Natural history and growth prediction model of pancreatic serous cystic neoplasms.

OBJECTIVE: Serous cystic neoplasms (SCN) are benign pancreatic cystic neoplasms that may require resection based on local complications and rate of growth. We aimed to develop a predictive model for the growth curve of SCNs to aid in the clinical decision making of determining need for surgical resection. METHODS: Utilizing a prospectively maintained pancreatic cyst database from a single institution, patients with SCNs were identified. Diagnosis confirmation included imaging, cyst aspiration, pathology, or expert opinion. Cyst size diameter was measured by radiology or surgery. Patients with interval imaging ≥3 months from diagnosis were included. Flexible restricted cubic splines were utilized for modeling of non-linearities in time and previous measurements. Model fitting and analysis were performed using R (V3.50, Vienna, Austria) with the rms package. RESULTS: Among 203 eligible patients from 1998 to 2021, the mean initial cyst size was 31 mm (range 5-160 mm), with a mean follow-up of 72 months (range 3-266 months). The model effectively captured the non-linear relationship between cyst size and time, with both time and previous cyst size (not initial cyst size) significantly predicting current cyst growth (p < 0.01). The root mean square error for overall prediction was 10.74. Validation through bootstrapping demonstrated consistent performance, particularly for shorter follow-up intervals. CONCLUSION: SCNs typically have a similar growth rate regardless of initial size. An accurate predictive model can be used to identify rapidly growing outliers that may warrant surgical intervention, and this free model (https://riskcalc.org/SerousCystadenomaSize/) can be incorporated in the electronic medical record.

Predictive growth modeling of Yersinia enterocolitica in fresh kimchi cabbage brassica pekinensis as a function of storage temperature.

We developed a predictive growth model of Yersinia enterocolitica for fresh Kimchi cabbages as a function of storage temperature (5-20 °C). The Baranyi equation used for primary modeling at these storage temperatures was suitable as a model for obtaining lag time (LT) and specific growth rate (SGR) (R2 = 0.97-0.98). As the temperature increased, the growth of Y. enterocolitica tended to increase, with SGR values of 0.33, 0.40, 0.60 and 0.68 log colony-forming units/h at 8, 11, and 15 °C, and LT values of 5.63, 3.54, 2.23 and 1.09 h, respectively. The secondary model was determined by the non-linear regression analysis. The suitability of the modeling results for the SGR and LT value was verified by determining the mean square error (<0.01), bias factor (0.919-0.999), and accuracy factor (1.032-1.136). The predicted models can be used to predict the growth of Y. enterocolitica in Kimchi cabbage at various temperatures and as an effective tool for maintaining the safe level of Y. enterocolitica in the production, processing, and distribution of fresh agricultural products.

[Analysis of Maximum Growth Rate and Construction of Predictive Growth Model for Bacillus cereusin Mashed Potato by Calorimetric Method].

The maximum growth rate (µmax) of Bacillus cereus was estimated using a non-destructive isothermal calorimetric method, and a growth prediction model was constructed based on the measurement results. SCD medium and mashed potato were inoculated with serial-diluted inoculum of B. cereus. Heat generation curves were determined using an isothermal calorimeter at 35, 25, and 15℃. The µmax was determined from the relationship between the increase in B. cereus cell number and incubation time, which was detected through the heat generation of the B. cereus biological process. Moreover, the growth prediction model was constructed using Ratkowsky's square-root model. The results of the growth prediction model based on the data of the calorimetric and conventional culture methods for SCD were expressed as √µCalmax=0.0354 (T-4.9)[R2=0.99] and √µCCMmax=0.0335 (T-5.0)[R2=0.99]; a similar equation was provided by both methods. Conversely, the results of the growth prediction model based on the calorimetric method data for mashed potato were given as √µCalmax=0.0390 (T-8.5)[R2=0.99]; the maximum growth rates at 30 and 20℃ were predicted as 0.70 and 0.20 (1/hr), respectively. The maximum growth rates obtained using the conventional culture method were 0.63 and 0.29 (1/hr), respectively, similar to the calorimetric method results. The predictive microbiological analysis using the calorimetric method enabled the rapid provision of a growth prediction equation, and the number of samples could be substantially reduced compared with that for the conventional culture method.

