Correction: Anti­biofilm effects of sinomenine against Staphylococcus aureus.

[This corrects the article DOI: 10.1007/s10068-022-01174-0.].

Characterization of the Bacterial Communities in Cichorium intybus According to Cultivation and Storage Conditions.

Chicory leaves (Cichorium intybus) are widely consumed due to their health benefits. They are mainly consumed raw or without adequate washing, which has led to an increase in food-borne illness. This study investigated the taxonomic composition and diversity of chicory leaves collected at different sampling times and sites. The potential pathogenic genera (Sphingomonas, Pseudomonas, Pantoea, Staphylococcus, Escherichia, and Bacillus) were identified on the chicory leaves. We also evaluated the effects of various storage conditions (enterohemorrhagic E. coli contamination, washing treatment, and temperature) on the chicory leaves' microbiota. These results provide an understanding of the microbiota in chicory and could be used to prevent food-borne illnesses.

Machine learning application for classification of Alzheimer's disease stages using 18F-flortaucipir positron emission tomography.

BACKGROUND: The progression of Alzheimer's dementia (AD) can be classified into three stages: cognitive unimpairment (CU), mild cognitive impairment (MCI), and AD. The purpose of this study was to implement a machine learning (ML) framework for AD stage classification using the standard uptake value ratio (SUVR) extracted from 18F-flortaucipir positron emission tomography (PET) images. We demonstrate the utility of tau SUVR for AD stage classification. We used clinical variables (age, sex, education, mini-mental state examination scores) and SUVR extracted from PET images scanned at baseline. Four types of ML frameworks, such as logistic regression, support vector machine (SVM), extreme gradient boosting, and multilayer perceptron (MLP), were used and explained by Shapley Additive Explanations (SHAP) to classify the AD stage. RESULTS: Of a total of 199 participants, 74, 69, and 56 patients were in the CU, MCI, and AD groups, respectively; their mean age was 71.5 years, and 106 (53.3%) were men. In the classification between CU and AD, the effect of clinical and tau SUVR was high in all classification tasks and all models had a mean area under the receiver operating characteristic curve (AUC) > 0.96. In the classification between MCI and AD, the independent effect of tau SUVR in SVM had an AUC of 0.88 (p < 0.05), which was the highest compared to other models. In the classification between MCI and CU, the AUC of each classification model was higher with tau SUVR variables than with clinical variables independently, which yielded an AUC of 0.75(p < 0.05) in MLP, which was the highest. As an explanation by SHAP for the classification between MCI and CU, and AD and CU, the amygdala and entorhinal cortex greatly affected the classification results. In the classification between MCI and AD, the para-hippocampal and temporal cortex affected model performance. Especially entorhinal cortex and amygdala showed a higher effect on model performance than all clinical variables in the classification between MCI and CU. CONCLUSIONS: The independent effect of tau deposition indicates that it is an effective biomarker in classifying CU and MCI into clinical stages using MLP. It is also very effective in classifying AD stages using SVM with clinical information that can be easily obtained at clinical screening.

Anti-biofilm effects of sinomenine against Staphylococcus aureus.

Staphylococcus aureus is a gram-positive foodborne pathogen capable of forming strong biofilms. This study identified that anti-biofilm natural compound against S. aureus. Sinomenine, a natural compound, showed significantly reduced biofilm formation (31.97-39.86%), but no effect on bacterial growth was observed. The dispersion of preformed biofilms was observed by confocal laser scanning microscopy (CLSM). qRT-PCR revealed that sinomenine treatment significantly up-regulated agrA by 3.8-fold and down-regulated icaA gene by 3.1-fold. These indicate that sinomenine treatment induces biofilm dispersal due to cell-cell adhesion, polysaccharide intercellular adhesin (PIA), and phenol-soluble modulin (PSM) peptides production. Our results suggest that sinomenine can be used as a promising agent for effectively controlling biofilm formation and dispersion, thereby making S. aureus more susceptible to the action of antimicrobial agents.

Predicting progression to dementia with "comprehensive visual rating scale" and machine learning algorithms.

Background and Objective: Identifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms. Methods: We included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated. Results: Of the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701-0.711) were used than when clinical data and cortical thickness (accuracy 0.650-0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression. Conclusions: Tree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

Microbiota Analysis and Microbiological Hazard Assessment in Chinese Chive (Allium tuberosum Rottler) Depending on Retail Types.

