Graphical abstract

Friedreich’s ataxia (FRDA) is caused primarily by expanded GAA repeats in intron 1 of both alleles of the FXN gene, which causes transcriptional silencing and reduced expression of frataxin mRNA and protein. FRDA is characterized by slowly progressive ataxia and cardiomyopathy. Symptoms generally appear during adolescence, and patients slowly progress to wheelchair dependency usually in the late teens or early twenties with death on average in the 4th decade. There are two known mature proteoforms of frataxin. Mitochondrial frataxin (frataxin-M) is a 130-amino acid protein with a molecular weight of 14,268 Da, and there is an alternatively spliced N-terminally acetylated 135-amino acid form (frataxin-E) with a molecular weight of 14,953 Da found in erythrocytes. There is reduced expression of frataxin in the heart and brain, but frataxin is not secreted into the systemic circulation, so it cannot be analyzed in serum or plasma. Blood is a readily accessible biofluid that contains numerous different cell types that express frataxin. We have found that pig blood can serve as an excellent surrogate matrix to validate an assay for frataxin proteoforms because pig frataxin is lost during the immunoprecipitation step used to isolate human frataxin. Frataxin-M is expressed in blood cells that contain mitochondria, whereas extra-mitochondrial frataxin-E is found in erythrocytes. This means that the analysis of frataxin in whole blood provides information on the concentration of both proteoforms without having to isolate the individual cell types. In the current study, we observed that the distributions of frataxin levels for a sample of 25 healthy controls and 50 FRDA patients were completely separated from each other, suggesting 100% specificity and 100% sensitivity for distinguishing healthy controls from FRDA cases, a very unusual finding for a biomarker assay. Additionally, frataxin levels were significantly correlated with the GAA repeat length and age of onset with higher correlations for extra-mitochondrial frataxin-E than those for mitochondrial frataxin-M. These findings auger well for using frataxin levels measured by the validated stable isotope dilution ultrahigh-performance liquid chromatography–multiple reaction monitoring/mass spectrometry assay to monitor therapeutic interventions and the natural history of FRDA. Our study also illustrates the utility of using whole blood for protein disease biomarker discovery and validation.

Background The COVID-19 pandemic has caused a severe shortage of personal protective equipment (PPE), especially N95 respirators. Efficient, effective and economically feasible methods for large-scale PPE decontamination are urgently needed. Aims (1) to develop protocols for effectively decontaminating PPE using vaporized hydrogen peroxide (VHP); (2) to develop novel approaches that decrease set up and take down time while also increasing decontamination capacity (3) to test decontamination efficiency for N95 respirators heavily contaminated by makeup or moisturizers. Methods We converted a decommissioned Biosafety Level 3 laboratory into a facility that could be used to decontaminate N95 respirators. N95 respirators were hung on metal racks, stacked in piles, placed in paper bags or covered with makeup or moisturizer. A VHP® VICTORY TM unit from STERIS was used to inject VHP into the facility. Biological and chemical indicators were used to validate the decontamination process. Findings N95 respirators individually hung on metal racks were successfully decontaminated using VHP. N95 respirators were also successfully decontaminated when placed in closed paper bags or if stacked in piles of up to six. Stacking reduced the time needed to arrange N95 respirators for decontamination by approximately two-thirds while almost tripling facility capacity. Makeup and moisturizer creams did not interfere with the decontamination process. Conclusions Respirator stacking can reduce the hands-on time and increase decontamination capacity. When personalization is needed, respirators can be decontaminated in labeled paper bags. Make up or moisturizers do not appear to interfere with VHP decontamination.

No abstract

Background The coronavirus disease 2019 (COVID-19) pandemic has resulted in the rapid uptake of telemedicine (TM) for routine cardiovascular care. Objectives To examine the predictors of TM utilization among ambulatory cardiology patients during the COVID-19 pandemic. Methods In this single centre retrospective study, all ambulatory cardiovascular encounters occurring between March 16th - June 19th, 2020 were assessed. Baseline characteristics by visit type (in-person, TM-phone, TM-video) were compared using Chi-square and student t-tests, with statistical significance defined by p value < 0.05. Multivariate logistic regression was used to explore the predictors of TM versus in-person care. Results 8446 patients (86% Non-Hispanic White, 42% female, median age 66.8 +/- 15.2 years) completed an ambulatory cardiovascular visit during the study period. TM-phone (n = 4,981, 61.5%) was the primary mode of ambulatory care followed by TM-video (n = 2693, 33.2%). Non-Hispanic Black race (OR 0.56; 95% CI: 0.35 - 0.94, p-value=0.02), Hispanic ethnicity (OR 0.53; 95% CI: 0.29 - 0.98, p = 0.04), public insurance (Medicaid OR 0.50; 95% CI:0.32 – 0.79, p = 0.003, Medicare OR 0.65; 95% CI: 0.47– 0.89, p = 0.009), zip-code linked median household income (MHI) of <$75,000, age >85 years, and patients with a diagnosis of heart failure were associated with reduced access to TM-video encounters and a higher likelihood of in-person care. Conclusions Significant disparities in TM-video access for ambulatory cardiovascular care exist among the elderly, lower income, as well as Black and Hispanic racial/ethnic groups.

Background Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. Objective This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. Methods We used natural language processing (NLP) algorithms to identify symptom- and medical condition–related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. Results Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition–related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). Conclusions COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population’s mental health status and enhance public health surveillance for infectious disease.

Social distancing has been one of the primary mitigation strategies in the United States to control the spread of novel coronavirus disease (COVID-19) and can be viewed as a multi-faceted public health measure. Using Twitter data, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine the most amplified tweets among social distancing facets. We analyzed a total of 259,529 unique tweets containing "coronavirus" from 115,485 unique users between January 23, 2020 and March 24, 2020 that were identified by the Twitter API as English and U.S.-based. Tweets containing specified keywords (determined a priori) were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. Tweets about social disruptiveness were most retweeted, and implementation tweets were most favorited. Social distancing tweets became overall more prevalent in the U.

Background: COVID-19 has stretched the ability of many institutions to supply needed personal protective equipment, especially N95 respirators. N95 decontamination and reuse programs provide one potential solution to this problem. Unfortunately, a comprehensive evaluation of the effects of decontamination on the integrity of various N95 models using a quantitative fit test (QTFT) approach is lacking. Aims: 1) To investigate the effects of up to eight rounds of vaporized H2O2 (VHP) decontamination on the integrity of N95 respirators currently in use in a hospital setting. 2) To examine if N95 respirators worn by one user can adapt to the face shape of a second user with no compromise of integrity following VHP decontamination. Methods: The PortaCount Pro+ Respirator Fit Tester Model 8038 was used to quantitatively define the integrity, measured by fit, of N95 respirators following decontamination with VHP. Findings: There was an observable downward trend in the integrity of Halyard Fluidshield 46727 N95 respirators throughout eight cycles of decontamination with VHP. The integrity of 3M 1870 N95 respirators was significantly reduced after the respirator was worn, decontaminated with VHP, and then quantitatively fit tested on a second user. Furthermore, we uncovered inconsistencies between qualitative fit test and QTFT results that may have strong implications on the fit testing method used by institutions. Conclusions: Our data revealed variability in the integrity of different N95 models after VHP decontamination and exposed potential limitations of N95 decontamination and reuse programs.