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Abstract Background Osteoarthritis (OA) is a degenerative joint disease caused by the deterioration of cartilage. However, the underlying mechanisms of OA pathogenesis remain elusive. Methods Hub genes were screened by bioinformatics analysis based on the GSE114007 and GSE169077 datasets. The Sprague–Dawley (SD) rat model of OA was constructed by intra-articular injection of a mixture of papain and L-cysteine. Hematoxylin–eosin (HE) staining was used to detect pathological changes in OA rat models. Inflammatory cytokine levels in serum were measured employing the enzyme-linked immunosorbent assay (ELISA). The reverse transcription quantitative PCR (RT-qPCR) was implemented to assess the hub gene expressions in OA rat models. The roles of PDK4 and the mechanism regulating the PPAR pathway were evaluated through western blot, cell counting kit-8 (CCK-8), ELISA, and flow cytometry assays in C28/I2 chondrocytes induced by IL-1β. Results Six hub genes were identified, of which COL1A1, POSTN, FAP, and CDH11 expressions were elevated, while PDK4 and ANGPTL4 were reduced in OA. Overexpression of PDK4 inhibited apoptosis, inflammatory cytokine levels (TNF-α, IL-8, and IL-6), and extracellular matrix (ECM) degradation protein expressions (MMP-3, MMP-13, and ADAMTS-4) in IL-1β-induced chondrocytes. Further investigation revealed that PDK4 promoted the expression of PPAR signaling pathway-related proteins: PPARA, PPARD, and ACSL1. Additionally, GW9662, an inhibitor of the PPAR pathway, significantly counteracted the inhibitory effect of PDK4 overexpression on IL-1β-induced chondrocytes. Conclusion PDK4 inhibits OA development by activating the PPAR pathway, which provides new insights into the OA management.

Abstract Objective We aimed to screen novel biomarkers for osteoarthritis (OA) using bioinformatic methods and explore its regulatory mechanism in OA development. Methods Differentially expressed genes were screened out from GSE98918 and GSE82107 datasets. Protein–protein interaction network and enrichment analysis were employed to search for hub gene and regulatory pathway. Hematoxylin–eosin, Safranin O-Fast green staining, and immunohistochemistry were performed to assess pathological damage. TNF-α, IL-1β, and IL-6 concentrations were determined by enzyme-linked immunosorbent assay. Real-time quantitative PCR was applied to verify expression of hub genes in OA model. The expression of key protein and pathway proteins was determined by western blot. Furthermore, Cell Counting Kit-8 and flow cytometry were conducted to explore the role of hub gene in chondrocytes. Results We identified 6 hub genes of OA, including ITGB1, COL5A1, COL1A1, THBS2, LAMA1, and COL12A1, with high prediction value. ITGB1 was screened as a pivotal regulator of OA and cAMP pathway was selected as the key regulatory pathway. ITGB1 was down-regulated in OA model. ITGB1 overexpression attenuated pathological damage and apoptosis in OA rats with the reduced levels of TNF-α, IL-1β and IL-6. ITGB1 overexpression activated cAMP pathway in vivo and vitro models. In vitro model, ITGB1 overexpression promoted cell viability, while inhibited apoptosis. ITGB1 overexpression also caused a decrease of TNF-α, IL-1β, and IL-6 concentrations. cAMP pathway inhibitor reversed the positive effect of ITGB1 on OA cell model. Conclusion ITGB1 is a novel biomarker for OA, which inhibits OA development by activating the cAMP pathway.

