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COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer's disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation. [ABSTRACT FROM AUTHOR]

Towards the end of 2019 in Wuhan, suspicions of a new dangerous virus circulating in the air began to arise. It was the start of the world pandemic coronavirus disease 2019 (COVID-19). Since then, considerable research data and review papers about this virus have been published. Hundreds of researchers have shared their work in order to achieve a better comprehension of this disease, all with the common goal of overcoming this pandemic. The coronavirus is structurally similar to influenza A. Both are RNA viruses and normally associated with comparable infection symptoms. In this review, different case studies targeting polymeric materials were appraised to highlight them as an indispensable tool to fight these RNA viruses. In particular, the main focus was how polymeric materials, and their versatile features could be applied in different stages of viral disease, i.e., in protection, detection and treatment. [ABSTRACT FROM AUTHOR]

As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large. [ABSTRACT FROM AUTHOR]

The use of artificial neural networks (ANNs) is a great contribution to medical studies since the application of forecasting concepts allows for the analysis of future diseases propagation. In this context, this paper presents a study of the new coronavirus SARS-COV-2 with a focus on verifying the virus propagation associated with mitigation procedures and massive vaccination campaigns. There were two proposed methodologies in making predictions 28 days ahead for the number of new cases, deaths, and ICU patients of five European countries: Portugal, France, Italy, the United Kingdom, and Germany. A case study of the results of massive immunization in Israel was also considered. The data input of cases, deaths, and daily ICU patients was normalized to reduce discrepant numbers due to the countries’ size and the cumulative vaccination values by the percentage of population immunized (with at least one dose of the vaccine). As a comparative criterion, the calculation of the mean absolute error (MAE) of all predictions presents the best methodology, targeting other possibilities of use for the method proposed. The best architecture achieved a general MAE for the 1-to-28-day ahead forecast, which is lower than 30 cases, 0.6 deaths, and 2.5 ICU patients per million people. [ABSTRACT FROM AUTHOR]

Disinfection of nosocomial pathogens in hospitals is crucial to combat healthcare-acquired infections, which can be acquired by patients, visitors and healthcare workers. However, the presence of a wide range of pathogens and biofilms, combined with the indiscriminate use of antibiotics, presents infection control teams in healthcare facilities with ongoing challenges in the selection of biocides and application methods. This necessitates the development of biocides and innovative disinfection methods that overcome the shortcomings of conventional methods. This comprehensive review finds the use of hydrogen peroxide vapour to be a superior alternative to conventional methods. Motivated by observations in previous studies, herein, we provide a comprehensive overview on the utilisation of hydrogen peroxide vapour as a superior high-level disinfection alternative in hospital settings. This review finds hydrogen peroxide vapour to be very close to an ideal disinfectant due to its proven efficacy against a wide range of microorganisms, safety to use, lack of toxicity concerns and good material compatibility. The superiority of hydrogen peroxide vapour was recently demonstrated in the case of decontamination of N95/FFP2 masks for reuse to address the critical shortage caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the COVID-19 pandemic. Despite the significant number of studies demonstrating antimicrobial activity, there remains a need to critically understand the mechanism of action by performing studies that simultaneously measure damage to all bacterial cell components and assess the correlation of this damage with a reduction in viable cell count. This can lead to improvement in antimicrobial efficacy and foster the development of superior approaches. [ABSTRACT FROM AUTHOR]

During the COVID-19 (coronavirus disease 2019) outbreak, many people were infected, and the symptoms may persist for several weeks or months for recovering patients. This is also known as "long COVID" and includes symptoms such as fatigue, joint pain, muscle pain, et cetera. The COVID-19 virus may trigger hyper-inflammation associated with cytokine levels in the body. COVID-19 can trigger inflammation in the joints, which can lead to osteoarthritis (OA), while long-term COVID-19 symptoms may lead to joint damage and other inflammation problems. According to several studies, sesame has potent anti-inflammatory properties due to its major constituent, sesamin. This study examined sesamin's anti-inflammatory, anti-osteoarthritis, and anti-COVID-19 effects. Moreover, in vivo and in vitro assays were used to determine sesamin's anti-inflammatory activity against the RAW264.7 and SW1353 cell lines. Sesamin had a dose-dependent effect (20 mg/kg) in a monoiodoacetic acid (MIA)-induced osteoarthritis rat model. Sesamin reduced paw swelling and joint discomfort. In addition, the findings indicated that sesamin suppressed the expression of iNOS (inducible nitric oxide synthase) and COX-2 (cyclooxygenase-2) in the RAW264.7 cell line within the concentration range of 6.25–50 μM. Furthermore, sesamin also had a suppressive effect on MMP (matrix metalloproteinase) expression in chondrocytes and the SW1353 cell line within the same concentration range of 6.25–50 μM. To examine the anti-viral activity, an in silico analysis was performed to evaluate sesamin's binding affinity with SARS-CoV-2 RdRp (severe acute respiratory syndrome coronavirus 2 RNA-dependent RNA polymerase) and human ACE2 (angiotensin-converting enzyme 2). Compared to the controls, sesamin exhibited strong binding affinities towards SARS-CoV-2 RdRp and human ACE2. Furthermore, sesamin had a higher binding affinity for the ACE2 target protein. This study suggests that sesamin shows potential anti-SARS-CoV-2 activity for drug development. [ABSTRACT FROM AUTHOR]

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission's stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a watershed moment in human history, causing a profound shift in the global landscape that has affected every aspect of our lives. In Chadaga et al. [[1]], the authors proposed a decision support system that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic, and blood markers. References 1 Chadaga K., Prabhu S., Bhat V., Sampathila N., Umakanth S., Chadaga R. A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence. [Extracted from the article]

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one. [ABSTRACT FROM AUTHOR]