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Yongbin Wang,1 Chang Feng,2 Jia Fu,2 Dongyi Liu2 1Department of Respiratory Medicine, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China; 2Department of Anesthesiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of ChinaCorrespondence: Dongyi Liu, Department of Anesthesiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, 247 Bei Yuan Street, Jinan, 250033, People’s Republic of China, Tel +86-17660085565, Email dyjn2011@126.comPurpose: The aim was to investigate the time for intubation, adverse events and the comfort score of ultrasound-guided internal branch of superior laryngeal nerve block in patients with severe chronic obstructive pulmonary disorder (COPD) undergoing awake fibreoptic nasotracheal intubation.Methods: Sixty patients with COPD who needed awake fibreoptic nasotracheal intubation were randomly and evenly divided into the ultrasound-guided internal branch of the superior laryngeal nerve block group (group S) and the control group (group C). All patients received procedural sedation with dexmedetomidine and adequate topical anaesthesia of the upper respiratory tract. Then, bilateral block was performed (with 2 mL of 2% lidocaine or the same volume of saline) followed by fibreoptic nasotracheal intubation. The primary outcomes were time for intubation, adverse reactions and comfort score. The secondary outcomes were haemodynamic changes and serum norepinephrine (NE) and adrenaline (AD) concentrations immediately before intubation (T0); immediately after intubation to the laryngopharynx (T1); and immediately (T2), 5 min (T3) and 10 min (T4) after intubation between the groups.Results: Compared with group C, the time for intubation, the incidence of adverse reactions and the comfort score in group S were significantly lower (P< 0.01). Compared with T0, the mean arterial pressure (MAP), heart rate (HR), NE and AD wer

Yuhan Xiao,1,&ast; Yanyan Li,1,&ast; Yuhong Li,2 Chongchong Yu,1 Yichun Bai,1 Lei Wang,3 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 3Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany&ast;These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, No. 601 Jinsui Road, Hongqi District, Xinxiang, Henan Province, 453003, People’s Republic of ChinaTel +86-373-3831646Email wybwho@163.comObjective: We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).Methods: The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).Results: The TBATS (0.27, {0,0}, -, {< 12,4>}) and SARIMA (0,1,(1,3))(0,1,1)12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 2

Wenhao Ding,1,&ast; Yanyan Li,1,&ast; Yichun Bai,1 Yuhong Li,2 Lei Wang,3 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 3Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany&ast;These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453000, People’s Republic of ChinaEmail wybwho@163.comObjective: COVID-19 may have a demonstrable influence on disease patterns. However, it remained unknown how tuberculosis (TB) epidemics are impacted by the COVID-19 outbreak. The purposes of this study are to evaluate the impacts of the COVID-19 outbreak on the decreases in the TB case notifications and to forecast the epidemiological trends in China.Methods: The monthly TB incidents from January 2005 to December 2020 were taken. Then, we investigated the causal impacts of the COVID-19 pandemic on the TB case reductions using intervention analysis under the Bayesian structural time series (BSTS) method. Next, we split the observed values into different training and testing horizons to validate the forecasting performance of the BSTS method.Results: The TB incidence was falling during 2005– 2020, with an average annual percentage change of − 3.186 (95% confidence interval [CI] − 4.083 to − 2.281), and showed a peak in March–April and a trough in January–February per year. The BSTS method assessed a monthly average reduction of 14% (95% CI 3.8% to 24%) in the TB case notifications from January–

Chongchong Yu,1,&ast; Chunjie Xu,2,&ast; Yuhong Li,3 Sanqiao Yao,1 Yichun Bai,1 Jizhen Li,1 Lei Wang,4 Weidong Wu,1 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, 100069, People’s Republic of China; 3National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 4Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany&ast;These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, 453000, Henan Province, People’s Republic of ChinaEmail wybwho@163.comObjective: The high morbidity, complex seasonality, and recurring risk of hand-foot-and-mouth disease (HFMD) exert a major burden in China. Forecasting its epidemic trends is greatly instrumental in informing vaccine and targeted interventions. This study sets out to investigate the usefulness of an advanced exponential smoothing state space framework by combining Box-Cox transformations, Fourier representations with time-varying coefficients and autoregressive moving average (ARMA) error correction (TBATS) method to assess the temporal trends of HFMD in China.Methods: Data from January 2009 to December 2019 were drawn, and then they were split into two segments comprising the in-sample training data and out-of-sample testing data to develop and validate the TBATS model, and its fitting and forecasting abilities were compared with the most frequently used seasonal autoregressive integrated moving average (SARIMA)

