ABSTRACT
Coronavirus disease (COVID-19) is an infectious disease caused by the coronavirus was first found in Wuhan, China in December 2019. It has infected more than 300 million people with more than 5 million of death cases. Until now, the virus is still evolving producing new variants of concern contributes to the increase the infection rate around the world. Thus, various diagnostic procedures are in need to help physicians in diagnosis disease certainly and rapidly. In this study, deep learning approach is used to classify normal and COVID-19 cases from CT scan images. Normalizer Free CNN network (NFNets) model is implemented on the images. Statistical measures such as accuracy, precision, sensitivity (also known as recall) are used to evaluate the performance of the model against the previous studies. Loss of 0.0842, accuracy of 0.7227, precision of 0.9751 and recall of 0.9727 are achieved. Thus, further optimization on the NFNets learning algorithm is required to improve the classification performanceClinical Relevance-Implementation of deep learning technique to automate diagnosis of diseases such as COVID-19 cases from CT scan images will simplify the clinical flow towards providing reliable intelligent aids for patient care. © 2022 IEEE.
ABSTRACT
Objective: To evaluate behavioral attitude and compliance of employees in adhering to COVID-19 preventive strategies. Methodology: This descriptive study was conducted in CMH Kharian Medical College in the month of September 2020. Convenient sampling was used 442 employees of CMH Kharian Medical College, including faculty, students and administrative/technical staff were included in the study. Prochaska model of behavior change (PMBC) was used to access behavior pattern. Questions were asked about adhering to WHO advised COVID-19 preventive strategies. Data were analyzed using SPSS version 25. Results: Out of 442 participants, 9.7%, 6.8% and 6.6% belonged to a category that didn't see any benefit of using face mask, washing hands for 20 seconds and keeping advised social distancing of minimum 3-6 feet in preventing COVID-19, and they did not have any intention to use these p r e v e n t i v e s t r a t e g i e s (b e l o n g t o P r e-contemplation phase of PMBC). Almost 1.3%, 1.8% and 0.8% of staff belonged to contemplation phase and were of opinion that they were enjoying their own way of life, and they were little concerned that if they continue like this, it might be hazardous to their health. Still they did not adhere to preventive strategies, owing to lack of motivation-hall mark of contemplation phase of PMBC. Conclusion: Majority of personnel (70%) of our medical school were found in maintenance phase once it comes to compliance of preventive strategies. There is dire need to work on remaining 30% of individuals so that those pertaining to pre-contemplation and other three phases do realize the significance of above mentioned preventive strategies in halting the prevention of COVID-19. (Rawal Med J 202;46:698-701).
ABSTRACT
Approximately one-third of patients with COVID-19 develop thrombocytopenia, and it is more commonly seen in those with severe infection. We present a 66-year-old gentleman with confirmed COVID-19 who developed severe thrombocytopenia during his admission. He was asymptomatic at diagnosis. He developed petechial rash over both arms, root of the mouth and under the tongue and was confirmed to have thrombocytopaenia. This was managed by Platelet transfusion. Thrombocytopenia is usually mild in patients with COVID-19 unlike our case. Nonetheless, a comprehensive evaluation of thrombocytopenia remains important as treatment of the underlying cause may reduce the risk of potentially catastrophic consequences. Clinicians should remain vigilant of the association between COVID-19 and thrombocytopenia, and instigate prompt supportive treatment accordingly. © 2021