Cost-effectiveness analysis of hepatitis E vaccination strategies among patients with chronic hepatitis B in China.

AIM: This study aimed to evaluate the cost-effectiveness of hepatitis E vaccination strategies in chronic hepatitis B (CHB) patients. METHODS: Based on the societal perspective, the cost-effectiveness of three hepatitis E vaccination strategies-vaccination without screening, screening-based vaccination, and no vaccination-among CHB patients was evaluated using a decision tree-Markov model, and incremental cost-effectiveness ratios (ICERs) were calculated. Values for treatment costs and health utilities were estimated from a prior investigation on disease burden, and values for transition probabilities and vaccination-related costs were obtained from previous studies and government agencies. Sensitivity analyses were undertaken for assessing model uncertainties. RESULTS: It was estimated that CHB patients superinfected with hepatitis E virus (HEV) incurred significantly longer disease course, higher economic burden, and more health loss compared to those with HEV infection alone (all p < 0.05). The ICERs of vaccination without screening and screening-based vaccination compared to no vaccination were 41,843.01 yuan/quality-adjusted life year (QALY) and 29,147.32 yuan/QALY, respectively, both lower than China's per-capita gross domestic product (GDP) in 2018. The screening-based vaccination reduced the cost and gained more QALYs than vaccination without screening. One-way sensitivity analyses revealed that vaccine price, vaccine protection rate, and decay rate of vaccine protection had the greatest impact on the cost-effectiveness analysis. Probabilistic sensitivity analyses confirmed the base-case results, and if the willingness-to-pay value reached per-capita GDP, the probability that screening-based vaccination would be cost-effective was approaching 100%. CONCLUSIONS: The disease burden in CHB patients superinfected with HEV is relatively heavy in China, and the screening-based hepatitis E vaccination strategy for CHB patients is the most cost-effective option.

Epidemiological and spatiotemporal analysis of severe fever with thrombocytopenia syndrome in Eastern China, 2011-2021.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease, which is caused by severe fever with thrombocytopenia syndrome virus (SFTSV) with high fatality. Recently, the incidence of SFTS increased obviously in Jiangsu Province. However, the systematic and complete analysis of spatiotemporal patterns and clusters coupled with epidemiological characteristics of SFTS have not been reported so far. METHODS: Data on SFTS cases were collected during 2011-2021. The changing epidemiological characteristics of SFTS were analyzed by adopting descriptive statistical methods. GeoDa 1.18 was applied for spatial autocorrelation analysis, and SaTScan 10.0 was used to identify spatio-temporal clustering of cases. The results were visualized in ArcMap. RESULTS: The annual incidence of SFTS increased in Jiangsu Province from 2011 to 2021. Most cases (72.4%) occurred during May and August with the obvious peak months. Elderly farmers accounted for most cases, among which both males and females were susceptible. The spatial autocorrelation and spatio-temporal clustering analysis indicated that the distribution of SFTS was not random but clustered in space and time. The most likely cluster was observed in the western region of Jiangsu Province and covered one county (Xuyi county) (Relative risk = 8.18, Log likelihood ratio = 122.645, P < 0.001) located in southwestern Jiangsu Province from January 1, 2017 to December 31, 2021. The Secondary cluster also covered one county (Lishui county) (Relative risk = 7.70, Log likelihood ratio = 94.938, P < 0.001) from January 1, 2017 to December 31, 2021. CONCLUSIONS: The annual number of SFTS cases showed an increasing tendency in Jiangsu Province from 2011 to 2021. Our study elucidated regions with SFTS clusters by means of ArcGIS in combination with spatial analysis. The results demonstrated solid evidences for the orientation of limited sanitary resources, surveillance in high-risk regions and early warning of epidemic seasons in future prevention and control of SFTS in Jiangsu Province.

Predicting incidence of hepatitis E using machine learning in Jiangsu Province, China.

