ABSTRACT
As a small group of academicians in a private medical university in Malaysia, we observed, in 2020, how the COVID-19 pandemic, changed our lives and the lives of our students. We would like to share our experiences in this commentary. It describes the contingency plans taken by the School of Medicine at International Medical University (IMU) to ensure that the delivery of the medical curriculum and its assessments has the best possible outcome in these tumultuous times, with plans to improve the delivery of teaching and learning, with an emphasis on technology-enhanced learning (TEL). We hope that this commentary is beneficial to those reading and that the contingency plans developed by IMU will help other institutions in the country and in this region navigate safely through the COVID-19 storm. © Malaysian Association of Education in Medicine and Health Sciences and Penerbit Universiti Sains Malaysia. 2023.
ABSTRACT
Machine learning and deep learning are aspects of artificial intelligence (AI) that help us to make the applications more accurate at predicting some real-life phenomenon without being specifically programmed or designed to do so. In this book chapter, we have discussed the COVID-19 virus, the importance of using face masks, and the existing models of face mask detection. The book chapter is analyzing the current model of sending an alert when a person is detected not wearing a mask and also comments on the improvements that can be done in the model. With the use of a ‘convolutional neural network,' we are making this model more efficient. We are attempting to briefly explain the model of face mask detection using various python libraries and deep learning efficiently. © 2023 Taylor & Francis Group, LLC.
ABSTRACT
Since inception of Corona Virus, 47.6 Cr. individuals got infected and 61L deaths occurred. Still it's going on and spreading across the world. Many health workers, researchers, experts, scientists are making efforts to slow down its pace & putting efforts in evaluating the techniques to detect it. For this, it is highly required to understand the virus & its versions. It is a part of SARS - Severe acute respiratory syndrome. To detect COVID, there are numerous ways but using Chest X-beams we are able to reduce the detection time and cost. To evaluate the Chest X-beams we need radiologists. So here, we develop a model to identify COVID X-beam in comparison to Normal X-beam. These days DL algo's are producing best results in classification. A pre-trained CNN models using large datasets is to preferred for image classification. Firstly our models need to be trained and then tested to recognize the images of X-beams of one of the either case. Logically we have to locate the best CNN model for diagnosis. © 2022 IEEE.
ABSTRACT
COVID-19 originated from Wuhan (China) in December 2019 and has spread rapidly throughout the world. As of August 2020, COVID 19 has affected more than 200 countries and has caused approximately 8 million casualties to date. Government agencies are announcing several mitigation measures based on the guidelines issued by the WHO and the UN. Presently, the top five countries badly affected by this virus are the USA, Brazil, India, Russia, and South Africa. Geographic Information System (GIS) is the technology used to detect, store, manipulate, and analyze spatial (latitude and longitude) information and may be used effectively for understanding and responding to the disease outbreak by real- or near real-time mapping of the diseases. In this study, we make an attempt to generate the Heatmaps (a graphical representation) in a GIS environment using QGIS (an open-source GIS software). Further, we extend the analysis of the study to the prediction of possible COVID-19 outbreak in India using SEIR (Susceptible—Exposed-Infectious-Recovered) model. SEIR model may be used for the prediction of any epidemic disease. This study may help researchers to analyze and predict the COVID-19. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
The unprecedented COVID-19 pandemic, with a zero-patient emerging to millions in few months, has spread across countries and is approaching nearly 280 million of cases worldwide. Billions of check kits are given to hospitals because of exploding cases of COVID-19. Hence, it becomes necessary to implement automatic detection system to restrain COVID-19 spreading. Here, we have demonstrated a convolutional neural network (CNN) model which is trained on X-ray, CT scan images of chest cavity collected from totally different sources to foretell COVID-19 patients and healthy persons. The model is thereby connected to a Web application, which is be made by using Hyper Text Markup Language (HTML), Cascaded Style Sheet (CSS), JavaScript (JS), jQuery, ECMA Script (ES6), and Flask (Python). The model will be providing us the probability so that huge numbers of tests can be done in few clicks with available medical scans. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Microenterprises have been valued for the development of emerging economies across demography. Though the opportunities of growth for microenterprises vary significantly across countries, their susceptibility to business environment-driven vulnerabilities, especially in emerging markets, has been of avid research interest. This study makes a novel contribution in viewing mindfulness as a differentiator in building resilience of microenterprises. Equating the need for disaster resilience to post-COV1D 19 business remodelling for microenterprises in emerging markets, the study delves into qualitative multi-case research to propose an extension of the model of microenterprises' survival postCOVID 19 pandemic by including mindfulness as a component. Three failed enterprises in three different sectors are taken as distinct experiments of structural, cognitive, and relational failure due to pandemic-induced disruptions. In this naturalistic enquiry, we validate the role of social capital in building resilience through detailed within-case descriptions and interpretations. Further, mindfulness is explored in the context of microenterprise resilience to build cognitive flexibility and alertness.
