ABSTRACT
The SARS-CoV-2 i.e., the novel severe acute respiratory syndrome corona virus;has caused massive loss of life. Mitigating this pandemic requires rapid inexpensive technologies for testing COVID-19. Optical sensors can be used to detect the Covid-19 virus by the surface Plasmon resonance phenomenon. Surface plasmon resonance sensors have good sensitivity, response times, fine resolution, and limits of detection. This paper, provides a brief overview on the COVID-19 effects, currently used testing technology, and potential of surface plasmon resonance optical sensors use for detecting this virus. © 2021 Institute of Physics Publishing. All rights reserved.
ABSTRACT
The transmission of SARS-CoV-2, the novel severe acute respiratory syndrome corona virus have caused the corona virus disease (Covid-19) worldwide pandemic. Overcoming this pandemic requires identifying patients to avoid further spread of the disease. Real-time, sensitive, and cost-efficient methods for detecting the COVID-19 virus are crucial. Optical sensors provide one such means to achieve this, especially using surface plasmon resonance due to its advantages such as high sensitivity and excellent detection limits. In this paper, we propose a sensor for COVID-19 detection which is based on a simple Kretschmann configuration with gold layers and thiol-tethered DNA for the ligand layer. Angle interrogation was used to obtain the sensitivity of this structure using Matlab numerical analysis. The performance of the sensor was investigated with two types of prisms, SF10 and SF11, while varying the gold layer thickness between 45 - 60 nm. This information was then used to determine which combination of prism and gold thickness are ideal for detecting COVID-19 using thiol-tethered DNA. Thiol-tethered DNA layer sensor showed the highest sensitivity of 137 degree/RIU when a SF10 prism was used with a 50 - 60 nm gold layer and thiol tethered DNA layer.
ABSTRACT
The unprecedented rise in the number of new coronavirus infections worldwide has prompted many researchers to use mathematical and machine-learning-based prediction models to predict future epidemic patterns that will help governments, health service providers, and society understand how to deal with this situation. Using different machine learning methodologies helps researchers to understand the trend curve clearly. These may lead to a better and more effective fight against the epidemic and reduce or end preventive measures, allowing people to return to their everyday lives. This study is based on an analysis of COVID-19 data of KSA. Also, it demonstrates the prediction of the new confirmed cases and death of COVID-19 in the next ten days from 8th July in KSA, which is considered the period of the performing Hajj in 2021. It uses machine learning models such as Support Vector Machine (SVM), Bayesian Edge (BR), Linear Regression (LR), and Moving Average (MA). Each model provides two types of predictions: the number of newly infected cases and deaths over the next 10 days. The results indicate that SVM and MA forecasts have high accuracy, followed by LR which performs well. The BR performs poorly in forecast scenarios when applied with the available data set in forecasting new confirmed cases. All models were accurate in predicting mortality, with the best performing model being SVM, followed by MA, LR, and BR. It also expects an increase in confirmed cases under the SVM model scenario to 511, 257 on 17th July from 496, 516 on 7th July in the actual daily cumulative cases. The number of deaths will rise to 8, 113 on 17th July from 7, 921 on 7th July in actual cumulative daily data. © 2021 IEEE.
ABSTRACT
Background: Coronavirus Disease 2019 (COVID-19) has spread throughout the world to become a pandemic, making the entire health sector overwhelmed. Hospitals as health service facilities continuously strive to provide optimal service to patients with the confirmed COVID-19 infection. Resident doctors usually handle COVID-19 patients at the Zainoel Abidin Hospital (ZAH). As the front liner in handling the COVID-19 pandemic, these resident doctors must have adequate knowledge and good attitudes in dealing with COVID-19 patients, especially regarding ethical and medicolegal. Aim: This study aims at finding out the level of knowledge and attitudes of resident doctors in dealing with the COVID-19 pandemic at the ZAH, Aceh. Methods: This was a cross sectional study conducted among resident doctors (n=80) in the ZAH during November-December 2020 with a self-administered online questionnaire. It had been tested for validity by experts with a Content Validity Ratio / CVR of 0.738 and reliability of 0.732. The data were analyzed using the frequency distribution test (univariate) and the Chi square correlation test (bivariate). Results and Discussion: The results show that the majority of respondents had inadequate knowledge of ethical and medicolegal issues in handling the COVID-19 pandemic (70.0%);however, they had a positive attitude in overcoming ethical and medicolegal issues, i.e.77.5%. The chi square statistical test result shows no correlation between knowledge and attitude with a p-value of 0.077 (p-value >0.05). Conclusions: Therefore, it can be concluded that the theoretical knowledge of doctors was not sufficient to identify ethical and medicolegal issues, but the attitude in dealing with ethical and medicolegal issues was satisfactory. This result offers a new opportunity and challenge to increase the awareness of doctors in applying their knowledge and attitudes towards handling the COVID-19 pandemic.
ABSTRACT
AIMS: To investigate factors that contribute to patient decisions regarding attendance for arthroplasty during the COVID-19 pandemic. METHODS: A postal questionnaire was distributed to patients on the waiting list for hip or knee arthroplasty in a single tertiary centre within the UK. Patient factors that may have influenced the decision to attend for arthroplasty, global quality of life (QoL) (EuroQol five-dimension three-level (EQ-5D-3L)), and joint-specific QoL (Oxford Hip or Knee Score) were assessed. Patients were asked at which 'COVID-alert' level they would be willing to attend an NHS and a "COVID-light" hospital for arthroplasty. Independent predictors were assessed using multivariate logistic regression. RESULTS: Of 540 distributed questionnaires, 400 (74.1%; 236 awaiting hip arthroplasty, 164 awaiting knee arthroplasty) complete responses were received and included. Less than half (48.2%) were willing to attend for hip or knee arthroplasty while a UK COVID-19 epidemic was in circulation (COVID-alert levels 3 to 5). Patients with worse joint-specific QoL had a preference to proceed with surgery at COVID-alert levels 3 to 5 compared to levels 1 and 2 (hip arthroplasty odds ratio (OR) 1.54 (95% confidence interval (CI) 1.45 to 1.63); knee arthroplasty OR 1.16 (1.07 to 1.26)). The odds of patients with worse joint-specific QoL being willing to attend for surgery at COVID-alert levels 3 to 5 increased further if surgery in a private, "COVID-light" hospital was available (hip arthroplasty OR 3.50 (95% CI 3.26 to 3.71); knee arthroplasty OR 1.41 (95% CI 1.29 to 1.53). CONCLUSION: Patient decisions surrounding elective surgery have been influenced by the global COVID-19 pandemic, highlighting the importance of patient involvement in ensuring optimized provision of elective surgery during these challenging times. Cite this article: Bone Jt Open 2021;2(4):261-270.