A case series of diabetic ketoacidosis associated with SGLT2 inhibitors

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are increasingly initiated as treatment for type 2 diabetes due to favourable cardiorenal characteristics. However, studies have identified an increased risk of diabetic ketoacidosis (DKA). We carried out a retrospective, case-based study at East and North Herts NHS Trust between February 2018 and December 2020. Fifteen cases of SGLT2i associated DKA were identified in people with presumed type 2 diabetes;33.3% were classed as euglycaemic DKA with a blood glucose of <11mmol/L. All cases were associated with a significant precipitating factor including diarrhoea, vomiting, reduced oral intake and sepsis. One case was related to COVID-19. Two people were subsequently found to have raised islet autoantibodies suggesting type 1 diabetes or latent autoimmune diabetes in adults. It is important that awareness of SGLT2i associated DKA is raised among users and health care practitioners, including the recognition of euglycaemic DKA. Sick day rules should be emphasised and reiterated at clinical encounters. Non-specialists in primary care, oncology and in perioperative settings should be empowered to advocate for temporary withdrawal and there should be readier access to blood ketone monitoring when required. When SGLT2i associated DKA occurs, due consideration should be given to evaluate the diabetes classification and investigate the circumstances of the event. Copyright © 2023 John Wiley & Sons.Copyright © 2023 John Wiley & Sons, Ltd.

Telerheumatology: A historical perspective

This chapter provides an overview of the historical context in the field of telemedicine and the origins of telerheumatology. A historical perspective lays the groundwork for the following chapters of the book and frames our discussion as telerheumatology concepts evolve and undergo transformation as the COVID-19 pandemic continues. The first part of the chapter discusses the major breakthroughs and accomplishments that set the stage for how we currently provide virtual care. The second part of the chapter is an oral history obtained from one of the pioneers of telemedicine. The story of his career allows for a unique way to review historical landmarks in telemedicine and illustrates their impact on current clinical practice. While telemedicine concepts have changed a great deal since the origins of providing care in this way, we do see the same themes and goals despite the ever-changing technology and dynamic healthcare delivery environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022. All rights reserved.

Clinical and social determinants of health features of SARS-CoV-2 infection among Black and Caribbean Hispanic patients with heart failure: The SCAN-MP Study.

Patients with heart failure (HF) often have multiple chronic conditions and are at increased risk for severe disease and mortality when infected by SARS-CoV-2, the virus that causes COVID-19. Furthermore, disparities in outcomes with COVID-19 have been associated with both racial/ethnic identity but also social determinants of health. Among older, urban-dwelling, minority patients with HF, we sought to characterize medical and non-medical factors associated with SARS-CoV-2 infection. Patients with HF living in Boston and New York City over 60 years of age participating in the Screening for Cardiac Amyloidosis with Nuclear Imaging (SCAN-MP) study between 12/1/2019 and 10/15/2021 (n = 180) were tested for nucleocapsid antibodies to SARS-CoV-2 and queried for symptomatic infection with PCR verification. Baseline testing included the Kansas City Cardiomyopathy Questionnaire (KCCQ), assessment of health literacy, biochemical, functional capacity, echocardiography, and a novel survey tool that determined living conditions, perceived risk of infection, and attitudes towards COVID-19 mitigation. The association of infection with prevalent socio-economic conditions was assessed by the area deprivation index (ADI). There were 50 overall cases of SARS-CoV-2 infection (28%) including 40 demonstrating antibodies to SARS-CoV-2 (indicative of prior infection) and 10 positive PCR tests. There was no overlap between these groups. The first documented case from New York City indicated infection prior to January 17, 2020. Among active smokers, none tested positive for prior SARS-CoV-2 infection (0 (0%) vs. 20 (15%), p = 0.004) vs. non-smokers. Cases were more likely to be taking ACE-inhibitors/ARBs compared to non-cases (78% vs 62%, p = 0.04). Over a mean follow-up of 9.6 months, there were 6 total deaths (3.3%) all unrelated to COVID-19. Death and hospitalizations (n = 84) were not associated with incident (PCR tested) or prior (antibody) SARS-CoV-2 infection. There was no difference in age, co-morbidities, living conditions, attitudes toward mitigation, health literacy, or ADI between those with and without infection. SARS-CoV-2 infection was common among older, minority patients with HF living in New York City and Boston, with evidence of infection documented in early January 2020. Health literacy and ADI were not associated with infection, and there was no increased mortality or hospitalizations among those infected with SARS-CoV-2.