ABSTRACT
Background: Obesity and high BMI are risk factors for COVID-19 affecting>190M adults in the US. Televisits increased from 15K to 25M in October 2020. With 218B downloads, apps are popular. During COVID19, obesity and weight loss apps could be useful for telehealth for doctors and patients with obesity and high BMI. Purpose: can doctors use obesity apps to help their patients and conversely, can patients use weight loss apps to improve their health during COVID-19? Methods: From the MD point of view (POV), search terms "obesity," "obese," and "overweight";from patient POV, search term,"weight loss" were used in Google search engine to identify the top 10 free obesity (OBE) apps, weight loss (WL) apps, respectively in Android and iOS stores. Android and iOS apps were ranked by downloads (DL) and reviews (RE), respectively. Apple does not provide download data. Inclusion criteria: Free, English, >500,000DL in Google Play Store, >100RE in Apple App Store. Parameters: COVID-19, gender, race, language accessibility, BMI, calorie count, diabetes, heart disease, hypertension, and fitness exercises. Obesity: Android(Downloads) vs iOS(Reviews). Android: 1-Lifesum(10M), 2-IdealWt(5M), 3-BMIcalc(5M), 4-WLTracker(1M), 5-WTtracker/BMIcalc(1M), 6-Weightloss Coach(1M), 7-BMI&WT Control(1/2M), 8-WTtrack/BMI(1/2M),9-BMI/idealWTcalc(1/2M),10-BMI/Weight Loss/Calc(1/2M);iOS: 1-BMICalc(22K), 2-MonitorYourWeight(14K), 3-aktiBMI(968), 4-BMICalc/WL(865), 5-Scelta: TrackMyWeight(831), 6-BMICalcforMen&Women(265),7-BMICalc/Manager(257), 8-BMICalc/WtTracker(237), 9-WtTrack/BMImonitor(127), 10-BMI/BMR&BodyFat(103). Weight Loss: Android(Downloads) vs iOS(Reviews). Android: 1-LoseWthome(50M), 2-Calcountmyfitness(50M),3-Losebellyfat(50M),4-LoseWtformen(50M), 5-LoseWtforwomen(50M),6-Monitoryourweight(10M), 7-Calcounterbyloseit(10M),8-Fatburnworkout(5M), 9-WLcalc/BMI(1M), 10-Fooducate(1M). iOS: 1-Myfitnesspal(1.3M),2-LoseitCalcounter(496k),3-fitbit(141K),4-Betterme(115K),5-F ooducate(68K),6-Bodyfast(62K),7-Caloriecounter-mynetdiary(34K), 8-Cronometer(28K),9-Controlmyweight(23K),10-Mydietcoach(20k). Gender=40/40;Race/ethnicity=0/40. COVID19 info= 0/40. Covid19 contact tracing=0/40. Diabetes=0/40;HTN=0/40 Blood pressure. Heart Disease: OBEapps=0/20;WLapps=2/20. Language options: OBEapps=5/20;WLapps=10/20. BMI: OBEapps=17/20;WLapps=19/20. Calorie count: OBEapps=4/20;WLapps=15/20. Fitness exercises: OBEapps=4/20;WLapps=10/20. Existing obesity and weight loss apps provide features for BMI calculation and calorie counting, but they do not provide any education about obesity associated comorbidities e. g. diabetes, hypertension and heart disease. Language barriers and racial/ethnic barriers play a role in health care delivery. Language options and different diets based on culture are not available in the apps. Sexual minority subpopulations have more risk factors for obesity and those issues are not addressed in the apps. Conclusion: eHealth apps for Obesity and Weight Loss may be useful as a starting point for the Physician-Patient interaction during COVID-19 office visits or televisits. As physicians, we can continue to play a role in public education during this pandemic.Presentation: No date and time listed
ABSTRACT
Introduction / Objective: In this study we aimed to elucidate the action of ultra-diluted (potency 6CH as per homeopathic pharmacopeia) ethanolic extract of Arsenicum album (Arsenic 6CH) on cytokine gene expression changes along with alteration of hepatic histology in Gallus gallus embryo (embryonated egg) challenged with the spike protein (S protein RBD of Delta SARS-CoV-2). Method(s): Allantoic fluid was collected during harvesting of the inoculated eggs after antigenic challenge. The gene expression analysis of interferons (IFN alpha, IFN beta, IFN gamma);transforming growth factor-beta 1 (TGF beta1);interleukins (IL-6, IL-8, IL-10, IL-1beta) were studied in real-time PCR. Pre- and post-treatment sets were separately studied. Hepatic tissue of the embryos was collected for histological study. Result(s): The most prominent changes were found with IL-10 expression, which was significantly increased directly by Arsenic 6CH;in pre-and post-treatment experimental sets, its level was further increased, while in control sets, such changes were not delineated. Some changes were found in relation to INF beta, IL-8, IL-1beta, and TGF beta1 in alcohol 6CH (potentized vehicle alcohol as per homeopathic pharmacopoeia), and there was no significant change in other experimental sets. Histological studies of the liver indicated definite protective action of Arsenic 6CH from hepatic pathological changes in both pre and post-treatment experimental sets. Conclusion(s): Our study indicates enhancement of beneficial anti-inflammatory cytokines with hepato-protective action by Arsenic 6CH in Gallus gallus embryo challenged with Delta SARS-CoV-2 spike protein RBD antigen inducing pathological changes. Copyright © 2022 are reserved by International Journal of Pharmaceutical Sciences and Research.
