COVID-19 on the spectrum: a scoping review of hygienic standards.

The emergence of COVID-19 in Wuhan, China, rapidly escalated into a worldwide public health crisis. Despite numerous clinical treatment endeavors, initial defenses against the virus primarily relied on hygiene practices like mask-wearing, meticulous hand hygiene (using soap or antiseptic solutions), and maintaining social distancing. Even with the subsequent advent of vaccines and the commencement of mass vaccination campaigns, these hygiene measures persistently remain in effect, aiming to curb virus transmission until the achievement of herd immunity. In this scoping review, we delve into the effectiveness of these measures and the diverse transmission pathways, focusing on the intricate interplay within the food network. Furthermore, we explore the virus's pathophysiology, considering its survival on droplets of varying sizes, each endowed with distinct aerodynamic attributes that influence disease dispersion dynamics. While respiratory transmission remains the predominant route, the potential for oral-fecal transmission should not be disregarded, given the protracted presence of viral RNA in patients' feces after the infection period. Addressing concerns about food as a potential viral vector, uncertainties shroud the virus's survivability and potential to contaminate consumers indirectly. Hence, a meticulous and comprehensive hygienic strategy remains paramount in our collective efforts to combat this pandemic.

Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models.

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms.

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Honey's Antioxidant and Antimicrobial Properties: A Bibliometric Study.

Research attention has been drawn to honey's nutritional status and beneficial properties for human health. This study aimed to provide a bibliometric analysis of honey's antioxidant and antimicrobial properties. The research advancements within this field from 2001 to 2022 were addressed using the Scopus database, R, and VOSviewer. Of the 383 results, articles (273) and reviews (81) were the most common document types, while the annual growth rate of published manuscripts reached 17.5%. The most relevant topics about honey's antimicrobial and antioxidant properties were related to the agricultural and biological sciences, biochemistry, and pharmacology. According to a keyword analysis, the most frequent terms in titles, abstracts, and keywords were honey, antimicrobial, antioxidant, bee, propolis, phenolic compounds, wound, antibacterial, anti-inflammatory, and polyphenols. A trend topic analysis showed that the research agenda mainly encompassed antioxidants, pathogens, and anti-infection and chemical agents. In a co-occurrence analysis, antioxidants, anti-infection agents, and chemistry were connected to honey research. The initial research focus of this domain was primarily on honey's anti-inflammatory and antineoplastic activity, wound healing, and antibacterial agents. The research agenda was enriched in the subsequent years by pathogens, propolis, oxidative stress, and flavonoids. It was possible to pinpoint past trends and ongoing developments and provide a valuable insight into the field of honey research.

The Role of Probiotics in Inducing and Maintaining Remission in Crohn's Disease and Ulcerative Colitis: A Systematic Review of the Literature.

Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammatory diseases of the gastrointestinal tract affecting millions of patients worldwide. The gut microbiome partly determines the pathogenesis of both diseases. Even though probiotics have been widely used as a potential treatment, their efficacy in inducing and maintaining remission is still controversial. Our study aims to review the present-day literature about the possible role of probiotics in treating inflammatory bowel diseases in adults. This research was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. We included studies concerning adult patients who compared probiotics with placebo or non-probiotic intervention. We identified thirty-three studies, including 2713 patients from fourteen countries. The role of probiotics in Crohn's disease was examined in eleven studies. Only four studies presented statistically significant results in the remission of disease, primarily when used for three to six months. On the other hand, in twenty-one out of twenty-five studies, probiotics proved effective in achieving or maintaining remission in ulcerative colitis. Supplementation with Bifidobacterium sp. or a combination of probiotics is the most effective intervention, especially when compared with a placebo. There is strong evidence supporting the usage of probiotic supplementation in patients with ulcerative colitis, yet more research is needed to justify their efficacy in Crohn's disease.

Principal methods for isolation and identification of soil microbial communities.

Soil microbial populations play crucial role in soil properties and influence below-ground ecosystem processes. Microbial composition and functioning changes the soil quality through decomposition of organic matter, recycling of nutrients, and biological control of parasites of plants. Moreover, the discovery that soil microbes may translate into benefits for biotechnology, management of agricultural, forest, and natural ecosystems, biodegradation of pollutants, and waste treatment systems maximized the need of scientists for the isolation and their characterization. Operations such as the production of antibiotics and enzymic activities from microorganisms of soil constitute objectives of industry in her effort to cope with the increase of population of earth and disturbance of environment and may ameliorate the effects of global climate change. In the past decades, new biochemical and molecular techniques have been developed in our effort to identify and classify soil bacteria. The goal of measuring the soil microbial diversity is difficult because of the limited knowledge about bacteria species and classification through families and orders. Molecular techniques extend our knowledge about microbial diversity and help the taxonomy of species. Measuring and monitoring soil microbial communities can lead us to better understanding of their composition and function in many ecosystem processes.