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INTRODUCTION: The use of anti‐epileptic drugs (AEDs) in degenerative dementia (DD) remains uncertain. We aimed to evaluate the association of early AED administration with subsequent DD occurrence. METHODS: Using a large nationwide database, we enrolled patients newly diagnosed with epilepsy from 2014 to 2019 (n = 104,225), and using propensity score matching, we divided them into treatment (those prescribed AEDs in 2014) and control groups. The primary outcome was subsequent DD occurrence in 2019. RESULTS: Overall, 4489 pairs of patients (2156 women) were matched. The odds ratio (treatment/control) for DD occurrence was 0.533 (95% confidence interval: 0.459–0.617). The DD proportions significantly differed between the treatment (340/4489 = 0.076) and control (577/4489 = 0.129) groups. DISCUSSION: Among patients newly diagnosed with epilepsy, compared to non‐use, early AED use was associated with a lower occurrence of subsequent DD. Further investigations into and optimization of early intervention for epilepsy in DD are warranted. Highlights: Anti‐epileptic drug (AED) use before epilepsy diagnosis was linked with a lower subsequent degenerative dementia (DD) occurrence.Identifying the epileptic phenotype was crucial for justifying early AED use in DD.AED use with an epilepsy diagnosis did not pose an additional risk of DD.The potential contribution of combination drug therapy to the strategy was noted. [ABSTRACT FROM AUTHOR]

Background: Frailty is a syndrome related to declining functioning attributed to aging. As a reversible, often improved condition, preventive measures for frailty can be effective. Objective: This study aimed to evaluate a frailty prevention program for older men comprising face‐to‐face meetings and an original social mobile application. We also examined the effect of COVID‐19‐associated social isolation on the implementation of this program. Methods: We conducted a mixed‐methods pilot study featuring a single group before and after comparison of 11 man participants, ≥65 years, living in Japanese cities with a spouse or relatives. Program outcome measures included frailty, loneliness, grip strength, step count and mental health status. Process evaluation considered the experience of participants through a semi‐structured interview. Results: The intervention period included an interval of self‐isolation owing to the spread of COVID‐19. Among the outcomes, grip strength decreased after intervention. No significant negative changes were observed in other items. Themes emerging from participant experiences with the program included 'Cooperation with researchers and contributing to research', 'Greater awareness of health', 'Interest in and experience of "newness"', 'Hesitation over active engagement', 'Casual ties with members' and 'Relationships that do not breach personal boundaries'. Conclusions: Although the COVID‐19 pandemic overlapped with the intervention, participants maintained their mental health. The program was a novel, stimulating experience for older men and demonstrated the usefulness of information and communication technology in supporting their social activities. Implications for practice: As a community health nursing initiative for older people, we propose activities that combine group and online support. Frailty prevention programs for older men must contain content that stimulates their interests, such as 'health' and 'newness'. [ABSTRACT FROM AUTHOR]

We developed 'Kikoeru', a pedometer and communication application (app) for older people, with functions that include voice conversations and messages, photograph sharing, and pedometer sharing among users. This pilot study aimed to clarify the effects of Kikoeru on social connectedness related to interactions through the app, subjective health, loneliness, and setting a target number of steps for seven men aged 65 and older who lived at home. Survey items consisted of the Ando-Osada-Kodama loneliness scale, target number of steps, and subjective health ratings. We utilized interviews, seeking to determine whether users felt connected when using the app. The app usage days were collected from the server. The social and physical effects of the app were assessed using a mix of quantitative and qualitative methods. Participants used the app for 54.9 days on average during the 60-day intervention period. Interviews revealed four social connectedness benefits: 'constructing reliance', 'remembering to consider friends', 'awareness of friends' daily lives and encouragement for me', and 'taking an interest in friends'. Loneliness decreased for four out of seven people, remained the same for three, and did not worsen for anyone. The target number of steps increased for three people, remained the same for four, and did not decrease for anyone. Average subjective health improved from 63.6 to 72.9 points out of 100 after the intervention. Results showed participants experienced social connectedness, reduced loneliness, and improved their target steps and health by using Kikoeru. Future large-scale studies with expanded sample sizes should be conducted. [ABSTRACT FROM AUTHOR]