With the application of ultra-high voltage (UHV) technology, a substantial amount of energy released from an internal arc fault in the transformer leads to the instantaneous decomposition of insulating oil into various flammable gases. This results in a rapid increase in internal pressure, making it highly probable for the transformer casing to rupture. Consequently, the insulating oil and its decomposition products disperse in the air, which in the presence of an ignition source, can trigger an explosive accident. To reveal the explosion characteristics of insulating oil, experiments and simulations are conducted under unconfined conditions. Besides, a quantitative explosion risk assessment is conducted. The results indicate that, at 20 ms, a high temperature and strong turbulence region appears at about 0.9 m from the center of the cloud and 0.7 m in height, and the combustible cloud is initiated near the region. The safety distances for humans free from the hazard of shock waves are 11.79 m, 13.74 m, and 21.63 m with the amount of insulating oil of 10 L, 15 L, and 20 L, respectively, and the safety distances for humans free from the hazard of thermal radiation are 21.2 m, 26.4 m, and 29.2 m respectively.

Abstract Background External fixators (EFs) and intramedullary nailing (IMN) are two effective methods for open tibial fractures. However, both methods have advantages and disadvantages, and the optimal surgical approach remains controversial. Thus, we performed a meta-analysis of randomized controlled trials (RCTs) to compare EF with IMN to evaluate their efficacy and safety. Methods A systematic study of the literature was conducted in relevant studies published in PubMed, Embase, the Cochrane Library, Web of Science, CNKI, CBM, Wanfang and Weipu from database inception to April 2022. All eligible literature was critically appraised for methodological quality via the Cochrane's collaboration tool. The primary outcome measurements included postoperative superficial infection, postoperative deep infection, union time, delayed union, malunion, nonunion, and hardware failure. Results Nine RCTs involving 733 cases were included in the current meta-analysis. The pooled results suggested that cases in the IMN group had a significantly lower postoperative superficial infection rate [risk ratio (RR) = 2.84; 95% confidence interval (CI) = 1.83 to 4.39; P 0.05). Conclusions Compared to EF, IMN had a significantly lower risk of postoperative superficial infection and malunion in patients with open tibial fractures. Meanwhile, IMN did not prolong the union time and increased the risk of the deep infection rate, delayed union rate and nonunion rate but had a higher hardware failure rate. The reanalysis of union time showed that it was significantly shorter in the IMN group than in the EF group after excluding the study with significant heterogeneity during sensitivity analysis. Therefore, IMN is recommended as a preferred method of fracture fixation for patients with open tibial fractures, but more attention should be given to the problem of hardware failure.

Abstract Background Despite current advances in gastric cancer treatment, disease metastasis and chemo-resistance remain as major hurdles against better overall prognosis. Previous studies indicated that IGHG1 as well as -Catenin serve as important regulators of tumor cellular malignancy. Therefore, understanding detailed molecular mechanism and identifying druggable target will be of great potentials in future therapeutic development. Methods Surgical tissues and gastric cancer cell lines were retrieved to evaluate IGHG1 expression for patients with or without lymph node/distal organ metastasis. Functional assays including CCK8 assay, Edu assay, sphere formation assay and transwell assay, wound healing assay, etc. were subsequently performed to evaluate the impact of IGHG1/-catenin axis on tumor cell proliferation, migration and chemo-resistance. Results Gastric cancer tissues and tumor cell lines demonstrated significantly higher level of IGHG1. Functional study further demonstrated that IGHG1 promoted proliferative and migration as well as chemo-resistance of gastric cancer tumor cells. Further experiments indicated that IGHG1 activated AKT/GSK-3/-Catenin axis, which played crucial role in regulation of proliferative and chemo-resistance of gastric cancer cells. Conclusion This study provided novel evidences that IGHG1 acted as oncogene by promotion of gastric cancer cellular proliferation, migration and chemo-resistance. Our research further suggested that IGHG1/AKT/GSK-3β/β-Catenin axis acted as novel pathway which regulated gastric cancer cellular malignant behavior. Our research might inspire future therapy development to promote overall prognosis of gastric cancer patients.

