CXCR7 promotes pulmonary vascular remodeling via targeting p38/MMP2 pathway in pulmonary arterial hypertension.

Background: A hallmark feature of pulmonary arterial hypertension (PAH) is the excessive proliferation of pulmonary artery smooth muscle cells (PASMCs) in the pulmonary arteries. The exact role of C-X-C motif chemokine ligand 12 (CXCL12)/chemokine receptor type 7 (CXCR7) in the PASMCs remains unknown. This study was conducted to investigate CXCR7's role in p38/MMP2 pathway and its effect on PASMCs. Methods: In this study, we examined the expression profile of CXCL12/CXCR7 in both hypoxic rats and PASMCs. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was used to measure the level of proliferation in PASMCs. Enzyme-linked immunosorbent assay (ELISA) and western blotting assays were applied to investigate the protein expression of the related molecules. Results: We found that a high level of CXCR7 was correlated with remodeled pulmonary arterioles in hypoxic rats. Moreover, CXCR7 protein levels were significantly increased by the induction of CXCL12, indicating that the CXCL12-CXCR7 axis participates in PAH. During hypoxia-PAH, CXCR7 inhibition reduces right ventricular systolic pressure (RVSP), the Fulton index, and pulmonary arteriosclerosis remodeling. Further study indicated inhibition CXCR7 reduced PASMCs by downregulating MMP2, via p38 MAPK pathway. It was additionally found that CXCL12/CXCR7 stimulated the phosphorylation of the p38 MAPK pathway, which was a contributing factor to the decrease in MMP2 expression following preconditioning with SB203580, which inhibited p38 MAPK. Conclusions: In summary, these findings suggest that CXCL12/CXCR7 plays a critical role in PAH, the therapy of which can be developed further by targeting its potential targets.

Parallel Multistage Wide Neural Network.

Deep learning networks have achieved great success in many areas, such as in large-scale image processing. They usually need large computing resources and time and process easy and hard samples inefficiently in the same way. Another undesirable problem is that the network generally needs to be retrained to learn new incoming data. Efforts have been made to reduce the computing resources and realize incremental learning by adjusting architectures, such as scalable effort classifiers, multi-grained cascade forest (gcForest), conditional deep learning (CDL), tree CNN, decision tree structure with knowledge transfer (ERDK), forest of decision trees with radial basis function (RBF) networks, and knowledge transfer (FDRK). In this article, a parallel multistage wide neural network (PMWNN) is presented. It is composed of multiple stages to classify different parts of data. First, a wide radial basis function (WRBF) network is designed to learn features efficiently in the wide direction. It can work on both vector and image instances and can be trained in one epoch using subsampling and least squares (LS). Second, successive stages of WRBF networks are combined to make up the PMWNN. Each stage focuses on the misclassified samples of the previous stage. It can stop growing at an early stage, and a stage can be added incrementally when new training data are acquired. Finally, the stages of the PMWNN can be tested in parallel, thus speeding up the testing process. To sum up, the proposed PMWNN network has the advantages of: 1) optimized computing resources; 2) incremental learning; and 3) parallel testing with stages. The experimental results with the MNIST data, a number of large hyperspectral remote sensing data, and different types of data in different application areas, including many image and nonimage datasets, show that the WRBF and PMWNN can work well on both image and nonimage data and have very competitive accuracy compared to learning models, such as stacked autoencoders, deep belief nets, support vector machine (SVM), multilayer perceptron (MLP), LeNet-5, RBF network, recently proposed CDL, broad learning, gcForest, ERDK, and FDRK.

Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data.

Understanding the spatiotemporal patterns of the COVID-19 impact on industrial production could improve the estimation of the economic loss and sustainable work resumption policies in cities. In this study, assuming and checking a correlation between the land surface temperature (LST) and industrial production, we applied the BFAST algorithm and linear regression models on multi-temporal MODIS data to derive monthly time-series deviation of LST with a spatial resolution of 1 × 1 km, to quantificationally explore the fine-scale spatiotemporal patterns of the COVID-19 control measures impact on industrial production, within Wuhan city. The results demonstrate that (1) the trend of time-series LST could partly reflect the impact of the COVID-19 pandemic on industrial production, and the year-around industrial production was less than expectations, with a fall of 14.30%; (2) the most serious COVID-19 impact on industrial production appeared in Mar. and Apr., then, after the lifting of lockdown, some regions (approximate 4.90%) firstly returned to expected levels in Jun, and almost all regions (98.49%) have completed the resumption of work and production before Nov.; (3) the southwest and south-central had more serious impact of the COVID-19 pandemic, approximate twice as much as that in the north and suburban, in Wuhan. The results and findings elaborated the spatiotemporal distribution and their changes during 2020 within Wuhan, which could provide a beneficial support for assessment of the COVID-19 pandemic and implementation of resumption plans for sustainable development.

Prognostic Value of Preoperative Albumin-to-Fibrinogen Ratio in Patients with Bladder Cancer.

