Association of Psychosocial Risk Factors and Outcomes in Heart Failure: Does COVID-19 Affect Outcomes?

Psychosocial risk factors (PSRFs) have emerged as crucial nontraditional risk factors affecting outcomes in patients with heart failure (HF). There is a paucity of data studying these risk factors in HF nationally. Additionally, whether the COVID-19 pandemic impacted outcomes remains unexplored, given the increased psychosocial risk during these times. Our objective is to assess the impact of PSRFs on the outcomes of HF and their comparison across non-COVID-19 and COVID-19 eras. Patients with a diagnosis of HF were selected using the 2019-2020 Nationwide Readmissions Database. Two cohorts were created based on the presence or absence of PSRFs and compared across non-COVID-19 and COVID-19 eras. We examined the association using hierarchical multivariable logistic regression models. A total of 305,955 patients were included, of which 175,348 (57%) had PSRFs. Patients with PSRFs were younger, less likely to be female, and had a higher prevalence of cardiovascular risk factors. All-cause readmissions were higher in patients with PSRFs in both the eras. All-cause mortality [odds ratio, OR 1.15 (1.04-1.27), P = 0.005] and composite of MACE [OR 1.11 (1.06-1.16), P < 0.001] were higher in patients in the non-COVID-19 era. Compared to 2019, patients with PSRFs and HF in 2020 had significantly higher all-cause mortality [OR 1.13 (1.03-1.24), P = 0.009]; however, the composite of MACE was comparable [OR 1.04 (1.00-1.09), P = 0.03]. In conclusion, the presence of PSRFs in patients with HF is associated with a significant increase in all-cause readmissions in COVID-19 and non-COVID-19 eras. The worse outcomes evident in the COVID-19 era highlights the importance of multidisciplinary care in this vulnerable population.

How COVID-19 informs business sustainability research: It's time for a systems perspective

In this essay, we argue that by taking a systems lens, sustainability researchers can better understand the implications of COVID-19 on business and society and prevent future pandemics. A systems lens asks management researchers to move from a firm-level perspective to one that also considers the broader socioecological context. We argue that for business to prevent future pandemics and assure future prosperity, business must recognize the limits to growth, alternative temporalities that do not pit the short against the long term, the nestedness of local phenomena in global systems, and leverage points that can reduce entrenched systems of social inequalities. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Effects of Cognitive Behavioral Therapy and Cash Transfers on Older Persons Living Alone in India : A Randomized Trial.

BACKGROUND: A growing number of older persons in developing countries live entirely alone and are physically, mentally, and financially vulnerable. OBJECTIVE: To determine whether phone-based cognitive behavioral therapy (CBT) or a cash transfer reduce functional impairment, depression, or food insecurity in this population. DESIGN: Randomized controlled trial. (ClinicalTrials.gov: NCT04225845; American Economic Association RCT Registry: AEARCTR-0007582). SETTING: Tamil Nadu, India, 2021. PARTICIPANTS: 1120 people aged 55 years and older and living alone. INTERVENTIONS: A 6-week, phone-based CBT and a 1-time cash transfer of 1000 rupees (U.S. $12 at market exchange rates) were evaluated in a factorial design. MEASUREMENTS: The World Health Organization Disability Assessment Schedule (WHODAS), the Geriatric Depression Scale, and food security, all measured 3 weeks after CBT for 977 people and 3 months after for 932. Surveyors were blind to treatment assignment. RESULTS: The WHODAS score (scale 0 to 48, greater values representing more impairment) decreased between baseline and the 3-week follow-up by 2.92 more (95% CI, -5.60 to -0.23) in the group assigned cash only than in the control group, and the depression score (ranging from 0 to 15, higher score indicating more depressive symptoms) decreased by 1.01 more (CI, -2.07 to 0.06). These effects did not persist to the 3-month follow-up, and CBT alone and the 2 together had no significant effects. There were no effects on food security. LIMITATIONS: The study cannot say whether more sustained or in-person therapy would have been effective, how results would translate outside of the COVID-19 period, or whether results in the consented sample differ from those in a larger population. Primary outcomes were self-reported. CONCLUSION: Among older people living alone, a small cash transfer was effective in alleviating short-term (3 weeks) functional impairment, produced a small but not clinically or statistically significant reduction in depression, and had no effect on food security. There were no short-term effects from CBT or the 2 interventions together. None of the interventions showed any effect at 3 months. PRIMARY FUNDING SOURCE: National Institute on Aging (NIA).

