Search

Salmonella is widely known as one of the most common foodborne pathogens, and antibiotics remain effective in clinical therapies against its infections. To guide better clinical antibiotic treatment, we analyzed the antimicrobial resistance (AMR) profiles of a nation-wide collection of 36,822 Salmonella isolates derived from sporadic diarrhea cases in China from 2014 to 2021. A panel of 15 antibiotics, including 10 critically important and 5 highly important antimicrobial agents for human medicine based on the WHO CIA List, was selected for AMR surveillance. Salmonella enterica serovar Typhimurium, Enteritidis, I 4,[5],12:i:-, London, and Stanley were turns to be the top five serotypes from human diarrhea cases in China. Antimicrobial susceptibility testing revealed that the majority of the isolates (87.2%) were resistant to at least one antimicrobial agent, and 66.5% were multidrug resistant (MDR). Salmonella isolates were found to be highly resistant to ampicillin (73.4%) while sensitive to imipenem (98.73%). Over the eight years, the isolates were demonstrated generally an increase in resistance to ampicillin, chloramphenicol, trimethoprim-sulfamethoxazole, and azithromycin, but displayed varied resistance profiles in terms of serotypes. Stanley (0.66–27.18%) and Agona (0.78–45.30%) had lower resistance rates compared to Typhimurium (1.11–85.6%), Enteritidis (1.55–91.29%), and I 4,[5],12:i:- (1.02–94.28%). In conclusion, our results provide systematic data on the resistance characteristics of Salmonella isolates from human diarrhea cases in China. Furthermore, this data identifies priorities for the clinical treatments of antibiotics.

Mobile colistin resistance (

This study aimed to understand the epidemiological characteristics of foodborne disease outbreaks related to meat and meat products in China from 2002 to 2017. Data collected from the National Foodborne Diseases Surveillance System and searched databases were analyzed. From 2002 to 2017, China reported 2815 outbreaks caused by foodborne diseases related to meat and meat products, resulting in 52,122 illnesses and 25,361 hospitalizations, and 96 deaths. Outbreaks were markedly seasonal and concentrated from May to September, accounting for 66.93%. Outbreaks were concentrated mainly in China's eastern coastal and southern regions. Unidimensional attribution analysis revealed that livestock meat was the most commonly implicated food category causing the outbreaks, accounting for 28.67%. Bacteria were the most common pathogenic cause of outbreaks, accounting for 51.94%.

Piezoelectric nanomaterials open new avenues in driving green catalysis processes (e.g., H

Introduction: Mushroom poisoning was the leading cause of foodborne disease outbreaks and outbreak-associated deaths in China. Mushroom poisoning outbreak surveillance can provide insight into the epidemiological characteristics of mushroom poisonings and guide policymaking and health education to reduce illnesses and deaths. Methods: Foodborne Disease Outbreak Surveillance System was upgraded in 2011 to collect foodborne disease outbreaks in China. Mushroom poisoning outbreaks during 2010-2020 were selected to analyze geographical distribution, seasonal distribution, and setting of food preparation. Results: A total of 10,036 outbreaks, which resulted in 38,676 illnesses and 788 deaths, were reported in this period. Mushroom poisonings occurred all over the country, but with highest incidence in the southwest and central China. Overall, 84.6% outbreaks were associated with food prepared in households, followed by 8.7% in street stalls, and 2.5% in canteens. Mushroom poisoning outbreaks clearly exhibited seasonality, and the peak season was summer through autumn. Outbreaks occurring between May and October accounted for 94.1% of total outbreaks, 92.4% illnesses, and 97.2% deaths. Conclusions: Mushroom poisoning is a food safety issue of higher concern in China. Targeted health education is essential to reduce mushroom poisoning, especially in southwest China. Citizens are advised to not collect or eat wild mushrooms.

