外送餐引起产气荚膜梭菌食物中毒流行病学调查分析

目的查明发生在某工厂的食源性疾病暴发的可疑食品、致病因子及危险因素等,并对事件调查过程中暴露出的问题进行探讨,为今后类似事件的防控和调查提供参考依据。方法病例定义为于2019年3月3日～3月4日期间在M工厂加班职工中发生腹痛、腹泻(≥3次/24 h)或呕吐症状之一者,运用描述性和分析性流行病学方法开展病例访谈和回顾性研究。收集病例的粪便标本、剩余食品和相关环境样品进行病原分离和采用聚合酶链式反应(PCR)检测阳性菌株的毒素基因。结果检索到病例106名,罹患率为73.6%(106/144),临床症状以腹泻(78.3%,83/106)、腹痛(78.3%,83/106)为主,部分半腹部胀气、胀痛(9.4%,10/106),无发热;流行曲线为点源暴露模式,潜伏期为2～22 h,可疑餐次为2019年3月3日的午餐;单因素分析和Logistic回归分析结果显示,发病与红烧鱼块[相对危险度(RR)=1.55, 95%置信区间(95%CI):1.29～1.85]、蒜苗肉丝(RR=1.26, 95%CI:1.01～1.57)和雪菜烧鸭血(RR=1.47, 95%CI:1.16～1.87)有关;在3份肛拭子、3份环境样品中检出产气荚膜梭菌,菌株和2份剩余食品均检测出α毒素和产气荚膜梭菌肠毒素(CPE)基因。送餐的D企业在加工经营中存在着被细菌污染并繁殖的条件。结论本次事件是因食用某供餐企业提供的配送餐引起的产气荚膜梭菌食物中毒,外送餐做好后应迅速冷却、低温储存,不能立即进食的,在食前要充分加热。     

一起疑似产气荚膜梭菌食物中毒事件的病原学分析

目的 分析一起疑似产气荚膜梭菌食物中毒事件的病原学。方法 利用荧光定量PCR方法对一起食物中毒患者的粪便进行产气荚膜梭菌基因的初筛, 根据荧光定量PCR初筛的提示结果进行细菌的分离培养, 对分离到的菌株进行质谱和生化鉴定、毒力基因检测和PFGE分析。结果 从5份食物中毒患者粪便中分离到5株携带肠毒素基因cpe的A型产气荚膜梭菌, 并且这5株菌的PFGE带型完全一致。结论 此次食物中毒致病因子为携带肠毒素基因cpe的A型产气荚膜梭菌, PFGE结果提示5株菌可能有同一来源。     

Evaluation of a predictive model for Clostridium perfringens growth during cooling

Proper temperature control is essential in minimizing Clostridium perfringens germination, growth, and toxin production. The U.S. Department of Agriculture Food Safety and Inspection Service offers two options for the cooling of meat products: follow a standard time-temperature schedule or validate that alternative cooling regimes result in no more than a 1-log CFU/g increase of C. perfringens and no growth of Clostridium botulinum. The Juneja 1999 model for C. perfringens growth during cooling may be helpful in determining whether the C. perfringens performance standard has been achieved, but this model has not been extensively validated. The objective of this study was to validate the Juneja 1999 model under a variety of temperature situations. The Juneja 1999 model for C. perfringens growth during cooling is fail safe when low (<1 log CFU/ml) or high (>3 log CFU/ml) observed increases occur during exponential cooling. The Juneja 1999 model consistently underpredicted growth at intermediate observed increases (1 to 3 log CFU/ml). The Juneja 1999 model also underpredicted growth whenever exponential cooling took place at two different rates in the first and second portions of the cooling process. This error may be due to faster than predicted growth of C. perfringens cells during cooling or to an inaccuracy in the Juneja 1999 model.     

Novel insights into the epidemiology of Clostridium perfringens type A food poisoning