Staying Virtual: A Survey Study of the Virtual Lecture Experience in Academic Medicine.

BACKGROUND: Increasing clinical demands can adversely impact academic advancement, including the ability to deliver lectures and disseminate scholarly work. The virtual lecture platform became mainstream during the height of the coronavirus-19 pandemic. Lessons learned from this period may offer insight into supporting academic productivity among physicians who must balance multiple demands, including high clinical workloads and family care responsibilities. We evaluated perceptions on delivering virtual lectures to determine whether virtual venues merit continuation beyond the pandemic's initial phase and whether these perceptions differ by gender and rank. METHODS: In a survey study, faculty who spoke in 1 of 3 virtual lecture programs in the Departments of Anesthesiology and Critical Care Medicine, Otolaryngology, and Radiology at a university hospital in 2020 to 2022 were queried about their experience. Speakers' motivations to lecture virtually and the perceived advantages and disadvantages of virtual and in-person lectures were analyzed using descriptive statistics and qualitative analyses. RESULTS: Seventy-two of 95 (76%) faculty members responded (40% women, 38% men, and 22% gender undisclosed). Virtual lectures supported the speakers "a lot" to "extremely" with the following goals: enhancing one's reputation and credibility (76%), networking (70%), receiving feedback (63%), and advancing prospects for promotion (59%). Virtual programs also increased the speakers' sense of accomplishment (70%) and professional optimism (61%) by at least "a lot," including instructors and assistant professors who previously had difficulty obtaining invitations to speak outside their institution. Many respondents had declined prior invitations to speak in-person due to clinical workload (66%) and family care responsibilities (58%). Previous opportunities to lecture in-person were also refused due to finances (39%), teaching (26%), and research (19%) requirements, personal medical conditions or disabilities (9%), and religious obligations (5%). Promotion was a stronger motivating factor to lecture virtually for instructors and assistant professors than for associate and full professors. By contrast, disseminating work and ideas was a stronger motivator for associate and full professors. Associate and full professors also reported greater improvement in work-related well-being than earlier career faculty from the virtual lecture experience. Very few differences were found by gender. CONCLUSIONS: Virtual lecture programs support faculty who might not otherwise have the opportunity to lecture in-person due to multiple constraints. To increase the dissemination of scholarly work and expand opportunities to all faculty, virtual lectures should continue even as in-person venues are reestablished.

Has the first year of the COVID-19 pandemic reversed the trends in CV mortality between 1999-2019 in the United States?

AIMS: Although cardiovascular (CV) mortality increased during the COVID-19 pandemic, little is known about how these patterns varied across key subgroups, include age, sex, and race and ethnicity, as well as by specific cause of CV death. METHODS AND RESULTS: The Centers for Disease Control WONDER database was used to evaluate trends in age-adjusted CV mortality between 1999 and 2020 among US adults aged 18 and older. Overall, there was a 4.6% excess CV mortality in 2020 compared to 2019, which represents an absolute excess of 62 802 deaths. The relative CV mortality increase between 2019 and 2020 was higher for adults under 55 years of age (11.9% relative increase), versus adults aged 55-74 (7.9% increase) and adults 75 and older (2.2% increase). Hispanic adults experienced a 9.4% increase in CV mortality (7 400 excess deaths) versus 4.3% for non-Hispanic adults (56 760 excess deaths). Black adults experienced the largest % increase in CV mortality at 10.6% (15 477 excess deaths) versus 3.5% increase (42 907 excess deaths) for White adults. Among individual causes of CV mortality, there was an increase between 2019 and 2020 of 4.3% for ischemic heart disease (32 293 excess deaths), 15.9% for hypertensive disease (13 800 excess deaths), 4.9% for cerebrovascular disease (11 218 excess deaths), but a decline of 1.4% for heart failure mortality. CONCLUSION: The first year of the COVID pandemic in the United States was associated with a reversal in prior trends of improved CV mortality. Increases in CV mortality were most pronounced among Black and Hispanic adults.

Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms.

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Audio texture analysis of COVID-19 cough, breath, and speech sounds.

The coronavirus disease (COVID-19) first appeared at the end of December 2019 and is still spreading in most countries. To diagnose COVID-19 using reverse transcription - Polymerase chain reaction (RT-PCR), one has to go to a dedicated center, which requires significant cost and human resources. Hence, there is a requirement for a remote monitoring tool that can perform the preliminary screening of COVID-19. In this paper, we propose that a detailed audio texture analysis of COVID-19 sounds may help in performing the initial screening of COVID-19. The texture analysis is done on three different signal modalities of COVID-19, i.e. cough, breath, and speech signals. In this work, we have used 1141 samples of cough signals, 392 samples of breath signals, and 893 samples of speech signals. To analyze the audio textural behavior of COVID-19 sounds, the local binary patterns LBP) and Haralick's features were extracted from the spectrogram of the signals. The textural analysis on cough and breath sounds was done on the following 5 classes for the first time: COVID-19 positive with cough, COVID-19 positive without cough, healthy person with cough, healthy person without cough, and an asthmatic cough. For speech sounds there were only two classes: COVID-19 positive, and COVID-19 negative. During experiments, 71.7% of the cough samples and 72.2% of breath samples were classified into 5 classes. Also, 79.7% of speech samples are classified into 2 classes. The highest accuracy rate of 98.9% was obtained when binary classification between COVID-19 cough and non-COVID-19 cough was done.

State-of-the-Art Clinical Microbiology in South Korea: Current Trends and Future Prospects.

Researchers and clinicians have repeatedly explored the clinical aspects of microorganisms because the human body is inhabited by several different microbial species and their strains [...].

COVID-19 and Remote Work: An Early Look at US Data

We report the results of a nationally-representative sample of the US population during the COVID-19 pandemic. The survey ran in two waves from April 1-5, 2020 and May 2-8, 2020. Of those employed pre-COVID-19, we find that about half are now working from home, including 35.2% who report they were commuting and recently switched to working from home. In addition, 10.1% report being laid-off or furloughed since the start of COVID-19. There is a strong negative relationship between the fraction in a state still commuting to work and the fraction working from home. We find that the share of people switching to remote work can be predicted by the incidence of COVID-19 and that younger people were more likely to switch to remote work. Furthermore, states with a higher share of employment in information work including management, professional and related occupations were more likely to shift toward working from home and had fewer people laid off or furloughed. We find no substantial change in results between the two waves, suggesting that most changes to remote work manifested by early April.

SARS-CoV-2 and other human coronaviruses: Mapping of protease recognition sites, antigenic variation of spike protein and their grouping through molecular phylogenetics.

In recent years, a total of seven human pathogenic coronaviruses (HCoVs) strains were identified, i.e., SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, and HCoV-HKU1. Here, we performed an analysis of the protease recognition sites and antigenic variation of the S-protein of these HCoVs. We showed tissue-specific expression pattern, functions, and a number of recognition sites of proteases in S-proteins from seven strains of HCoVs. In the case of SARS-CoV-2, we found two new protease recognition sites, each of calpain-2, pepsin-A, and caspase-8, and one new protease recognition site each of caspase-6, caspase-3, and furin. Our antigenic mapping study of the S-protein of these HCoVs showed that the SARS-CoV-2 virus strain has the most potent antigenic epitopes (highest antigenicity score with maximum numbers of epitope regions). Additionally, the other six strains of HCoVs show common antigenic epitopes (both B-cell and T-cell), with low antigenicity scores compared to SARS-CoV-2. We suggest that the molecular evolution of structural proteins of human CoV can be classified, such as (i) HCoV-NL63 and HCoV-229E, (ii) SARS-CoV-2, and SARS-CoV and (iii) HCoV-OC43 and HCoV-HKU1. In conclusion, we can presume that our study might help to prepare the interventions for the possible HCoVs outbreaks in the future.