Background: Both nutritional status and coagulation function are closely associated with prognosis in patients with bladder cancer (BC). This study aimed to investigate the prognostic value of albumin-to-fibrinogen ratio (AFR) for BC patients underwent radical cystectomy (RC) or transurethral resection of bladder tumor (TURBT), and develop predictive nomograms based on AFR. Methods: We retrospectively collected medical records of 358 BC patients who underwent RC or TURBT between January 2012 and December 2018. The whole cohort was divided into the training (215 patients, 60.06%) and validation cohorts (143 patients, 39.94%) based on surgery dates. The training cohort was applied to select characteristics and construct nomograms, while the validation cohort was used to verify the nomograms independently. Endpoints of the current study included overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS). Prognostic values of AFR and other characteristics were evaluated using univariate and multivariate Cox regression analyses and compared using the concordance-index (C-index). Nomograms for OS, DSS and DFS were constructed based on both-directional stepwise Cox proportional hazards regression analysis and evaluated by the receiver operating characteristic (ROC) curve, the C-index and calibration plot. Results: In whole cohort, 86 patients (24.02%) were classified into low AFR group and had worse OS (hazard ratio [HR]: 4.079, 95% confidence interval [CI]: 2.085-7.982, P < 0.001), DSS (HR: 3.012, 95% CI: 1.302-6.966, P = 0.010) and DFS (HR: 1.863, 95% CI: 1.204-2.883, P = 0.005) compared to BC patients in high AFR group. Meanwhile, the AFR processed better prognostic power than albumin and fibrinogen, individually. Multivariate Cox analysis indicated that AFR was an independent prognostic factor for OS (HR: 2.601, 95% CI: 1.057-6.395, P = 0.037) and DFS (HR: 1.971, 95% CI: 1.049-3.703, P = 0.035). Novel nomograms, incorporating AFR, tumor grade and tumor multifocality, were constructed and successfully validated for predictions of OS, DSS and DFS in BC. Conclusions: Preoperative AFR was identified as an independent prognostic predictor for OS and DFS of BC patients underwent surgery. The nomograms incorporating AFR provided accurate predictions for OS, DSS and DFS, which could help urologists in better clinical decision-making.

Impact of structural features of Sr/Fe co-doped HAp on the osteoblast proliferation and osteogenic differentiation for its application as a bone substitute.

The potential of external ions doped biomaterials has been extensively explored; however, the co-doped biomaterials remain typically unexplored for their biological properties for precise biomedical applications. The current study was aimed to explore the impact of structural features of Sr/Fe co-doped hydroxyapatite (HAp) bionanomaterial on osteoblastic proliferation and osteogenic differentiation for its application as a bone substitute. A 10 mol% isomorphous co-doping of strontium and iron with respect to calcium was carried into HAp in the solid solution. Raman spectroscopy verified the presence of major functional groups of apatite structure together with the carbonate peaks. The Sr/Fe co-doped HAp bionanomaterials showed slightly negative zeta potential (at neutral pH), versatile DLS particle size (140-205 nm), high BET surface area (186 m2/g), and narrow width pore size (13-19 nm). TG/DTA analysis showed low thermal stability of the Sr/Fe co-doped HAp groups. The nanoparticles showed an initial burst release of amoxicillin for 15 h followed by extended-release up to 81 h and demonstrated an excellent antibacterial activity by producing inhibition zones of 17.6 ± 0.3 mm and 19.5 mm ± 0.4 mm for Escherichia coli and Staphylococcus aureus. Live/dead cell staining and MTT assay confirmed the non-toxic nature of Sr/Fe co-doped HAp bionanomaterial towards MC3T3-E1 cells. Further, an improved ALP activity, increased calcium deposition, enhanced RUNX2 expression, and regulated OPN and OCN expression levels suggest in MC3T3-E1 cells demonstrate the maturation of osteoblasts. This study provides the unique advantages of the co-doping approach for the applications Sr/Fe co-doped HAp bionanomaterials as a bone substitute.

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

Transition Patterns of Weight Status and Their Associated Factors among Elementary School Children: A Longitudinal Cohort Study Using Multistate Markov Model.

Background: Childhood obesity is a recognized public health concern worldwide. It is essential to study the natural progression of obesity in the interest of prevention. This study aimed to describe the dynamic changes in weight status among elementary school children and identify the significant factors influencing the progression or regression of weight status. Methods: This study involved 928 elementary school children who were followed up annually during their elementary school years. Heights, weights, and vital capacity (VC) were measured each school year. A multistate Markov model containing three weight states was fit to longitudinal weight status data. Results: Children with healthy weight and obesity tended to stay in their preceding weight state. Children with overweight, in contrast, were more likely to move to the other two states. The mean sojourn time in obesity and in overweight states was 5.15 years (95% confidence interval [CI]: 4.22-6.3) and 2 years (95% CI: 1.76-2.28), respectively. Children in lower grades, those with a lower VC index, those with a higher initial BMI, those with a higher annual weight increment, and boys were at increased risk of progression to overweight or obesity, with a decreased probability of regression. Conclusions: Children with obesity were more resistant to recovery than those with overweight. Prevention and intervention measures should be adopted early when abnormal weight onset occurs. The multistate Markov model was an advanced tool to analyze dynamic changes in status and identify significant factors for progression and regression and helped to develop prevention and intervention targeting strategies.

