Socioeconomic, meteorological factors and spatiotemporal distribution of human brucellosis in China between 2004 and 2019-A study based on spatial panel model.

BACKGROUND: Human brucellosis continues to be a great threat to human health in China. The present study aimed to investigate the spatiotemporal distribution of human brucellosis in China from 2004 to 2019, to analyze the socioeconomic factors, meteorological factors and seasonal effect affecting human brucellosis incidence in different geographical regions with the help of spatial panel model, and to provide a scientific basis for local health authorities to improve the prevention of human brucellosis. METHODS: The monthly reported number and incidence of human brucellosis in China from January 2004 to December 2019 were obtained from the Data Center for China Public Health Science. Monthly average air temperature and monthly average relative humidity of 31 provincial-level administrative units (22 provinces, 5 autonomous regions and 4 municipalities directly under the central government) in China from October 2003 to December 2019 were obtained from the National Meteorological Science Data Centre. The inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield and gross pastoral product of 31 provincial-level administrative units in China from 2004 to 2019 were obtained from the National Bureau of Statistics of China. The temporal and geographical distribution of human brucellosis was displayed with Microsoft Excel and ArcMap software. The spatial autocorrelation and hotspot analysis was used to describe the association among different areas. Spatial panel model was constructed to explore the combined effects on the incidence of human brucellosis in China. RESULTS: A total of 569,016 cases of human brucellosis were reported in the 31 provincial-level administrative units in China from January 2004 to December 2019. Human brucellosis cases were concentrated between March and July, with a peak in May, showing a clear seasonal increase. The incidence of human brucellosis in China from 2004 to 2019 showed significant spatial correlations, and hotspot analysis indicated that the high incidence of human brucellosis was mainly in the northern China, particularly in Inner Mongolia, Shanxi, and Heilongjiang. The results from spatial panel model suggested that the inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature (the same month, 2-month lagged and 3-month lagged), average relative humidity (the same month) and season variability were significantly associated with human brucellosis incidence in China. CONCLUSIONS: The epidemic area of human brucellosis in China has been expanding and the spatial clustering has been observed. Inner Mongolia and adjacent provinces or autonomous regions are the high-risk areas of human brucellosis. The inventory of cattle and sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature, average relative humidity and season variability played a significant role in the progression of human brucellosis. The present study strengthens the understanding of the relationship between socioeconomic, meteorological factors and the spatial heterogeneity of human brucellosis in China, through which 'One Health'-based strategies and countermeasures can be provided for the government to tackle the brucellosis menace.

Effects of doxofylline as an adjuvant on severe exacerbation and long-term prognosis for COPD with different clinical subtypes.

OBJECTIVE: This study aimed to investigate the effectiveness of doxofylline as an adjuvant in reducing severe exacerbation for different clinical subtypes of chronic obstructive pulmonary disease (COPD). METHODS: The clinical trial was an open-label non-randomized clinical trial that enrolled patients with COPD. The patients were divided into two groups (doxofylline group[DG] and non-doxofylline group[NDG]) according to whether the adjuvant was used. Based on the proportion of inflammatory cells present, the patients were divided into neutrophilic, eosinophilic, and mixed granulocytic subtypes. The rates of severe acute exacerbation, use of glucocorticoids, and clinical symptoms were followed up in the first month, the third month, and the sixth month after discharge. RESULTS: A total of 155 participants were included in the study. The average age of the participants was 71.2 ± 10.1 years, 52.3% of the patients were male, and 29.7% of the participants had extremely severe cases of COPD. In the third month after discharge the numbers of patients exhibiting severe exacerbation among the neutrophilic subtype were 5 (6.6%) in the DG versus 17 (22.4%) in the NDG (incidence rate ratio[IRR] = 0.4 [95% CI: 0.2-0.9] P = 0.024). In the sixth month after discharge, the numbers were 3 (3.9%) versus 13 (17.1%; IRR = 0.3 [95%; CI: 0.1-0.9], P = 0.045), and those for the eosinophilic subtype were 0 (0.0%) versus 4 (14.8%), P = 0.02. In the eosinophilic subtype, the results for forced expiratory volume in the first second and maximal mid-expiratory flow were significantly higher in the DG. The mean neutrophil and eosinophil levels were significantly lower than in the NDG among the neutrophilic subtype, and the neutrophil percentage was lower than in the NDG among the eosinophilic subtype. At the six-month follow-up, the dose adjustment rates of the neutrophilic and eosinophilic subtypes showed a significant difference (P< 0.05). CONCLUSIONS: As an adjuvant drug, doxofylline has a good therapeutic effect on patients with the neutrophilic and eosinophilic clinical subtypes of COPD. It can reduce the incidence of severe exacerbation, the use of glucocorticoids, and inflammatory reactions in the long term (when used for a minimum of 3 months).

Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966-2020).

Background: Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods: PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results: Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion: This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.

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.

Mendelian randomization analyses in ocular disease: a powerful approach to causal inference with human genetic data.

Ophthalmic epidemiology is concerned with the prevalence, distribution and other factors relating to human eye disease. While observational studies cannot avoid confounding factors from interventions, human eye composition and structure are unique, thus, eye disease pathogenesis, which greatly impairs quality of life and visual health, remains to be fully explored. Notwithstanding, inheritance has had a vital role in ophthalmic disease. Mendelian randomization (MR) is an emerging method that uses genetic variations as instrumental variables (IVs) to avoid confounders and reverse causality issues; it reveals causal relationships between exposure and a range of eyes disorders. Thus far, many MR studies have identified potentially causal associations between lifestyles or biological exposures and eye diseases, thus providing opportunities for further mechanistic research, and interventional development. However, MR results/data must be interpreted based on comprehensive evidence, whereas MR applications in ophthalmic epidemiology have some limitations worth exploring. Here, we review key principles, assumptions and MR methods, summarise contemporary evidence from MR studies on eye disease and provide new ideas uncovering aetiology in ophthalmology.

A probabilistic model for co-occurrence analysis in bibliometrics.

The co-occurrence analysis of Medical Subject Heading (MeSH) terms extracted from the PubMed database is popularly used in bibliometrics. Practically for making the result interpretable, it is necessary to apply a certain filter procedure of co-occurrence matrix for removing the low-frequency items due to their low representativeness. Unfortunately, there is rare research referring to determine a critical threshold to remove the noise of co-occurrence matrix. Here, we proposed a probabilistic model for co-occurrence analysis that can provide statistical inferences about whether the paired items co-occur randomly. With help of this model, the dimensionality of co-occurrence matrix could be reduced according to the selected threshold. The conceptual model framework, simulation and practical applications are illustrated in the manuscript. Further details (including all reproducible codes) can be downloaded from the project website: https://github.com/xizhou/co-occurrence-analysis.git.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Defining matrix Gla protein expression in the Dunkin-Hartley guinea pig model of spontaneous osteoarthritis.

BACKGROUND: Matrix Gla (γ-carboxyglutamate) protein (MGP) is considered a strong inhibitor of ectopic calcification, and it has been associated with OA severity, although not conclusively. We utilized male Dunkin-Hartley (DH) guinea pigs to investigate the expression of MGP throughout aging and disease pathogenesis in a spontaneous model. METHOD: Twenty-five male DH guinea pigs were obtained and nurtured to several timepoints, and then randomly and equally divided by age into five subgroups (1-, 3-, 6-, 9-, and 12-months, with the 1-month group as the reference group). DH guinea pigs in each group were euthanized at the designated month-age and the left or right medial tibial plateaus cartilages were randomly excised. OA severity was described by modified Mankin Score (MMS) at microscopy (Safranin O/Fast Green stain). Proteomic evaluation using isobaric tags for relative and absolute quantification (iTRAQ) was performed to validate the age-related changes in the MGP profiles, and immunohistochemistry (IHC) methods were applied for semi-quantitative determination of MGP expression in articular cartilage. RESULTS: The histopathologic findings validated the increasing severity of cartilage degeneration with age in the DH guinea pigs. The MMS showed significant, stepwise (every adjacent comparison P < 0.05) disease progression with month-age. The iTRAQ indicated that MGP levels increased significantly with advancing age (P < 0.05), as supported by the IHC result (P < 0.05). CONCLUSION: Increased expression of MGP in male DH guinea pigs was present throughout aging and disease progression and may be link to increased OA severity. Further studies are needed to investigate and confirm the association between MGP levels and OA severity.

The effects of dehiscence and fenestration before orthodontic treatment on external apical root resorption in maxillary incisors.