Myocardial Work Efficiency, A Novel Measure of Myocardial Dysfunction, Is Reduced in COVID-19 Patients and Associated With In-Hospital Mortality.

Background: Although troponin elevation is common in COVID-19, the extent of myocardial dysfunction and its contributors to dysfunction are less well-characterized. We aimed to determine the prevalence of subclinical myocardial dysfunction and its association with mortality using speckle tracking echocardiography (STE), specifically global longitudinal strain (GLS) and myocardial work efficiency (MWE). We also tested the hypothesis that reduced myocardial function was associated with increased systemic inflammation in COVID-19. Methods and Results: We conducted a retrospective study of hospitalized COVID-19 patients undergoing echocardiography (n = 136), of whom 83 and 75 had GLS (abnormal >-16%) and MWE (abnormal <95%) assessed, respectively. We performed adjusted logistic regression to examine associations of GLS and MWE with in-hospital mortality. Patients were mean 62 ± 14 years old (58% men). While 81% had normal left ventricular ejection fraction (LVEF), prevalence of myocardial dysfunction was high by STE; [39/83 (47%) had abnormal GLS; 59/75 (79%) had abnormal MWE]. Higher MWE was associated with lower in-hospital mortality in unadjusted [OR 0.92 (95% CI 0.85-0.99); p = 0.048] and adjusted models [aOR 0.87 (95% CI 0.78-0.97); p = 0.009]. In addition, increased systemic inflammation measured by interleukin-6 level was associated with reduced MWE. Conclusions: Subclinical myocardial dysfunction is common in COVID-19 patients with clinical echocardiograms, even in those with normal LVEF. Reduced MWE is associated with higher interleukin-6 levels and increased in-hospital mortality. Non-invasive STE represents a readily available method to rapidly evaluate myocardial dysfunction in COVID-19 patients and can play an important role in risk stratification.

The role of sex and inflammation in cardiovascular outcomes and mortality in COVID-19.

OBJECTIVE: Higher mortality in COVID-19 in men compared to women is recognized, but sex differences in cardiovascular events are less well established. We aimed to determine the independent contribution of sex to stroke, myocardial infarction and death in the setting of COVID-19 infection. METHODS: We performed a retrospective cohort study of hospitalized COVID-19 patients in a racially/ethnically diverse population. Clinical features, laboratory markers and clinical events were initially abstracted from medical records, with subsequent clinician adjudication. RESULTS: Of 2060 patients, myocardial injury (32% vs 23%, p = 0.019), acute myocardial infarction (2.7% vs 1.6%, p = 0.114), and ischemic stroke (1.8% vs 0.7%, p = 0.007) were more common in men vs women. In-hospital death occurred in 160 men (15%) vs 117 women (12%, p = 0.091). Men had higher odds of myocardial injury (odds ratio (OR) 2.04 [95% CI 1.43-2.91], p < 0.001), myocardial infarction (1.72 [95% CI 0.93-3.20], p = 0.085) and ischemic stroke (2.76 [95% CI 1.29-5.92], p = 0.009). Despite adjustment for demographics and cardiovascular risk factors, male sex predicted mortality (HR 1.33; 95% CI:1.01-1.74; p = 0.041). While men had significantly higher markers of inflammation, in sex-stratified analyses, increase in interleukin-6, C-reactive protein, ferritin and d-dimer were predictive of mortality and myocardial injury similarly in both sexes. CONCLUSIONS: Adjusted odds of myocardial injury, ischemic stroke and all-cause mortality, but not myocardial infarction, are significantly higher in men compared to women with COVID-19. Higher inflammatory markers are present in men but associated similarly with risk in both men and women. These data suggest that adverse cardiovascular outcomes in men vs. women are independent of cardiovascular comorbidities.

