Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

某福利院一起肠炎沙门菌食源性疾病的调查分析

目的 对某福利院一起肠炎沙门菌食源性疾病进行调查和溯源,为研究相关食源性疾病提供参考。方法采用流行病学、食品卫生学和脉冲场凝胶电泳(PFGE)同源性分析等方法,分析本次食源性疾病事件。结果 确认本次事件中病例23名,患病率5.65%(23/407)；现场采集病例肛拭子15份和厨房工作人员肛拭子3份、留样菜品3份、水果2份以及冰棒1份,其中从11份病例肛拭子中检出肠炎沙门菌,PFGE结果显示11株肠炎沙门菌的DNA条带图谱相似性为96.4%,聚类分析为同一型,结合流行病学调查,初步判断菌株来自同一克隆系。结论 综合流行病学、食品卫生学和实验室检测结果,确定为一起肠炎沙门菌引起的食源性疾病,福利院应加强对特殊人群的饮食安全管理,制定相应的食源性疾病突发事件应急处理预案,防止此类事故再发生。     

Efficacy of thyme oil-alginate-based coating in reducing foodborne pathogens on fresh-cut apples

In this study, the inhibition of an alginate-based edible coating (EC) containing thyme oil (0.05%, 0.35% and 0.65%) was evaluated against Listeria monocytogenes, Salmonella Typhimurium, Staphylococcus aureus and Escherichia coli O157:H7 inoculated onto fresh-cut apples. To investigate the antibacterial mechanism of thyme oil, the constituent compounds of that were analysed by gas chromatography-mass spectrometry (GC-MS), and the cellular damage of pathogens was observed by scanning electron microscopy (SEM). Results showed that alginate-based EC containing thyme oil effectively inhibited the growth of pathogens on fresh-cut apples. GC-MS analysis revealed thymol (47.23%) as the major compounds in thyme oil. SEM showed that the cell membrane of foodborne pathogens was damaged by thyme oil, causing their inactivation. Treatment with alginate-based EC containing 0.05% thyme oil preserved the sensory characteristics of fresh-cut apples. Therefore, using alginate-based EC with thyme oil may represent a potential approach to preserve and enhance the safety of fresh-cut apples.     

Practical in-storage interventions to control foodborne pathogens on fresh produce

Although tremendous efforts have been made to ensure fresh produce safety, various foodborne outbreaks and recalls occur annually. Most of the current intervention strategies are evaluated within a short timeframe (less than 1 h), leaving the behavior of the remaining pathogens unknown during subsequent storages. This review summarized outbreak and recall surveillance data from 2009 to 2018 obtained from government agencies in the United States to identify major safety concerns associated with fresh produce, discussed the postharvest handling of fresh produce and the limitations of current antimicrobial interventions, and reviewed the intervention strategies that have the potential to be applied in each storage stage at the commercial scale. One long-term (up to 12 months) prepacking storage (apples, pears, citrus among others) and three short-term (up to 3 months) postpacking storages were identified. During the prepacking storage, continuous application of gaseous ozone at low doses (≤1 ppm) is a feasible option. Proper concentration, adequate circulation, as well as excess gas destruction and ventilation systems are essential to commercial application. At the postpacking storage stages, continuous inhibition can be achieved through controlled release of gaseous chlorine dioxide in packaging, antimicrobial edible coatings, and biocontrol agents. During commercialization, factors that need to be taken into consideration include physicochemical properties of antimicrobials, impacts on fresh produce quality and sensory attributes, recontamination and cross-contamination, cost, and feasibility of large-scale production. To improve fresh produce safety and quality during storage, the collaboration between researchers and the fresh produce industry needs to be improved.     

基因芯片法在食源性疾病中诊断效果及影响多因素Logistic分析研究

目的对比基因芯片法在食源性疾病诊断中的效果,并对影响多因素进行logistic分析。方法选择180例临床表现符合食源性疾病诊断标准的患者作为研究对象,随机均分为实验组和对照组各90例,对照组采用传统的常规培养检测方法,实验组采用基因芯片法进行检测,对比2种方法的检出率、检测耗时以及检测灵敏度。结果实验组的检出率和检测灵敏度均高于对照组的检出率和检测灵敏度,对照组的检测耗时大约是实验组检测耗时的8.24倍。结论相比常规方法,应用基因芯片法的诊断速度更快、准确率更高,在诊断食源性疾病中的应用效果更佳。通过对单因素的χ~2和t检验,确定对食源性疾病有直接影响的多个因素。对影响食源性疾病的多个因素进行Logistic分析,分析结果表明在本次研究分析中,影响较大的因素是人们的饮食卫生以及进食规律。     

食用农产品中产志贺毒素大肠埃希菌污染与家庭厨房食物安全现状分析及防控措施

本文通过对食用农产品中产志贺毒素大肠埃希菌(STEC)来源与传播途径分析,阐述了STEC污染与家庭厨房食物安全之间的关系,并对当前世界各国食品中STEC的监管情况进行阐述,从而提出我国控制食用农产品中STEC进入家庭厨房的解决方案。     

乳品中食源性致病菌快速检测技术研究进展

近年来食源性疾病事件频发,食源性疾病已成为全球化的公共卫生问题,其中有害微生物污染占了较大比重,而乳品因营养丰富可以为众多微生物提供生长所需的营养物质易被其污染。传统的微生物检测方法虽然设备简单、成本低廉,但普遍检测周期长、操作繁琐,对人员操作水平和检验经验要求高。本文综述了分子生物学技术、免疫学技术、光谱技术、基质辅助激光解吸电离飞行时间质谱技术、生物传感器技术以及流式细胞技术在乳品食源性致病菌快速检测的研究进展,展望了乳品中食源性致病菌检测技术向在线化、便捷化、高效化发展前景,以期为后续研究提供参考和借鉴。     

Effect of processing on the microbiological quality and safety of chicken carcasses at slaughterhouse

Microbiological contamination of chicken meat depends on the conditions under which the animals are reared, slaughtered and processed. The aim of this study was to determine the influence of farm origin and processing stages at slaughterhouse on the microbial safety and quality of chicken. Samples of chicken carcasses from three different farms were taken from a slaughterhouse. Mesophiles, Escherichia coli, coagulase positive Staphylococcci counts, presence of Listeria monocytogenes,Campylobacter and Salmonella were determined at five sampling points: after defeathering, after evisceration, after washing, after chilling and after cutting. Chilling reduced log numbers of mesophiles, coagulase positive Staphylococci and E. coli by 0.85, 1.52 and 2.2 log units, respectively. Salmonella was not detected after chilling. High prevalence of Campylobacter spp was observed at all the stages ranging between 84% and 100%. L. monocytogenes was not detected in chicken carcasses after defeathering. However, it was detected after evisceration and after washing and chilling. The most critical stage for L. monocytogenes contamination was the portioning operation, the prevalence in breast and legs being 88% and 84%, respectively.