Effectiveness of Passenger Attraction Policies: A Pre-COVID-19 and Post-lockdown Comparison

The effectiveness of passenger attraction policies designed to induce a modal shift from private mode of transport to public mode of transport is tested frequently as a solution to the constantly dropping ridership rates in public transportation (PT). However, with COVID-19 pandemic in the picture, will the policies that were previously tested effective stand the test of time? Using the work-trip data collected from employees working in Thiruvananthapuram City, this study compares the effectiveness of six passenger attraction policies, aimed at decreasing the travel time and travel cost parameters, in a pre-COVID-19 and a post-lockdown scenario. Fuzzy logic-based mode choice models are developed to perform policy sensitivity analysis. The policies such as improving PT coverage and supply, introducing parking prohibition on major streets, operating non-stop bus services, reducing return-trip fares, early bird pre-peak hour discounts, and providing monthly PT season tickets are tested. The results show that, compared to the pre-COVID-19 model, the effectiveness of two out of the six policies reduces for the post-lockdown model, and the two policies being related to the travel cost parameter. The six policies are found to induce a private to public modal shift ranging from 5.8 to 7.9% for the pre-COVID-19 scenario, while the post-lockdown model gives a shift ranging from 5.8 to 7.1%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Predictive Score for Patients Hospitalized With COVID-19 in Resource-Limited Settings.

Background and aims The second wave of coronavirus disease 2019 (COVID-19) has been devastating in India and many developing countries. The mortality reported has been 40% higher than in the first wave, overwhelming the nation's health infrastructure. Despite a better understanding of the disease and established treatment protocols including steroids and heparin, the second wave was disastrous. Subsequent waves have the potential to further cripple healthcare deliveries, also affecting non-COVID-19 care across many developing economies. It is then important to identify and triage high-risk patients to best use the limited resources. Routine tests such as neutrophil and monocyte counts have been identified but have not been successfully validated uniformly, and their utility is still being understood in COVID-19. Various predictive models that are available require online resources and calculators and additionally await validation across all populations. These, although useful, might not be available or accessible across all institutions. It is then important to identify easy-to-use scores that utilize tests done routinely. In identifying with this goal, we did a retrospective review of the institutional database to identify potential predictors of intensive care unit (ICU) admission and mortality in patients hospitalized during the second wave who accessed healthcare at our academic setup. Results Three predictors of mortality and four predictors of ICU admission were identified. Absolute neutrophil count was a common predictor of both ICU admission and mortality but with two separate cut points. An absolute neutrophil count of >4,200 predicted need for ICU admission (odds ratio (OR): 3.1 (95% confidence interval (CI): 2.0, 4.8)), and >7,200 predicted mortality (adjusted OR: 4.2 (95% CI: 1.9, 9.4)). We observed that a blood urea level greater than 45 was predictive of needing ICU care (adjusted OR: 8.0 (95% CI: 3.7, 17.6)). In our dataset, serum ferritin of >500 was predictive of ICU admission (adjusted OR: 2.7 (95% CI: 1.2, 5.9)). We noted a right shift of partial pressure (p50 is the oxygen tension at which hemoglobin is 50% saturated) (p50c) in SARS-CoV-2 as a predictor of ICU care (OR: 2.6 (95% CI: 1.7, 3.9)) when partial pressure is >26.5. In our analysis, a serum protein of less than 7 g/dL (OR: 2.8 (95% CI: 1.7, 4.4)) was a predictive variable for ICU admission. An LDH value of >675 was predictive of severity with a need for ICU admission (OR: 9.2 (95% CI: 5.4, 15.5)) in our series. We then assigned a score to each of the predictive variables based on the adjusted odds ratio. Conclusion We identified a set of easy-to-use predictive variables and scores to recognize the subset of patients hospitalized with COVID-19 with the highest risk of death or clinical worsening requiring ICU care.

