Accuracy of Pedicle Screw Placement in Scoliosis Surgery: A Comparison between Conventional Computed Tomography-Based and O-Arm-Based Navigation Techniques

Study DesignRetrospective study.PurposeWe compared the accuracy of O-arm-based navigation with computed tomography (CT)-based navigation in scoliotic surgery.Overview of LiteratureNo previous reports comparing the results of O-arm-based navigation with conventional CT-based navigation in scoliotic surgery have been published.MethodsA total of 222 pedicle screws were implanted in 29 patients using CT-based navigation (group C) and 416 screws were implanted in 32 patients using O-arm-based navigation (group O). Postoperative CT was performed to assess the screw accuracy, using the established Neo classification (grade 0: no perforation, grade 1: perforation <2 mm, grade 2: perforation ≥2 and <4, and grade 3: perforation ≥4 mm).ResultsIn group C, 188 (84.7%) of the 222 pedicle screw placements were categorized as grade 0, 23 (10.4%) were grade 1, 11 (5.0%) were grade 2, and 0 were grade 3. In group O, 351 (84.4%) of the 416 pedicle screw placements were categorized as grade 0, 52 (12.5%) were grade 1, 13 (3.1%) were grade 2, and 0 were grade 3. Statistical analysis showed no significant difference in the prevalence of grade 2.3 perforations between groups C and O. The time to position one screw, including registration, was 10.9±3.2 minutes in group C, but was significantly decreased to 5.4±1.1 minutes in group O.ConclusionsO-arm-based navigation facilitates pedicle screw insertion as accurately as conventional CT-based navigation. The use of O-arm-based navigation successfully reduced the time, demonstrating advantages in the safety and accuracy of pedicle screw placement for scoliotic surgery

Gigantic chiroptical enhancements in polyfluorene copolymers bearing bulky neomenthyl groups : importance of alternating sequences of chiral and achiral fluorene units

An alternating copolymer consisting of achiral and chiral units emits remarkably efficient CPL on photo-excitation. The main-chain twist bias is enhanced by thermal annealing by the factor of 10(4) in the ground state. An anisotropy factor of the polymer in the excited state is greater by approximately an order of magnitude compared with that in the ground sate

Antibacterial Properties of Melanoidins Produced from Various Combinations of Maillard Reaction against Pathogenic Bacteria

Novel melanoidins are produced by the Maillard reaction. Here, melanoidins with high antibacterial activity were tested by examining various combinations of reducing sugars and amino acids as reaction substrates. Twenty-two types of melanoidins were examined by combining two reducing sugars (glucose and xylose) and eleven L-isomers of amino acids (alanine, arginine, glutamine, leucine, methionine, phenylalanine, proline, serine, threonine, tryptophan, and valine) to confirm the effects of these melanoidins on the growth of Listeria monocytogenes at 25 degrees C. The melanoidins produced from the combination of D-xylose with either L-phenylalanine (Xyl-Phe) or L-proline (Xyl-Pro), for which absorbance at 420 nm was 3.5 +/- 0.2, completely inhibited the growth of L. mono-cytogenes at 25 degrees C for 48 h. Both of the melanoidins exhibited growth inhibition of L. monocytogenes which was equivalent to the effect of nisin (350 IU/mL). The antimicrobial spectrum of both melanoidins was also investigated for 10 different species of bacteria, including both Gram-positive and Gram-negative species. While Xyl-Phe-based melanoidin successfully inhibited the growth of Bacillus cereus and Brevibacillus brevis, Xyl-Probased melanoidin inhibited the growth of Salmonella enterica Typhimurium. However, no clear trend in the antimicrobial spectrum of the melanoidins against different bacterial species was observed. The findings in the present study suggest that melanoidins generated from xylose with phenylalanine and/or proline could be used as potential novel alternative food preservatives derived from food ingredients to control pathogenic bacteria. IMPORTANCE Although the antimicrobial effect of melanoidins has been reported in some foods, there have been few comprehensive investigations on the antimicrobial activity of combinations of reaction substrates of the Maillard reaction. The present study comprehensively investigated the potential of various combinations of reducing sugars and amino acids. Because the melanoidins examined in this study were produced simply by heating in an autoclave at 121 degrees C for 60 min, the targeted melanoidins can be easily produced. The melanoidins produced from combinations of xylose with either phenylalanine or proline exhibited a wide spectrum of antibiotic effects against various pathogens, including Listeria monocytogenes, Bacillus cereus, and Salmonella enterica Typhimurium. Since the antibacterial effect of the melanoidins on L. monocytogenes was equivalent to that of a nisin solution (350 IU/mL), we might expect a practical application of melanoidins as novel food preservatives

Prediction of population behavior of Listeria monocytogenes in food using machine learning and a microbial growth and survival database

