Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.

BACKGROUND: It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy". METHODS: We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method. RESULTS: The hepatitis B epidemics fluctuates irregularly during the period 2005-2007(APC = 8.7, P = 0.246) and 2015-2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18-35%) within the first three months in 2020,17% (95% CI 7.7-26%) within the first six months in 2020, and 10% (95% CI19-22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001). CONCLUSION: The COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

HMFT: Hyperspectral and Multispectral Image Fusion Super-Resolution Method Based on Efficient Transformer and Spatial-Spectral Attention Mechanism.

Due to the imaging mechanism of hyperspectral images, the spatial resolution of the resulting images is low. An effective method to solve this problem is to fuse the low-resolution hyperspectral image (LR-HSI) with the high-resolution multispectral image (HR-MSI) to generate the high-resolution hyperspectral image (HR-HSI). Currently, the state-of-the-art fusion approach is based on convolutional neural networks (CNN), and few have attempted to use Transformer, which shows impressive performance on advanced vision tasks. In this paper, a simple and efficient hybrid architecture network based on Transformer is proposed to solve the hyperspectral image fusion super-resolution problem. We use the clever combination of convolution and Transformer as the backbone network to fully extract spatial-spectral information by taking advantage of the local and global concerns of both. In order to pay more attention to the information features such as high-frequency information conducive to HR-HSI reconstruction and explore the correlation between spectra, the convolutional attention mechanism is used to further refine the extracted features in spatial and spectral dimensions, respectively. In addition, considering that the resolution of HSI is usually large, we use the feature split module (FSM) to replace the self-attention computation method of the native Transformer to reduce the computational complexity and storage scale of the model and greatly improve the efficiency of model training. Many experiments show that the proposed network architecture achieves the best qualitative and quantitative performance compared with the latest HSI super-resolution methods.

Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Nuclear Radiation Knowledge and Anxiety Levels among Residents around a Nuclear Power Plant in Liaoning Province, China.

ABSTRACT: Awareness of radiation-related knowledge (RRK) and nuclear energy-related knowledge (NERK) among residents around a nuclear power plant (NPP), as well as their concerns about a NPP, were investigated. A face-to-face survey was conducted among 1,775 residents within 30 km around the NPP in Liaoning Province, China. A single-item Likert scale, Spearman's/Pearson's correlation coefficients, Student's t-test, ANOVA, and multiple-linear regression analysis were employed. Awareness of RRK and NERK among residents around the NPP was 27.7% and 36.6%, respectively. The anxiety level of respondents was negatively corelated with the distance from their residence to the NPP and age. Also, 55.6% of respondents thought that the publicity about nuclear energy/NPPs was insufficient, and 82.7% of respondents wanted to know relevant information about NPPs. Awareness of RRK and NERK among residents around the NPP was relatively low, which was related to education, occupation, and income. The anxiety level among residents was related to distance and age. The public was eager to know about RRK and NERK. These findings indicate that the publicity and education of RRK and NERK among residents around the NPP should be strengthened.

Prognostic significance of long noncoding RNA HOTAIR in hepatocellular carcinoma: A protocol for systematic review and meta-analysis.

BACKGROUND: Homeobox transcript antisense intergenic RNA (HOTAIR), a long noncoding RNA, has been reported to associate with the prognosis of patients with hepatocellular carcinoma (HCC) in several studies, however, the definite conclusion has not been obtained for conflicting results across different studies. The aim of this study is to determine the association of HOTAIR expression with overall survival, progression-free survival, and clinical features in HCC. METHODS: PubMed, Cochrane Library, and Embase will be comprehensively searched to seek the relevant studies. The studies meeting the inclusion criteria will be included into this systematic review and meta-analysis. A combination of hazard ratio and 95% confidence interval is used to estimate the impact of HOTAIR expression on the overall survival and progression-free survival in HCC. The relationship between HOTAIR expression and clinical features of HCC is evaluated using the odds ratio and 95% confidence interval. The study quality is evaluated with the "risk of bias assessment" tool in Cochrane System Assessment Manual or Newcastle-Ottawa Scale. The subgroup analysis, publication bias, and sensitivity analysis are performed. RESULTS: This study provides a strict and classic protocol for systematic review and meta-analysis to determine the prognostic significance of HOTAIR expression in HCC. The findings of this systematic review and meta-analysis may provide a novel diagnostic indicator and potential therapeutic target of HCC. ETHICS AND DISSEMINATION: This study is only a protocol for systematic review and meta-analysis, and all data used in this study is acquired through published studies. Therefore, the ethical review is not needed for this study. REGISTRATION NUMBER: INPLASY202230050.