Clostridium perfringens food poisoning ranks among the most common gastrointestinal diseases in developed countries. The disease is caused by C. perfringens enterotoxin (CPE) encoded by cpe and produced by less than 5% of C. perfringens type A strains. Molecular epidemiological research in the past 15 years has focused on the reservoirs and routes of cpe-positive C. perfringens aiming to clarify the role and epidemiology of chromosomal and plasmid-borne cpe-carrying strains. This literature review highlights novel aspects in the epidemiology of CPE-mediated diseases. We suggest that (1) chromosomal and plasmid-borne cpe-carrying C. perfringens strains are genetically and epidemiologically distinct and have adapted to different environments; (2) not only chromosomal but also plasmid-borne cpe-carrying C. perfringens strains cause food poisonings; (3) other CPE-mediated diseases, such as antibiotic-associated and sporadic diarrhea, associated with plasmid-borne cpe-positive strains, may be food-related; (4) the role of animals as the main reservoir of cpe-positive C. perfringens needs to be reconsidered; (5) humans serve as an important reservoir of cpe-positive C. perfringens, introducing a contamination risk into foods through handling; and (6) the current standard procedures to diagnose C. perfringens food poisoning fail to detect and isolate many C. perfringens strains, distorting the epidemiological understanding of C. perfringens food poisoning.     

Predictive model for growth of Clostridium perfringens during cooling of cooked cured chicken

Estimates of the growth kinetics of Clostridium perfringens from spores at temperatures applicable to the cooling of cooked cured chicken products are presented. A model for predicting relative growth of C. perfringens from spores during cooling of cured chicken is derived using a nonlinear mixed effects analysis of the data. This statistical procedure has not been used in the predictive microbiology literature that has been written for microbiologists. However, recently software systems have been including this statistical procedure. The primary growth curves, based on the stages of cell development, identify two parameters: (1) germination, outgrowth, and lag (GOL) time, or lag phase time; and (2) exponential growth rate, egr. The mixed effects model does not consider GOL and egr as constants, but as random variables that would, in all likelihood, differ for different cooling events with the same temperature. As such, it is estimated that the egr, for a given temperature, has a CV of approximately 19%. The model obtained by the mixed effects model is compared to the one obtained by the more traditional two-stage approach. The estimated parameters from the derived models are virtually the same. The model predicts, for example, a geometric mean relative growth of about 9·4 with an upper 95% confidence limit of 21·3 when cooling the product from 51°C to 12°C in 8 h, assuming log linear decline in temperature with time. C. perfringens growth from spores was not observed at a temperature of 12°C for up to 3 weeks.     

Predictive model for growth of Clostridium perfringens during cooling of cooked ground chicken

Traditional methodologies for development of microbial growth models under dynamic temperature conditions do not take into account the organism's history. Such models have been shown to be inadequate in predicting growth of the organisms under dynamic conditions commonly encountered in the food industry. The objective of the current research was to develop a predictive model for Clostridium perfringens spore germination and outgrowth in cooked chicken products during cooling by incorporating a function to describe the prior history of the microbial cell in the secondary model. Incorporating an assumption that growth kinetics depends in an explicit way on the cells' history could provide accurate estimates of growth or inactivation.Cooked, ground uncured chicken was inoculated with C. perfringens spores, and from this chicken, samples were formed and vacuum packaged. For the isothermal experiments, all samples were incubated in a constant temperature water baths stabilized at selected temperatures between 10 and 51 °C and sampled periodically. The samples were cooled from 54.4 to 27 °C and subsequently from 27 to 4 °C at different time periods (cooling rates) for dynamic cooling experiments. The standard model provided predictions that varied from the observed mean log₁₀ growth values by magnitudes up to about 0.65 log₁₀. However, for a selected memory model, estimates of log₁₀ relative growth provided predictions within 0.3 log₁₀ of the mean observed log₁₀ growth values. These findings point to an improvement of predictions obtained by memory models over those obtained by the standard model. More study though is needed to validate the selected model.Industrial relevanceMention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.     

Predictive model for growth of Clostridium perfringens during cooling of cooked ground pork

A predictive dynamic model for Clostridium perfringens spore germination and outgrowth in cooked pork products during cooling is presented. Cooked, ground pork was inoculated with C. perfringens spores and vacuum packaged. For the isothermal experiments, all samples were incubated in a water bath stabilized at selected temperatures between 10 and 51 °C and sampled periodically. For dynamic experiments, the samples were cooled from 54.4 to 27 °C and subsequently from 27 to 4 °C for different time periods, designated as x and y hours, respectively. The growth models used were based on a model developed by Baranyi and Roberts (1994), which incorporates a constant, referred to as the physiological state constant, q₀. The value of this constant captures the cells' history before the cooling begins. To estimate specific growth rates, data from isothermal experiments were used, from which a secondary model was developed, based on a particular form of Ratkowsky's 4-parameter equation. Using the data from dynamic experiments and the Ratkowsky model, an optimal value of q₀ (=0.01375) was derived minimizing the mean square error of predictions. However, using this estimate, the model had a tendency to over-predict relative growth when there was observed small amounts of relative growth, and under-predict relative growth when there was observed large relative growth. To provide more fail-safe estimates, rather than using the derived value of q₀, a value of 0.04 is recommended. The predictive model with this value of q₀ would provide more fail-safe estimates of relative growth and could aid producers and regulatory agencies with determining disposition of products that were subjected to cooling deviations.Industrial relevanceSafe time/temperature for cooling of cooked pork is very important to guard against the pathogen in cooked products. Predictive model will assist industry to determine compliance with regulatory performance standards and to ensure microbiological safety of cooked products.     