What is already known on this topic? Vibrio parahaemolyticus (V. parahaemolyticus) is frequently resistant to common antimicrobials such as ampicillin and generally highly susceptible to most clinically used antimicrobials. What is added by this report? V. parahaemolyticus were highly resistant to cefazolin and ampicillin: 94.4% and 37.0%, respectively. However, it was below 3% resistance to all 10 other antimicrobials including clinically relevant agents and even imipenem. The overall levels of antimicrobial resistance and multidrug resistance were 95.1% and 3.3%, respectively. The distribution of antimicrobial resistance and the multidrug resistance had regional, temporal, sexual, and isolated source strain variation. What are the implications for public health practice? This study provides data on drug resistance of V. parahaemolyticus in Chinese clinical settings, which will help develop a public health strategy.

Background: The true incidence of acute gastrointestinal illness in China is underrecognized by surveillance systems. The aims of this study were to estimate the incidence and prevalence of self-reported AGI in the community of China, and to investigate sociodemographic and epidemiological determinants of AGI.Methods: We conducted a 12-months cross-sectional population-based survey in eight provinces of China during 2014-2015. The survey determined the prevalence and incidence of acute gastrointestinal illness (AGI) in the total permanent resident population in China according to the census of the population in 2010. The random multilevel population sample was stratified by geographic, population, and socioeconomic status. We used a recommended case definition of AGI, with diarrhea（three loose or watery stools）and/or any vomiting in a four-week recall. A face-to-face survey was conducted by selecting the member in the household with the most recent birthday. Results: Among 56,704 sampled individuals, 948 (1,134 person-time) fulfilled the case definition; 98.5% reported diarrhea. This corresponds to 2.3% (95% CI:1.9%-2.8%) of an overall standardized four-week prevalence and 0.3 (95% CI: 0.23-0.34) episodes per person-year of annual adjusted incidence rate. There was no significant difference between males and females. The incidence rates were higher mong urban residents, and in the spring and summer. In the whole study period, 50% of the cases sought medical care, of which 3.9% were hospitalized and 14.3% provided a biological sample for laboratory identification of the causative agent. Children aged 0–4 and young adults aged 15–24, people living in rural areas and people who traveled frequently had higher prevalence of AGI. Conclusion: Results showed that AGI represents a substantial burden in China, and will contribute to the estimation of the global burden of AGI. Complemented with data on the etiologies of AGI, these estimates will form the basis to estimate the burden of foodborne diseases in China.

Foodborne botulism is a rare, potentially fatal illness resulting from the ingestion of foods contaminated with preformed botulinum neurotoxin types A, B, E, or F, produced byData from 22 of 31 provincial-level administrative divisions (PLADs) of the National Foodborne Disease Outbreaks Surveillance System during 2004-2020 and Embase, China National Knowledge Infrastructure (CNKI), Wanfang Data, and Chinese Science and Technique Journals (CQVIP) from January 2004 to December 2020 to identify indexed publications in the Chinese literature using the following search terms "botulism," or "botulinum toxin," or "Clostridium botulinum." The number and proportion of outbreaks, illnesses, and deaths by PLAD, food types, and contributing factors were calculated.During 2004-2020, a total of 80 foodborne botulism outbreaks occurred in China, involving 386 illnesses and 55 deaths; most outbreaks were reported between June and August, with a sharp peak in January; 22 out of 31 PLADs reported foodborne botulism outbreaks, Xinjiang reported the largest number of outbreaks (20), followed by Qinghai (13); the most commonly implicated foods were home-prepared traditional processed stinky tofu and dried beef, accounting for 51.25% events. Improper processing and improper storage in contributing factors accounted for 77.50% outbreaks. Initial misdiagnosis occurred in 27.50% of cases.Outbreaks of foodborne botulism had a high case-fatality rate. Targeted food safety and popularization education to farmers and herdsmen in Xinjiang and Qinghai related to botulism prevention should be carried out, and timely outbreak investigation and hospital surge capacity should be improved.

