exposure to heme may occur in pathological processes, including neovascularization, internal hemorrhage, ischemia, and reperfusion. Based on years of research, in this review, we aimed to summarize the underlying mechanisms by which heme contributes to the development of cardiovascular diseases through oxidative stress, relative pathway gene expression regulation and phenotypic changes in cells. Excess heme plays a detrimental role in atherosclerosis, heart failure, myocardial ischemia-reperfusion injury, degenerative aortic valve stenosis, cardiac iron overload. Recent researches revealed that in some cases heme involved in cardiac damage though ferroptosis. Thus, heme concentrations beyond normal levels are dangerous. Further research on the role of heme in cardiovascular diseases is needed.

Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost.Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing.Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established.Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare.Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

The COVID-19 pandemic has caused serious economic and social consequences. Recent research shows that the pandemic has not only caused a physical health crisis but also caused many psychological and mental crises. Based on the contemporary cognitive-behavioral models, this article offers a conceptual analysis of how the pandemic affects individual mental health and coping behaviors from the perspective of individual economic status, individual context, and social context. The analysis shows that (1) the pandemic has led to increased economic uncertainty, increased unemployment and underemployment pressure, increased income uncertainty, and different degrees of employment pressure and economic difficulties; (2) these difficulties have stimulated different levels of mental health problems, ranging from perceived insecurity (environmental, food safety, etc.), worry, fear, to stress, anxiety, depression, etc., and the mental health deterioration varies across different groups, with the symptoms of psychological distress are more obvious among disadvantageous groups; and (3) mental health problems have caused behavior changes, and various stress behaviors such as protective behaviors and resistive behaviors. Future research directions are suggested.

The COVID-19 pandemic and its related public health restrictions are having an increasingly serious impact on mental health, and measures need to be taken to curb this trend. The positive relationship between physical exercise and mental health has been well-established, but during the COVID-19 pandemic, with various restrictions, the space and facilities for physical exercise are limited. This article explores the relationship between physical exercise and mental health during the COVID-19 pandemic based on the latest research findings published in 2019–2021. We offer a novel model that consists of three central arguments. First, physical exercises during COVID-19, especially supervised exercises, are conducive to enhancing happiness and improving mental health. Second, physical exercise reduces people's anxiety, sadness and depression during the COVID-19 pandemic. Third, the maintenance and improvement of mental health are related to the intensity and frequency of physical exercise. Intensive and frequent physical exercise are conducive to maintaining mental health. Finally, this article proposes important directions for future research.

Abstract Background Gastric cancer (GC) is one of the most prevalent and deadly malignancies worldwide. Accumulating reports have indicated the participation of long non-coding RNAs (lncRNAs) in the onset and progression of GC. Methods GSE109476 data was utilized to screen out lncRNAs dysregulated in GC. Gene expressions were determined by qRT-PCR and western blot. Both in vitro and in vivo experiments were carried out to assess the function of HOXC-AS1 in GC. The association between genes was verified via RIP, ChIP, CoIP, RNA pull down and luciferase reporter assays, as appropriate. Results HOXC-AS1 was discovered to be upregulated in GC and located both in cytoplasm and in nucleus in GC cells. Functionally, inhibition of HOXC-AS1 restrained GC cell growth and metastasis both in vitro and in vivo. Moreover, HOXC-AS1 was proved to be trans-activated by c-MYC in GC. In return, HOXC-AS1 positively regulated MYC expression in GC through targeting miR-590-3p/MYC axis in cytoplasm and modulating BRG1/β-catenin complex-activated MYC transcription in nucleus. Furthermore, the rescue assays verified that MYC mediated HOXC-AS1-affected GC progression. Conclusion Our research illustrated a feedback loop of HOXC-AS1-MYC in aggravating GC cell growth and metastasis, highlighting HOXC-AS1 as a promising target for GC diagnosis and treatment.

