ABSTRACT
The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.
ABSTRACT
In recent times, the COVID-19 epidemic has spread to over 170 nations. Authorities all around the world are feeling the strain of COVID-19 since the total of infected people is rising as well as they does not familiar to handle the problem. The majority of current research effort is thus being directed on the analysis of COVID-19 data within the framework of the machines learning method. Researchers looked the COVID 19 data to make predictions about who would be treated, who would die, and who would get infected in the future. This might prompt governments worldwide to develop strategies for protecting the health of the public. Previous systems rely on Long Short -Term Memory (LSTM) networks for predicting new instances of COVID-19. The LSTM network findings suggest that the pandemic might be over by June of 2020. However, LSTM may have an over-fitting issue, and it may fall short of expectations in terms of true positive. For this issue in COVID-19 forecasting, we suggest using two methods such as Cat Swarm Optimization (CSO) for reducing the inertia weight linearly and then artificial intelligence based binomial distribution is used. In this proposed study, we take the COVID-19 predicting database as an contribution and normalise it using the min-max approach. The accuracy of classification is improved with the use of the first method to choose the optimal features. In this method, inertia weight is added to the CSO optimization algorithm convergence. Death and confirmed cases are predicted for a certain time period throughout India using Convolutional Neural Network with Partial Binomial Distribution based on carefully chosen characteristics. The experimental findings validate that the suggested scheme performs better than the baseline system in terms of f-measure, recall, precision, and accuracy.
ABSTRACT
Background: Severe Acute Respiratory Syndrome Corona Virus Disease 2019 (COVID-19) is responsible for the COVID-19 pandemic causing respiratory illness worldwide. Hence there is an urgent need for simple, rapid, and accurate tests for diagnosis. Performance characteristic of Rapid Antigen detection test for identifying sensitivity and specificity with gold standard Real-Time Polymerase Chain Reaction and correlate the significance of non-specific parameters like CRP(C- Reactive Protein), IL-6(Interleukin-6), Procalcitonin for diagnosis of COVID-19. Method(s): The Rapid Antigen Detection test was compared with Real-Time Polymerase Chain Reaction to detect SARS CoV-2 in respiratory specimens. 100 respiratory samples (mainly nasopharyngeal and throat swab) and for CRP, IL6, and Procalcitonin serum samples were obtained from COVID-19 suspected cases, mostly in-patients at Saveetha Medical College from April 2021- to September 2021. Result(s): Out of 100 samples, 67% were positive, 33% were negative for SARS-CoV2 RNA by RT-PCR assay. When compared with Rapid Antigen Test, the RT-PCR test showed 83.8% sensitivity and 59.3% specificity, while non-specific parameters correlation in diagnosis of COVID-19 showed CRP insignificant, IL-6 and Procalcitonin significant. Conclusion(s): RT-PCR is considered the standard gold method for diagnosing COVID-19. On comparing RT-PCR with other non-specific tests like CRP, Procalcitonin, IL-6 showed Procalcitonin and IL-6 can be considered non-specific tests for diagnosing COVID-19. Copyright © 2022 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
ABSTRACT
INTRODUCTION: These are unprecedented times, where a global pandemic disrupts all aspects of local clinical practice. Yet, providing care for people with diabetic foot disease remains crucial: as clinicians caring for these patients, during peak of covid 19 many branches of medicine have adapted to telemedicine, this is difficult and challenging for the diabetic foot which often requires "hands on" treatment. METHODOLOGY: All the diabetic foot disease cases treated in various tertiary care medical colleges during covid period 1March2020 to 31 August 2021 admitted with RTPCR covid positive are taken for study. RESULTS : Total number of covid positive patients admitted and treated are 29,170 of them 9421 are DIABETIC, 143 had diabetic foot disease 7 patients undergone Below Knee AMPUTATION 19-PATIENTS NEED AMPUTAION OF DIGITS, 117 NEED DEEP DEBRIDEMENT, no mortality due to covid19, diabetic foot patients, but delayed wound healing, infections, AND INTENSIVE CARE ADMISSIONS ARE MORE COMPARED TO OTHER PATIENTS. CONCLUSION: There is no doubt that the covid-19 experience when handling diabetic foot problems will likely transform our approach to the management of diabetic foot disease especially in the areas of digital health and smart technology.double care, vaccination, mask, social distancing frequent soapand water hand wash all are necessary with adquate diabetic control. patient education plays a major role, daily mirror examination of feet and any ulcer, blister even painless should be attended immediately in order to prevent the vicious cycle of sepsis-uncontrolled DIABETES.