5-aminotetrazole (5AT) has been widely used as a fuel in SPGGs for its high nitrogen content, heat resistance, and environmentally friendly product. However, 5AT-based propellants still have disadvantages, such as a high exhaust temperature and unstable combustion rate, which somewhat limit their application. Given that transition metal oxides are typically employed in small quantities to enhance the performance of solid propellants, this study selected nickel oxide (NiO) nanoparticles as a catalyst and employed them in conjunction with 5AT via mechanical ball milling to investigate their impact on the pyrolysis behavior of 5AT. It was found that the nanoscale NiO particles can significantly reduce the thermal degradation temperature of 5AT according to TG-DSC tests. The calculation of the energy required to initiate the pyrolysis of 5AT using three kinetic methods, namely Friedman (FR), Flynn–Wall–Ozawa (FWO), and Kissinger–Akahira–Sunose (KAS), indicated that the use of NiO nanoparticles can reduce the energy required by more than 46 kJ mol−1, thereby increasing the likelihood of 5AT pyrolysis. Meanwhile, the reduced thermal safety parameters indicated that NiO makes 5AT more susceptible to thermal decomposition due to thermal explosion transition, so more care is needed for the storage of 5AT. Moreover, the TG-FTIR test was conducted to study the pyrolysis mechanism with or without NiO; the results showed that NiO exerts different catalytic effects on the gas products. The results from this study can offer direction and recommendations for future research on solid propellants.

Brain tumor segmentation is an important content in medical image processing, and it is also a very common research in medicine. Due to the development of modern technology, it is very valuable to use deep learning (DL) and multimodal MRI images to study brain tumor segmentation. In order to solve the problems of low efficiency and low accuracy of brain tumor segmentation, this paper proposes DL to conduct research on multimodal MRI image segmentation, aiming to make accurate diagnosis and treatment for doctors. In addition, this paper constructs an automatic diagnosis system for brain tumors, uses GLCM and discrete wavelet transform (DWT) to extract features from MRI images, and then uses a convolutional neural network (CNN) for final diagnosis; finally, through four. The comparison of the results between the two algorithms proves that the CNN algorithm has the better processing power and higher efficiency.

A simplified model that calculates the deflagration pressure–time curves of a hydrogen explosion was proposed. The deflagration parameters (pressure peak, duration, deflagration index, and impulse) of hydrogen–air mixtures with different hydrogen concentrations were experimentally investigated. The results show that the pressure curves calculated by the model are consistent with experimental data pertaining to a methane and hydrogen explosion. By comparison, the pressure peak and deflagration index are found to be influenced by the aspect ratio and surface area of vessels. The impulse and explosion times at fuel-lean hydrogen concentrations are greater than those at fuel-rich concentrations. When the hydrogen concentration is between 34 vol.% and 18 vol.%, the greatest explosion damage effect is formed by both the overpressure and the impulse, which should be considered for hydrogen explosion safety design in industrial production.

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC > 60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

Rare earth elements (REEs) are widely used in various industries. The open-pit mining and chemical extraction of REEs in the weathered crust in southern Jiangxi, China, since the 1970s have provoked severe damages to the environment. After 2010, different restorations have been implemented by various enterprises, which seem to have a spatial variability in both management techniques and efficiency from one mine to another. A number of vegetation indices, e.g., normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), enhanced vegetation index (EVI) and atmospherically resistant vegetation index (ARVI), can be used for this kind of monitoring and assessment but lack sensitivity to subtle differences. For this reason, the main objective of this study was to explore the possibility to develop new, mining-tailored remote sensing indicators to monitor the impacts of REE mining on the environment and to assess the effectiveness of its related restoration using multitemporal Landsat data from 1988 to 2019. The new indicators, termed mining and restoration assessment indicators (MRAIs), were developed based on the strong contrast of spectral reflectance, albedo, land surface temperature (LST) and tasseled cap brightness (TCB) of REE mines between mining and postmining restoration management. These indicators were tested against vegetation indices such as NDVI, EVI, SAVI and generalized difference vegetation index (GDVI), and found to be more sensitive. Of similar sensitivity to each other, one of the new indicators was employed to conduct the restoration assessment of the mined areas. Six typically managed mines with different restoration degrees and management approaches were selected as hotspots for a comparative analysis to highlight their temporal trajectories using the selected MRAI. The results show that REE mining had experienced a rapid expansion in 1988–2010 with a total mined area of about 66.29 km2 in the observed counties. With implementation of the post-2010 restoration measures, an improvement of varying degrees in vegetation cover in most mines was distinguished and quantified. Hence, this study with the newly developed indicators provides a relevant approach for assessing the sustainable exploitation and management of REE resources in the study area.