Abstract Background Soil salinization and alkalization are among the major agricultural threats that affect crop productivity worldwide, which are increasing day by day with an alarming rate. In recent years, several halophytes have been investigated for their utilization in soil remediation and to decipher the mechanism of salt-tolerance in these high salt tolerant genetic repositories. Suaeda salsa is an annual halophytic herb in the family Amaranthaceae, displaying high salt and alkali-resistance and having nutritive value. However, the fundamental biological characteristics of this valuable plant remain to be elucidated until today. Results In this study, we observed the morphology and development of Suaeda salsa, including seed morphology, seed germination, plant morphology, and flower development. Using microscopy, we observed the male and female gametophyte developments of Suaeda salsa. Also, chromosome behaviour during the meiosis of male gametophyte was studied. Eventually, the genome size of Suaeda salsa was estimated through flow cytometry using Arabidopsis as reference. Conclusions Our findings suggest that the male and female gametophyte developments of Suaeda salsa are similar to those of the model plant Arabidopsis, and the diploid Suaeda salsa contains nine pairs of chromosomes. The findings also indicate that the haploid genome of Suaeda salsa is approximately 437.5 MB. The observations and results discussed in this study will provide an insight into future research on Suaeda salsa.

By introducing pre-compression/inverse moment through prestressing tendons or rods, prestressed concrete (PC) structures could overcome conventional concrete weakness in tension, and thus, these tendons or rods are widely accepted in a variety of large-scale, long-span structures. Unfortunately, prestressing tendons or rods embedded in concrete are vulnerable to degradation due to corrosion. These embedded members are mostly inaccessible for visual or direct destructive assessments, posing challenges in determining the prestressing level and any corrosion-induced damage. As such, ultrasonic guided waves, as one of the non-destructive examination methods, could provide a solution to monitor and assess the health state of embedded prestressing tendons or rods. The complexity of the guided wave propagation and scattering in nature, as well as high variances stemming from the structural uncertainty and noise interference PC structures may experience under complicated operational and harsh environmental conditions, often make traditional physics-based methods invalid. Alternatively, the emerging machine learning approaches have potential for processing the guided wave signals with better capability of decoding structural uncertainty and noise. Therefore, this study aimed to tackle stress level prediction and the rod embedded conditions of prestressed rods in PC structures through guided waves. A deep learning approach, convolutional neural network (CNN), was used to process the guided wave dataset. CNN-based prestress level prediction and embedding condition identification of rods were established by the ultrasonic guided wave technique. A total of fifteen scenarios were designed to address the effectiveness of the stress level prediction under different noise levels and grout materials. The results demonstrate that the deep learning approaches exhibited high accuracy for prestressing level prediction under structural uncertainty due to the varying surrounding grout materials. With different grout materials, accuracy could reach up to 100% under the noise level of 90 dB, and still maintain the acceptable range of 75% when the noise level was as high as 70 dB. Moreover, the t-distributed stochastic neighbor embedding technology was utilized to visualize the feature maps obtained by the CNN and illustrated the correlation among different categories. The results also revealed that the proposed CNN model exhibited robustness with high accuracy for processing the data even under high noise interference.

Ganoderma lucidum (Lingzhi) polysaccharide peptide (GL-pp) is a component of the globally acknowledged traditional Chinese medicine Ganoderma lucidum; Ganoderma lucidum is known for its sedative, hypnotic, immune regulatory, antitumor, and other pharmacological effects. In recent years, sleep disorders have been linked to many diseases and human body disorders, including cancer. Some experimental studies in mice found that sleep fragmentation could promote tumor development and progression. However, effects on GL-pp on tumor metastasis under circumstances of sleep disorders have rarely been studied. Thus, in this study, we used mice with sleep fragmentation (SF) bearing B16-F10-luc-G5 melanoma tumors to investigate the effect of SF on melanoma metastasis. Furthermore, we investigated the antitumor and antimetastatic effects of GL-pp (80 mg/kg) in mice suffering from SF and bearing B16-F10-luc-G5. Then, whole proteomics was used to analyze the differences in protein expression in the lung tissue between SF mice bearing B16-F10-luc-G5 with and without GL-pp administration. High-throughput pyrosequencing of 16S rRNA was also used to analyze the impact of GL-pp on the gut microbiota composition in SF mice bearing B16-F10-luc-G5. Last, the effects of GL-pp on macrophage polarization and TNF-α serum levels were detected. Collectively, we found that SF significantly facilitated the B16-F10-luc-G5 melanoma tumor metastasis in mice, while GL-pp significantly reduced B16-F10-luc-G5 melanoma tumor metastasis under the condition of SF, in which proteomics and gut microbiota had been changed greatly.