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork.

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R2 of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R2 ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and µmax (all R2 ≥ 0.97) or Lag (all R2 ≥ 0.98). Growth models were validated by the actual test values, with Bf and Af close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (105 CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

Evaluation of microbiological quality and safety of fresh-cut fruit products at retail levels in Korea.

The risk of foodborne illnesses caused by pathogens could be increased in fresh-cut fruit products owing to contamination during processing. Therefore, this study was conducted to investigate the microbiological quality and safety of commercial fresh-cut fruit products in Korea. Additionally, the growth of Listeria monocytogenes in selected fresh-cut fruits was evaluated, and their growth curves were analyzed using predictive growth modeling. The mean count of total aerobic bacteria, coliforms, and yeast/mold was 3.67±1.73 log10 CFU/g, 1.54±1.01 log10 CFU/g, and 3.81±1.51 log10 CFU/g, respectively. Escherichia coli, Staphylococcus aureus, Escherichia coli O157:H7, L. monocytogenes, Salmonella spp., and Cyclospora spp. were not detected in any of the tested samples. Only Bacillus cereus was detected in a few samples at the mean level of 1.72±0.13 log10 CFU/g. The growth of L. monocytogenes varied depending on the type of fruit; they grew well in non-acidic fresh-cut fruit products during storage at 10 °C.

Estimating Listeria monocytogenes Growth in Ready-to-Eat Chicken Salad Using a Challenge Test for Quantitative Microbial Risk Assessment.

Currently, there is a growing preference for convenience food products, such as ready-to-eat (RTE) foods, associated with long refrigerated shelf-lives, not requiring a heat treatment prior to consumption. Because Listeria monocytogenes is able to grow at refrigeration temperatures, inconsistent temperatures during production, distribution, and at consumer's household may allow for the pathogen to thrive, reaching unsafe limits. L. monocytogenes is the causative agent of listeriosis, a rare but severe human illness, with high fatality rates, transmitted almost exclusively by food consumption. With the aim of assessing the quantitative microbial risk of L. monocytogenes in RTE chicken salads, a challenge test was performed. Salads were inoculated with a three-strain mixture of cold-adapted L. monocytogenes and stored at 4, 12, and 16 °C for eight days. Results revealed that the salad was able to support L. monocytogenes' growth, even at refrigeration temperatures. The Baranyi primary model was fitted to microbiological data to estimate the pathogen's growth kinetic parameters. Temperature effect on the maximum specific growth rate (µmax ) was modeled using a square-root-type model. Storage temperature significantly influenced µmax of L. monocytogenes (p < 0.05). These predicted growth models for L. monocytogenes were subsequently used to develop a quantitative microbial risk assessment, estimating a median number of 0.00008726 listeriosis cases per year linked to the consumption of these RTE salads. Sensitivity analysis considering different time-temperature scenarios indicated a very low median risk per portion (<-7 log), even if the assessed RTE chicken salad was kept in abuse storage conditions.

Predictive Growth Model of Listeria monocytogenes Under Fluctuating Temperature Conditions in Pasteurized Milk by Using Real-Time Polymerase Chain Reaction.

The aim of this study was to evaluate the application of real-time polymerase chain reaction (PCR)-based quantification as a rapid and accurate tool for the monitoring and prediction of Listeria monocytogenes growth in pasteurized milk under constant and fluctuating temperature conditions. The growth of L. monocytogenes was monitored under constant temperature conditions at 4°C, 10°C, 15°C, 20°C, and 35°C. High correlation was obtained between the bacterial growth rate and incubation temperature, where the R2 of the slope of the square root model was calculated to be 0.993 and 0.996 for real-time PCR and the conventional culture method, respectively. Moreover, the obtained maximum specific growth rate (µmax) data plots were correlated with 188 L. monocytogenes µmax data points from the existing model according to ComBase database, with an R2 of 0.961 for real-time PCR and of 0.931 for the conventional culture method. The growth models were examined under three different patterns of fluctuating temperature conditions ranging from 2°C to 30°C. The prediction results fell within ±20% of the relative error zone, showing that real-time PCR quantification could be used for fast, sensitive, and specific bacterial growth monitoring with high-throughput results. Real-time PCR should be considered a promising option and powerful tool for the construction of a bacterial growth prediction model for safety risk analysis in the dairy industry.