Hepatitis E is an increasingly serious worldwide public health problem that has attracted extensive attention. It is necessary to accurately predict the incidence of hepatitis E to better plan ahead for future medical care. In this study, we developed a Bi-LSTM model that incorporated meteorological factors to predict the prevalence of hepatitis E. The hepatitis E data used in this study are collected from January 2005 to March 2017 by Jiangsu Provincial Center for Disease Control and Prevention. ARIMA, GBDT, SVM, LSTM and Bi-LSTM models are adopted in this study. The data from January 2009 to September 2014 are used as the training set to fit models, and data from October 2014 to March 2017 are used as the testing set to evaluate the predicting accuracy of different models. Selecting models and evaluating the effectiveness of the models are based on mean absolute per cent error (MAPE), root mean square error (RMSE) and mean absolute error (MAE). A total of 44 923 cases of hepatitis E are detected in Jiangsu Province from January 2005 to March 2017. The average monthly incidence rate is 0.35 per 100 000 persons in Jiangsu Province. Incorporating meteorological factors of temperature, water vapour pressure, and rainfall as a combination into the Bi-LSTM Model achieved the state-of-the-art performance in predicting the monthly incidence of hepatitis E, in which RMSE is 0.044, MAPE is 11.88%, and MAE is 0.0377. The Bi-LSTM model with the meteorological factors of temperature, water vapour pressure, and rainfall can fully extract the linear and non-linear information in the hepatitis E incidence data, and has significantly improved the interpretability, learning ability, generalisability and prediction accuracy.

Comparative genome analysis of 12 Shigella sonnei strains: virulence, resistance, and their interactions.

Shigellosis is a highly infectious disease that is mainly transmitted via fecal-oral contact of the bacteria Shigella. Four species have been identified in Shigella genus, among which Shigella flexneri is used to be the most prevalent species globally and commonly isolated from developing countries. However, it is being replaced by Shigella sonnei that is currently the main causative agent for dysentery pandemic in many emerging industrialized countries such as Asia and the Middle East. For a better understanding of S. sonnei virulence and antibiotic resistance, we sequenced 12 clinical S. sonnei strains with varied antibiotic-resistance profiles collected from four cities in Jiangsu Province, China. Phylogenomic analysis clustered antibiotic-sensitive and resistant S. sonnei into two distinct groups while pan-genome analysis reveals the presence and absence of unique genes in each group. Screening of 31 classes of virulence factors found out that type 2 secretion system is doubled in resistant strains. Further principle component analysis based on the interactions between virulence and resistance indicated that abundant virulence factors are associated with higher levels of antibiotic resistance. The result present here is based on statistical analysis of a small sample size and serves basically as a guidance for further experimental and theoretical studies.

Epidemiologic characteristics and influencing factors of cluster infection of COVID-19 in Jiangsu Province.

To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary infection (5 days) was longer than that of patients without secondary infection (3 days) (F = 9.761, P = 0.002). Asymptomatic patients and non-familial clusters had impacts on the size of the clusters. The average reduction in the Rt value in family clusters (26.00%, 0.26 ± 0.22) was lower than that in other clusters (37.00%, 0.37 ± 0.26) (F = 4.400, P = 0.039). Early detection of asymptomatic patients and early reports of non-family clusters can effectively weaken cluster infections.

Epidemiology of 631 Cases of COVID-19 Identified in Jiangsu Province Between January 1st and March 20th 2020: Factors Associated with Disease Severity and Analysis of Zero Mortality.

BACKGROUND This retrospective study aimed to investigate the factors associated with disease severity and patient outcomes in 631 patients with COVID-19 who were reported to the Jiangsu Commission of Health between January 1 and March 20, 2020. MATERIAL AND METHODS We conducted an epidemiological investigation enrolling 631 patients with laboratory-confirmed COVID-19 from our clinic from January to March 2020. Patients' information was collected through a standard questionnaire. Then, we described the patients' epidemiological characteristics, analyzed risk factors associated with disease severity, and assessed causes of zero mortality. Additionally, some key technologies for epidemic prevention and control were identified. RESULTS Of the 631 patients, 8.46% (n=53) were severe cases, and no deaths were recorded (n=0). The epidemic of COVID-19 has gone through 4 stages: a sporadic phase, an exponential growth phase, a peak plateau phase, and a declining phase. The proportion of severe cases was significantly different among the 4 stages and 13 municipal prefectures (P<0.001). Factors including age >65 years old, underlying medical conditions, highest fever >39.0°C, dyspnea, and lymphocytopenia (<1.0×109/L) were early warning signs of disease severity (P<0.05). In contrast, earlier clinic visits were associated with better patient outcomes (P=0.029). Further, the viral load was a potentially useful marker associated with COVID-19 infection severity. CONCLUSIONS The study findings from the beginning of the COVID-19 epidemic in Jiangsu Province, China showed that patients who were more than 65 years of age and with comorbidities and presented with a fever of more than 39.0°C developed more severe disease. However, mortality was prevented in this initial patient population by early supportive clinical management.