ABSTRACT
Introduction: The coronavirus disease 2019 (covid-19) pandemic has had a substantial effect on surgeons and patients who require surgical care. providing care for patients with surgical disease requires a unique and intimate relationship between the patient and surgeon, and this interaction and contact cannot be replaced by telehealth. as such, the surgical workforce has faced distinct challenges compared with nonsurgical specialties during the covid-19 pandemic. specific issues include the best approach to protect health care personnel and the patient;the ability to efficiently regulate delivery of surgical care;the detrimental effects on patients with surgical disease;the financial implications of the pandemic on health care systems;the management of surgical workforce shortages;the implications for education, research, and career development;and the emotional toll to all involved. Aim of study :to find out the incidence, complications, and out come of patients presented with surgical emergencies with covid-19 Methodology: All the covid positive patients confirmed by RTPCR presented with acute surgical emergency including LSCS are taken for study the study period is between march2019 to august2021 in various medical college hospitals, are taken for study. Results : Of the 25,609 patients admitted in the hospital with covid 19 required 338 lscs, 43, first trimester abortions,31 required emergency laporatomy(acute intestinal abstuction7,obstucted, strangulated ingunal hernia 9 acute appendicitis 15,) 143 diabetic foot disease.
ABSTRACT
INTRODUCTION: As of December 18, 2020, there have been nearly 17 million cases of COVID-19 in the United States. Early studies reported intubation rates between 12.2-29.2% with varying criteria between sites and as the pandemic has progressed. With the rapid spread of infection and increased use of ventilators, triage of patients based on factors such as their past medical history can help guide resource allocation. This retrospective study examined a large multi-center cohort of patients with COVID-19 infection to identify risk factors associated with intubation. METHODS: Adult patients with an emergency department or inpatient encounter from December 1, 2019 to June 30, 2020 with COVID-19 were included in the Cerner COVID-19 dataset, which was extracted from 62 hospital systems throughout the United States. Only patients with at least two previous visits in the past 5 years were included. ICD-10 diagnosis codes, demographic data, and discharge status were collected and classified based on intubation status. Logistic regression model identified independently associated risk factors for intubation. RESULTS:18,467 patients with confirmed SARS-CoV-2 infection were identified, with 5,525 seen only in the ED and 12,942 hospitalized. The mean age was 54.3 ± 20.5 years, the mean body mass index (BMI) was 30.42 ± 11.72, 8,397 (45.6%) patients were male, and 938 (7.2%) were intubated. 1,829 (14.1%) patients died in the hospital and 4,185 (22.7%) had a non-routine discharge, which encompasses home health care, short-term hospital, other facility including intermediate care and skilled nursing home. Using multivariate logistic regression, patients who were intubated were more likely to present with stroke, pneumonia, acute kidney injury, septic shock, respiratory failure, cardiac arrest, and encephalopathy. From patient demographics and past medical history, male sex (p=0.011), age > 35 (p=0.035 for 35-49 years old;p<0.001 for >50 years old), BMI > 30 (p=0.013 for BMI 30-40;p=0.004 for BMI >40), and previous respiratory failure (p=0.014) were independently associated with intubation. CONCLUSIONS: Among patients with COVID-19, male sex, increased age, BMI > 30, and previous respiratory failure were independently associated with the risk for intubation. Intubated patients were also more likely to present with stroke, pneumonia, acute kidney injury, septic shock, respiratory failure, cardiac arrest, and encephalopathy. Clinicians should be aware of these independent risk factors for intubation to facilitate resource allocation and minimize the possibility of unanticipated crash intubations.