INTRODUCTION: The objective of this study was to investigate the effects of dehiscence and fenestration on external apical root resorption (EARR) in maxillary incisors. METHODS: Seventy-eight patients were selected for this study. We set dehiscence, fenestration, sex, extraction, or nonextraction, tooth position, initial age, and duration of treatment as independent variables and EARR as the dependent variable. General statistical descriptions for these variables were made by mean, standard deviation and occurrence rates, etc. To make the data visualization and find more information, 2 heat maps were made. Generalized estimation equation analysis was performed to determine associations between EARR and independent variables. RESULTS: The occurrence rates of dehiscence and fenestration in maxillary incisors were 14.77% and 10.74%, respectively. The average value of EARR was 1.09 ± 0.87 mm in this study. Dehiscence, tooth position, extraction, initial age, and duration had significant correlations with EARR. The ratio of exponent B was 1:1.643 for dehiscence and nondehiscence, whereas fenestration and sex had no significant association with EARR. CONCLUSIONS: The amount of EARR at maxillary incisors in patients with dehiscence before orthodontic treatment might be less than that in patients without it, and different incisors might have different contributions to it. However, the low value of exponent B for dehiscence meant that there might be other unknown factors that were involved in this study.

Nursing Scheduling Mode and Experience from the Medical Teams in Aiding Hubei Province During the COVID-19 Outbreak: A Systematic Scoping Review of 17 Studies.

INTRODUCTION: Due to COVID-19 outbreak, since January 24, 2020, national medical teams from across the country and the armed forces have been dispatched to aid Hubei. The present review was designed to timely summarize the existing frontline information about nursing scheduling mode with special focus on the length of shifts with the aim to contribute to improve the nurses' job satisfaction and the quality of nursing services. METHODS: Articles from Jan 2020 to October 2020 were retrieved from China National Knowledge Infrastructure, Wanfang Data and Weipu Information, with the terms "COVID-19", "designated hospital", "Hubei-assisted", "makeshift hospital", "nursing", "nursing shift", "whole-system takeover" and variations of these, in the title and abstract fields and the Boolean combinations of these words as the retrieval strategy. RESULTS: Seventeen journal articles have been included in the target field, from the nurses in aiding Hubei Province, four kinds of shift length, 2-hour (h), 3-h, 4-h and 6-h shift have been considered, the main nursing scheduling mode adopted in designated hospitals for COVID-19 patients was dynamic scheduling based on workload, flexible scheduling based on working hours, workload and the number of critically ill patients admitted, humanized scheduling based on the daily reported health status of the nurses, and professional-integrated scheduling according to the professional distribution of nurses on the basis of four-hour shift length, and in makeshift hospitals for mild patients, the scheduling mode was 6-h based correspondingly. CONCLUSION: The descriptive results of the present systematic review shed light on the challenges and practical solutions of nursing scheduling mode in the context of cross-regional medical assistance. Additionally, the present systematic review could provide the academic community of nurses, nurse managers and administrators with baseline information and scientific productions from the content's points of view in the target field.

Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.

OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. METHOD: We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). RESULT: There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). CONCLUSION: The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.

Quantitative spectral CT evaluation of kidney tumors with the stretched-exponential nonlinear regression analysis model.

BACKGROUND: This study aimed to use the stretched-exponential nonlinear regression analysis model to explore the value of the energy spectral curve in the differential diagnosis of clear cell renal cell carcinoma (ccRCC), minimal fat renal angiomyolipoma (RAML), and hypovascular renal cell carcinoma. METHODS: Sixty-five cases with renal tumors were enrolled retrospectively who had undergone a preoperative multiphase spectral CT scan of the kidney in pre-enhance and double-phase enhanced scanning. The normalized iodine concentrations (NIC) of these lesions, normal renal cortex, and psoas major were measured and calculated. The spectral curves of these lesions and normal tissues were analyzed to calculate the stretched-exponential index (α) and b value with the stretched-exponential nonlinear regression analysis model (y=-b·Xα). The differences between α, b value, and NIC of these lesions and normal tissues in pre-enhance and two enhanced phases were compared using one-way ANOVA. The correlation between α, b value, and NIC was evaluated using the Pearson coefficient test, with significance assigned at the 5% level. RESULTS: There was no significant difference in α value between the groups in pre-enhance scanning. In the Cortical phase (CP), there were no significant differences in NIC and α value between minimal fat RAML and hypovascular renal cell carcinoma, or between ccRCC and the normal renal cortex. However, in the nephrographic phase (NP), a significant difference in α value was found between minimal fat RAML and hypovascular renal cell carcinoma, but no difference in NIC between them. In NP, there were significant differences in NIC and α values between ccRCC and the normal renal cortex. In CP and NP, there were significant differences between the psoas major and other groups in all parameters. For b value, in pre-enhance scanning, there was a significant difference between the psoas major and other groups, and between ccRCC and the normal renal cortex. There was no significant difference between other groups. After enhancement, in CP and NP, significant differences were observed between the psoas major and other groups in b value, but no significant differences were observed between all renal tumors and the normal renal cortex. A linear correlation was found between α values and NIC in CP (r=0.780, P=0.00) and NP (r=0.693, P=0.00). The b values and NIC had a low correlation in CP, with no correlation in NP. CONCLUSIONS: Quantitative spectral CT with the stretched-exponential nonlinear regression analysis model may enhance the differential diagnosis ability for renal tumors. Its clinical value remains to be further explored in other types of soft tissue lesions.