Interventions in Live Poultry Markets for the Control of Avian Influenza: A Systematic Review and Meta-analysis.

BACKGROUND: This review aimed to provide constructive suggestions for the control and management of avian influenza through quantitative and qualitative evaluation of the impact of different live poultry market (LPM) interventions. METHODS: Both English and Chinese language databases were searched for articles that were published on or before 9 November 2018. After extraction and assessment of the included literature, Stata14.0 was applied to perform a meta-analysis to explore the impacts of LPM interventions. RESULTS: A total of 19 studies were identified. In total, 224 human, 3550 poultry, and 13 773 environment samples were collected before the intervention; 181 people, 4519 poultry, and 9562 environments were sampled after LPM interventions. Avian influenza virus (AIV) detection rates in the LPM environment (odds ratio [OR], 0.393; 95% confidence interval [CI], 0.262-0.589) and the incidence of AIV infection (OR, 0.045; 95% CI, 0.025-0.079) were significantly lower after LPM interventions, while interventions were not significantly effective in reducing AIV detection in poultry samples (OR, 0.803; 95% CI, 0.403-1.597). CONCLUSIONS: LPM interventions can reduce AIV human infections and the detection rate of AIV in market environments.

Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China.

BACKGROUND: Infectious diarrhea can lead to a considerable global disease burden. Thus, the accurate prediction of an infectious diarrhea epidemic is crucial for public health authorities. This study was aimed at developing an optimal random forest (RF) model, considering meteorological factors used to predict an incidence of infectious diarrhea in Jiangsu Province, China. METHODS: An RF model was developed and compared with classical autoregressive integrated moving average (ARIMA)/X models. Morbidity and meteorological data from 2012 to 2016 were used to construct the models and the data from 2017 were used for testing. RESULTS: The RF model considered atmospheric pressure, precipitation, relative humidity, and their lagged terms, as well as 1-4 week lag morbidity and time variable as the predictors. Meanwhile, a univariate model ARIMA (1,0,1)(1,0,0)52 (AIC = - 575.92, BIC = - 558.14) and a multivariable model ARIMAX (1,0,1)(1,0,0)52 with 0-1 week lag precipitation (AIC = - 578.58, BIC = - 578.13) were developed as benchmarks. The RF model outperformed the ARIMA/X models with a mean absolute percentage error (MAPE) of approximately 20%. The performance of the ARIMAX model was comparable to that of the ARIMA model with a MAPE reaching approximately 30%. CONCLUSIONS: The RF model fitted the dynamic nature of an infectious diarrhea epidemic well and delivered an ideal prediction accuracy. It comprehensively combined the synchronous and lagged effects of meteorological factors; it also integrated the autocorrelation and seasonality of the morbidity. The RF model can be used to predict the epidemic level and has a high potential for practical implementation.

A case-control study on the risk factors for hemorrhagic fever with renal syndrome.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is an endemic communicable disease in China, accounting for 90% of total reported cases worldwide. In this study, the authors want to investigate the risk factors for HFRS in recent years to provide the prevention and control advices. METHODS: A community-based, 1:2 matched case-control study was carried out to investigate the risk factors for HFRS. Cases were defined as laboratory-confirmed cases that tested positive for hantavirus-specific IgM antibodies. Two neighbourhood controls of each case were selected by sex, age and occupation. Standardized questionnaires were used to collect information and identify the risk factors for HFRS. RESULTS: Eighty-six matched pairs were investigated in the study. The median age of the cases was 55.0 years, 72.09% were male, and 73.26% were farmers. In the multivariate logistic regression analysis, cleaning spare room at home (OR = 3.310, 95%CI 1.335-8.210) was found to be risk factor for infection; storing food and crops properly (OR = 0.279 95%CI 0.097-0.804) provided protection from infection. CONCLUSION: Storing food and crops properly seemed to be protective factor, which was important for HFRS prevention and control. More attention should be paid to promote comprehensive health education and behaviour change among high-risk populations in the HFRS endemic area.

Outbreak of Vaccinia Virus Infection from Occupational Exposure, China, 2017.

Human infections with vaccinia virus (VACV), mostly from laboratory accidents or contact with infected animals, have occurred since smallpox was eradicated in 1980. No recent cases have been reported in China. We report on an outbreak of VACV from occupational exposure to rabbit skins inoculated with VACV.

Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China.