Dual-Path Residual "Shrinkage" Network for Side-Scan Sonar Image Classification.

The underwater environment is complicated and changeable and contains many noises, making it difficult to detect a particular object in the underwater environment. At present, the main seabed detection technology explores the seabed environment with sonar equipment. However, the characteristics of underwater sonar imaging (e.g., low contrast, blurred edges, poor texture, and unsatisfactory quality) have serious negative influences on such image classification. Therefore, in this study, we propose a dual-path deep residual "shrinkage" network (DP-DRSN) module, which is a simple and effective neural network attention module that can classify side-scan sonar images. Specifically, the module can extract background and feature texture information of the input feature mapping through different scales (e.g., global average pooling and global max pooling), whereas scale information passes through a two-layer 1 × 1 convolution to increase nonlinearity. This helps realize cross-channel information interaction and information integration simultaneously before outputting threshold parameters in a sigmoid layer. The parameters are then multiplied by the average value of the input feature mapping to obtain a threshold, which is used to denoise the image features using the soft threshold function. The proposed DP-DRSN study provided higher classification accuracy and efficiency than other models. In this way, the feasibility and effectiveness of DP-DRSN in image classification of side-scan sonar are proven.

Vehicle Detection in Remote Sensing Image Based on Machine Vision.

Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people's hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-PANet is used as the neck network. After the upper sampling, depthwise separable convolution is used to greatly avoid the lack of small target feature information in the convolution of the shallow network and increase the semantic information in the high-level network. Finally, Gamma correction is used to preprocess the image before image training, which effectively reduces the interference of shadow and other factors on training. The experiment proves that the method has a better effect on small targets obscured by shadows and under the color similar to the background of the picture, and the accuracy is significantly improved based on the original algorithm.

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. METHODS: We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. RESULTS: There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. CONCLUSIONS: The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

Trends of Annual Whole-body Occupational Radiation Exposure for Industrial Practices in China (2009-2018).

ABSTRACT: The national status and dose trends on the occupational exposure to ionizing radiation in industrial practices for 2009-2018 in China are presented in terms of seven occupational categories. A total of 504,538 industrial radiation workers were monitored for the period 2009-2018, with a continuous increase in the number of workers from 23,789 in 2009 to 66,017 in 2018. The annual average effective doses were 0.399, 0.425, 0.392, 0.376, 0.346, 0.355, 0.312, 0.305, 0.270, and 0.230 mSv from 2009 to 2018, respectively, which were well lower than the recommended occupational dose limit of 20 mSv y-1 for radiation workers. The Mann-Kendall test result shows a statistically significant decreasing trend at a rate of 0.02 mSv y-1 in average annual effective doses (p<0.001). In addition, more than 95.4% of radiation workers in industrial practices received an average annual effective dose less than the public dose limit of 1 mSv. It was also found that the average annual effective doses in industrial radiography and well logging were significantly higher than those in five other categories (p<0.001). Based on these observations, it is still necessary to control and manage the workplace and radiation workers to control occupational exposure as low as reasonably achievable, especially for the workers engaged in these two activities.

Antecedent infections in Guillain-Barré syndrome: a single-center, prospective study.