Chinese chive (Allium tuberosum Rottler) has potential risks associated with pathogenic bacterial contamination as it is usually consumed raw. In this study, we investigated the microbiota of Chinese chives purchased from traditional markets and grocery stores in March (Spring) and June (Summer) 2017. Differences in bacterial diversity were observed, and the microbial composition varied across sampling times and sites. In June, potential pathogenic genera, such as Escherichia, Enterobacter, and Pantoea, accounted for a high proportion of the microbiota in samples purchased from the traditional market. A large number of pathogenic bacteria (Acinetobacter lwoffii, Bacillus cereus, Klebsiella pneumoniae, and Serratia marcescens) were detected in the June samples at a relatively high rate. In addition, the influence of the washing treatment on Chinese chive microbiota was analyzed. After storage at 26°C, the washing treatment accelerated the growth of enterohemorrhagic Escherichia coli (EHEC) because it caused dynamic shifts in Chinese chive indigenous microbiota. These results expand our knowledge of the microbiota in Chinese chives and provide data for the prediction and prevention of food-borne illnesses.

Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data.

Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.

Factors associated with the difference between the incidence and case-fatality ratio of coronavirus disease 2019 by country.

Coronavirus disease (COVID-19) has been spreading all over the world; however, its incidence and case-fatality ratio differ greatly between countries and between continents. We investigated factors associated with international variation in COVID-19 incidence and case-fatality ratio (CFR) across 107 northern hemisphere countries, using publicly available COVID-19 outcome data as of 14 September 2020. We included country-specific geographic, demographic, socio-economic features, global health security index (GHSI), healthcare capacity, and major health behavior indexes in multivariate models to explain this variation. Multiple linear regression highlighted that incidence was associated with ethnic region (p < 0.05), global health security index 4 (GHSI4) (beta coefficient [ß] 0.50, 95% Confidence Interval [CI] 0.14-0.87), population density (ß 0.35, 95% CI 0.10-0.60), and water safety level (ß 0.51, 95% CI 0.19-0.84). The CFR was associated with ethnic region (p < 0.05), GHSI4 (ß 0.53, 95% CI 0.14-0.92), proportion of population over 65 (ß 0.71, 95% CI 0.19-1.24), international tourism receipt level (ß - 0.23, 95% CI - 0.43 to - 0.03), and the number of physicians (ß - 0.37, 95% CI - 0.69 to - 0.06). Ethnic region was the most influential factor for both COVID-19 incidence (partial [Formula: see text] = 0.545) and CFR (partial [Formula: see text] = 0.372), even after adjusting for various confounding factors.

Changing influenza activity in the Southern hemisphere countries during the COVID-19 pandemic.

INTRODUCTION: While the reduction in influenza cases in the Northern hemisphere in 2020 has been widely reported, the influenza transmission dynamics in the Southern hemisphere remain uncharacterized. METHODS: This study analysed the change in influenza-positive proportion (IPP) between 2010-2019 and 2020 in countries in the Southern hemisphere with ≤40% missing IPP data in FluNet to assess how coronavirus disease 2019 (COVID-19) relates to influenza activity. The analysis considered the incidence of COVID-19 reported by the World Health Organization and the implementation date of non-pharmaceutical interventions (NPIs) reported by the Oxford COVID-19 Government Response Tracker. RESULTS: In each of the seven included countries, the average IPP was lower in 2020 than in 2010-2019 (P < 0.01), with the largest difference being 31.1% (95% confidence interval 28.4-33.7%). In Argentina, Bolivia, Chile and South Africa, higher IPPs were observed during epidemiological weeks 4-16 in 2020 compared with the same weeks in 2010-2019. The IPP increased after NPIs were implemented in Argentina and South Africa, but started to decline in Bolivia, Chile, Madagascar and Paraguay before NPI implementation. CONCLUSIONS: Influenza burden and activity decreased in 2020 in the Southern hemisphere. The temporal decline in influenza activity varied between countries.

Trends in Viral Respiratory Infections During COVID-19 Pandemic, South Korea.

We compared weekly positivity rates of 8 respiratory viruses in South Korea during 2010-2019 and 2020. The overall mean positivity rate for these viruses decreased from 54.7% in 2010-2019 to 39.1% in 2020. Pandemic control measures might have reduced the incidence of many, but not all, viral respiratory infections.

The Serial Interval of COVID-19 in Korea: 1,567 Pairs of Symptomatic Cases from Contact Tracing.