Influenza activity is subject to environmental factors. Accurate forecasting of influenza epidemics would permit timely and effective implementation of public health interventions, but it remains challenging. In this study, we aimed to develop random forest (RF) regression models including meterological factors to predict seasonal influenza activity in Jiangsu provine, China. Coefficient of determination (R2) and mean absolute percentage error (MAPE) were employed to evaluate the models' performance. Three RF models with optimum parameters were constructed to predict influenza like illness (ILI) activity, influenza A and B (Flu-A and Flu-B) positive rates in Jiangsu. The models for Flu-B and ILI presented excellent performance with MAPEs <10%. The predicted values of the Flu-A model also matched the real trend very well, although its MAPE reached to 19.49% in the test set. The lagged dependent variables were vital predictors in each model. Seasonality was more pronounced in the models for ILI and Flu-A. The modification effects of the meteorological factors and their lagged terms on the prediction accuracy differed across the three models, while temperature always played an important role. Notably, atmospheric pressure made a major contribution to ILI and Flu-B forecasting. In brief, RF models performed well in influenza activity prediction. Impacts of meteorological factors on the predictive models for influenza activity are type-specific.

Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.

BACKGROUND: Hand, foot and mouth disease (HFMD) is a rising public health problem and has attracted considerable attention worldwide. The purpose of this study was to develop an optimal model with meteorological factors to predict the epidemic of HFMD. METHODS: Two types of methods, back propagation neural networks (BP) and auto-regressive integrated moving average (ARIMA), were employed to develop forecasting models, based on the monthly HFMD incidences and meteorological factors during 2009-2016 in Jiangsu province, China. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were employed to select model and evaluate the performance of the models. RESULTS: Four models were constructed. The multivariate BP model was constructed using the HFMD incidences lagged from 1 to 4 months, mean temperature, rainfall and their one order lagged terms as inputs. The other BP model was fitted just using the lagged HFMD incidences as inputs. The univariate ARIMA model was specified as ARIMA (1,0,1)(1,1,0)12 (AIC = 1132.12, BIC = 1440.43). And the multivariate ARIMAX with one order lagged temperature as external predictor was fitted based on this ARIMA model (AIC = 1132.37, BIC = 1142.76). The multivariate BP model performed the best in both model fitting stage and prospective forecasting stage, with a MAPE no more than 20%. The performance of the multivariate ARIMAX model was similar to that of the univariate ARIMA model. Both performed much worse than the two BP models, with a high MAPE near to 40%. CONCLUSION: The multivariate BP model effectively integrated the autocorrelation of the HFMD incidence series. Meanwhile, it also comprehensively combined the climatic variables and their hysteresis effects. The introduction of the climate terms significantly improved the prediction accuracy of the BP model. This model could be an ideal method to predict the epidemic level of HFMD, which is of great importance for the public health authorities.

Surveillance for severe hand, foot, and mouth disease from 2009 to 2015 in Jiangsu province: epidemiology, etiology, and disease burden.

BACKGROUND: Severe hand, foot, and mouth disease (HFMD) is a common childhood illness caused by various enteroviruses. The disease has imposed increased burden on children younger than 5 years old. We aimed to determine the epidemiology, CNS complication, and etiology among severe HFMD patients, in Jiangsu, China. METHODS: Epidemiological, clinical, and laboratory data of severe HFMD cases were extracted from 2009 to 2015. The CNS complication, annually severe illness rates, mortality rates, severity-PICU admission rates, severity-hospitalization rates, and so on were analyzed to assess the disease burden of severe HFMD. All analyses were stratified by time, region, population, CNS involvement and serotypes. The VP1 gene from EV-A71, CV-A16, CV-A6, CV-A10 and other enteroviruses isolates was amplified. Phylogenetic analysis was performed using MEGA5.0. RESULTS: Seven thousand nine hundred ninety-four severe HFMD cases were reported, of them, 7224 cases were inpatients, 611 were PICU inpatients, and 68 were fatal. The average severe illness rate, mortality rate, severity-fatality rate, severity-PICU admission rate, and severity-hospitalization rate were 14.54, 0.12,8506, 76,430, and 903,700 per 1 million, respectively. The severe illness rate was the highest in the 12-23 months age group, and the greatest mortality rate was in the 6-11 months age group. Geographical difference in severe illness rate and mortality were found. Patients infected with EV-A71 were at a higher proportion in different CNS involvement even death. EV-A71, CV-A16 and other enteroviruses accounted for 79.14, 6.49, and 14.47%, respectively. A total of 14 non-EV-A71/ CV-A16 genotypes including CV-A2, CV-A4, CV-A 6, CV-A9, CV-A10, CV-B1, CV-B2, CV-B3, CV-B4, CV-B5, E-6, E-7, E-18, and EV-C96 were identified. Phylogentic analyses demonstrated that EV-A71 strains belonged to subgenotype C4a, while CV-A16 strains belonged to sub-genotype B1a and sub-genotype B1b of genotype B1. CV-A6 strains were assigned to genogroup F, and CV-A10 strains belonged to genogroup D. CONCLUSIONS: Future mitigation policies should take into account the age, region heterogeneities, CNS conditions and serotype of disease. Additional a more rigorous study between the mild and severe HFMD should be warranted to elucidate the difference epidemiology, pathogen spectrum and immunity patterns and to optimize interventions in the following study.