Predictive model for growth of Clostridium perfringens during cooling of cooked uncured beef

This paper considers growth models including one based on Baranyi's equations for growth and the other based on the logistic function. Using a common approach for constructing dynamic models for predicting Clostridium perfringens growth in ready-to-eat uncured beef during cooling, there was no appreciable difference between the models' predictions when the population of cells was within the lag or exponential phases of growth. The developed models can be used for designing safe cooling processes; however, the discrepancies between predicted and observed growths obtained in this study, together with discrepancies reported in other papers using the same, or similar methodology as used in this paper, point to a possible inadequacy of the derived models. In particular, the appropriateness of the methodology depends on the appropriateness of using estimated growth kinetics obtained from experiments conducted in isothermal environments for determining coefficients of differential equations that are used for predicting growth in constantly changing (dynamic) environments. The coefficients are interpreted as instantaneous specific rates of change that are independent of prior history. However, there is no known scientific reason that would imply the truth of this assumption. Incorporating a different, less restrictive assumption, allowing for a dependency on the prior history of cells for these kinetic parameters, might lead to models that provide more accurate estimates of growth. For example, a cooling scenario of 54.4-27 degrees C in 1.5h, the average predicted and observed log(10) relative growths were 1.1log(10) and 0.66log(10), respectively, a difference of 0.44log(10,) whereas, when assuming a particular dependency of history, the predicted value was 0.8log(10). More research is needed to characterize the behavior of growth kinetic parameters relative to prior history in dynamic environments.     

Predictive modeling and probabilistic risk assessment of Clostridium perfringens in hamburgers and sandwiches

This study aimed to develop a mathematical model for the survival of Clostridium perfringens in hamburgers and sandwiches and to evaluate their microbial risk. The primary model was developed in hamburgers using 4 strains of C. perfringens at 5, 10, 15, 25 and 37 °C, and the kinetic parameters of the primary model were fitted well with the Weibull model (R²?≥?0.95). The secondary model was developed and validated in hamburgers and sandwiches using the Davey model, which was evaluated by B_f, A_f, and RMSE values within the acceptable range. A probabilistic risk model was developed and simulated using @Risk program to estimate the probability of infection (P_inf) of C. perfringens based on the data on prevalence (n?=?100), time, temperature, and consumption of hamburgers and sandwiches (150.00?±?20.96 g). Based on the simulation model, the mean C. perfringens exposure dose was 0.00976 CFU/g, and the estimated mean P_inf was 1.78?×?10^–13, which was very low in comparison with the current available data. The proposed model and the result can thus be useful to establish risk management options and microbial criteria for C. perfringens contamination in hamburgers and sandwiches.

     

Development of a model to predict growth of Clostridium perfringens in cooked beef during cooling

The objective of this work was to develop a new model to predict the growth of Clostridium perfringens in cooked meat during cooling. All data were collected under changing temperature conditions. Individual growth curves were fit using DMFit. Germination outgrowth and lag (GOL) time was modeled versus temperature at the end of GOL using conservative assumptions. Each growth curve was used to estimate a series of exponential growth rates at a series of temperatures. The squareroot model was used to describe the relationship between the square root of the average exponential growth rate and effective temperature. Predictions from the new model were in close agreement with the data used to create the model. When predictions from the model were compared with new observations, fail-dangerous predictions were made a majority of the time. When GOL time was predicted exactly, many fail-dangerous predictions shifted toward the fail-safe direction. Two important facts regarding C. perfringens should impact future modeling research with this organism and may have broader food safety policy implications: (i) the normal variability in the response of the organism from replicate to replicate may be quite large (1 log CFU) and may exceed the current U.S. Food Safety Inspection Service performance standard, and (ii) the accuracy of the GOL time model has a profound influence upon the overall prediction, with small differences in GOL time prediction (approximately 1 h) having a very large effect on the predicted final concentration of C. perfringens.     