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study.

BACKGROUND: The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions. OBJECTIVE: This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone. METHODS: A cohort of 393 participants enrolled in Digbi Health's personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data. RESULTS: Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks. CONCLUSIONS: Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants' genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficacy.

Ongoing Clinical Trials of Vaccines to Fight against COVID-19 Pandemic.

Coronavirus disease 2019 (COVID-19) has developed as a pandemic, and it created an outrageous effect on the current healthcare and economic system throughout the globe. To date, there is no appropriate therapeutics or vaccines against the disease. The entire human race is eagerly waiting for the development of new therapeutics or vaccines against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Efforts are being taken to develop vaccines at a rapid rate for fighting against the ongoing pandemic situation. Amongst the various vaccines under consideration, some are either in the preclinical stage or in the clinical stages of development (phase-I, -II, and -III). Even, phase-III trials are being conducted for some repurposed vaccines like Bacillus Calmette-Guérin, polio vaccine, and measles-mumps-rubella. We have highlighted the ongoing clinical trial landscape of the COVID-19 as well as repurposed vaccines. An insight into the current status of the available antigenic epitopes for SARS-CoV-2 and different types of vaccine platforms of COVID-19 vaccines has been discussed. These vaccines are highlighted throughout the world by different news agencies. Moreover, ongoing clinical trials for repurposed vaccines for COVID-19 and critical factors associated with the development of COVID-19 vaccines have also been described.

Multimodality Imaging for Cardiac Evaluation in Patients with COVID-19.

PURPOSE OF REVIEW: A growing number of cardiovascular manifestations resulting from the novel SARS-CoV-2 coronavirus (COVID-19) have been described since the beginning of this global pandemic. Acute myocardial injury is common in this population and is associated with higher rates of morbidity and mortality. The focus of this review centers on the recent applications of multimodality imaging in the diagnosis and management of COVID-19-related cardiovascular conditions. RECENT FINDINGS: In addition to standard cardiac imaging techniques such as transthoracic echocardiography, other modalities including computed tomography and cardiac magnetic resonance imaging have emerged as useful adjuncts in select patients with COVID-19 infection, particularly those with suspected ischemic and nonischemic myocardial injury. Data have also emerged suggesting lasting COVID-19 subclinical cardiac effects, which may have long-term prognostic implications. With the spectrum of COVID-19 cardiovascular manifestations observed thus far, it is important for clinicians to recognize the role, strengths, and limitations of multimodality imaging techniques in this patient population.

Immunoinformatics Approach for the Identification and Characterization of T Cell and B Cell Epitopes towards the Peptide-Based Vaccine against SARS-CoV-2.

Presently, immunoinformatics is playing a significant role in epitope identification and vaccine designing for various critical diseases. Using immunoinformatics, several scientists are trying to identify and characterize T cell and B cell epitopes as well as design peptide-based vaccine against SARS-CoV-2. In this review article, we have tried to discuss the importance in adaptive immunity and its significance for designing the SARS-CoV-2 vaccine. Moreover, we have attempted to illustrate several significant key points for utilizing immunoinformatics for vaccine designing, such as the criteria for selection and identification of epitopes, T cell epitope, and B cell epitope prediction and different emerging tools/databases for immunoinformatics. In the current scenario, a few immunoinformatics studies have been performed for various infectious pathogens and related diseases. Thus, we have also summarized and included these current immunoinformatics studies in this review article. Finally, we have discussed about the probable T cell and B cell epitopes and their identification and characterization for vaccine designing against SARS-CoV-2.

Our Collective Tensions: Paradox Research Community’s Response to COVID-19

In this commentary on three articles from dozens of paradox theory scholars on paradox approaches to examining the COVID-19 pandemic and how the COVID-19 pandemic informs paradox theory, the authors involved in coordinating the collection of three papers discuss the process of bringing together scholars from around the world to discuss the pandemic. Four other preeminent paradox theorists offer additional commentaries on the papers in this Collection.