Clusters of Human Infection and Human-to-Human Transmission of Avian Influenza A(H7N9) Virus, 2013-2017.

To detect changes in human-to-human transmission of influenza A(H7N9) virus, we analyzed characteristics of 40 clusters of case-patients during 5 epidemics in China in 2013-2017. Similarities in number and size of clusters and proportion of clusters with probable human-to-human transmission across all epidemics suggest no change in human-to-human transmission risk.

Phylogenetic analysis of human G9P[8] rotavirus strains circulating in Jiangsu, China between 2010 and 2016.

Rotavirus A (RVA) is the leading cause of acute viral gastroenteritis in children under 5 years of age worldwide. G9P[8] is a common RVA genotype that has been persistently prevalent in Jiangsu, China. To determine the genetic diversity of G9P[8] RVAs, 7 representative G9P[8] strains collected from Suzhou Children's Hospital between 2010 and 2016 (named JS2010-JS2016) were analyzed through whole-genome sequencing. All evaluated strains showed the Wa-like constellation G9-P[8]-I1-R1-C1-M1-A1-N1-T1-E1-H1. Furthermore, phylogenetic analysis revealed that the VP7 genes of all strains clustered into lineage G9-III and G9-VI. With the exception of strain JS2012 (P[8]-4), the VP4 sequences of all strains belonged to the P[8]-3 lineage. Sequencing further revealed that amino acid substitutions were present in the antigenic regions of the VP7 and VP4 genes of all strains. Moreover, there were multiple substitutions in antigenic sites I and II of the nonstructural protein 4 (NSP4) genes, whereas the other NSP genes were relatively conserved. In conclusion, our phylogenetic analysis of these 7 G9P[8] strains suggests that RVA varied across regions and time. Therefore, our findings suggest that continued surveillance is necessary to explore the molecular evolutionary characteristics of RVA for better prevention and treatment of acute viral gastroenteritis.

Co-circulation of multiple genotypes of influenza A (H7N9) viruses in eastern China, 2016-2017.

Five epidemic waves of human infection with influenza A (H7N9) virus have emerged in China since spring 2013. We previously described the epidemiological characterization of the fifth wave in Jiangsu province. In this study, 41 H7N9 viruses from patients and live-poultry markets were isolated and sequenced to further elucidate the genetic features of viruses of the fifth wave in Jiangsu province. Phylogenetic analysis revealed substantial genetic diversity in the internal genes, and 18 genotypes were identified from the 41 H7N9 virus strains. Furthermore, our data revealed that 41 isolates from Jiangsu contained the G186V and Q226L/I mutations in their haemagglutinin (HA) protein, which may increase the ability of these viruses to bind the human receptor. Four basic amino acid insertions were not observed in the HA cleavage sites of 167 H7N9 viruses from Jiangsu, which revealed that highly pathogenic avian influenza (HPAI) H7N9 viruses did not spread to Jiangsu province in the fifth wave. These findings revealed that multiple genotypes of H7N9 viruses co-circulated in the fifth wave in Jiangsu province, which indicated that the viruses have undergone ongoing evolution with genetic mutation and reassortment. Our study highlights the need to constantly monitor the evolution of H7N9 viruses and reinforce systematic influenza surveillance of humans, birds, and pigs in China.

Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis.