Although coronavirus disease 2019 (COVID-19) is an ongoing pandemic, the mean serial interval was measured differently across nations. Through the Korean national COVID-19 contact tracing system, we were able to investigate personal contacts in all symptomatic cases in Korea from January 20 to August 3, 2020. The mean serial interval was calculated by the duration between the symptom onset of the infector and infectee, and became shorter after the case definition changed to include not-imported cases in Korea on February 20, 2020. The mean serial interval before and after this fifth case definition was 6.12 and 3.93 days based on the infectors' symptom onset date, respectively, and 4.02 days in total with the median of 3 days. Older age and women lead to longer serial intervals.

Leaf-associated microbiota on perilla (Perilla frutescens var. frutescens) cultivated in South Korea to detect the potential risk of food poisoning.

Perilla (Perilla frutescens) is a commonly consumed herb with various health benefits in Asia. However, the risks of food-borne illness owing to the presence of pathogens on perilla leaves have not been evaluated. In this study, we evaluated the microbiota of perilla leaves harvested in South Korea using Illumina MiSeq sequencing of the 16S rRNA gene. In total, 2,743,003 sequencing reads were obtained, and 92-437 operational taxonomic units were observed in all samples. Bacterial loads were quantified, and the diversity indices were compared. Differences in the microbiota among sampling times and regions were also investigated. Proteobacteria and Firmicutes were predominant phyla at both times. At the class level, the bacterial communities were composed primarily of Alphaproteobacteria, Bacilli, and Gammaproteobacteria. Diverse bacterial taxa, such as Bacillus, uncultured family Enterobacteriaceae, and Sphingomonas were detected, and the representative pathogenic species (i.e., Acinetobacter lwoffii, Klebsiella pneumoniae, and Staphylococcus aureus) were quantified by qRT-PCR. The results of the co-occurrence network analysis showed characteristics of bacterial taxa in the microbiome on perilla leaves and provided insights into the roles of correlations among diverse microbes, including potential pathogens. Based on these results, the potential risk of food-borne illness from consumption of perilla leaves may be higher in July than in April. In summary, the microbial compositions determined in this study can be used as a base data for food-safety management for prediction and prevention of future outbreaks.

Analysis of the Microbiota on Lettuce (Lactuca sativa L.) Cultivated in South Korea to Identify Foodborne Pathogens.

Lettuce (Lactuca sativa L.) is a major ingredient used in many food recipes in South Korea. Lettuce samples were collected during their maximum production period between April and July in order to investigate the microbiota of lettuce during different seasons. 16S rRNA gene-based sequencing was conducted using Illumina MiSeq, and real-time PCR was performed for quantification. The number of total bacterial was greater in lettuce collected in July than in that collected in April, albeit with reduced diversity. The bacterial compositions varied according to the site and season of sample collection. Potential pathogenic species such as Bacillus spp., Enterococcus casseliflavus, Klebsiella pneumoniae, and Pseudomonas aeruginosa showed season-specific differences. Results of the network co-occurrence analysis with core genera correlations showed characteristics of bacterial species in lettuce, and provided clues regarding the role of different microbes, including potential pathogens, in this microbiota. Although further studies are needed to determine the specific effects of regional and seasonal characteristics on the lettuce microbiota, our results imply that the 16S rRNA gene-based sequencing approach can be used to detect pathogenic bacteria in lettuce.

Characterization and evaluation of antimicrobial activity of actinonin against foodborne pathogens.

This study revealed the antimicrobial properties of actinonin against major foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, Salmonella Typhimurium, Staphylococcus aureus, and Vibrio vulnificus. Among them, actinonin caused growth defect in S. Typhimurium and V. vulnificus. Minimal inhibitory concentration (MIC) values of actinonin were determined by broth microdilution methods. The MICs of actinonin were ≤0.768 µg/ml for S. Typhimurium and ≤0.192 µg/ml for V. vulnificus. Susceptibility to actinonin in both pathogens was measured by colony-forming ability and disc diffusion test. The results showed actinonin had antimicrobial activity against S. Typhimurium and V. vulnificus in a dose-dependent manner. The inhibitory effects on swarming motility were determined, and cytotoxicity of each pathogen against HeLa cells was decreased significantly by actinonin treatment. Furthermore, actinonin showed an antimicrobial efficacy in food models infected with these pathogens. These results demonstrate that actinonin is potentially an effective agent for food sanitization or preservation.