Superoxide dismutase activity and risk of cognitive decline in older adults: Findings from the Chinese Longitudinal Healthy Longevity Survey.

BACKGROUND: The association between superoxide dismutase (SOD) activity and cognitive decline in older adults remains controversial. OBJECTIVES: This study was designed to examine the association between plasma superoxide dismutase (SOD) activity and cognitive decline in older population. METHOD: We analyzed the follow-up data from 2012 to 2014 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), a community-based longitudinal survey in Chinese longevity areas. A total of 1004 Chinese adults aged 60 years and older were included in this study. Plasma SOD activity was assessed. Cognitive function was evaluated by Mini-Mental State Examination (MMSE) in Chinese version. Modified Poisson regression was performed to investigate the association between plasma SOD activities with cognitive decline. Restricted cubic spline was performed to determine the dose-response relationship. RESULTS: Participants in the highest quartile of SOD activity had an increased risk of cognitive decline compared with those in the lowest quartile (relative risk [RR] = 1.32, 95% confidence interval [CI]: 1.00-1.74, P = 0.051).Using cut-off points determined by Chi-square automatic interaction detector analysis (CHAID), the multivariable relative risks (RRs; 95% CI) for the lowest category, second highest, and the highest versus the third highest category of SOD activity were 0.56 (0.34-0.92), 1.26 (1.03-1.54), and 0.96 (0.70-1.31), respectively. CONCLUSIONS: Higher SOD activity was associated with elevated risk of cognitive decline among Chinese older adults.

Simple Risk Score for Prediction of Early Recurrence of Hepatocellular Carcinoma within the Milan Criteria after Orthotopic Liver Transplantation.

Ten to twenty percent of the hepatocellular carcinoma (HCC) patients fulfilling the Milan criteria (MC) recurred within three years after orthotopic liver transplantation (OLT). We therefore utilize a training cohort to develop an improved prognostic model for predicting the recurrence in these patients. By univariate and multivariate analysis, AFP level [cut-off value: 321 ng/mL, area under the curve (AUC) = 0.724, 95% confidence interval (CI) = 0.604-0.843, P < 0.001] and cytokeratin-19 (CK19) and glypican-3 (GPC3) expression pattern from nine putative prognostic factors were entered in risk factor scoring model to conjecture the tumor recurrence. In the training cohort, the AUC value of the model was 0.767 (95% CI = 0.645-0.890, P < 0.001), which was the highest among all the elements. The model's performance was then assessed using a validation cohort. In the validation cohort, the AUC value of the model was 0.843 (95% CI = 0.720-0.966, P < 0.001) which was higher than any other elements. The results indicated that model had high performance with good discrimination ability and significantly improved the predictive capacity for the recurrence of HCC patients within MC after OLT.

A flexible approach to azasugars: asymmetric total syntheses of (+)-castanospermine, (+)-7-deoxy-6-epi-castanospermine, and (+)-1-epi-castanospermine.

The asymmetric total synthesis of natural azasugars (+)-castanospermine, (+)-7-deoxy-6-epi-castanospermine, and synthetic (+)-1-epi-castanospermine has been accomplished in nine to ten steps from a common chiral building block (S)-8. The method features a powerful chiral relay strategy consisting of a highly diastereoselective vinylogous Mukaiyama-type reaction with either chiral or achiral aldehydes (> or = 95% de; de=diastereomeric excess) and a diastereodivergent reduction of tetramic acids, which allows formation of three continuous stereogenic centers with high diastereoselectivities. The method also provides a flexible access to structural arrays of 5-(alpha-hydroxyalkyl)tetramic acids, such as 17/34, and 5-(alpha-hydroxyalkyl)-4-hydroxyl-2-pyrrolidinones, such as 18 and 25/35 a. The method constitutes the first realization of the challenging chiral synthons A and D and thus of the conceptually attractive retrosynthetic analysis shown in Scheme 1 in a highly enantioselective manner.

A flexible approach to (S)-5-alkyl tetramic acid derivatives: application to the asymmetric synthesis of (+)-preussin and protected (3S,4S)-AHPPA.

[reaction: see text] A flexible asymmetric approach to 5-alkyl tetramic acid derivatives is described, which is based on the use of 9 as the first synthetic equivalent to chiral nonracemic tetramic acid 5-carbanionic synthon 9b. The existence of the carbanion intermediate 9b was proven by trapping with trimethylchlorosilane. Application of the present method to the synthesis of antifungal alkaloid (+)-preussin, as well as protected (3S,4S)-AHPPA 6, is also described.