Background Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management. Objective We aimed to design a spatial–temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases. Methods We designed a novel end-to-end framework to predict foodborne disease risk by using a multigraph structural long short-term memory neural network, which can utilize an encoder–decoder to achieve multistep prediction. In particular, to capture multiple spatial correlations, we divided regions by administrative area and constructed adjacent graphs with metrics that included region proximity, historical data similarity, regional function similarity, and exposure food similarity. We also integrated an attention mechanism in both spatial and temporal dimensions, as well as external factors, to refine prediction accuracy. We validated our model with a long-term real-world foodborne disease data set, comprising data from 2015 to 2019 from multiple provinces in China. Results Our model can achieve F1 scores of 0.822, 0.679, 0.709, and 0.720 for single-month forecasts for the provinces of Beijing, Zhejiang, Shanxi and Hebei, respectively, and the highest F1 score was 20% higher than the best results of the other models. The experimental results clearly demonstrated that our approach can outperform other state-of-the-art models, with a margin. Conclusions The spatial–temporal risk prediction model can take into account the spatial–temporal characteristics of foodborne disease data and accurately determine future disease spatial–temporal risks, thereby providing support for the prevention and risk assessment of foodborne disease.

Haff disease is an unexplained rhabdomyolysis syndrome occurring within 24 h of consumption of certain types of cooked fish or crustaceans. The current epidemiological status of crayfish-transmitted Haff disease in China was investigated by analyzing retrospective data linked with cases of the rhabdomyolysis syndrome recorded during the period 2016–2017. This information was acquired from the National Foodborne Disease Surveillance and Reporting System. These data were filtered, based on the disease definition and a markedly elevated serum creatine phosphokinase level, that is, fivefold or greater increase than normal levels. Data analysis indicated that crayfish-transmitted Haff disease was mainly concentrated in the middle and lower reaches of the Yangtze River Valley, and the current epidemiological curve was notable seasonal. Most patients were aged 20–49 years and the proportion of female patients was higher than male patients. Based on this initial study, it will be necessary to follow up the trace-back investigation of crayfish poisoning reports; further analyze the correlation between the aquatic environment of the associated crayfish together with establishing virulence factors of crayfish-transmitted Haff disease; and explore whether certain individuals have receptor pathways to trigger rhabdomyolysis syndrome.

BACKGROUND Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management. OBJECTIVE We aimed to design a spatial–temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases. METHODS We designed a novel end-to-end framework to predict foodborne disease risk by using a multigraph structural long short-term memory neural network, which can utilize an encoder–decoder to achieve multistep prediction. In particular, to capture multiple spatial correlations, we divided regions by administrative area and constructed adjacent graphs with metrics that included region proximity, historical data similarity, regional function similarity, and exposure food similarity. We also integrated an attention mechanism in both spatial and temporal dimensions, as well as external factors, to refine prediction accuracy. We validated our model with a long-term real-world foodborne disease data set, comprising data from 2015 to 2019 from multiple provinces in China. RESULTS Our model can achieve F1 scores of 0.822, 0.679, 0.709, and 0.720 for single-month forecasts for the provinces of Beijing, Zhejiang, Shanxi and Hebei, respectively, and the highest F1 score was 20% higher than the best results of the other models. The experimental results clearly demonstrated that our approach can outperform other state-of-the-art models, with a margin. CONCLUSIONS The spatial–temporal risk prediction model can take into account the spatial–temporal characteristics of foodborne disease data and accurately determine future disease spatial–temporal risks, thereby providing support for the prevention and risk assessment of foodborne disease.

BACKGROUND Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life. OBJECTIVE We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested. METHODS We extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy. RESULTS The gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens. CONCLUSIONS Data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

Based on the verified application in China's National Foodborne Disease Outbreak Surveillance System, machine learning techniques have been indicated to have very positive effects. We summarize current positive attempts on improving case reporting, helping diagnosing and outbreak prediction. Based on the attempts, we propose a general framework to facilitate future improvements for a more intelligent foodborne disease surveillance system, which can greatly help human health.