In predictive microbiology, statistical models are employed to predict bacterial population behavior in food using environmental factors such as temperature, pH, and water activity. As the amount and complexity of data increase, handling all data with high-dimensional variables becomes a difficult task. We propose a data mining approach to predict bacterial behavior using a database of microbial responses to food environments. Listeria monocytogenes, which is one of pathogens, population growth and inactivation data under 1,007 environmental conditions, including five food categories (beef, culture medium, pork, seafood, and vegetables) and temperatures ranging from 0 to 25 degrees C, were obtained from the ComBase database (www.combase.cc). We used eXtreme gradient boosting tree, a machine learning algorithm, to predict bacterial population behavior from eight explanatory variables: 'time', 'temperature', 'pH', 'water activity', 'initial cell counts', 'whether the viable count is initial cell number', and two types of categories regarding food. The root mean square error of the observed and predicted values was approximately 1.0 log CFU regardless of food category, and this suggests the possibility of predicting viable bacterial counts in various foods. The data mining approach examined here will enable the prediction of bacterial population behavior in food by identifying hidden patterns within a large amount of data

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty

Conventional regression analysis using the least-squares method has been applied to describe bacterial behavior logarithmically. However, only the normal distribution is used as the error distribution in the least-squares method, and the variability and uncertainty related to bacterial behavior are not considered. In this paper, we propose Bayesian statistical modeling based on a generalized linear model (GLM) that considers variability and uncertainty while fitting the model to colony count data. We investigated the inactivation kinetic data of Bacillus simplex with an initial cell count of 10(5) and the growth kinetic data of Listeria monocytogenes with an initial cell count of 10(4). The residual of the GLM was described using a Poisson distribution for the initial cell number and inactivation process and using a negative binomial distribution for the cell number variation during growth. The model parameters could be obtained considering the uncertainty by Bayesian inference. The Bayesian GLM successfully described the results of over 50 replications of bacterial inactivation with average of initial cell numbers of 10(1), 10(2), and 10(3) and growth with average of initial cell numbers of 10(-1), 10(0), and 10(1). The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 10(1) were within the 95% prediction interval. In addition, parameter uncertainty could be expressed as an arbitrary probability distribution. The analysis procedures can be consistently applied to the simulation process through fitting. The Bayesian inference method based on the GLM clearly explains the variability and uncertainty in bacterial population behavior, which can serve as useful information for risk assessment related to food borne pathogens

Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling

Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen's levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model's parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model's parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 10⁷ CFU/ml at 65℃, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157: H7 and Listeria monocytogenes with enteric microflora in a small-intestine model

Aims The biological events occurring during human digestion help to understand the mechanisms underlying the dose-response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine. Methods and Results The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased. Conclusions Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived. Significance and Impact of the Study Competition experiments in a small-intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose-response relationships

Why Does Cronobacter sakazakii Survive for a Long Time in Dry Environments? Contribution of the Glass Transition of Dried Bacterial Cells

To investigate the mechanism of adaptation of Cronobacter sakazakii to desiccation stress, the present study focused on the glass transition phenomenon of dried bacterial cells, using a thermomechanical technique. The mechanical glass transition temperature (T-g) of dried C. sakazakii cells per se, prepared by different drying methods (air drying and freeze-drying) and with different water activity (a(w)) levels (0.43, 0.57, 0.75, and 0.87), were determined. In addition, we investigated the survival of two strains of C. sakazakii (JCM 1233 and JCM 2127) prepared by different drying methods under different storage temperatures (4, 25, and 42 degrees C) and a(w) conditions (0.43 and 0.87). While the T-g of the air-dried C. sakazakii cells increased as the a(w) decreased, the freeze-dried C. sakazakii cells showed an unclear a(w) dependency of the T-g. Air-dried C. sakazakii cells showed a higher T-g than freeze-dried C. sakazakii cells at an aw of 20 degrees C, the dried C. sakazakii cells survived stably regardless of the drying method. In contrast, when the difference between the T-g and storage temperature was reduced to <10 degrees C, the viable cell numbers in dried C. sakazakii cells were quickly decreased. Thus, the T-g is a key factor affecting the desiccation tolerance of C. sakazakii. IMPORTANCE The mechanical glass transition temperature (T-g) of dried Cronobacter sakazakii cells varied depending on differences in drying methods and water activity (a(w)) levels. Because the T-g of the dried bacterial cells varied depending on the drying method and a(w), the T-g will play an important role as an operational factor in the optimization of dry food processing for controlling microbial contamination in the future. Furthermore, the differences between the T-g and storage temperature were introduced as an integrated index for survival of bacterial cells under a desiccation environment that took into consideration the differences in the drying methods and aw levels. As the difference between the T-g and storage temperature decreased to <10 degrees C, the dried C. sakazakii cells were inactivated quickly, regardless of the drying methods. The relationship between T-g and storage temperature will contribute to understanding the desiccation tolerance of bacterial cells