Digitalization of Online Classes Among Higher Secondary Students in the Emerging Shift of Post Covid-19 (Second Wave)

The second wave of COVID-19 in India has left higher secondary school students befuddled, unhappy, and unsure about their future. During the second wave of the COVID-19 epidemic, a number of factors influence the effectiveness of online learning. Hence, the main objective of this research paper is focused on understanding the factors influencing online learning among higher secondary students. Researchers identified variables such as attitude, tools and technology, and quality of teaching and social support through extensive literature review. The research study adopted snowball sampling technique and used a survey-based online questionnaire for collecting the data;responses were obtained from 394 respondents from the state of Kerala in India. PLS-SEM was used to test the proposed hypotheses. The results of the study indicate that quality of teaching is the only factor that impacts the effectiveness of online classes among higher secondary students. Attitude, technology and tools, and social support are observed to have insignificant impact on online learning effectiveness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Anticoagulation and Antiplatelet Therapy for Prevention of Venous and Arterial Thrombotic Events in Critically Ill Patients With COVID-19: COVID-PACT.

BACKGROUND: The efficacy and safety of prophylactic full-dose anticoagulation and antiplatelet therapy in critically ill COVID-19 patients remain uncertain. METHODS: COVID-PACT (Prevention of Arteriovenous Thrombotic Events in Critically-ill COVID-19 Patients Trial) was a multicenter, 2×2 factorial, open-label, randomized-controlled trial with blinded end point adjudication in intensive care unit-level patients with COVID-19. Patients were randomly assigned to a strategy of full-dose anticoagulation or standard-dose prophylactic anticoagulation. Absent an indication for antiplatelet therapy, patients were additionally randomly assigned to either clopidogrel or no antiplatelet therapy. The primary efficacy outcome was the hierarchical composite of death attributable to venous or arterial thrombosis, pulmonary embolism, clinically evident deep venous thrombosis, type 1 myocardial infarction, ischemic stroke, systemic embolic event or acute limb ischemia, or clinically silent deep venous thrombosis, through hospital discharge or 28 days. The primary efficacy analyses included an unmatched win ratio and time-to-first event analysis while patients were on treatment. The primary safety outcome was fatal or life-threatening bleeding. The secondary safety outcome was moderate to severe bleeding. Recruitment was stopped early in March 2022 (≈50% planned recruitment) because of waning intensive care unit-level COVID-19 rates. RESULTS: At 34 centers in the United States, 390 patients were randomly assigned between anticoagulation strategies and 292 between antiplatelet strategies (382 and 290 in the on-treatment analyses). At randomization, 99% of patients required advanced respiratory therapy, including 15% requiring invasive mechanical ventilation; 40% required invasive ventilation during hospitalization. Comparing anticoagulation strategies, a greater proportion of wins occurred with full-dose anticoagulation (12.3%) versus standard-dose prophylactic anticoagulation (6.4%; win ratio, 1.95 [95% CI, 1.08-3.55]; P=0.028). Results were consistent in time-to-event analysis for the primary efficacy end point (full-dose versus standard-dose incidence 19/191 [9.9%] versus 29/191 [15.2%]; hazard ratio, 0.56 [95% CI, 0.32-0.99]; P=0.046). The primary safety end point occurred in 4 (2.1%) on full dose and in 1 (0.5%) on standard dose (P=0.19); the secondary safety end point occurred in 15 (7.9%) versus 1 (0.5%; P=0.002). There was no difference in all-cause mortality (hazard ratio, 0.91 [95% CI, 0.56-1.48]; P=0.70). There were no differences in the primary efficacy or safety end points with clopidogrel versus no antiplatelet therapy. CONCLUSIONS: In critically ill patients with COVID-19, full-dose anticoagulation, but not clopidogrel, reduced thrombotic complications with an increase in bleeding, driven primarily by transfusions in hemodynamically stable patients, and no apparent excess in mortality. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04409834.