BACKGROUND: With the increasing incidence of scrub typhus in recent years, it is of great value to analyse the spatial and temporal distribution of scrub typhus by applying micro-geographical studies at a reasonably fine scale, and to guide the control and management. METHODS: We explored the use of maximum entropy modelling method to confirm the spatial and temporal distribution of scrub typhus according to the occurrence locations of human cases in Jiangsu Province. The risk prediction map under specific environmental factors was therefore drawn by projecting the training model across China. The area under the curve and the omission rate were used to validate the model. Meanwhile, Jackknife tests were applied to enumerate the contribution of different environmental variables, then to predict the final model. The predicted results were validated by using China's known occurrence locations. RESULTS: A total of 566 occurrence locations with known 4865 scrub typhus occurrence records were used in our study. The number of female cases was higher than male cases, with a proportion of 1.17:1, and people in any age group could be infected. The number of cases presented an inverted-U relation with age. The percentage of cases aged from 60 to 69 years old was the highest, accounting for 30.50% of all cases. Ecological niche modelling results indicated that the locations of scrub typhus cases, which was of great importance in the disease transmission cycle, had a certain ecological niche with environmental elements in many dimensions. Moreover, the key environmental factors for determining scrub typhus occurrence were temperature (including temperature seasonality, min temperature of coldest month, mean diurnal range, and monthly mean temperature), precipitation of wettest month, and land cover types. The risk prediction maps indicated that mid-eastern China was the potential risk areas for scrub typhus of "autumn type". Meanwhile, in our results, Guangdong Province was the high-risk region for "autumn type" scrub typhus, where cases were mainly reported as "summer type". CONCLUSION: The combination of climatic and geographic factors with GIS methods is an appropriate option to analyse and estimate the spatial and temporal distribution of scrub typhus.

Significantly elevated number of human infections with H7N9 virus in Jiangsu in eastern China, October 2016 to January 2017.

Since first identified in 2013, the H7N9 virus has caused several waves of human infections in China, with a current wave including a number of patients with very severe disease. Jiangsu is one of the most impacted provinces, whereby as of 31 January 2017, the number of human infections (n = 109) in the ongoing fifth H7N9 wave has exceeded the sum of those in the four preceding ones. Ten of 13 cities in Jiangsu have been affected, and clustered infections as well as one co-infection with seasonal influenza have been observed. With a median age of 58 years and 74.3% (81/109) of patients being male, the characteristics of cases are similar to those in previous waves, however patients with H7N9 seem to have an accelerated disease progression. Preliminary case fatality remains above 30%. No significant viral mutations have been found in key functional loci. Environmental H7N9 detection rate and number of days with high risk ambient temperatures were both significantly elevated during the month of December 2016 when most human infections were reported. A number of municipal governments in Jiangsu have implemented live poultry market closures to impede viral transmission to humans. A detectable decline in human infections has been observed in these municipalities and the entire province since January 2017.

Comparison of Free Total Amino Acid Compositions and Their Functional Classifications in 13 Wild Edible Mushrooms.

Thirteen popular wild edible mushroom species in Yunnan Province, Boletus bicolor, Boletus speciosus, Boletus sinicus, Boletus craspedius, Boletus griseus, Boletus ornatipes, Xerocomus, Suillus placidus, Boletinus pinetorus, Tricholoma terreum, Tricholomopsis lividipileata, Termitomyces microcarpus, and Amanita hemibapha, were analyzed for their free amino acid compositions by online pre-column derivazation reversed phase high-performance liquid chromatography (RP-HPLC) analysis. Twenty free amino acids, aspartic acid, glutamic acid, serine, glycine, alanine, praline, cysteine, valine, methionine, phenylalanine, isoleucine, leucine, lysine, histidine, threonine, asparagines, glutamine, arginine, tyrosine, and tryptophan, were determined. The total free amino acid (TAA) contents ranged from 1462.6 mg/100 g in B. craspedius to 13,106.2 mg/100 g in T. microcarpus. The different species showed distinct free amino acid profiles. The ratio of total essential amino acids (EAA) to TAA was 0.13-0.41. All of the analyzed species showed high contents of hydrophobic amino acids, at 33%-54% of TAA. Alanine, cysteine, glutamine, and glutamic acid were among the most abundant amino acids present in all species. The results showed that the analyzed mushrooms possessed significant free amino acid contents, which may be important compounds contributing to the typical mushroom taste, nutritional value, and potent antioxidant properties of these wild edible mushrooms. Furthermore, the principal component analysis (PCA) showed that the accumulative variance contribution rate of the first four principal components reached 94.39%. Cluster analysis revealed EAA composition and content might be an important parameter to separate the mushroom species, and T. microcarpus and A. hemibapha showed remarkable EAA content among the 13 species.