Ligand-based optimization to identify novel 2-aminobenzo[d]thiazole derivatives as potent sEH inhibitors with anti-inflammatory effects.

Inhibition of the soluble epoxide hydrolase (sEH) is a promising new therapeutic approach in the treatment of inflammation. Driven by the in-house database product lead 1, a hybridization strategy was utilized for the design of a series of novel benzo [d]thiazol derivatives. To our delight, D016, a byproduct of compound 9, was obtained with an extraordinarily low IC50 value of 0.1 nM but poor physical and chemical properties. After removal of a non-essential urea moiety or replacement of the urea group by an amide group, compounds 15a, 17p, and 18d were identified as promising sEH inhibitors, and their molecular binding modes to sEH were constructed. Furthermore, compounds 15a and 18d exhibited more effective in vivo anti-inflammatory effect than t-AUCB in carrageenan-induced mouse paw edema. Compound 15a also showed moderate metabolic stability with a half-time of 34.7 min. Although 18d was unstable in rat liver microsomes, it might be a "prodrug". In conclusion, this study could provide valuable insights into discovery of new sEH inhibitors, and compounds 15a and 18d were worthy of further development as potential drug candidates to treat inflammation.

Comparison of ARIMA model and XGBoost model for prediction of human brucellosis in mainland China: a time-series study.

OBJECTIVES: Human brucellosis is a public health problem endangering health and property in China. Predicting the trend and the seasonality of human brucellosis is of great significance for its prevention. In this study, a comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more suitable for predicting the occurrence of brucellosis in mainland China. DESIGN: Time-series study. SETTING: Mainland China. METHODS: Data on human brucellosis in mainland China were provided by the National Health and Family Planning Commission of China. The data were divided into a training set and a test set. The training set was composed of the monthly incidence of human brucellosis in mainland China from January 2008 to June 2018, and the test set was composed of the monthly incidence from July 2018 to June 2019. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) were used to evaluate the effects of model fitting and prediction. RESULTS: The number of human brucellosis patients in mainland China increased from 30 002 in 2008 to 40 328 in 2018. There was an increasing trend and obvious seasonal distribution in the original time series. For the training set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 338.867, 450.223 and 10.323, respectively, and the MAE, RSME and MAPE of the XGBoost model were 189.332, 262.458 and 4.475, respectively. For the test set, the MAE, RSME and MAPE of the ARIMA(0,1,1)×(0,1,1)12 model were 529.406, 586.059 and 17.676, respectively, and the MAE, RSME and MAPE of the XGBoost model were 249.307, 280.645 and 7.643, respectively. CONCLUSIONS: The performance of the XGBoost model was better than that of the ARIMA model. The XGBoost model is more suitable for prediction cases of human brucellosis in mainland China.

A prognostic gene model of immune cell infiltration in diffuse large B-cell lymphoma.

BACKGROUND: Immune cells in the tumor microenvironment are an important prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, information on the heterogeneity and risk stratification of these cells is limited. We sought to develop a novel immune model to evaluate the prognostic intra-tumoral immune landscape of patients with DLBCL. METHODS: The ESTIMATE and CIBERSORT algorithms were used to estimate the numbers of 22 infiltrating immune cells based on the gene expression profiles of 229 patients with DLBCL who were recruited from a public database. The least absolute shrinkage and selection operator (Lasso) penalized regression analyses and nomogram model were used to construct and evaluate the prognostic immunoscore (PIS) model for overall survival prediction. An immune gene prognostic score (IGPS) was generated by Gene Set Enrichment Analysis (GSEA) and Cox regression analysis was and validated in an independent NCBI GEO dataset (GSE10846). RESULTS: A higher proportion of activated natural killer cells was associated with a poor outcome. A total of five immune cells were selected in the Lasso model and DLBCL patients with high PIS showed a poor prognosis (hazard ratio (HR) 2.16; 95% CI [1.33-3.50]; P = 0.002). Differences in immunoscores and their related outcomes were attributed to eight specific immune genes involved in the cytokine-cytokine receptor interaction and chemokine signaling pathways. The IGPS based on a weighted formula of eight genes is an independent prognostic factor (HR: 2.14, 95% CI [1.40-3.28]), with high specificity and sensitivity in the validation dataset. CONCLUSIONS: Our findings showed that a PIS model based on immune cells is associated with the prognosis of DLBCL. We developed a novel immune-related gene-signature model associated with the PIS model and enhanced the prognostic functionality for the prediction of overall survival in patients with DLBCL.