During 2013-2017, a total of 211 cases of listeriosis were reported by 64 sentinel hospitals in China to a national foodborne disease surveillance network. The average case-fatality rate was 31.2% for perinatal cases and 16.4% for nonperinatal cases. Sequence types 87 and 8 were the most prevalent types.

To improve the early detection of geographically dispersed common-source foodborne outbreaks, a national foodborne disease molecular tracing network (TraNet) was launched in 2013 based on the existing laboratory-based foodborne disease surveillance system in China. TraNet consists of 32 provincial and many municipal Centers for Disease Control and Prevention that conduct molecular subtyping using pulsed-field gel electrophoresis (PFGE) on foodborne pathogens submitted by sentinel hospitals within their jurisdictions, and submit these PFGE profiles to a national database for further analysis. Currently, TraNet plays a significant role in the identification of etiologic causes and tracks contaminated food during foodborne outbreaks.

Foodborne disease is one of the most important public health issues worldwide. China faces various and unprecedented challenges in all aspects of the food chain. Data from laboratory-based foodborne disease surveillance systems from 2013 to 2016, as well as different regions and ages, can be found along with differences in the patterns of pathogens detected with diverse characteristics. Vibrio parahaemolyticus has been the leading cause of infectious diarrhea in China, especially among adults in coastal regions. Salmonella has been a serious and widely distributed pathogen responsible for substantial socioeconomic burden. Shigella was mostly identified in Northwest China and the inland province (Henan) with less-developed regions among children under 5 years. Data from foodborne disease outbreak reporting system from 2011 to 2016 showed that poisonous animals and plant factors responsible for most deaths were poisonous mushrooms (54.7%) in remote districts in southwest regions. The biological hazard that caused most cases reported (42.3%) was attributed to V. parahaemolyticus, the leading cause of foodborne outbreaks. In this review, we summarize the recent monitoring approach to foodborne diseases in China and compare the results with those in developed countries.

Based on the angle of the "pathogenic bacteria-food", to analyze the cause of events in canteen from 2002 to 2016 in China, and to provide scientific basis for prevention and control of food borne diseases.Collect and finish the food borne disease outbreaks events in canteen from 2002 to 2016, do the descriptive analysis of the number of events, cases and deaths by the different cause food, pathogenic factors and link, in order to do the multi-dimensional attribution analysis.From 2002 to 2016, there were 2129 food poisoning events in canteen in China. By the analysis of single dimensional attribution, the main cause food were vegetables, meat and fungi, respectively, the total events of 46. 09%, 16. 91% and 10. 53%. Bio-contaminants was the primary cause of pathogenic factor, accounting for 34. 05%. Production and processing were the main trigger links, accounting for 43. 31%. By the multidimensional attribution analysis, the highest number of cases(426) caused by green beans contain saponi, there were 404 incidents caused by improper production and processing. Poisonous mushrooms caused 125 toxic incidents, among them, some incidents(75) were caused by accidental consumption and misuse.It is suggested to regard green bean as the main cause food, focus on the aspects of production and processing and strengthen supervision, in order to reduce the occurrence of disease.

Background Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life. Objective We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested. Methods We extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy. Results The gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens. Conclusions Data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

Foodborne disease remains a major public health problem worldwide. To understand the epidemiology and changes of foodborne disease in China, data reported to the National Foodborne Disease Outbreak Surveillance System during 2003–2017 were collected. A total of 19,517 outbreaks, which resulted in 235,754 illnesses, 107,470 hospitalizations, and 1,457 deaths, were reported in this period. Of the 13,307 outbreaks with known etiology, 31.8% of outbreaks were caused by poisonous mushrooms, followed by Vibrio parahaemolyticus (11.3%), saponin (8.0%), Salmonella (6.8%), nitrite (6.4%), pesticide (4.8%), Staphylococcus aureus (4.2%) and Bacillus cereus (3.0%). Among 18,955 outbreaks with reported setting, 46.6% were associated with food prepared in a household, followed by 22.5% with food prepared in a restaurant, and 18.4% prepared in a canteen. Of the 13,305 outbreaks associated with a single food category, fungi (mainly poisonous mushroom) were the most commonly implicated food category, followed by meats, vegetables, aquatic animals, condiments, poisonous plants (such as saponin, tung oil or seed, aconite) and grains (such as rice, noodle, rice noodle). Analysis of foodborne disease outbreaks can provide insight into the most important causative agents and sources of foodborne disease, and assist public health agencies determine the high-risk etiology and food pairs, specific points of contamination and settings to reduce foodborne disease illnesses.