Landslides are one of the major geohazards threatening human society. This study was aimed at conducting such a hazard risk prediction and zoning based on an efficient machine learning approach, Random Forest (RF), for Ruijin, Jiangxi, China. Multiple geospatial and geo-environmental data such as land cover, NDVI, landform, rainfall, stratigraphic lithology, proximity to faults, to roads and to rivers, depth of the weathered crust, etc., were utilized in this research. After pre-processing, including digitization, linear feature buffering and value assignment, 19 hazard-causative factors were eventually produced and converted into raster to constitute a 19-band geo-environmental dataset. 155 observed landslides that had truly taken places in the past 10 years were utilized to establish a vector layer. 70 % of the disaster sites (points) were randomly selected to compose a training set (TS) and the remained 30 % to form a validation set (VS). A number of non-risk samples were identified in low slope (< 1-3°) areas and also added to the TS and VS in the similar percentage. Then, RF-based classification algorithm was employed to model the probability of landslide occurrence using the above 19-band dataset as predictive variables and TS for training. After performance evaluation, the RF-based model was applied back to the integrated dataset to calculate the probability of the hazard occurrence in the whole study area. The predicted map was evaluated versus both TS and VS and found of high reliability in which the Overall Accuracy (OA) and Kappa Coefficient (KC) are 91.49 % and 0.8299 respectively. In terms of the risk probability, the predicted map was further zoned into different risk grades to constitute landslide risk map. Modeling results also revealed the order of importance of the 19 causative factors, and the most important ones are the proximity to roads, slope, May-July rainfall, NDVI and elevation. We hence conclude that the RF algorithm is able to achieve the risk prediction with high accuracy and reliability, and this study may provide an operational methodology for geohazard risk mapping and assessment. The results of this study can serve as reference for the local authorities in prevention and early warning of landslide hazard. [ABSTRACT FROM AUTHOR]

Objective: Infective endocarditis is usually caused by Streptococcus sanguinis and characterized by inflammatory responses in the endocardium. This study aimed to investigate if the new compound (3R)-5,6,7-trihydroxy-3-isopropyl-3-methylisochroman-1-one (TIM) isolated from Alpinia katsumadai Hayata could provide protection against lipoteichoic acid (LTA)-induced cell damage in embryonic rat heart cells (H9c2). Methods: LTA-induced cell damage was established in H9c2, and the protective effects of TIM against the cell damage were examined at different concentrations (0.1–2.5 μM). The inflammatory response and oxidative stress in H9c2 cells were also measured. Results: Treatment with TIM (0.1–2.5 μM) significantly decreased LTA-induced toxicity in H9c2 cells, which was indicated by increase in cell viability, elevation in the mitochondrial membrane potential, decrease in the release of cytochrome-c and DNA damage, inhibition of caspase-3/9 activities, and change in apoptosis-related protein expression in LTA-treated H9c2 cells. TIM treatment also significantly attenuated the redox imbalance in H9c2 cells by decreasing malondialdehyde and intracellular reactive oxygen species levels as well as by enhancing superoxide dismutase activities and glutathione levels by increasing nuclear factor (erythroid-derived 2)-like 2 protein expression. Moreover, TIM treatment decreased interleukin 1 β, interleukin 12, and tumor necrosis factor α levels by inhibiting nuclear factor kappa B protein expression. Conclusion: Our data indicated that TIM protected H9c2 cells against LTA-induced toxicity, at least partially through inhibiting the inflammatory response and oxidative stress, providing scientific rational to develop TIM to treat infective endocarditis. [ABSTRACT FROM AUTHOR]