Clinical trial transparency regulations: Implications to various scholarly publishing stakeholders

As thousands of 2019 Corona virus disease (Covid-19) clinical trials are continuously getting added to various registries these days, good practices on data sharing and transparency have become one of the prime topics of discussion than ever before. Although trial registration is considered a crucial step, there is a lack of integration between registration and published literature. Trial outcomes are a matter of public interest, but sponsor compliances are not adequate with the recommended guidelines. Although the global recognition of data transparency increases day by day, there is still a long journey to travel. It is high time that scholarly publishing stakeholders should put in a collaborative effort to check author compliance. In this article, we aimed to comprehend and discuss the imperative roles of various scholarly publishing stakeholders in improving clinical trial transparency during this pandemic situation and highlight the changing paradigm towards the pressing need for reporting clinical trial data more effectively.

Sentimental Analysis on Online Education Using Machine Learning Models

Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students’ mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students’ learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils’ feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students’ feelings. Students’ favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Experiences of COVID-19-Recovered Healthcare Workers in a Tertiary Hospital in the Philippines: A Mixed-Method Inquiry.

COVID-19 pandemic affected the mental health of the global population. Among the most vulnerable are the healthcare workers (HCWs) who got infected but returned to the frontline after recovery. Currently, there is a dearth of information and understanding on the psychological status and actual lived experience of the recovered HCWs in the Philippines. The present study investigated the psychological status and experiences of 93 COVID-19-recovered HCWs from a tertiary hospital in the Philippines using a mixed-method approach, particularly the explanatory-sequential design. Participants completed the Impact of Event Scale-Revised, and the Depression, Anxiety, and Stress Scale-21 in the quantitative phase. Selected participants took part in focus group discussions in the qualitative phase. Integrated results showed that our participants experienced significant COVID-19-related distress (mean IES-R score = 25.5; partial impact), anxiety (mean subscale score = 7.4; mild), and depression (mean subscale score = 8.1; mild). Certain sociodemographic and professional characteristics and the length of quarantine days appear to affect the psychometric scores. The quantitative results are supported by the participant's description of recovery experiences as living in uncertainty, distress, fatigue, dissociation, and valuation of life. In summary, adequate psychological support and intervention program should be prioritized and provided by hospital management for recovered HCWs to prevent the development of more serious mental health concerns that may significantly affect their tasks in caring for patients and in-hospital management.

KNOWLEDGE OF SAFETY PRECAUTIONS AND EMERGENCY MANAGEMENT DURING COVID PANDEMIC AMONG DENTISTS IN SAUDIARABIA: CROSS-SECTIONAL STUDY

Recently, with the emergence of world pandemic called COVID-19 virus all over the world, dental practitioners have stood out as high risked front liners. The aim of this study is to analyse the knowledge and management of emergency and safety precautions implemented by dentists during the pandemic of COVID-19 in Saudi Arabia. An online survey was used for this cross-sectional study using google forms and was distributed to dental professionals who works in government hospitals, private clinics, and academic universities in Saudi Arabia. Statistical evaluation was done using the data that was obtained from 355 dentists (academicians, private practitioners, military and government employees), with the power of the sample being 0.85. Relevant awareness regarding the incubation period and symptoms of COVID-19 virus was observed among the dental professionals. Preparedness and perception among dental professionals seem to be satisfactory and statistically significant. Obligatory improvements should be provided through educational campaigns.