ABSTRACT
The coronavirus, one of the deadliest virus erupted in Wuhan, China in December and has claimed millions of lives worldwide and infected too. This virus has off-late demonstrated mutations thus making it difficult for the health professionals to adopt a uniform means of cure. Many people due to lack of support have confined themselves at home. The hospitals too are running short of equipment and support systems. Thus, computational connectivity between the patients at home and the hospitals needs to be established. The objective of this paper is to propose a framework/model that connects all the stakeholders so that either in regular monitoring or in emergency cases help can be provided to them. It has been well established through research and case studies that critical factors associated with this disease are oxygen level (SPO2), pulse rate, fever, chest infection, cough causing choking, and breathlessness. Data shall be collected, stored, and analyzed for the above symptoms and for this cloud storage and blockchain technology would be used. It has been established through various studies that non-clinical techniques like AI and machine learning prove to be effective for the prediction and diagnosis of COVID-19. Using this theory as the standard basis, machine learning models like SVM, Naive Bayes, and decision trees can be used for the analysis, diagnosis, and prediction. Using IoT and its variants, remote monitoring of patient, and consultation can be provided to the patient. Appropriate action would be taken. In addition, a mobile application would enable the patients to gather or read about experiences of other patients. Thus, it would be established through the proposed framework, that an integrated approach of technologies has a great potential in such applications and offers several advantages.
ABSTRACT
Mathematical Analysis of Infectious Diseases updates on the mathematical and epidemiological analysis of infectious diseases. Epidemic mathematical modeling and analysis is important, not only to understand disease progression, but also to provide predictions about the evolution of disease. One of the main focuses of the book is the transmission dynamics of the infectious diseases like COVID-19 and the intervention strategies. It also discusses optimal control strategies like vaccination and plasma transfusion and their potential effectiveness on infections using compartmental and mathematical models in epidemiology like SI, SIR, SICA, and SEIR. The book also covers topics like: biodynamic hypothesis and its application for the mathematical modeling of biological growth and the analysis of infectious diseases, mathematical modeling and analysis of diagnosis rate effects and prediction of viruses, data-driven graphical analysis of epidemic trends, dynamic simulation and scenario analysis of the spread of diseases, and the systematic review of the mathematical modeling of infectious disease like coronaviruses. © 2022 Elsevier Inc. All rights reserved.
ABSTRACT
Background: Foreign bodies of the Aerodigestive tract in children are increasingly common and reached a peak during the Covid Pandemic. They usually present as a acute respiratory emergency. Management of these foreign bodies pose a technical challenge and have a definite risk of morbidity and mortality even in expert hands. Method(s): Our retrospective study is from November 2016 to November 2021. Result(s): We had 29 patients in this study period. 16 boys and 13 girls. Youngest child was 11 months and oldest child was 17 years. Most children were below 5 years (19)-65%. Metal foreign bodies was the highest (11)-37% in our series. Outcomes were analyzed in terms of types of foreign body, duration of procedure, postoperative ventilation, ICU care and morbidity. Conclusion(s): Most patients were treated successfully by endoscopy (27/29)-93%. 2 patients underwent a surgery-(2/29)-7%. we encountered no deaths in our series. Endoscopic retrieval of foreign bodies is a technical challenge in small children. Maximum technical difficulty was with vegetable foreign bodies as they enlarge in size with time. We report the successful management of the foreign bodies in a tertiary care setup with excellent anaesthesia help and Pediatric Intensive Care Unit (PICU) backup.