Assessment of Escherichia coli O157:H7 growth in ground beef in the Greek chill chain.

A predictive mathematical model of the effect of temperature (10-47 °C) on the growth of Escherichia coli O157:H7 in natural contaminated ground beef was developed. The estimated values for the cardinal parameters Tmin, Tmax, Topt and the optimum maximum specific growth rate (µopt) of E. coli O157:H7 were found to be 3.36, 46.87, 43.16 °C and 1.385/h, respectively. The developed model was further validated against observed growth of E. coli O157:H7 in ground beef at non - isothermal chilling conditions by using two periodically changing temperature profiles with temperature ranging from 0 to 15 °C. Overall the model predicted satisfactorily the growth of E. coli O157:H7 in ground beef at dynamic temperature conditions. The model was combined with temperature data collected from ground beef chill chain in Greece in order to assess the growth of the pathogen from purchase of the product at retail to consumption. Retail storage average temperature from 50 retail cabinets in Greek super markets ranged from 0.1 to 7.4 °C with a mean of 3.2 °C and a mean standard deviation of 1.7 °C. The predicted growth of the pathogen after 7 days of storage at retail ranged between 0 and 2.03 log10 CFU/g, with an average growth to 0.31 log10 CFU/g. The growth of the pathogen during transportation from retail to domestic refrigerators ranged between 0.03 and 0.45 log10 CFU/g, with an average growth to 0.16 log10 CFU/g. The average temperature of 160 domestic refrigerators ranged from -2.7 to 18.1 °C. Differences in the temperature among the shelves of the refrigerators were observed. The predicted growth of E. coli O157:H7 in ground beef stored in domestic refrigerators for 1 day ranged between 0.00 and 2.3 log10 CFU/g. For a scenario storage of ground beef in retail for 3 days, transportation from retail domestic refrigerators over a period of 6 h and storage in domestic refrigerators for 3 days the 99th percentile of the total growth was 4.83 log10 CFU/g for storage at the upper self of the domestic refrigerator. The data and models provided in the present work can be further used in a quantitative risk assessment model of E. coli O157:H7 in ground beef consumed in Greece.

Predictive Modeling for the Growth of Aeromonas hydrophila on Lettuce as a Function of Combined Storage Temperature and Relative Humidity.

This study developed predictive growth models of Aeromonas hydrophila on lettuce as a function of combined storage temperature (15-35°C) and relative humidity (RH, 60-80%) using a polynomial equation. The primary model of specific growth rate, lag time, and maximum population density showed a good fit (R2 ≥ 0.95) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary model was verified by mean square error (0.0001-0.8848), bias factor (Bf = 0.962-1.009), and accuracy factor (Af = 1.002-1.104). The newly developed secondary models for A. hydrophila could be incorporated into the tertiary modeling program to predict the growth of A. hydrophila as a function of combined temperature and RH. The developed model may be useful to predict potential A. hydrophila growth on lettuce, which is important for food safety purpose during the overall food chain of lettuce from farm to table. It could offer reliable and useful information of growth kinetics for the quantification of microbial risk assessment of A. hydrophila on lettuce.

