ABSTRACT
Objective: Oral blood blister, also known as angina bullosa hemorrhagica (ABH), is a rare lesion involving the oral cavity and agitates patients due to its dreadful appearance. This review aims to summarize oral blood blister cases in the literature. Methods: This study is based on the PRISMA guideline. An online search was conducted in PubMed/MEDLINE and Scopus databases without any restriction, and 45 articles were included. Results: Oral blood blister was slightly more prevalent in women, with a ratio of 1.09. The patients' average age was 59.93, and more than half of them were in their lives fourth to sixth decades. Half of the lesions were located on the palate, whereas the tongue, buccal mucosa, lips, the floor of the mouth, and uvula were the other common sites, respectively. Almost one-third of the cases were asymptomatic;however, pain, bleeding, and burning sensation were common symptoms in others. Eating trauma was the most relevant causative factor of this entity (57 %), yet no admissible cause was mentioned in 25 % of the cases. Hypertension, diabetes mellitus, and endocrine disease were among the most frequently reported underlying disorders. COVID-19 has been reported in a confined number of cases. In 60 % of cases, no therapeutic intervention was mentioned, while using mouthwash (6.2 %) and topical analgesics (5.1 %) as means of medicament were also mentioned in the literature. Conclusion: Oral blood blister is more common in middle-aged and elderly patients and is slightly more frequent in women. Physical trauma is the major cause of this lesion. © 2023 Asian AOMS, ASOMP, JSOP, JSOMS, JSOM, and JAMI
ABSTRACT
Contact tracing has become a vital practice in reducing the spread of COVID-19 among staff in all industries, especially those in high-risk occupations such as healthcare workers. Our research team has investigated how wearable IoT devices can alleviate this problem by utilizing 802.11 wireless beacon frames broadcasted from pre-existing access points in a building to achieve room-level localization. Notable improvements to this low-cost localization technique's accuracy are achieved via machine learning by implementing the random forest algorithm. Using random forest, historical data can train the model and make more informed decisions while tracking other nodes in the future. In this project, employees' and patients' locations while in a building (e.g., a healthcare facility) can be time-stamped and stored in a database. With this data available, contact tracing can be automated and accurately conducted, allowing those who have been in contact with a confirmed positive COVID-19 case to be notified and quarantined immediately. This paper presents the application of the random forest algorithm on broadcast frame data collected in February of 2020 at Sentara RMH in Harrisonburg, Virginia, USA. Our research demonstrates the combination of affordability and accuracy possible in an IoT beacon frame-based localization system that allows for historical recall of room-level localization data.
ABSTRACT
The epidemiological aspects of the viral dynamic of the SARS-CoV-2 have become increasingly crucial due to major questions and uncertainties around the unaddressed issues of how corpse burial or the disposal of contaminated waste impacts nearby soil and groundwater. Here, a theoretical framework base on a meshless algorithm using the moving least squares (MLS) shape functions is adopted for solving the time-fractional model of the viral diffusion in and across three different environments including water, tissue, and soil. Our computations predict that by considering the α (order of fractional derivative) best fit to experimental data, the virus has a traveling distance of 1 m m in water after 22, regardless of the source of contamination (e.g., from tissue or soil). The outcomes and extrapolations of our study are fundamental for providing valuable benchmarks for future experimentation on this topic and ultimately for the accurate description of viral spread across different environments. In addition to COVID-19 relief efforts, our methodology can be adapted for a wide range of applications such as studying virus ecology and genomic reservoirs in freshwater and marine environments.
ABSTRACT
With the advent of the Covid-19 virus in early 2020 and the worldwide spread of the disease, various attempts have been made to identify and classify patients infected by COVID-19. According to the latest information in the protocol provided by the ministry of Health and Medical Treatment of Iran, patients' statuses are divided into four phases. Each phase of the disease has its own characteristics. In this study, information about patients with Covid-19 are collected by physicians specialized in infectious diseases. Patients' characteristics are classified based on clinical symptoms, laboratory parameters, and radiological images. In the proposed method, patients are classified according to their features, without supervision, and labels. The obtained results are compared with physicians' diagnosis. The results have revealed that the accuracy of the self-organized mapping is 92.5%. In addition, after clustering, the most important features of clusters are extracted by using the PCA method. After analysis and classification, the most important common features which can distinguish considered patients from other patients are: age, lung involvement and SPO2. Based on these features, the initial screening of patients can be performed with a high reliability. © 2020 IEEE.