ABSTRACT
People are being compelled to adopt a "work from home"strategy because of the COVID-19 outbreak. In today's world, the Internet has evolved into a powerful tool for social connection. People's reliance on digital platforms creates opportunities for deception. Phishing websites are one of the types of internet security issues that target human vulnerabilities rather than software flaws. It is defined as the act of impersonating someone else to steal sensitive information such as usernames and passwords from online users. In this research, we provide an intelligent system for identifying phishing websites that work as an extension to an internet browser and automatically warn the user when a phishing website is detected. © 2022 IEEE.
ABSTRACT
Background: As the oncology patient population grows, continued recruitment of talented individuals is essential. Meaningful and highly educational clinical exposures play a crucial role in a resident's decision to pursue fellowship in oncology. Currently, the majority of oncology exposure occurs in the inpatient setting, where trainees are confronted with many competing clinical demands, leaving minimal time for teaching. Prior work has demonstrated that dedicated time for teaching supplemented with high-yield educational material improves trainee satisfaction and interest in a particular field. We hypothesize that implementing an asynchronous inpatient video oncology curriculum will improve resident clinical competency and satisfaction. Methods: This is an ongoing, single institution educational intervention using the Kern method for curriculum development in the Johns Hopkins Osler Internal Medicine Residency (IRB00307077). A targeted needs assessment has been developed to identify gaps in medical knowledge, assess preparedness in managing common solid tumor inpatient diagnoses, and explore satisfaction with prior educational experiences on this service. Based on these results, ten educational videos will be created by content experts and aligned with the learning objectives of the Osler residency program and ABIM blueprint. Trainees will have dedicated time to watch the videos during their solid tumor rotation. We plan to evaluate effectiveness of our curriculum by measuring completion rates and pre- and post-video multiple choice responses. We will also assess knowledge retention and resident satisfaction at three and six months post-rotation with online questionnaires. Results: Annual Osler program ACGME survey data review revealed a high level of resident dissatisfaction with the oncology clinical experience compared with other rotations. While 83.3% of residents rated their general internal medicine experience as “excellent,” only 52.4% rated their oncology experience as “excellent.” Needs assessment data collection has been delayed by the COVID-19 pandemic but is ongoing. Following topic selection, videos will be created in April 2022 with plans for video curriculum implementation in July 2022. Conclusions: In order to attract and retain trainees in the field of oncology, it is imperative that educational curricula adapts to meet learners' needs. Efficient, evidence-based instructional strategies designed to promote clinical competency for internal medicine residents on an inpatient solid tumor service may foster engagement and rotation satisfaction. We are currently evaluating how an asynchronous video inpatient oncology curriculum may significantly improve the inpatient internal medicine trainee experience. If successful, this curriculum can be adapted for other trainees and practitioners new to the field of oncology.
ABSTRACT
Objective: Describe clinical characteristics of COVID-19 in patients with Parkinson's disease (PD). Background: Individuals with PD have an increased risk of severe illness and poor outcomes in the context of COVID-19, though more research is needed. Design/Methods: We retrospectively analyzed clinical data from 18 consecutive patients with PD and COVID-19 presenting to a suburban hospital in the Northwest United States. Results: Our sample was predominantly male (61%) with a mean age of 71. The most common presenting symptoms of COVID-19 were cough (72%), fever (61%), and shortness of breath (50%). The most common comorbid conditions were hypertension (50%), obstructive sleep apnea (33%), dementia (33%), cardiovascular disease (22%), diabetes mellitus (22%), and lung disease (11%). Two patients (11%) had a history of deep brain stimulation, one of whom was also on an apomorphine pump trial. Two patients were asymptomatic with one testing positive on routine screening following an outbreak at her residential facility and the other testing positive prior to a sleep study. The latter received monoclonal antibody infusion as part of FDA emergency use authorization. Six patients (33%) required hospitalization. Eight patients (44%) had exacerbation of underlying PD symptomology. Five of six patients with PD dementia presented with encephalopathy. Two patients presented with increased tremor, one with increased lightheadedness, and one with increased falls. Two patients died. Both deceased patients had PD dementia and presented with encephalopathy. Conclusions: In this cohort of 18 patients with PD and COVID-19, 44% experienced exacerbation of PD symptomology including encephalopathy in those with dementia, tremor, lightheadedness, and falls. Both mortalities were associated with baseline dementia and encephalopathy as a presenting symptom. The results of our study further highlight the risk of severe illness and poor outcomes in those with PD and COVID-19. These findings will be helpful for treating physicians, researchers, individuals with PD and their carepartners.