Listeria monocytogenes, a ubiquitous bacterium in nature, can lead to human listeriosis through food consumption. Listeriosis is a rare, preventable, and treatable foodborne disease but can cause hospitalizations and fatalities. We reviewed the literature published in China to better understand the prevalence of L. monocytogenes in food products, incidence of human listeriosis, and characteristics of L. monocytogenes strains in China. The average prevalence of L. monocytogenes in Chinese food products in 28 provinces was 4.42%, with the highest prevalence of 8.91% in meat-poultry products, followed by aquatic animals, Chinese salad and salad, rice and flour products, and so on. Two hundred fifty-three invasive listeriosis cases were reported from 2011 to 2016 in 19 provinces, and the overall case-fatality rate was 25.7% with no deaths reported of pregnant women and children. L. monocytogenes strains were generally susceptible to most antibiotics, with ampicillin and penicillin G still effective in treatment. The predominant sequence types (STs) in food were ST9 and ST8, while clinically ST87 was most common ST in China. The national human listeriosis pilot surveillance started in 2013, and a total of 133 listeriosis cases have been collected until now. On the basis of the surveillance program, further research should be conducted to uncover the reason for the prevalence and pathogenic mechanism of the highly epidemiological hypervirulent ST87 strains in China.

This study firstly analyzed the antibiotic resistance, biochemical typing, and pulsed-field gel electrophoresis typing of 45 Bifidobacterium strains commonly used in health foods. Most strains were resistant to antibiotics but their antibiotic resistance rates were not high: Fos (56.52%), TET (43.48%), CRO (21.74%), AMC (15.22%), GEN (13.04%), RIF (10.87%), CHL (8.7%), CTX (6.52%), VAN (4.35%), and ERY (4.35%). The 45 strains could be divided into 14 pulsed-field gel electrophoresis types, of which the strain numbers of six pulsed-field gel electrophoresis types were more than one. All the Bifidobacterium strains could be divided into nine types by API50CHL biochemical identification. The same species displayed same biochemical typings, expect for B. animalis. Furthermore, the results confirmed that the same pulsed-field gel electrophoresis-type strains had closer antibiotic resistance patterns, and the same biochemical-type strain also had similar antibiotic resistance patterns.

Microbiological food safety surveillance is a system that collects data regarding food contamination by foodborne pathogens, parasites, viruses, and other harmful microbiological factors. It helps to understand the spectrum of food safety, timely detect food safety hazards, and provide relevant data for food safety supervision, risk assessment, and standards-setting. The study discusses the microbiological surveillance of food safety in China, and introduces the policies and history of the national microbiological surveillance system. In addition, the function and duties of different organizations and institutions are provided in this work, as well as the generation and content of the surveillance plan, quality control, database, and achievement of the microbiological surveillance of food safety in China.

