Is chlorhexidine mouthwash effective in lowering COVID-19 viral load? A systematic review.

OBJECTIVE: This review aims to determine whether there is considerable evidence that mouthwashes containing chlorhexidine (CHX) lower the COVID-19 virus load in saliva. MATERIALS AND METHODS: A comprehensive literature search was carried out in PubMed/Medline, EMBASE, LILACS, Scopus, Web of Science and Cochrane Library, Google Scholar, Open Gray, and ProQuest electronic databases using the keywords: "coronavirus infections" or "coronavirus" or "covid 2019" or "sars 2" or "sars-cov-2" or "sars-cov-19" or "severe acute respiratory syndrome coronavirus 2" or "coronavirus infection" or "severe acute respiratory pneumonia outbreak" and "CHX" or "CHX Hydrochloride" or "CHX Digluconate." A manual search of the articles was also conducted utilizing the reference lists of articles. The in vitro experimental and clinical studies that tested CHX mouthwash were included. Study selection was not restricted or limited to a specific gender, age, ethnicity of individuals, or time of publication. A mix of keywords and proper truncations were used to search for databases. RESULTS: Twelve studies (7 clinical and 5 in vitro) published between 2020 and 2021 were included in this systemic review. Five randomized controlled trials and one clinical case series demonstrated the effectiveness of CHX in reducing the oral viral load; one was inconclusive. Of the five in vitro studies, three showed that CHX is effective against SARS-CoV-2, and two studies denied the effectiveness of CHX. All in vitro studies tested CHX activity concentrations of 0.2, 0.12, and 0.1%. One study reported more than a 99.9% reduction in SARS-CoV-2 viral load in a minimal contact time of 30 seconds. CHX exhibited potent antiviral activity at higher concentrations without cytotoxicity. CONCLUSIONS: Despite differences in the published research, CHX at different concentrations may be effective in lowering the SARS-COV-2 viral load in saliva.

Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology dataset. In the proposed MOKELM-CPED model, the data first undergoes pre-processing to transform the medical data into useful format. Followed by, data classification process is performed by following Kernel Extreme (SOS) optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection efficiency. In order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner, a comprehensive experimental analysis was conducted and the results were inspected under diverse aspects. The outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

Performance improvement of decision tree: A robust classifier using tabu search algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.