OBJECTIVE: To investigate the spectrum of antecedent infections in Chinese patients with Guillain-Barré syndrome (GBS) and analyze the infections-related clinical phenotypes locally. METHODS: A prospective case-control study of 150 patients diagnosed with GBS and age- and sex-matched neurological and healthy controls was performed to investigate recent infections of 14 pathogens serologically and collect the clinical data during a follow-up of 12 months. RESULTS: In total, 53% of patients with GBS had a positive serology for recent infection, including Campylobacter jejuni (27%), influenza A (17%) and B (16%), hepatitis A virus (5%), dengue virus (3%), cytomegalovirus (3%), Epstein-Barr virus (3%), Mycoplasma pneumoniae (2%), herpes simplex virus (2%), varicella-zoster virus (1%), and rubella virus (1%). Serology for infections of hepatitis E virus, Haemophilus influenzae, and Zika virus was negative. There was a higher frequency of C. jejuni, influenza A, influenza B, and hepatitis A virus infections in GBS patients than both the neurological and healthy controls. C. jejuni infection was more frequent in younger GBS patients and was associated with antibodies against GM1, GalNAc-GD1a, and GM1:galactocerebroside complex. Influenza B infection was associated with a pure motor form of GBS. INTERPRETATION: C. jejuni, influenza A, influenza B, and hepatitis A virus serve as the most common cause of antecedent infections in GBS locally. Influenza B-related GBS may represent a pure motor phenotype. Differences in the infectious spectrum worldwide may contribute to the geographical clinical heterogeneity of GBS.

Oxymatrine can attenuate pathological deficits of Alzheimer's disease mice through regulation of neuroinflammation.

Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by progressive learning and cognitive damage. Several hypotheses such as amyloid cascade hypothesis, hyper-phosphorylated τ hypothesis, and energy metabolism hypothesis have been proposed to elucidate the disease. However, the exact mechanism of AD remains unclear and current therapeutic strategies are miserable. Cumulative evidence showed that neuroinflammation plays a significant role in the pathogenesis of the AD. Oxymatrine (OMT), a plant-derived bioactive compound, has anti-viral, anti-fibrosis, and anti-tumor effects through the involvement of several immune-related signaling pathways. Whether OMT can attenuate the pathology of AD is largely unknown. In this manuscript, we found that treatment of OMT can significantly improve cognitive and learning abilities of AD mice during various behavioral test. Treatment of OMT can significantly reduce the densities of Aß plaques and astrocyte clusters in the neocortex and hippocampus of AD mice. Furthermore, treatment of OMT significantly reduced the concentration of pro-inflammatory cytokines including IL-6, IL-1ß, TNF-α and IL-17A in AD mice. Taken together, our data indicate that OMT may serve as a potential drug for AD.

Neonatal Crohn's disease with Oral ulcer as the first symptom caused by a compound heterozygote mutation in IL-10RA: a case report.

OBJECTIVE: To investigate the clinical and genetic characteristics of neonatal Crohn's disease (CD), improve recognition of neonatal CD, and reduce the number of patients that are missed or misdiagnosed. METHODS: A 10-day-old Chinese girl with oral ulcers was admitted to the Department of Neonatology. She later developed a rash and perianal disease, but without diarrhea and stool abnormalities. The patient and her parents underwent next-generation sequencing. RESULTS: The results showed that the patient carries a compound heterozygous mutation in the interleukin-10 receptor A (IL-10RA) (NM_001558.3) gene. One heterozygous mutation was c.301 c > T, P. (Arg 101 Trp) in exon 3 of IL-10RA (a missense mutation), and the other was c. 537G > A, P. (Thr 179 =) in exon 4 of IL 10RA (a synonymous mutation). The patient's father also carries the c.301 c > T, P. (Arg 101 Trp) heterozygous mutation in exon 3 of IL-10RA, whereas her mother carries the c.537G > A, P. (Thr 179 =) heterozygous mutation in exon 4 of IL-10RA. CONCLUSIONS: The results show that a compound heterozygous mutation in IL-10RA is associated with neonatal CD. Oral ulcers with a rash and perianal disease may be an early symptom of neonatal CD; therefore, such patients should undergo genetic identification as soon as possible.

Human Umbilical Cord Wharton's Jelly Derived Mesenchymal Stromal Cells May Attenuate Sarcopenia in Aged Mice Induced by Hindlimb Suspension.