Connectedness among older people is essential for healthy communities, especially among rural populations where limited social interaction and associated health effects may be cause for concern. In this qualitative descriptive study, we explored older rural people's perception of connectedness through a communication application. The study assessed 10 participants (mean age = 76.2 years) living in rural Japan who regularly participated in a senior citizens' club. From July 2019 to January 2020, the participants used a social media application developed by our research team to meet the needs of older people. Semi‐structured interviews were conducted. Six themes representing older rural people's perception of connectedness were identified: (1) thoughtful consideration for members strengthened even without them meeting face‐to‐face, (2) encouragement received from familiar members, (3) joy in sharing daily routine with neighbors, (4) courage to advance through face‐to‐face interaction, (5) willingness to continue club membership, and (6) fear of disrupting club's harmony. Participants who used the application felt compensated for the lack of social interaction opportunities in rural settings and strengthened their existing relationships. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusions A variety of myositis‐specific autoantibodies have been identified in sera from patients with idiopathic inflammatory myopathies, and they play a crucial role in tailoring personalized disease management. In particular, autoantibodies against 3‐hydroxy‐3‐methylglutaryl‐coenzyme A reductase (HMGCR) are now recognized as a key tool for diagnosing immune‐mediated necrotizing myopathy. The current gold standard for detecting anti‐HMGCR autoantibodies involves immunoprecipitation (IP) using radiolabeled proteins from cell extracts or purified proteins produced by in vitro transcription/translation (IVTT). Unfortunately, this radioisotope labeling is technically intricate and not suitable for routine laboratory use. To address this, we developed a novel assay called “Bio‐IVTT‐IP” for detecting anti‐HMGCR autoantibodies, which uses a nonradioactive biotinylated recombinant HMGCR protein produced by IVTT.We collected 14 clinical specimens containing anti‐HMGCR autoantibodies from patients with immune‐mediated necrotizing myopathy, which were validated using the gold standard IP assay, and a set of 35 control samples from patients with other autoimmune diseases, such as systemic lupus erythematosus.The Bio‐IVTT‐IP assay successfully identified 14 clinical samples positive for anti‐HMGCR. We confirmed that the performance of the Bio‐IVTT‐IP assay was completely consistent with that of the gold standard IP assay using radiolabeled proteins. Notably, no immunoprecipitates were found in control samples from patients with other autoimmune diseases.These findings show that the Bio‐IVTT‐IP assay can serve as a potential alternative to the gold standard IP assay for detecting anti‐HMGCR autoantibodies. It can be considered a practical immunodiagnostic tool for diagnosing immune‐mediated necrotizing myopathy, particularly owing to its accessibility in routine laboratory settings. [ABSTRACT FROM AUTHOR]

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 105 and the growth kinetic data of Listeria monocytogenes with an initial cell count of 104. 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 101, 102, and 103 and growth with average of initial cell numbers of 10–1, 100, and 101. The accuracy of the developed model revealed that more than 90% of the observed cell numbers except for growth with initial cell numbers of 101 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. [ABSTRACT FROM AUTHOR]

The objective of this study was to separately describe the fitting uncertainty and the variability of individual cell in bacterial survival kinetics during isothermal and non-isothermal thermal processing. The model describing bacterial survival behavior and its uncertainties and variabilities during non-isothermal inactivation was developed from survival kinetic data for Bacillus simplex spores under fifteen isothermal conditions. The fitting uncertainties in the parameters used in the primary Weibull model was described by using the bootstrap method. The variability of individual cells in thermotolerance and the true randomness in the number of dead cells were described by using the Markov chain Monte Carlo (MCMC) method. A second-order Monte Carlo (2DMC) model was developed by combining both the uncertainties and variabilities. The 2DMC model was compared with reduction behavior under three non-isothermal profiles for model validation. The bacterial death estimations were validated using experimentally observed surviving bacterial count data. The fitting uncertainties in the primary Weibull model parameters, the individual thermotolerance heterogeneity, and the true randomness of inactivated spore counts were successfully described under all the iso-thermal conditions. Furthermore, the 2DMC model successfully described the variances in the surviving bacterial counts during thermal inactivation for all three non-isothermal profiles. As a template for risk-based process designs, the proposed 2DMC simulation approach, which considers both uncertainty and variability, can facilitate the selection of appropriate thermal processing conditions ensuring both food safety and quality. [ABSTRACT FROM AUTHOR]

