Guinea Pig Model for Evaluating the Potential Public Health Risk of Swine and Avian Influenza Viruses

BACKGROUND: The influenza viruses circulating in animals sporadically transmit to humans and pose pandemic threats. Animal models to evaluate the potential public health risk potential of these viruses are needed. METHODOLOGY/PRINCIPAL FINDINGS: We investigated the guinea pig as a mammalian model for the study of the replication and transmission characteristics of selected swine H1N1, H1N2, H3N2 and avian H9N2 influenza viruses, compared to those of pandemic (H1N1) 2009 and seasonal human H1N1, H3N2 influenza viruses. The swine and avian influenza viruses investigated were restricted to the respiratory system of guinea pigs and shed at high titers in nasal tracts without prior adaptation, similar to human strains. None of the swine and avian influenza viruses showed transmissibility among guinea pigs; in contrast, pandemic (H1N1) 2009 virus transmitted from infected guinea pigs to all animals and seasonal human influenza viruses could also horizontally transmit in guinea pigs. The analysis of the receptor distribution in the guinea pig respiratory tissues by lectin histochemistry indicated that both SAα2,3-Gal and SAα2,6-Gal receptors widely presented in the nasal tract and the trachea, while SAα2,3-Gal receptor was the main receptor in the lung. CONCLUSIONS/SIGNIFICANCE: We propose that the guinea pig could serve as a useful mammalian model to evaluate the potential public health threat of swine and avian influenza viruses

Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Associations between cancer history, social distancing behaviors, and loneliness in adults during the COVID-19 pandemic.

BackgroundDuring the COVID-19 pandemic, enforced social distancing initiatives have highlighted differences in social distancing practices and the resulting loneliness in various populations. The objective of this study was to examine how cancer history and social distancing practices relate to loneliness during COVID-19.Methods and findingsParticipants from previous studies (N = 32,989) with permission to be re-contacted were invited to complete a survey online, by phone, or by mail between June and November 2020. Linear and logistic regression models were used to determine the associations between cancer history, social distancing, and loneliness.ResultsAmong the included participants (n = 5729), the average age was 56.7 years, 35.6% were male, 89.4% were White, and 54.9% had a cancer history (n = 3147). Individuals with a cancer history were more likely to not contact people outside of their household (49.0% vs. 41.9%, pConclusionsFindings from this study can inform efforts to support the mental health of individuals susceptible to loneliness during the COVID-19 pandemic

Resiliency among Women's Health Initiative women aged 80 and older by race, ethnicity, and neighborhood socioeconomic status.

ObjectivesA comprehensive examination of resilience by race, ethnicity, and neighborhood socioeconomic status (NSES) among women aged ≥80 is needed, given the aging of the US population, increasing longevity, and growing racial and ethnic diversity.MethodsParticipants were women aged ≥80 enrolled in the Women's Health Initiative (WHI). Resilience was assessed with a modified version of the Brief Resilience Scale. Descriptive statistics and multiple linear regression examined the association of demographic, health, and psychosocial variables with resilience by race, ethnicity, and NSES.ResultsParticipants (n=29,367, median age=84.3) were White (91.4%), Black (3.7%), Hispanic (1.9%), and Asian (1.7%) women. There were no significant differences by race and ethnicity on mean resiliency scores (p=0.06). Significant differences by NSES were observed regarding mean resiliency scores between those with low NSES (3.94±0.83, out of 5) and high NSES (4.00±0.81). Older age, higher education, higher self-rated health, lower stress, and living alone were significant positive correlates of resilience in the sample. Social support was correlated with resilience among White, Black, and Asian women, but not for Hispanic women. Depression was a significant correlate of lower resilience, except among Asian women. Living alone, smoking, and spirituality were significantly associated with higher resilience among women with moderate NSES.DiscussionMultiple factors were associated with resilience among women aged ≥80 in the WHI. Despite some differing correlates of resilience by race, ethnicity, and NSES, there were many similarities. These results may aid in the design of resilience interventions for the growing, increasingly diverse population of older women