ABSTRACT
It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks. © 2022 by Dr Avinash Rajkumar, Pankhuri Agarwal, Dr Mohit Rastogi, Dr Vipin Jain, Dr Chanchal Chawla and Dr Manoj Agarwal.
ABSTRACT
Aims: High-dose corticosteroids reduce mortality in covid- 19 patients requiring oxygen therapy. Dexamethasone-induced hyperglycaemia is a known complication and linked to increased morbidity and mortality. The National Inpatient Diabetes Covid-19 Response Group produced guidance on corticosteroid therapy in covid-19 patients (June 2020). We assessed using the MicroGuide app® to help implement this guidance. Methods: We audited patients that received Dexamethasone and supplemental oxygen for covid-19 during two admission peaks -Oct/ Nov 2020 and Jan/ Feb 2021 (sample n = 30). Along with education and diabetes team ward-rounds, between peaks our trust introduced a guideline summary using the Microguide® smartphone-app. Results: In Oct/Nov20, 15 had known diabetes vs. 15 patients not known to have diabetes;11 and 19 respectively in Jan/Feb21. Capillary blood glucose (CBG) checks carried out as per guidelines in 27% of patients Oct/Nov20 vs. 64% of patients in Jan/Feb21. 60% experienced CBG >12mmol/l at least once in Oct/Nov20, compared with 70% in Jan/Feb21. Diabetic emergencies (DKA/HHS) were ruled out by ketone/pH testing in 57% in Oct/Nov20, improving to 76% in Jan/Feb21 (all had U+E checked). Of the insulin naïve population who experienced hyperglycaemia (n = 15 both), 27% were commenced on intermediate acting insulin Oct/Nov20 compared to 60% in Jan/ Feb21. Conclusion: Most patients experienced hyperglycaemia during admission. CBG monitoring by nursing staff improved between peaks, as did ruling out emergencies. Commencing intermediate-acting insulin if hyperglycaemic, as recommended, improved too. We believe the MicroGuide® app improved these outcomes as training and education of staff was limited during this time period, and this offered remote digital and easy accessible guidance.
ABSTRACT
Myopia, or nearsightedness, is the most common refractive error in children and young adults.[1] Myopia has increasingly become a concern due to the unawareness and stigma among parents.[2] Furthermore, the COVID-19 pandemic has added to the previous woes. The prolonged use of digital screens among children has increased the incidence of myopia and accelerated its progression.[3] Indeed, the incidence and prevalence of myopia have increased considerably.
ABSTRACT
COVID-19 causes high rates of mortality and morbidity in older adults, especially those with pre-existing conditions. Since epilepsy is associated with premature mortality, we aimed to evaluate in-hospital outcomes, including mortality, in older compared (>65) to younger adults (<65) with COVID-19 and epilepsy. We hypothesized that adults >65 years with epilepsy would have higher mortality despite adjustment for comorbidity. This retrospective study in a large multicenter New York health system included consecutive patients with epilepsy admitted with COVID-19 between 3/15/2020-5/17/2021. Epilepsy was identified using a validated ICD-CM based case definition. Outcomes were level of respiratory support, ICU admission, and mortality. Chi-square tests, Fisher's exact tests, Student's t-tests and Mann-Whitney U or Kolmogorov Smirnov tests were conducted as appropriate. Multivariable logistic regression models were generated to examine factors associated with mortality. We identified 173 older and 161 younger adults with epilepsy and COVID-19. Median age of older (>65) compared to younger (<65) adults was 74 vs. 52 (p<.001). A larger proportion of older adults died in hospital (35.8% vs. 23%, p=.01). Older adults were less likely to be discharged to home (21.4% vs. 38.5%, p<.001) and more likely to go to a chronic care facility (19.7% vs. 10.0%, p<.001). Ventilation status (35.8% vs. 39.8%, p=.45) or ICU admission rate (34.7% vs. 44.1%, p=.08) were not significantly different between the age groups. Older adults had higher odds of mortality after adjusting for sex, race, language and Charlson Comorbidity Index (CCI) (OR, 2.04;95% CI, 1.22-3.40, p=0.01). Within the over 65 group, increasing years of age (OR 1.07;95% CI 1.02-1.12, p=0.01), and increasing CCI score (OR 1.16, 95% CI 1.01-1.32, p=0.03) were associated with in-hospital mortality while sex, race, and language were not. Our study found higher in-hospital mortality in older compared to younger adults with epilepsy diagnosed with COVID-19. Consistent with prior work, increasing age and increasing number of comorbid diseases was associated with increased odds of mortality, reinforcing the need to communicate risks of multimorbidity and COVID-19 in older adults with epilepsy.