The control of bacterial reduction is important to maintain food safety during thermal processing. The goal of this study was to illustrate and describe variability in bacterial population behavior during thermal processing as a probability distribution based on individual cell heterogeneity regarding heat resistance. Toward this end, we performed a Monte Carlo simulation via computer, and compared and validated the simulated estimations with observed values. Weibullian fitted parameters were estimated from the kinetic survival data of Bacillus simplex during thermal treatment at 94 °C. The variability in reductions of bacterial sporular populations was illustrated using Monte Carlo simulation based on the Weibull distribution of the parameters. In particular, variabilities in viable spore counts and survival probability of the B. simplex spore population were simulated in various replicates. We also experimentally determined the changes in survival probability and distributions of survival spore counts; notably, these were successfully predicted by the Monte Carlo simulation based on the kinetic parameters. The kinetic parameter-based Monte Carlo simulation could thus successfully illustrate bacterial population behavior variability during thermal processing as a probability distribution. The simulation approach may contribute to improving food quality through risk-based processing designs and enhance risk assessment model accuracy. • Appropriate modeling which can bring minimum processing should be used for keeping food quality. • Prior models for predicting bacterial survival do not describe heterogeneity in survival cell count. • We used a kinetic parameter-based Monte Carlo simulation to model distribution in bacterial counts. • Variability as a probability distribution were derived through 60 replicates experiment. • Simulation may improve food quality and safety via risk-based processing design. [ABSTRACT FROM AUTHOR]

Highlights • Convolution of exponential distributions described the time required for a specific decrease in the number of cells. • Variability in the number of survivors has followed a Poisson distribution. • Although there are some differences between the probabilistic approach and the kinetic method, the difference is less than 5% when the probability of a population containing survivors is below 0.1. Abstract The traditional log-linear inactivation kinetics model considers microbial inactivation as a process that follows first-order kinetics. A basic concept of log reduction is decimal reduction time (D -value), which means time/dose required to kill 90% of the relevant microorganisms. D -value based on the first-order survival kinetics model is insufficient for reliable estimations of bacterial survivors following inactivation treatment. This is because the model does not consider the inactivation curvature and variability in bacterial inactivation. However, although the D -value has some limitations, it is widely used for risk assessment and sterilization time estimation. In this study, stochastic inactivation models are used in place of the conventional D -value to describe the probability of a population containing survivors. As representative bacterial inactivation normally follows a log-linear or log-Weibull model, we calculate the time required for a specific decrease in the number of cells and the number of survival cells as a probability distribution using the stochastic inactivation of individual cells in a population. We compare the probability of a population containing survivors calculated via the D -value, an inactivation kinetics model, and the stochastic formula. The stochastic calculation can be approximately estimated via a kinetic curvature model with less than 5% difference below the probability of a population containing survivors 0.1. This stochastic formula indicates that the D -value model would over- or under-estimate the probability of a population containing survivors when applied to inactivation kinetics with curvature. The results presented in this study show that stochastic analysis using mathematical models that account for variability in the individual cell inactivation time and initial cell number would lead to a realistic and probabilistic estimation of bacterial inactivation. [ABSTRACT FROM AUTHOR]

