ABSTRACT
Our retrospective study aimed to evaluate the effectiveness of monoclonal antibodies (casirivimab and imdevimab) on mild cases of coronavirus disease 2019 patients admitted to the tertiary care center. A total of 161 patients were evaluated of which the test group consisted of 79 and the control group of 82. In the test group the patients had been administered with diluted 250 ml of 0.9 % sodium chloride along with co-formulated casirivimab (600 mg) and imdevimab (600 mg) solution intravenously and in the control group the patients were administered standard coronavirus disease 2019 treatment protocol. The monitoring of patients in both groups was done at least 1 h after drug infusion in the designated room. Post-treatment designed interviews were taken to evaluate the effectiveness of treatment. This retrospective analysis discovered a significant association of symptoms with the group at 48 h for injected and non-injected patients and 1 mo from the chi-square test after injecting monoclonal antibodies. There is no significant association of symptoms with the groups at 3 mo. A significant difference in the symptom distribution through different time points in the injected group and not injected group was observed. From the pairwise McNemar's test, a significant difference in the symptoms between each time in 48 h, the difference was p=0.0075 and after 1 mo, p<0.001 points in both groups. The combination of casirivimab and imdevimab could be considered a treatment of choice for vaccinated, non-vaccinated and mild to highrisk coronavirus disease 2019 patients.
ABSTRACT
COVID-19 respiratory viral disease has changed the entire research communities' focus towards Diseases, Health Care, Treatment and related resources decision making and services. Fuzzy approach gives simplification and improvement in processing logic and speed applied to Data analytics and testing of algorithm for different business purposes may be future research domain. Significant COVID-19 analytics can help to classify and prioritize the resources the future consequences to take enhanced self-ruled decisions, to recognise and design pattern in data spread to design strategic policies for medical, health care units and stakeholders.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
This paper attempts to give an overview of the system which is designed keeping social distancing guidelines in mind. Our system will detect in real-time, if the person in the captured live video is wearing a mask properly or not using a mask detecting algorithm developed using deep learning and neural networks with an accuracy of 96.05%. If and only if the person is wearing a mask, they will be allowed to scan the iris and hence record their attendance, which can be stored in excel or CSV formats. The location of iris biometric is translated to a real-life position in the 3D space with the resolution of 0.lmm. To scan the located biometric this system comprises a robotic arm. End effector of this robotic arm traverses to the translated position of the person's eye to scan iris with an iris scanner. The system employs a 'four degrees of motion' robotic arm that can autonomously align itself to the iris with an accuracy of 96.86%. It is battery operated and has a cylindrical workspace with maximum range of 300mm, hence it is easily deployable in institutions requiring secure authorization while monitoring COVID-19 safety norms. © 2022 IEEE.
ABSTRACT
COVID-19 respiratory viral disease has changed the entire research communities' focus towards Diseases, Health Care, Treatment and related resources decision making and services. Fuzzy approach gives simplification and improvement in processing logic and speed applied to Data analytics and testing of algorithm for different business purposes may be future research domain. Significant COVID-19 analytics can help to classify and prioritize the resources the future consequences to take enhanced self-ruled decisions, to recognise and design pattern in data spread to design strategic policies for medical, health care units and stakeholders. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
COVID - 19 pandemic has brought many problems after recovery. The problems ranges from body functions to all other small issues related to health. But the major impact of COVID - 19 has been on mental health resulting into suicidal tendencies in many patients. Due to lockdown in many parts of country, people have been in their home for a major part of time which has resulted into changes in their mental condition. Socializing has been minimal and going out to work also was hampered due to work from home. In January 2021, 41% adults have been reported with problems of mental fatigue. The efficiency has come down, the outcome of work has been to 80% - 85%, business has gone down because of less innovation and creativeness ability and other related problems. Yoga is giving an impetus to life for many, so to cope up with mental problems the various asanas is providing remedies which will be effective for the entire life. The asanas are being promoted by many yoga experts. The various recommended asanas will provide support to patients having mental problems which are very simple. The yoga guru send a message that if the asanas are done regularly and systematically to get relieved from many different ailment. If the recommended asanas are performed each day it will lead to a better life even if you are suffering from any long term ailment. In this article few postures (asana's) which can be performed at any time in the day but early morning will always be preferred (empty stomach) to get good result. All the asanas should be performed in presence of a trained person after due consultation with your physician. © The Electrochemical Society
ABSTRACT
Introduction: Coronavirus disease 2019 (COVID-19) is caused bysevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2),which was first identified in Wuhan, China in December 2019.Sincethe beginning of the coronavirus disease 2019 (COVID-19) pandemic,many studies have described the quantitative blood findings seen inCOVID-19 patients. However, morphologic changes have beendescribed by only few studies. We report morphologic and quantitative changes in peripheral blood of COVID-19 patients.Aims &Objectives: To examine the complete blood count (CBC),characteristics of the cells detected on peripheral blood smear (PBS)and to correlate whether these findings are related to the clinicalcourse by evaluating the smear.Materials &Methods: Clinical features, complete blood count andperipheral blood smears findings of 100 RT-PCR positive COVID-19patients were evaluated in pathology department of Indira GandhiGovernment Medical College, Nagpur. Patients were divided intodifferent groups depending on thrombocytopenia, bicytopenia andpancytopenia. Peripheral smears of patients were evaluated for cellular morphology. The relationship between the CBC and PBS of allpatients with the severity of disease was analyzed.Result: Red blood cells did not show any significant abnormalities.Neutrophils, lymphocytes showed changes in morphology. Neutrophils showed neutrophillia with pseudo Pelger-Huet anomaly,abnormal segmentation, abnormal shapes of nuclei, pyknotic nuclei,toxic granules in cytoplasm. Relative lymphopenia with atypicallymphocytes, large granular lymphocytes were also seen. Eosinophilswere low which could be attributed to use of steroids in covid 19patients.There was no significant abnormality in basophil andmonocytes. Platelets were decreased and giant platelets were found infew cases.Study showed positive correlation between the severity ofdisease and neutrophil abnormalities and thrombocytopenia. Presenceof mature lymphocytes showed negative correlation with diseaseseverity.Conclusions: CBC and PBS are inexpensive and rapid tests whichcan be done at any institute. Neutrophillic abnormalities and lymphocytopenia along with large granular lymphocytes are the findingsassociated with COVID 19 infection, while high initial mature lymphocyte at the time of diagnosis may be an indicator of betterprognosis and short duration of hospital stay.
ABSTRACT
COVID-19 is an infectious disease that causes respiratory problems similar to those caused by SARS-CoV (2003). In this paper, we propose a prospective screening tool wherein we use chest CT scans to diagnose the patients for COVID-19 pneumonia. We use a set of open-source images, available as individual CT slices, and full CT scans from a private Indian Hospital to train our model. We build a 2D segmentation model using the U-Net architecture, which gives the output by marking out the region of infection. Our model achieves a sensitivity of 0.96 (95% CI: 0.88-1.00) and a specificity of 0.88 (95% CI: 0.82-0.94). Additionally, we derive a logic for converting our slice-level predictions to scan-level, which helps us reduce the false positives.