Identification and quantification of viable Lacticaseibacillus rhamnosus in probiotics using validated PMA-qPCR method.

The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus, which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR Cq values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pure cultures, probiotics as food ingredients, and compound probiotic products. Noteworthy is the congruity observed between measured and theoretical values within a 95% confidence interval of the upper and lower limits of agreement, demonstrating the relative trueness of this method. Moreover, accurate results were obtained when viable L. rhamnosus ranging from 103 to 108 CFU/mL. The comprehensive appraisal of PMA-qPCR performances provides potential industrial applications of this new technology in quality control and supervision of probiotic products.

Abundant Neutrophils-initiated Acute Myocardial Injury Following Coxsackievirus A6 Infection.

Coxsackievirus (CV) A6 is currently considered as a predominant pathogen of hand, foot, and mouth disease (HFMD), and is occasionally linked to myocardial injury. We first established a mouse model of CVA6-induced myocardial injury. Next, we analyzed the immune cell phenotypes CVA6-infected mice hearts by FACS, and found that CVA6 led to massive neutrophils infiltration, suggesting their potential link with the occurrence of myocardial injury. We further used either αGr-1 or αLy6G antibody to deplete neutrophils, and found that neutrophil-depleted animals showed decreased cardiac enzymes, lower degree pathology in hearts, and reduced inflammatory cytokine production compared to isotype controls. Finally, we confirmed the involvement of neutrophils in myocardial injury of clinical patients with severe HFMD. Overall, our study suggests that excessive neutrophils contribute to myocardial injury caused by CVA6 infection, which provides new insight into myocardial injury during the development of HFMD severity and the outcome of immune cell-mediated therapies.

IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks.

Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis.

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg's analysis (p = 0.835) and Egger's analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.

BACKGROUND: Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. MATERIALS AND METHODS: We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)-neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA-NNAR hybrid model were established for comparison and estimation. RESULTS: The wavelet-based SARIMA-NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. CONCLUSIONS: The wavelet-based SARIMA-NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.

Identification of immune and metabolic predictors of severe hand-foot-mouth disease.

Hand, foot and mouth disease (HFMD) is an infectious disease that affects mostly children. The children with HFMD also have other immune and metabolic disorders. However, the association of these disorders with the severity of HFMD has not yet been determined. In this study, we used a case-control study design to examine the correlation of immune and metabolic disorders with HFMD development in children. 406 mild and severe patients were recruited and divided into different subgroups based on the number of days from the initial onset time to hospitalization (1, 2, 3, 4, and ≥5 days). Logistic regression model was used to define the predictors of severe HFMD. We found that the patients from rural area (OR = 1.76, 95% CI [1.19~2.63], P = 0.005) or with body temperature of >39°C (OR = 2.14, 95% CI [1.12~4.12], P = 0.022) exhibited higher risk for severe symptoms. In addition, the risk increased with the rise of body temperature by using a Chis-quare trend test (P = 0.01). We also found that a decreased number of eosinophils was an predictor of severe HFMD at 1, 2, 3,and 4 days post infection (dpi). Decreased levels of Na+, Cl-, and creatine kinase were also predictors at 1 and ≥5 dpi. On the other hand, elevated level of globulin was a predictor for severe HFMD at 4 dpi and ≥5 dpi, and the increased number of neutrophils or increased level of alkaline phosphatase posed risk for severe HFMD at 3 and ≥5 dpi. Our results suggested that rural living, hyperpyrexia, changes in the immune system that include the numbers of eosinophils and neutrophils and the levels of IgG and globulin, and metabolic alterations, such as the levels of alkaline phosphatase, Na+, Cl-, and creatine kinase in peripheral blood are predictors of severe HFMD.

Developing a Nomogram for Risk Prediction of Severe Hand-Foot-and-Mouth Disease in Children.