Jizhen Li,1 Yuhong Li,2 Ming Ye,3 Sanqiao Yao,1 Chongchong Yu,1 Lei Wang,4 Weidong Wu,1 Yongbin Wang1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2National Center for Tuberculosis Control and Prevention, China Center for Disease Control and Prevention, Beijing, People’s Republic of China; 3Preventive Medicine Clinic, Xinxiang Center for Disease Control and Prevention, Xinxiang, Henan Province, People’s Republic of China; 4Center for Musculoskeletal Surgery, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt–Universität Zu Berlin and Berlin Institute of Health, Berlin, GermanyCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453000, People’s Republic of ChinaEmail wybwho@163.comObjective: The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to assess and forecast the epidemic patterns of TB in Tibet.Methods: The TB incidence from January 2006 to December 2017 was obtained, and then the time series was partitioned into training subsamples (from January 2006 to December 2016) and testing subsamples (from January to December 2017). Among them, the training set was used to develop the EEMD-SARIMA-NARNN combined model, whereas the testing set was used to validate the forecasting performance of the model. Whilst the forecasting accuracy level of this novel method was compared with the basic SARIMA model, basic NARNN model, error-trend-seasonal (ETS) model, and traditional SARIMA-NARNN mixtur

Yongbin Wang,1,* Chunjie Xu,2,* Sanqiao Yao,1 Yingzheng Zhao,1 Yuchun Li,1 Lei Wang,3 Xiangmei Zhao1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People’s Republic of China; 3Center for Musculoskeletal Surgery, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität Zu Berlin and Berlin Institute of Health, Berlin, Germany*These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang 453000, Henan Province, People’s Republic of ChinaTel +86 373 383 1646Email wybwho@163.comObjective: The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model.Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models.Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD,A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, th

Yongbin Wang,1,* Chunjie Xu,2,* Yuchun Li,1 Weidong Wu,1 Lihui Gui,1 Jingchao Ren,1 Sanqiao Yao1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sanqiao Yao; Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453000, People’s Republic of ChinaTel +86 373831646Email 1153483589@qq.com; wybwho@163.comPurpose: Qinghai province has invariably been under an ongoing threat of tuberculosis (TB), which has not only been an obstacle to local development but also hampers the prevention and control process for ending the TB epidemic. Forecasting for future epidemics will serve as the base for early detection and planning resource requirements. Here, we aim to develop an advanced detection technique driven by the recent TB incidence series, by fusing a seasonal autoregressive integrated moving average (SARIMA) with a neural network nonlinear autoregression (NNNAR).Methods: We collected the TB incidence data between January 2004 and December 2016. Subsequently, the subsamples from January 2004 to December 2015 were employed to measure the efficiency of the single SARIMA, NNNAR, and hybrid SARIMA-NNNAR approaches, whereas the hold-out subsamples were used to test their predictive performances. We finally selected the best-performing technique by considering minimum metrics including the mean absolute error, root-mean-squared error, mean absolute percentage error and mean error rate .Results: During 2004– 2016, the reported TB cases totaled 71,080 resulting in the morbidity of 97.624 per 100,000 persons annually in Qinghai province and showed notable peak activi

Yongbin Wang,1,* Chunjie Xu,2,* Jingchao Ren,1 Weidong Wu,1 Xiangmei Zhao,1 Ling Chao,1 Wenjuan Liang,1 Sanqiao Yao1 1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People’s Republic of China; 2Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yongbin WangDepartment of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan 453000, People’s Republic of ChinaTel +86 373 383 1646Email wybwho@163.comObjective: Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China.Methods: We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects.Results: In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead foreca