Implementation of Various Modulation Techniques using Scilab: A User-Friendly Solution

COVID 19 situation is a big blow to students specially undergraduates wherein they severely suffered and are restricted to the online classes. Core subjects in electronics and communication engineering requires hardware laboratories wherein the theoretical explanation is made clearer by practical implementations. Analog communication is supposed to be the fundamental core subject of electronics with hardware lab wherein student can have a clear idea of various modulated waveforms. In this situation when all are restricted to homes a user-friendly solution came up, an open-source software tool wherein students and faculties can implement all hardware related experiments easily. Scilab is a replacement for licensed software such as MATLAB with simple syntax which can be easily understood. In this paper scilab codes are developed for various modulation techniques such as amplitude modulation (AM), frequency modulation (FM) and pulse amplitude modulation (PAM) which can be used to make the students comfortable with the concepts of modulation. A feedback survey was taken by the undergraduate students of electronics and communication engineering and it clearly shows that students are interested and comfortable in using scilab programming and wished to use this tool while implementing major project. © 2022 IEEE.

Stress, Anxiety and Depression of Medical Teachers during COVID-19

Introduction: Medical teachers have been experienced different kinds of psychological stress and anxiety during COVID-19 .The pandemic has not only affected the mental state of students , since teachers have also accumulated a high level of stress since beginning of crisis .This stress has often been accompanied by symptoms of anxiety ,depression and sleep disturbances as consequence of the increased workload resulting from home teaching. Aim: The aim of study was to investigate the stress, anxiety and depression on medical teachers during COVID-19. Materials and methods: The study was carried out in Saveetha medical college. An online survey was conducted and disturbuted to the medical teachers via the google forms containing questionnaire session. The level of stress measures based on the 10 items perceived stress scale .The level of anxiety was measured based on the 7 items generalized anxiety disorder scale. Results: A total of 370 participants responded to perceived stress component of survey of whom 17% had high stress, 67% had moderate stress, 15% had low perceived stress. Being female was significantly associated with moderate /high stress. A Total of 201 participants responded to generalized anxiety disorder component of the survey ,of whom had mild anxiety (28%), had moderate anxiety (39%) and 46% had severe anxiety .A total of 169 participants responded to the depression component of survey of whom 72% had high depression 18% low depression. Conclusion: A considerable proportion of medical teachers stress and anxiety during COVID-19 outbreak .There a need to establish mechanisms to reduce the risks of stress and anxiety among medical teachers.

Regression Based Machine Learning to Generate and Validate a Metric for Food Insecurity

With the increased cost of living, exacerbated by COVID-19, thousands in New Jersey lack secure access to nutritious food. Given the importance of the issue, this research aims to produce an accurate metric using accessible data to quantify food insecurity. 16 potential explanatory variables, such as median household income and homeless population, were chosen as their values were defined across all NJ counties from 2015-2019. Using multiple linear regression, 14 unique metrics were created after four different variable pruning methods. The leading metric, with an adj. R2 value of 0.932, demonstrates the correlation between food insecurity, population, median household income, total population with health insurance, and population with private health insurance. The implementation of this metric could serve as a tool in predicting areas of food insecurity, highlighting affiliated factors, and revealing connections between racial populations. © 2021 IEEE.

Forecasting Evictions to Determine Areas at High Risk of Housing Insecurity

The COVID-19 pandemic has contributed to an escalating housing crisis in the United States. After the onset of pandemic-related lockdowns in March of 2020, eviction morato-riums were swiftly enacted, enabling millions of tenants in rental residences to remain in their homes while unemployment surged and families lacked the resources to pay rent. With the majority of these moratoriums scheduled to lift by the end of 2021, many tenants will face imminent eviction. This paper outlines the development of a multivariable, time-series regression model that can be used to forecast eviction rates as a function of changing economic conditions in a given geographical area and time period. When the model is applied to New Jersey in the current time period, the results reveal a buildup of evictions, which upon the lifting of the eviction moratorium will significantly intensify the existing housing insecurity crisis. © 2021 IEEE.