OBJECTIVE: Early recognition of children with severe Hand-Foot-and-Mouth disease (HFMD) is especially important, as severe cases are associated with poor prognosis. To accomplish this, authors designed a quantitative assessment tool to build a nomogram to assist in clinical diagnosis. METHODS: A total of 2332 HFMD patients were enrolled in this study; 1750 cases in the mild group and 582 cases in the severe group. Analysis of all of the data was performed using R software version 3.4.3. Multivariate logistic regression was utilized to screen predictors to construct a nomogram model. Finally, predictive performance of the model was evaluated using a receiver operating characteristic (ROC) curve and classifier calibration plot. RESULTS: A nomogram was constructed with five variables: age, peak temperature, fever duration, pathogen, and vomiting. For the nomogram, the area under the curve was 0.87, and the model prediction accuracy rate was 85.2%. Depending upon the comparison of the area under the ROC curve, the nomogram model was superior to the traditional pediatric clinical illness score (PCIS). With the help of the Hosmer-Lemeshow test and resampling model calibration curve, the fitting performance of the nomogram was stable. CONCLUSIONS: With advantages such as simplicity, intuitiveness, and practicality, the nomogram (including age, peak temperature, fever duration, pathogen, and vomiting) is capable of predicting severe HFMD and has certain auxiliary value in clinical applications.

Parental posttraumatic stress and child behavioral problems in world trade center responders.

BACKGROUND: We investigated trans-generational associations between Post Traumatic Stress Disorder (PTSD) symptoms in World Trade Center (WTC) responders and behavioral problems in their children. METHODS: Participants were WTC responders-8034 police and 8352 non-traditional (eg, construction workers)-with one or more children at the time of their first visit to the World Trade Center Health Program (WTC-HP). Self-report questionnaires were administered approximately 4 years after the 9/11 WTC attack. RESULTS: A total of 31.4% of non-traditional and 20.0% of police responders reported behavioral problems in their children. Non-traditional responder status, female sex, Hispanic ethnicity, more life stressors, more WTC-related PTSD symptoms, and dysphoric arousal symptoms were significant correlates of behavioral problems in responders' children. CONCLUSIONS: Specific parental sociodemographic, psychosocial and clinical characteristics, as well as PTSD symptom severity, were significant correlates of child behavior problems. Findings encourage monitoring and early intervention for children of disaster responders, particularly those at highest risk.

Pulmonary edema following central nervous system lesions induced by a non- mouse-adapted EV71 strain in neonatal BALB/c mice.

BACKGROUND: Enterovirus (EV) infection has been a serious health issue in Asia-Pacific region. It has been indicated that the occurrence of fatal hand foot and mouth disease (HFMD) cases following EV71 infection is mainly attributed to pulmonary edema. However, the development of pulmonary disorders after EV71 infection remains largely unknown. To establish an EV71-infected animal model and further explore the underlying association of central nervous system (CNS) invasion with pulmonary edema, we isolated a clinical source EV71 strain (ZZ1350) from a severe case in Henan Province. METHODS: We evaluated the cytotoxicity of ZZ1350 strain and the susceptibility in 3-day-old BALB/c mice with intraperitoneal, intracerebral and intramuscular inoculation. Various histopathological and immunohistochemical techniques were applied to determine the target organs or tissue damage after infection. Correlation analysis was used to identify the relationship between CNS injury and pulmonary disorders. RESULTS: Our experimental results suggested that ZZ1350 (C4 subtype) had high cytotoxicity against African green monkey kidney (Vero) cells and human rhabdomyosarcoma (RD) cells and neonatal BALB/c mice were highly susceptible to the infection with ZZ1350 through three different inoculation routes (2 × 106 pfu/mouse) exhibiting severe neurological and respiratory symptoms that were similar to clinical observation. Viral replication was found in brain, spinal cord, skeletal muscle, lung, spleen, liver, heart of infected mice and these sections also showed histopathological changes. We found that brain histology score was positive correlated with lung histology score in total experimental mice and mice under the three inoculation routes (P < 0.05). At the same time, there were positive correlations between spinal cord score and lung score in total experimental mice and mice with intracerebral inoculation (P < 0.05). CONCLUSIONS: ZZ1350 strain is effective to establish animal model of EV71 infection with severe neurological and respiratory symptoms. The development of pulmonary disorders after EV71 infection is associated with severity of CNS damage.

Application and Comparison of Laboratory Parameters for Forecasting Severe Hand-Foot-Mouth Disease Using Logistic Regression, Discriminant Analysis and Decision Tree.