Predictive model for growth of Clostridium perfringens during cooling of cooked uncured meat and poultry

Comparison of Clostridium perfringens spore germination and outgrowth in cooked uncured products during cooling for different meat species is presented. Cooked, uncured product was inoculated with C. perfringens spores and vacuum packaged. For the isothermal experiments, all samples were incubated in a water bath stabilized at selected temperatures between 10 and 51 °C and sampled periodically. For dynamic experiments, the samples were cooled from 54.4 to 27 °C and subsequently from 27 to 4 °C for different time periods, designated as x and y hours, respectively. The growth models used were based on a model developed by Baranyi and Roberts (1994. A dynamic approach to predicting bacterial growth in food. Int. J. Food Micro. 23, 277-294), which incorporates a constant, referred to as the physiological state constant, q₀. The value of this constant captures the cells’ history before the cooling begins. To estimate specific growth rates, data from isothermal experiments were used, from which a secondary model was developed, based on a form of Ratkowsky’s 4-parameter equation. The estimated growth kinetics associated with pork and chicken were similar, but growth appeared to be slightly greater in beef; for beef, the maximum specific growth rates estimated from the Ratkowsky curve was about 2.7 log₁₀ cfu/h, while for the other two species, chicken and pork, the estimate was about 2.2 log₁₀ cfu/h. Physiological state constants were estimated by minimizing the mean square error of predictions of the log₁₀ of the relative increase versus the corresponding observed quantities for the dynamic experiments: for beef the estimate was 0.007, while those for pork and chicken the estimates were about 0.014 and 0.011, respectively. For a hypothetical 1.5 h cooling from 54 °C to 27° and 5 h to 4 °C, corresponding to USDA-FSIS cooling compliance guidelines, the predicted growth (log₁₀ of the relative increase) for each species was: 1.29 for beef; 1.07 for chicken and 0.95 log₁₀ for pork. However, it was noticed that for pork in particular, the model using the derived q₀ had a tendency to over-predict relative growth when the observed amount of relative growth was small, and under-predict the relative growth when the observed amount of relative growth was large. To provide more fail-safe estimate, rather than using the derived value of q₀, a value of 0.04 is recommended for pork.     

Inhibitory effects of nisin against Clostridium perfringens food poisoning and nonfood-borne isolates

The enterotoxigenic Clostridium perfringens type A is the causative agent of C. perfringens type A food poisoning (FP) and nonfood-borne (NFB) human gastrointestinal diseases. Due to its ability to form highly resistant endospores, it has become a great concern to the meat industry to produce meat free of C. perfringens. In this study, we evaluated the antimicrobial effect of nisin against C. perfringens FP and NFB isolates. No inhibitory effect of nisin was observed against germination of spores of both FP and NFB isolates in laboratory medium. However, nisin effectively arrested outgrowth of germinated spores of C. perfringens in rich medium. Interestingly, germinated spores of NFB isolates possessed higher resistance to nisin than that of FP isolates. Furthermore, nisin exhibited inhibitory effect against vegetative growth of both FP and NFB isolates in laboratory medium, with vegetative cells of NFB isolates showing higher resistance than that of FP isolates. However, the antimicrobial activity of nisin against C. perfringens was significantly decreased in a meat model system. In conclusion, although nisin showed inhibitory effect against spore outgrowth and vegetative cells of C. perfringens FP and NFB isolates in laboratory conditions, no such effect was observed against C. perfringens spores inoculated into a meat model system.     

Clostridium perfringens: A Dynamic Foodborne Pathogen

Clostridium perfringens is a spore-forming bacterium and natural inhabitant of soil and the intestinal tracts of many warm-blooded animals, including humans. The ubiquitous nature of this bacterium and its spores makes it a frequent problem for the food industry and establishments where large amounts of food are prepared. C. perfringens causes potentially lethal foodborne diseases in humans, including food poisoning and necrotic enteritis. This bacterium could be controlled properly following safety rules such as adequate heating and cooling of food during processing. Unfortunately, large C. perfringens outbreaks, sometimes with fatal outcomes are still frequently reported. This paper describes the main characteristics of C. perfringens that allow the bacterium to survive and grow in foods, and cause human disease as well as discusses strategies to control this microorganism during food processing.     