Abstract Decimal reduction time (D -value) based on the first-order survival kinetics model is not sufficient for reliable estimation of the bacterial survivors of inactivation treatment because the model does not consider inactivation curvature. However, even though doubt exists in the calculation of D -value, it is still widely used for risk assessment and sterilisation time estimation. This paper proposes an approach for estimating the time-to-inactivation and death probability of bacterial population that considers individual cell heterogeneity and initial number of cells via computer simulation. In the proposed approach, Weibull and Poisson distributions are respectively used to provide individual cell inactivation time variability and initial number of cells variability. Our simulation results show that the time-to-inactivation significantly depends on kinetics curvature and initial number of cells. For example, with increases in the initial number of cells, the respective variance of the time-to-inactivation of log-linear, concave downward curve, and concave upward curve remains constant, decreases, and increases, respectively. The death probability contour plot was successfully generated via our computer simulation approach without using D -value estimation. Further, the death probability calculated using our stochastic approach was virtually the same as that obtained using inactivation kinetics. We validated the simulation by using literature data for acid inactivation of Salmonella population. The results of this study indicate that inactivation curvature can replace D -value extrapolation to estimate the death probability of bacterial population. Further, our computer simulation facilitates realistic estimation of the time-to-inactivation of bacterial population. The R code used for the above stochastic calculation is outlined. Highlights • Bacterial time-to-inactivation and death probability estimation approach is proposed • Proposed approach considers individual cell heterogeneity and initial number of cells • Decimal reduction time extrapolation is replaced with stochastics estimation • The variance of the time-to-inactivation depends on kinetics curvature and initial number of cells. [ABSTRACT FROM AUTHOR]

Abstract Stochastic models take into account the uncertainty and variability of predictions in quantitative microbial risk assessment. However, a model that considers thermal inactivation conditions can better predict whether or not bacteria in food are alive. To this end, we describe a novel probabilistic modelling procedure for accurately predicting thermal end point, in contrast to conventional kinetic models that are based on extrapolation of the D value. We used this new model to investigate changes in the survival probability of Salmonella enterica serotype Oranienburg during thermal processing. These changes were accurately described by a cumulative gamma distribution. The predicted total bacterial reduction time with a survival probability of 10−6—the commercial standard for sterility—was significantly shorter than that predicted by the conventional deterministic kinetic model. Thus, the survival probability distribution can explain the heterogeneity in total reduction time for a bacterial population. Furthermore, whereas kinetic methodologies may overestimate the time required for inactivation, our method for determining survival probability distribution can provide an accurate estimate of thermal inactivation and is therefore an important tool for quantitative microbial risk assessment of foods. [ABSTRACT FROM AUTHOR]

This study proposes a novel methodology for risk assessment of products with extremely low risk. The method is based on the analysis of those iterations that result in illness occurrence. It is demonstrated using a hypothetical scenario on listeriosis from pasteurized milk heated at 72°C–75 °C for 15–20 s and analysed which combinations of factors resulted in illness. Sixty-one cases of listeriosis were predicted from 10 billion simulations, representing a realistically large number of servings for this product. According to the model simulations, the illness cases were caused by extremely high doses resulting from three rare situations occurring concurrently: high initial level, less effective pasteurization (due to high thermotolerance of L. monocytogenes contaminants) and extremely high microbial growth during domestic storage (due to poor storage conditions). However, listeriosis was not always observed even if the three situations occurred. Infectivity (dose–response parameter) had the strongest relevance to illness occurrence. Notably, the results of the sensitivity analysis varied depending on the output variable (microbial concentration at exposure or illness occurrence). Furthermore, the correlation-based sensitivity analysis for the illness occurrence provided unreliable results, discouraging this approach for food products with an extremely low illness probability. Considering that number of illnesses and not exposure is the output variable most relevant for risk management, we propose an innovative method based on graphical representations for sensitivity analysis in low-risk products. • Sixty-one listeriosis cases were estimated per ten billion servings of pasteurized milk. • Exploring small risks of huge numbers of servings needs a large number of Monte Carlo iterations. • Listeriosis only occurs when subsequently high initial level, little bacterial reduction and high growth take place. • Sensitivity analysis depends on the output variable: illness occurrence or (log-)microbial concentration. • Graphical methods are effective for analysing the sensitivity in low-risk foods. [ABSTRACT FROM AUTHOR]