BACKGROUND: In recent years, the prevalence of hand-foot-mouth disease (HFMD) in China and some other countries has caused worldwide concern. Mild cases tend to recover within a week, while severe cases may progress rapidly and tend to have bad outcome. Since there is no vaccine for HFMD and anti-inflammatory treatment is not ideal. In this study, we aimed to establish a valid forecasting model for severe HFMD using common laboratory parameters. METHODS: Retrospectively, 77 severe HFMD cases from Zhengzhou Children's hospital in the peaking period between years 2013 to 2015 were collected, with 77 mild HFMD cases in the same area. The study recorded common laboratory parameters to assist in establishment of the severe HFMD model. After screening the important variables using Mann-Whitney U test, the study also matched the logistic regression (LR), discriminant analysis (DA), and decision tree (DT) to make a comparison. RESULTS: Compared with that of the mild group, serum levels of WBC, PLT, PCT, MCV, MCH, LCR, SCR, LCC, GLO, CK-MB, K, S100, and B in the severe group were higher (p < 0.05), while MCR, EOR, BASOR, SCC, MCC, EO, BASO, NA, CL, T, Th, and Th/Ts were lower (p < 0.05). Five indicators including MCR, LCC, Th, CK-MB, and CL were screened out by LR and the same for DA, and five variables including EO, LCC, CL, GLO, and MCC screened out by DT. The area under the curve (AUC) of LR, DA, and DT was 0.805, 0.779 and 0.864, respectively. CONCLUSIONS: The findings were that common laboratory indexes were effectively used to distinguish the mild HFMD cases and severe HFMD cases by LR, DA, and DT, and DT had the best classification effect with an AUC of 0.864.

Time series analysis of hand-foot-mouth disease hospitalization in Zhengzhou: establishment of forecasting models using climate variables as predictors.

BACKGROUND: Large-scale outbreaks of hand-foot-mouth disease (HFMD) have occurred frequently and caused neurological sequelae in mainland China since 2008. Prediction of the activity of HFMD epidemics a few weeks ahead is useful in taking preventive measures for efficient HFMD control. METHODS: Samples obtained from children hospitalized with HFMD in Zhengzhou, Henan, China, were examined for the existence of pathogens with reverse-transcriptase polymerase chain reaction (RT-PCR) from 2008 to 2012. Seasonal Autoregressive Integrated Moving Average (SARIMA) models for the weekly number of HFMD, Human enterovirus 71(HEV71) and CoxsackievirusA16 (CoxA16) associated HFMD were developed and validated. Cross correlation between the number of HFMD hospitalizations and climatic variables was computed to identify significant variables to be included as external factors. Time series modeling was carried out using multivariate SARIMA models when there was significant predictor meteorological variable. RESULTS: 2932 samples from the patients hospitalized with HFMD, 748 were detected with HEV71, 527 with CoxA16 and 787 with other enterovirus (other EV) from January 2008 to June 2012. Average atmospheric temperature (T{avg}) lagged at 2 or 3 weeks were identified as significant predictors for the number of HFMD and the pathogens. SARIMA(0,1,0)(1,0,0)52 associated with T{avg} at lag 2 (T{avg}-Lag 2) weeks, SARIMA(0,1,2)(1,0,0)52 with T{avg}-Lag 2 weeks and SARIMA(0,1,1)(1,1,0)52 with T{avg}-Lag 3 weeks were developed and validated for description and predication the weekly number of HFMD, HEV71-associated HFMD, and Cox A16-associated HFMD hospitalizations. CONCLUSION: Seasonal pattern of certain HFMD pathogens can be associated by meteorological factors. The SARIMA model including climatic variables could be used as an early and reliable monitoring system to predict annual HFMD epidemics.

Mouse transgenesis in a single locus with independent regulation for multiple fluorophores.

A major barrier to complex experimental design in mouse genetics is the allele problem: combining three or more alleles is time-consuming and inefficient. Here, we solve this problem for transgenic animals with a simple modification of existing BAC transgenesis protocols, and generate triple-colored 'prism' mice in which the major cell types of the brain: neurons, astrocytes, and oligodendrocytes, are each labeled with a distinct fluorophore. All three fluorophores are expressed from the same locus, yet each fluorophore is expressed in an independent temporal and spatial pattern. All three transgenes are generally co-inherited across multiple generations with stable genomic copy number and expression patterns. This generic solution should permit more sophisticated experimental manipulations to assess functional interactions amongst populations of cell types in vivo in a more rapid and efficient manner.