Thymoquinone ameliorates the PM2.5-induced lung injury in rats.

BACKGROUND: This study aims to explore the effect of thymoquinone (TQ) on particulate matter 2.5 (PM2.5)-induced lung injury. METHODS: The PM2.5 sample was provided by Shenyang Environment Monitor Central Station. Lung injury was established by intratracheal instillation PM2.5 (7.5 mg/kg) followed by TQ treatment (20 and 40 mg/kg) for 14 d in rats. Hematoxylin and eosin (HE) and Evans blue dye (EBD) staining were detected on lung tissues. ELISA, real-time PCR, western blotting and TUNEL assays were also performed. RESULTS: The data showed that TQ diminished lung injury and EBD accumulation. The number of macrophages, neutrophils, eosinophils, and lymphocytes was ameliorated after TQ treatment. In addition, TQ suppressed the inflammation reaction parameters (interleukin-1ß and -6, IL-1ß and IL-6; tumor necrosis factor-α, TNF-α) and oxidative stress in PM2.5-induced lung injury. The levels of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase (HO-1) were increased due to the treatment of TQ. The number of TUNEL-positive cells was prominently reduced in TQ-treated rats compared with that in PM2.5 group. Intratracheal instillation PM2.5 activated autophagy, whilst TQ blocked it in lung. CONCLUSIONS: Taken together, this study provides the first in vivo evidence that TQ suppresses inflammation, oxidative stress, apoptosis, and autophagy in PM2.5-induced lung injury.

Global Research Output and Theme Trends on Climate Change and Infectious Diseases: A Restrospective Bibliometric and Co-Word Biclustering Investigation of Papers Indexed in PubMed (1999-2018).

Climate change is a challenge for the sustainable development of an international economy and society. The impact of climate change on infectious diseases has been regarded as one of the most urgent research topics. In this paper, an analysis of the bibliometrics, co-word biclustering, and strategic diagram was performed to evaluate global scientific production, hotspots, and developing trends regarding climate change and infectious diseases, based on the data of two decades (1999-2008 and 2009-2018) from PubMed. According to the search strategy and inclusion criteria, a total of 1443 publications were found on the topic of climate change and infectious diseases. There has been increasing research productivity in this field, which has been supported by a wide range of subject categories. The top highly-frequent major MeSH (medical subject headings)/subheading combination terms could be divided into four clusters for the first decade and five for the second decade using a biclustering analysis. At present, some significant public health challenges (global health, and travel and tropical climate, etc.) are at the center of the whole target research network. In the last ten years, "Statistical model", "Diarrhea", "Dengue", "Ecosystem and biodiversity", and "Zoonoses" have been considered as emerging hotspots, but they still need more attention for further development.

Different Clinical Manifestations of Human Brucellosis in Pregnant Women: A Systematic Scoping Review of 521 Cases from 10 Countries.

INTRODUCTION: This systematic scoping review aims to assess the frequency and severity of clinical manifestations of pregnant women with brucellosis. METHODS: Three literature databases, PubMed, Web of Science and China National Knowledge Infrastructure (CNKI), and two search engines (Google and Yahoo) were adopted to identify the relevant articles that published until 31 December 2019. Two investigators independently screened the publications and extracted the data; the case reports and case series which described at least two symptoms or clinical manifestations of pregnant women with brucellosis were included. RESULTS: A total of 27 articles describing the information of 521 pregnant women with brucellosis were included. Serum agglutination test was the most common laboratory test in the diagnosis of brucellosis. A total of 36 clinical manifestations were extracted from the included articles, and the most common clinical manifestations were fever (400, 76.8%), joint pain/swelling/arthralgia (389, 74.7%), sweats (382, 73.3%), fatigue/asthenia/weakness (262, 50.3%) and back pain (189, 36.3%). Among the 32 included individual cases that with available obstetric outcome information, 10 (31.3%) suffered preterm delivery, 12 (37.5%) had an abortion and 3 (9.8%) had intrauterine fetal death. CONCLUSION: Brucellosis is popular and threatening for pregnant women. Regarding the localized body system complications, osteoarticular system was mostly involved, the obstetrics outcomes were severe among pregnant women with brucellosis. The detailed clinical and epidemiological characteristics in this scoping review may add a better and more complete understanding of the disease for both physicians and policy-makers, and provide evidence for timely diagnosis, adequate therapy and better prevention.