Conceptually new bifunctional chiral ligands were developed. The axially chiral N,N-bidentate phenanthroline ligand (S)-1 was found to be effective for Pd-catalyzed asymmetric allylic substitution of allyl acetate and dialkyl malonate. The intramolecular Lewis basic group from the hydroxybinaphthyl structure of (S)-1 played a pivotal role in the high reactivity and enantioselectivity. [ABSTRACT FROM AUTHOR]

We construct a diagram between the endomorphism algebra of a two-term projective module complex over a self-injective algebra and the endomorphism algebras of its homology groups and apply the diagram to the tilting theory of self-injective algebras. We give an example of recovering the endomorphism algebra of a two-term tilting complex over a self-injective algebra from the endomorphism algebras of its homology groups with some additional structures which appear in the diagram. [ABSTRACT FROM AUTHOR]

The flame characteristics of pool fires such as their height and oscillation frequency vary depending on gravity. To improve our understanding of the effects of gravity on flame characteristics, we experimentally investigated small-scale pool fires under conditions of normal to low gravity; using the drop tower at Hirosaki University in Japan to obtain arbitrary low-gravity acceleration, which varied from 1G to 0.55G. Our experiments include analyses of the flame height and oscillation frequency of puffing phenomenon caused by buoyancy under low-gravity environments with a high-speed camera. To clarify the factor of puffing occurrence factor, experiments were performed using eleven different sizes of fuel pan and three different kinds of fuels. It emerged that the flame height and puffing frequency declined with decreasing gravity, whereas the puffing phenomenon which occurred with fuel pan size rose with decreasing gravity. The puffing phenomenon was suppressed with decreasing gravity and the puffing frequency under low-gravity environments was summarized by the relationship between the Strouhal-and Froude numbers. The occurrence of the puffing phenomenon, namely the puffing-limit, was summarized by the relation between the ( H / D ) limit and Grashof number, and found to be inversely proportional. [ABSTRACT FROM AUTHOR]

We show that every two-term tilting complex over an Artin algebra has a tilting module over a certain factor algebra as a homology group. Also, we determine the endomorphism algebra of such a homology group, which is given as a certain factor algebra of the endomorphism algebra of the two-term tilting complex. Thus, every derived equivalence between Artin algebras given by a two-term tilting complex induces a derived equivalence between the corresponding factor algebras. [ABSTRACT FROM PUBLISHER]

Campylobacter jejuni -related foodborne diseases are mainly attributed to the consumption of undercooked chicken meat and cross-contaminated produce. This study aimed to develop a survival kinetics model, based on the Weibull model, for predicting foodborne C. jejuni survival during gastric digestion in a model stomach. We previously confirmed that C. jejuni can survive temperatures up to 62 °C; therefore, certain types of grilled chicken skewers (yakitori) were examined for C. jejuni survival during simulated gastric digestion. C. jejuni survival on a chicken thigh following grilling was examined to confirm the foods for digestion experiments. Further, C. jejuni survival during model digestion was investigated through simultaneous digestion of raw chicken and cross-contaminated iceberg lettuce. The model stomach pH increased from 1.5 to 6.0 immediately after yakitori ingestion and did not decrease below 4.0 within 3 h of digestion. Gastric digestion did not significantly contribute to C. jejuni inactivation (<1.5 log reduction after 3 h digestion). Our model could predict C. jejuni survival kinetics in simulated gastric fluid under varying pH during model digestion. This approach can be used to predict C. jejuni survival rates following digestion to improve food safety and reduce Campylobacter- related disease outbreaks. • Predicting Campylobacter survival is a critical for food safety measure. • We developed a model for predicting C. jejuni survival in common foods. • Model stomach pH was dynamic, simulating in vivo conditions. • The model predicted C. jejuni survival under varying pH in model digestion. [ABSTRACT FROM AUTHOR]