Growth potential of Clostridium perfringens during cooling of cooked meats

Many meat-based food products are cooked to temperatures sufficient to inactivate vegetative cells of Clostridium perfringens, but spores of this bacterium can survive, germinate, and grow in these products if sufficient time, temperature, and other variables exist. Because ingestion of large numbers of vegetative cells can lead to concomitant sporulation, enterotoxin release in the gastrointestinal tract, and diarrhea-like illness, a necessary food safety objective is to ensure that not more than acceptable levels of C. perfringens are in finished products. As cooked meat items cool they will pass through the growth temperature range of C. perfringens (50 to 15 degrees C). Therefore, an important step in determining the likely level of C. perfringens in the final product is the estimation of growth of the pathogen during cooling of the cooked product. Numerous studies exist dealing with just such estimations, yet consensual methodologies, results, and conclusions are lacking. There is a need to consider the bulk of C. perfringens work relating to cooling of cooked meat-based products and attempt to move toward a better understanding of the true growth potential of the organism. This review attempts to summarize observations made by researchers and highlight variations in experimental approach as possible explanations for different outcomes. An attempt is also made here to identify and justify optimal procedures for conducting C. perfringens growth estimation in meat-based cooked food products during cooling.     

Dynamic computer simulation of Clostridium perfringens growth in cooked ground beef

The objective of this study was to develop a computer simulation algorithm to dynamically estimate and predict the growth of Clostridium perfringens in cooked ground beef. The computational algorithm was based on the implicit form of the Gompertz model, the growth kinetics of C. perfringens in cooked ground beef, and the fourth-order Runge-Kutta numerical method. This algorithm was validated using a cocktail of three strains of C. perfringens spores grown under isothermal, square-waved, linear cooling, and exponential cooling temperature profiles. In general, the results of computer simulation matched closely with the experimental data with the absolute errors less than 0.5 log(10) CFU/g. This method may be a useful tool for the food industry, regulatory agencies, distributors, and retailers to predict the effect of temperature abuse on the microbial safety of C. perfringens and other foodborne pathogens in processed meat products.     

Predictive model for growth of Clostridium perfringens in cooked cured pork

Mathematical models have been developed and used for predicting growth of foodborne pathogens in various food matrices. However, these early models either used microbiological media or other model systems to develop the predictive models. Some of these models have been shown to be inaccurate for applications in meat and specific food matrices, especially under dynamic conditions, such as constantly changing temperatures that are encountered during food processing. The objective of this investigation was to develop a model for predicting growth of Clostridium perfringens from spore inocula in cured pork ham. Isothermal growth of C. perfringens at various temperatures from 10 to 48.9 degrees C were evaluated using a methodology that employed a numerical technique to solve a set of differential equations. The estimated theoretical minimum and maximum growth temperatures of C. perfringens in cooked cured pork were 13.5 and 50.6 degrees C, respectively. The kinetic and growth parameters obtained from this study can be used in evaluating growth of C. perfringens from spore populations during dynamically changing temperature conditions such as those encountered in meat processing. Further, this model can be successfully used to design microbiologically "safe" cooling regimes for cured pork hams and similar products.     

Optimizing sporulation of Clostridium perfringens

Many sporulation media have been developed for Clostridium perfringens, but none stimulates sporulation for all strains. The aim of our experiments was to develop a sporulation method using Duncan and Strong (DS) medium, which supports sporulation of a wide variety of strains. Different inoculation levels were tested, and the effects of sporulation-promoting substances and acid shock were evaluated. Furthermore, DS medium was compared with other sporulation media. Highest spore numbers in DS medium were obtained with a 10% 24-h fluid thioglycollate broth inoculum (5.0 x 10(5)/ml). Addition of theophylline and replacement of starch by raffinose increased spore yields for some strains, but most strains were not affected (average increases in log N/ml of 0.2 and 0.3, respectively). One strain was enhanced by the addition of bile, but other strains were strongly inhibited (average decrease in log N/ml of 2.5); agar did not influence sporulation. Neither short-time acid exposure nor addition of culture supernatant fluids of well-sporulating strains resulted in higher spore numbers in DS medium. None of the tested methods enhanced sporulation in general; only strain-dependent effects were obtained. Peptone bile theophylline medium was the most promising sporulation medium tested; peptone bile theophylline starch medium yielded highest spore numbers (2.5 x 10(5)/ml), but some strains failed to sporulate. In conclusion, adding theophylline to DS medium may optimize sporulation of C. perfringens, but peptone bile theophylline medium with or without starch is most suitable.