Smart Online Exam Proctoring Assist for Cheating Detection

Online exams are the most preferred mode of exams in online learning environment. This mode of exam has been even more prevalent and a necessity in the event of a forced closure of face-to-face teaching such as the recent Covid-19 pandemic. Naturally, conducting online exams poses much greater challenge to preserving academic integrity compared to conducting on-site face-to-face exams. As there is no human proctor for policing the examinee on site, the chances of cheating are high. Various online exam proctoring tools are being used by educational institutes worldwide, which offer different solutions to reduce the chances of cheating. The most common technique followed by these tools is recording of video and audio of the examinee during the whole duration of exam. These videos can be analyzed later by human examiner to detect possible cheating case. However, viewing hours of exam videos for each student can be impractical for a large class and thus detecting cheating would be next to impossible. Although some AI-based tools are being used by some proctoring software to raise flags, they are not always very useful. In this paper we propose a cheating detection technique that analyzes an exam video to extract four types of event data, which are then fed to a pre-trained classification model for detecting cheating activity. We formulate the cheating detection problem as a multivariate time-series classification problem by transforming each video into a multivariate time-series representing the time-varying event data extracted from each frame of the video. We have developed a real dataset of cheating videos and conduct extensive experiments with varying video lengths, different deep learning and traditional machine learning models and feature sets, achieving prediction accuracy as high as 97.7%.

Neutrophil Count is Predicitive of Mortality in COVID(Long): An Easy-to-Use Biomarker Based Modelto Predict Mortality at Admission

Introduction: The second wave of COVID has been devastating inIndia and many developing countries. The mortality has been reported40% higher than in the first wave overwhelming the nation's healthinfrastructure. Despite better understanding of the disease andestablished treatment protocols including steroids and heparin;thesecond wave was disastrous. Subsequent waves have the potential tofurther cripple health care deliveries affecting non COVID care alsoacross many developing economies. It is then important to identifyand triage high risk patients to best use the limited resources.Aims &Objectives: The objective of this study was to identifypotential predictors of mortality in the second wave who accessedhealth care at our academic setup.Materials &Methods: All patients admitted at our centre from 01February through June 15 2021 were included in the analysis. Areduced set of potential predictor variables was selected a priori,which included routine investigations sent on patient admission at ourcenter. These were bundled as groups namely;coagulation markers(INR, APTT, Fibrinogen, d-Dimer), Inflammatory markers (ESR,CRP, Ferritin), Hemogram, Liver function tests, Renal function tests,Arterial blood gas analytics and Glucose levels at admission (measured using the arterial blood gas analyzer).We used a two stage model building process.Result: We collected data from 790 patients. The overall mortalityrate was 10% (79 patients). The median age of patients in the cohortwas 57 years (range 1-99). Patients travelled a distance of 25 km (1-262 km) to seek care. We identified 78 candidate predictor variablesmeasured at hospital admission.n entering variables into a logisticregression model [least absolute shrinkage and selection operator] 4variables were retained within the final model. We identified 4important (Table 1) predictors of mortality by using this modelling:LDH, Oxygen Saturation in Abg (SO2), Neutrophil count and Glucose level at admission >LDH 675 U/L, Oxygen SO2 C 94%Neutrophil count C 7000/mm3 and Glucose value > 132 mg/dL].Using a ROC a 'c' measure of 0.834 corresponded to the modeldiscriminating the response.Conclusions: In our analysis, 4 variables which include LDH, Oxygen saturation, Neutrophil count and Glucose measurements atadmission are important predictors of mortality. Their role need moreresearch;possibly reflective of roles of NETs in the inflammatorycascade of severe covid.