Shiga toxin-producing Escherichia coli (STEC) O157 is an important foodborne pathogen. The aims of this study were to determine genetic relatedness of STEC O157 isolated from foods in China. STEC O157 isolates from food were characterized by virulence gene typing, antibiotyping, Multi-locus sequence typing (MLST), pulsed-field gel electrophoresis analysis (PFGE), Multi-locus variable number tandem repeat analysis (MLVA), and the single nucleotide polymorphism (SNP) clade typing. Of the 30 STEC O157 isolates analyzed, all isolates harbored eae, exhA, stx1 and/or stx2 genes with stx2c subtype predominating. By MLST, they were relatively homogenous with only 4 STs. PFGE and MLVA generated 22 pulsotyples and 23 patterns, respectively, which showed considerable diversity. Only one clade 8 isolate was detected. These results indicate that STEC O157 isolates from foods in China were heterogeneous. There was no correlation between genotypic characteristics and sources of isolates. Since different subtyping methods often give different discriminatory powers, the use of more than one subtyping approach is necessary in providing a more accurate picture of the genetic diversity of STEC O157. Four isolates were from ready-to-eat meat or salads, underscores the risk of infections. There is a need for surveillance of STEC O157 in foods and clinically and implementation of prevention strategies to prevent outbreaks of STEC O157 infections in China.

Vibrio vulnificus is a gram-negative bacterium that naturally occurs in estuarine waters and marine environment. Consumption of seafood contaminated with V. vulnificus or exposure to the contaminated water causes septicemia, gastroenteritis or wound infections in humans. This study aimed at identifying the prevalence of V. vulnificus in shrimps in Chinese seafood market. From June to September 2009, 239 samples encompassing six shrimp species were collected from ten cities in China. To quantify the prevalence and the levels of V. vulnificus in shrimps, the most probable number (MPN) method was used combining with the polymerase chain reaction (PCR) which was used to classify the isolates by detecting V. vulnificus hemolysin gene ( vvhA ). One hundred and forty out of 239 samples were positive for V. vulnificus , with the geometric mean level at 153.3 MPN/g. The prevalence and levels of V. vulnificus in shrimps varied in different geographic areas, with the values for the samples in the South cities (179.6 MPN/g) remarkably higher than that in the North cities (7.6 MPN/g). In addition, we found that 61.7% (79/128) of the V. vulnificus positive samples contained C-type (Clinical) strains and 8 contained E-type (Environmental) strains. The antimicrobial susceptibility of the isolates to 12 antibiotics was tested as well. Most of the 169 isolates remained susceptible to the majority of antimicrobials tested, whereas some strains tend to be less sensitive to amikacin, ampicillin, tetracycline and gentamicin. In conclusion, V. vulnificus is commonly found in retail marine shrimp in Chinese seafood market, and most of the shrimp samples contained C-type strains, which may pose a potential threat to human health.

The aim of this study was to analyze the antibiotic resistance and pulsed-field gel electrophoresis (PFGE) genotypes of Lactobacillus strains present in fermented dairy products available on the Chinese market and to analyze the correlation between these. A total of 33 Lactobacillus strains identified in fermented dairy products were tested for resistance to 16 antibiotics using the broth microdilution method and also analyzed by PFGE. Almost all of the strains were multidrug resistant and showed high resistance rates to fosfomycin (97.0%), chloramphenicol (87.9%), vancomycin (87.9%), ceftriaxone (81.8%), imipenem (66.7%), trimethoprim/sulfamethoxazone (54.6%), tetracycline (33.3%), cefotaxime (21.2%), gentamicin (18.2%), erythromycin (12.1%), gatifloxacin (12.1%), amoxicillin/clavulanic acid (12.1%), rifampin (9.1%), and clindamycin (9.1%). Based on the PFGE results, the 33 strains were subtyped into 17 pulsotypes (PFPs, pulsed-field profiles), of which six were represented by more than one strain each. In conclusion, the Lactobacillus strains identified here in fermented dairy products available on the Chinese market showed a high rate of antibiotic resistance. Lactobacillus strains were genotyped well by PFGE; the resistance patterns of the strains among the same PFP were not necessarily similar to each other.