Growth kinetics of Escherichia coli O157: H7 on the epicarp of fresh vegetables and fruits

ABSTRACT Despite the increasing reports on the incidence of fresh vegetables and fruits as a possible vehicle for human pathogens, there is currently limited knowledge on the growth potential of Escherichia coli O157:H7 on different plant substrates. This study analyzed the selective adhesion and growth of E. coli O157:H7 on chili habanero (Capsicum chinense L.), cucumber (Cucumis sativus), radish (Raphanus sativus), tomato (Lycopersicon esculentum), beet (Beta vulgaris subsp. vulgaris), and onion (Allium cepa L.) under laboratory conditions. The Gompertz parameters were used to determine the growth kinetics. Scanning electron microscopy was used to visualize the adhesion of E. coli O157:H7 on the epicarp of the samples. Predictive models were constructed to compare the growth of E. coli O157:H7 on the samples with different intrinsic factors and to demonstrate the low selectivity of the pathogen. No significant difference was observed in the lag-phase duration (LPD), generation time (GT), and exponential growth rate (EGR) of the pathogen adhered to the samples. The interaction between the microorganism and the substrate was less supportive to the growth of E. coli O157:H7 for onion, whereas for tomato and cucumber, the time for the microorganism to attain the maximum growth rate (M) was significantly longer than that recorded for other samples.

Growth kinetics of Escherichia coli O157:H7 on the epicarp of fresh vegetables and fruits.

Despite the increasing reports on the incidence of fresh vegetables and fruits as a possible vehicle for human pathogens, there is currently limited knowledge on the growth potential of Escherichia coli O157:H7 on different plant substrates. This study analyzed the selective adhesion and growth of E. coli O157:H7 on chili habanero (Capsicum chinense L.), cucumber (Cucumis sativus), radish (Raphanus sativus), tomato (Lycopersicon esculentum), beet (Beta vulgaris subsp. vulgaris), and onion (Allium cepa L.) under laboratory conditions. The Gompertz parameters were used to determine the growth kinetics. Scanning electron microscopy was used to visualize the adhesion of E. coli O157:H7 on the epicarp of the samples. Predictive models were constructed to compare the growth of E. coli O157:H7 on the samples with different intrinsic factors and to demonstrate the low selectivity of the pathogen. No significant difference was observed in the lag-phase duration (LPD), generation time (GT), and exponential growth rate (EGR) of the pathogen adhered to the samples. The interaction between the microorganism and the substrate was less supportive to the growth of E. coli O157:H7 for onion, whereas for tomato and cucumber, the time for the microorganism to attain the maximum growth rate (M) was significantly longer than that recorded for other samples.

Development of an absorbance-based response model for monitoring the growth rates of Arcobacter butzleri as a function of temperature, pH, and NaCl concentration.

In this study, the growth of Arcobacter butzleri in poultry was evaluated as a function of storage temperature (5, 22.5, and 40°C), pH (5, 7, and 9), and NaCl concentration (0, 4, and 8%). A predictive model was developed using the absorbance-based response surface methodology to describe the growth rate. The primary model was obtained to predict a growth rate with a good fit (R2≥0.95), and the secondary model was obtained by nonlinear regression analysis and calculated as follows: Growth rate=-2.267274-0.024181 (Temp)+0.6459384 (pH)+0.1926227 (NaCl)+0.0024661 (Temp×pH)-0.001312 (Temp×NaCl)-0.018802 (pH×NaCl)+0.000467 (Temp2)-0.041711 (pH2)- 0.007426 (NaCl2). Our data showed that the growth of A. butzleri can be completely inhibited at a pH of 5 (in the absence of NaCl, at 5°C) and at a pH of 9 (in the presence of 8% NaCl, at 5°C). The surface response model was statistically significant, with P<0.0001, as evident from the Fisher F test and from coefficient determination (R2, 0.95). This model was also verified by the bias factor (Bf, 0.839), accuracy factor (Af, 1.343), and mean square error (MSE, 0.0138). The newly developed secondary models of growth rate for A. butzleri could possibly be incorporated into a tertiary modeling program such as Pathogen Modeling Program (U.S. Department of Agriculture [USDA]) and Food Micro Model (in the United Kingdom). As a result, they could be used to predict the growth kinetics of A. butzleri as a function of a combination of environmental factors. Ultimately, the developed model can be used to reduce A. butzleri in poultry production, processing, and distribution, thereby enhancing food safety.