Urbanization has been proven to be a critical factor in modifying local or regional climate characteristics. This research aims to examine the impact of urbanization on extreme climate indices in the Yangtze River Economic Belt (YREB), China, by using meteorological observation data from 2000 to 2019. Three main steps are involved. First, a clustered threshold method based on remote-sensing nighttime light data is used to extract urban built-up areas, and urban and rural meteorological stations can be identified based on the boundary of urban built-up areas. Nonparametric statistical tests, namely, the Mann–Kendall test and Sen’s slope, are then applied to measure the trend characteristics of extreme climate indices. Finally, the urbanization contribution rate is employed to quantify the impact of urbanization on extreme climate indices. The results indicate that urbanization has a more serious impact on extreme temperature indices than on extreme precipitation indices in the YREB. For extreme temperature indices, urbanization generally causes more (less) frequent occurrence of warm (cold) events. The impact of urbanization on different extreme temperature indices has heterogeneous characteristics, including the difference in contamination levels and spatial variation of the impacted cities. For extreme precipitation indices, only a few cities impacted by urbanization are detected, but among these cities, urbanization contributes to increasing the trend of all indices.

Welding is widely used in the connection of metallic structures, including welded joints in oil/gas metallic pipelines and other structures. The welding process is vulnerable to the inclusion of different types of welding defects, such as lack of penetration and undercut. These defects often initialize early-age cracking and induced corrosion. Moreover, welding-induced defects often accompany other types of mechanical damage, thereby leading to more challenges in damage detection. As such, identification of weldment defects and interaction with other mechanical damages at their early stage is crucial to ensure structural integrity and avoid potential premature failure. The current strategies of damage identification are achieved using ultrasonic guided wave approaches that rely on a change in physical parameters of propagating waves to discriminate as to whether there exist damaged states or not. However, the inherently complex nature of weldment, the complication of damages interactions, and large-scale/long span structural components integrated with structure uncertainties pose great challenges in data interpretation and making an informed decision. Artificial intelligence and machine learning have recently become emerging methods for data fusion, with great potential for structural signal processing through decoding ultrasonic guided waves. Therefore, this study aimed to employ the deep learning method, convolutional neural network (CNN), for better characterization of damage features in terms of welding defect type, severity, locations, and interaction with other damage types. The architecture of the CNN was set up to provide an effective classifier for data representation and data fusion. A total of 16 damage states were designed for training and calibrating the accuracy of the proposed method. The results revealed that the deep learning method enables effectively and automatically extracting features of ultrasonic guided waves and yielding high precise prediction for damage detection of structures with welding defects in complex situations. In addition, the effectiveness and robustness of the proposed methods for structure uncertainties using different embedding materials, and data under noise interference, was also validated and findings demonstrated that the proposed deep learning methods still exhibited a high accuracy at high noise levels.

The filling material of the karst collapse column (KCC) is easy to be activated by mining. During this process, the mechanical properties of KCC fillings change, and its water resisting capacity constantly deteriorates and thus often leads to water inrush disaster. In this study, the samples of KCC fillings were taken on-site and then were remolded by the consolidation drainage method. The variation laws of the compressive strength, tensile strength, cohesive stress, internal friction angle, and permeability of the filling samples with respect to the consolidation pressure and moisture content were tested and analyzed. Based on an engineering example, the yield and activation and particle loss of the filling material of the KCC are analyzed. A mechanism for the lagging water inrush of KCC in the process of mining is proposed. The main results of the present study can be concluded concisely as follows. (1) The KCC fillings show obvious soft rock characteristics in the process of uniaxial compression and Brazilian split. The ratio of the uniaxial compressive strength to splitting tensile strength is between 12 : 1 and 8 : 1. The larger the consolidation pressure or the smaller the moisture content, the larger the ratio. (2) With the increase of consolidation pressure or the decrease of moisture content, the uniaxial compressive strength, elastic modulus, splitting tensile strength, cohesive stress, and internal friction angle of the filling material of the KCC increase linearly, while its permeability increases exponentially. (3) When the crack field of the surrounding rocks of the stope is connected with the KCC, its filling material will continue to yield, activate, and migrate under the fluid-solid coupling effect and finally result in the lagging water inrush from the KCC.