To evaluate the antimicrobial resistance of Vibrio parahaemolyticus isolated from shellfish and its habitat in Sichuan, Fujian and Guangxi.The susceptibility of 186 isolates of Vibrio parahaemolyticus to 8 antibiotics were determined by broth microdilution susceptibility test. The antibiotics of ampicillin, cefotaxime, ceftazidime, gentamicin, tetracycline, chloramphenicol, ciprofloxacin and trimethoprim/sulfamethoxazole were used.The antibiotic resistance rate were 69.35% in which ampicillin resistance was the most prevalent. The geometric mean of ampicillin MIC value of all isolates was greater than the interpretation value of resistance. Among the 186 tested isolates, the multiple antibiotic resistance and/or intermediate resistance was 4. All strains were 100% sensitive to cefotaxime, ceftazidime, chloramphenicol and ciprofloxacin. The ampicillin susceptibility spectrum of aquaculture farm was the highest among the three sectors as 73.68% and Sichuan was the highest among the three provinces as 70.94% although there were no significant differences. There were 44 samples out of which 2 and above strains were isolated, and the susceptibility spectrum polymorphism rate of strains isolated from the same sample was 77.27% (34/44).The ampicillin resistance rate is relatively high, and shellfish habitat may be the main source of antimicrobial resistance of Vibrio parahaemolyticus. There is an urgent need to strengthen the surveillance and management of Vibrio parahaemolyticus in the shellfish breeding process.

To test the aerobic plate count examining capability of microbiology laboratories, to ensure the accuracy and comparability of quantitative bacteria examination results, and to improve the quality of monitoring.The 4 different concentration aerobic plate count piece samples were prepared and noted as I, II, III and IV. After homogeneity and stability tests, the samples were delivered to monitoring institutions. The results of I, II, III samples were logarithmic transformed, and evaluated with Z-score method using the robust average and standard deviation. The results of IV samples were evaluated as "satisfactory" when reported as10 CFU/piece or as "not satisfactory" otherwise. Pearson χ2 test was used to analyze the ratio results.309 monitoring institutions, which was 99.04% of the total number, reported their results. 271 institutions reported a satisfactory result, and the satisfactory rate was 87.70%. There was no statistical difference in satisfactory rates of I, II and III samples which were 81.52%, 88.30% and 91.40% respectively. The satisfactory rate of IV samples was 93.33%. There was no statistical difference in satisfactory rates between provincial and municipal CDC.The quality control program has provided scientific data that the aerobic plate count capability of the laboratories meets the requirements of monitoring tasks.

Vibrio parahaemolyticus is an important foodborne pathogen, of which the 03:K6 serotype caused many outbreaks in different countries since 1996. Based on the 10 years data (1992-2001) from China, gastroenteritis caused by Vibrio parahaemolyticus accounted for 31.1% of foodborne disease outbreaks that were resulted from microorganisms. Most environmental strains of Vibrio parahaemolyticus are non-pathogenic strains. However, clinical strains can producethermostable direct hemolysin (TDH), TDH-related hemolysin, and other virulence factors. Here we reviewed three commonly used molecular markers for Vibrio parahaemolyticus, including species-specific genes, the virulence genes and pandemic group-specific genes, so that to provide references for the rapid detection of Vibrio parahaemolyticus and the identification of its pathogenic factor.

Foodborne disease, a rapid-growing public health problem, has become the highest-priority topic for food safety. The threat of foodborne disease has stimulated interest in enhancing public health surveillance to detect outbreaks rapidly. To advance research on food risk assessment in China, China National Center for Food Safety Risk Assessment (CFSA) sponsored a project to construct an online correlation analysis system for foodborne disease surveillance beginning in October 2012. They collect foodborne disease clinical data from sentinel hospitals across the country. They want to analyze the foodborne disease outbreaks existed in the collected data and finally find the link between pathogen, incriminated food sources and infected persons. Rapid detection of outbreaks is a critical first step for the analysis. The purpose of this paper is to provide approaches that can be applied to an online system to rapidly find local and sporadic foodborne disease outbreaks out of the collected data. Specifically, we employ DBSCAN for local outbreaks detection and solve the parameter self-adaptive problem in DBSCAN. We also propose a new approach named K-CPS (K-Means Clustering with Pattern Similarity) to detect sporadic outbreaks. The experimental results show that our methods are effective for rapidly mining local and sporadic outbreaks from the dataset.