Feifei Feng,1 Peng Cheng,2 Huanan Zhang,1 Nannan Li,1 Yuxin Qi,3 Hui Wang,1 Yongbin Wang,1 Wei Wang1 1Department of Respiratory Medicine, The Second Hospital of Shandong University, Jinan, Shandong 250033, People’s Republic of China; 2Department of Neural Medicine, The Second Hospital of Shandong University, Jinan, Shandong 250033, People’s Republic of China; 3Department of Respiratory Medicine, Jinan People’s Hospital, Jinan, Shandong 250033, People’s Republic of ChinaCorrespondence: Wei WangDepartment of Respiratory Medicine, The Second Hospital of Shandong University, 247 Beiyuan Street, Jinan, Shandong 250033, People’s Republic of ChinaTel +86 17660080618Email sducp@qq.comPurpose: Silicosis is an occupational disease caused by inhalation of silica and there are no effective drugs to treat this disease. Tanshinone IIA (Tan IIA), a traditional natural component, has been reported to possess anti-inflammatory, antioxidant, and anti-fibrotic properties. The current study’s purpose was to examine Tan IIA’s protective effects against silica-induced pulmonary fibrosis and to explore the underlying mechanisms.Methods: 48 male SD rats were randomly divided into four groups (n=12): i) Control group; ii) Silicosis group; iii) Tan IIA group; iv) Silicosis +Tan IIA group. Two days after modeling, the rats of Tan IIA group and Silicosis +Tan IIA group were given intraperitoneal administration 25 mg/kg/d Tan IIA for 40 days. Then, the four groups of rats were sacrificed and the lung inflammatory responses were measured by ELISA, lung damage and fibrosis were analyzed by hematoxylin and eosin (H&E) staining and Masson staining, the expression levels of collagen I, fibronectin and α-smooth muscle actin (α-SMA) were measured by immunohistochemistry. The markers of oxidative stress were measured by commercial kits, and the activity of the TGF-β1/Smad and NOX4, Nrf2/ARE signal

Qiaoji Qin, Nan Lin, Huan Huang, Xuezhi Zhang, Xuelei Cao, Yongbin Wang, Peng LiEmergency Department, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong, People’s Republic of ChinaBackground and purpose: Ginsenoside Rg1 (GS Rg1), as an important active substance of Panax ginseng, has been proven to have elaborate cardioprotective effects. The purpose of this study was to detect that GS Rg1 attenuates cardiac oxidative stress and inflammation in streptozotocin (STZ)-induced diabetic rats (DM).Methods: Cardiac function was assessed by heart rate and blood pressure. Markers relevant to myocardial oxidative stress and antioxidant capacity, and inflammatory reaction factors were detected. The mRNA and protein expression were detected by RT-qPCR and Western blot, respectively.Results: GS Rg1 treatment significantly reduced the symptoms of cardiac hypertrophy and hypertension, and also decreased oxidative stress, inflammation response, NF-κB expression and NLRP3 inflammasome expression. GS Rg1 enhanced mitochondrial biogenesis by increasing PGC-1α, complex III and complex â…£ expression. GS Rg1 treatment significantly increased the expression of AMPK, Nrf2 and HO-1 in cardiac tissues.Conclusion: GS Rg1 exhibited protective effect against STZ-induced cardiac dysfunction, which is potentially associated with AMPK/Nrf2/HO-1 signal pathway.Keywords: Ginsenoside Rg1, oxidative stress, inflammation, AMPK, Nrf, HO-1

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.

As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.

Strained Si HBTs have been demonstrated for the first time with a maximum current gain (P) of 3700 using a relaxed Si(0.85)Ge(0.15) virtual substrate, Si(0.7)Ge(0.3) base and strained Si emitter. This represents 10x and 27x larger gain compared with pseudomorphic SiGe HBTs and Si control BJTs which were manufactured in parallel and had current gains of 334 and 135, respectively. The strained Si HBTs exhibited satisfactory breakdown voltage (2.5 V) compared with SiGe HBTs (2.7 V) and Si BJTs (4.5 V) and excellent control of collector off-state leakage (< 20 fA)., QC 20120328