BACKGROUND Since the use of human umbilical cord Wharton's Jelly derived mesenchymal stromal cells (hWJ-MSCs) to treat sarcopenia has not been explored, we studied the effects of hWJ-MSCs in aged male C57BL/6J mice with sarcopenia induced by hindlimb suspension, and explored the potential mechanism. MATERIAL AND METHODS Hindlimb suspension was used to induce sarcopenia in 24-month-old C57BL/6J mice and green fluorescent protein-tagged hWJ-MSCs and controls were transplanted into mice via tail vein or local intramuscular injection. After hWJ-MSC transplantation, changes in whole body muscle strength and endurance, gastrocnemius muscle weight and myofiber cross-sectional area (CSA) were studied. Proliferation of skeletal muscle stem cell, apoptosis, and chronic inflammation were also investigated. RESULTS We demonstrated that whole body muscle strength and endurance, gastrocnemius muscle mass, and CSA were significantly increased in hWJ-MSC-transplanted mice than in controls (P<0.05). In hWJ-MSC-transplanted mice, apoptotic myonuclei was reduced, and BrdU and Pax-7 expression indices of gastrocnemius muscles were increased (P<0.05). Tumor necrosis factor (TNF)-α and interleukin (IL)-6 were downregulated, and IL-4 and IL-10 were upregulated (P<0.05). CONCLUSIONS hWJ-MSCs may ameliorate sarcopenia in aged male C57BL/6J mice induced by hindlimb suspension, and this may be via activation of resident skeletal muscle satellite cells, reduction of apoptosis, and less chronic inflammation.

Oxymatrine attenuates cognitive deficits through SIRT1-mediated autophagy in ischemic stroke.

To investigate if oxymatrine could ameliorate hippocampus ischemia/reperfusion (I/R) injury induced in rats and explore the possible mechanism. Rats were randomly divided into four groups: sham group, I/R group, I/R + OMT-treated group, I/R + Vehicle-treated group. Oxymatrine or vehicle solution was intraperitoneally injected OMT (150 mg/kg) 60 min before renal ischemia respectively. Water maze was measured; cell apoptosis was assessed by doing TUNEL assay and detecting the expression of P53, Bax, and Cleaved-Caspase-3; autophagy were assessed by measuring the expression of LC3 and P62. The expression of SIRT1 was also detected. Oxymatrine treatment alleviated histological injury in I/R rats, inhibiting apoptosis, promoting autophagy and accompanied by upregulated expression of SIRT1 proteins. Oxymatrine may attenuate hippocampus ischemia/reperfusion injury through upregulation SIRT1, further influencing the processes of apoptosis and autophagy.

Effect of erythropoietin combined with hypothermia on serum tau protein levels and neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy.

Although hypothermia therapy is effective to treat neonatal hypoxic-ischemic encephalopathy, many neonatal patients die or suffer from severe neurological dysfunction. Erythropoietin is considered one of the most promising neuroprotective agents. We hypothesized that erythropoietin combined with hypothermia will improve efficacy of neonatal hypoxic-ischemic encephalopathy treatment. In this study, 41 neonates with moderate/severe hypoxic-ischemic encephalopathy were randomly divided into a control group (hypothermia alone for 72 hours, n = 20) and erythropoietin group (hypothermia + erythropoietin 200 IU/kg for 10 days, n = 21). Our results show that compared with the control group, serum tau protein levels were lower and neonatal behavioral neurological assessment scores higher in the erythropoietin group at 8 and 12 days. However, neurodevelopmental outcome was similar between the two groups at 9 months of age. These findings suggest that erythropoietin combined with hypothermia reduces serum tau protein levels and improves neonatal behavioral neurology outcome but does not affect long-term neurodevelopmental outcome.

IgG-degrading enzyme of Streptococcus pyogenes (IdeS) prevents disease progression and facilitates improvement in a rabbit model of Guillain-Barré syndrome.

Autoantibodies binding to peripheral nerves followed by complement deposition and membrane attack complex formation results in nerve damage in Guillain-Barré syndrome (GBS). Strategies to remove the pathogenic autoantibodies or block the complement deposition benefit most patients with GBS. Immunoglobulin G-degrading enzyme of Streptococcus pyogenes (IdeS) is a cysteine protease which cleaves IgG antibodies into F(ab')2 and Fc fragments. In this study, using a rabbit model of axonal GBS, acute motor axonal neuropathy (AMAN), we demonstrated that IdeS treatment significantly reduced the disruption of Nav channels as well as activated C3 deposition at the anterior spinal root nodes of Ranvier in AMAN rabbits. IdeS significantly promoted the clinical recovery of AMAN rabbits and there were significant lower frequencies of axonal degeneration in anterior spinal roots of AMAN rabbits with IdeS treatment compared to the saline controls. Our data support that IdeS treatment is a promising therapeutic strategy for GBS.