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Given the challenges that SARS-CoV-2 variants have caused in terms of rapid spread and reduced vaccine efficacy, a rapid and cost-effective assay that can detect new and emerging variants is greatly needed worldwide. We have successfully applied the xenonucleic acid-based molecular-clamping technology to develop a multiplex reverse-transcription quantitative real-time PCR assay for SARS-CoV-2 multivariant detection. The assay was used to test 649 nasopharyngeal swab samples that were collected for clinical diagnosis or surveillance. The assay was able to correctly identify all 36 Delta variant samples as it accurately detected the D614G, T478K, and L452R mutations. In addition, the assay was able to correctly identify all 34 Omicron samples by detecting the K417N, T478K, N501Y, and D614G mutations. This technique reliably detects a variety of variants and has an analytical sensitivity of 100 copies/mL. In conclusion, this novel assay can serve as a rapid and cost-effective tool to facilitate large-scale detection of SARS-CoV-2 variants., Importance: We have developed a multiplex reverse-transcription quantitative real-time PCR (RT-qPCR) testing platform for the rapid detection of SARS-CoV-2 variants using the xenonucleic acid (XNA)-based molecular-clamping technology. The XNA-based RT-qPCR assay can achieve high sensitivity with a limit of detection of about 100 copies/mL for variant detection which is much better than the next-generation sequencing (NGS) assay. Its turnaround time is about 4 hours with lower cost and a lot of Clinical Laboratory Improvement Amendments (CLIA) labs own the instrument and meet skillset requirements. This assay provides a rapid, reliable, and cost-effective testing platform for rapid detection and monitoring of known and emerging SARS-CoV-2 variants. This testing platform can be adopted by laboratories that perform routine SARS-CoV-2 PCR testing, providing a rapid and cost-effective method in lieu of NGS-based assays, for detecting, differentiating, and monitoring SARS-CoV-2 variants. This assay is easily scalable to any new variant(s) should it emerge., Competing Interests: The authors declare no conflict of interest.

Background & Aims: Bioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (X c ) within population-specific equations to predict body composition., Objective: We compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations., Methods: Raw R, X c , impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIA SUP4 ), octapolar supine (BIA SUP8 ), and octapolar standing (BIA STA8 ) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST., Results: Data from 73 (23.2 ± 4.8 y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2-50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIA SUP8 and BIA STA8 , with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIA SUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equation, all three devices showed significant (p < 0.01) CE from -1.6 to -2.9 kg., Conclusions: Raw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: John Shepherd reports financial support was provided by Hologic Inc. Steven Heymsfield reports a relationship with Tanita Corporation that includes: board membership. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Background and Aims: Body fat distribution, i.e., visceral (VAT), subcutaneous adipose tissue (SAT) and intramuscular fat, is important for disease prevention, but sex and ethnic differences are not well understood. Our aim was to identify anthropometric, demographic, and lifestyle predictors for these outcomes., Methods and Results: The cross-sectional ShapeUp!Kids study was conducted among five ethnic groups aged 5-18 years. All participants completed questionnaires, anthropometric measurements, and abdominal MRI scans. VAT and SAT areas at four lumbar levels and muscle density were assessed manually. General linear models were applied to estimate coefficients of determination (R 2 ) and to compare the fit of VAT and SAT prediction models. After exclusions, the study population had 133 male and 170 female participants. Girls had higher BMI-z scores, waist circumference (WC), and SAT than boys but lower VAT/SAT and muscle density. SAT, VAT, and VAT/SAT but not muscle density differed significantly by ethnicity. R 2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R 2 increased from 0.85 to 0.88 (girls) and 0.62 to 0.71 (boys) when WC was added while VAT models improved from 0.62 to 0.65 (girls) and 0.57 to 0.62 (boys). VAT values were significantly lower among Blacks than Whites with little difference for the other groups., Conclusion: This analysis in a multiethnic population identified BMI-z scores and WC as the major predictors of MRI-derived SAT and VAT and highlights the important ethnic differences that need to be considered in diverse populations., (Copyright © 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)

Background & Aims: The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA)., Methods: Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults., Results: 67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95 % limits of agreement [LOA] -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time., Conclusions: Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice., Competing Interests: Conflicts of interest TK is an employee of Hologic, Inc. however, the sponsors had no role in the in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results., (Published by Elsevier Ltd.)

Background: Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics., Methods: Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy., Results: Predicted DXA scans achieve R 2 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R 2 s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition., Conclusions: This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility., (© 2024. The Author(s).)

Background: Athletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels., Methods: The Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D 2 O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry., Results: In total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water 6CM /FFM 6CM ) had substantial variation in both males (63-73 %) and females (58-78 %). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin's Concordance Coefficient [CCC] = 0.90-0.95) whereas the other measures had lesser agreement (CCC <0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0 %). Increasing quartiles (significant p < 0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status., Conclusion: The criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations., Summary Box: This study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes., Competing Interests: Conflict of interest JS received an investigator-initiated grant from Hologic, Inc. to fund the larger study and other grants on body composition from Hologic and GE Healthcare. SH is on the Medical Advisory Board for Tanita Corporation. TK is a current employee at Hologic. The remaining authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Educational interventions for asthma inhaler technique have been identified as successful in improving technique in children, yet inhaler technique has not improved over time. New approaches should be considered, including the use of technology-based interventions such as smartphone and tablet applications. Adoption and implementation of such technology in healthcare has been historically slow. This mixed-methods study aimed to identify the barriers and facilitators of delivering and receiving asthma inhaler education for children in a hospital setting, including technology-based interventions., Methods: Children with asthma, their caregivers, and healthcare professionals who regularly provide asthma education, were invited to participate in a qualitative interview and brief questionnaire to describe their experiences, knowledge, beliefs, and recommendations about asthma education delivery. The Theoretical Domains Framework was used to develop questions for the semistructured moderator guide, questionnaire, and provide the rigorous evaluation framework for deductive thematic analysis., Results: Sixteen interviews and questionnaires were conducted with participants. Overall, healthcare professionals perceived more barriers in asthma inhaler education delivery than asthmatic children and their caregivers to receiving the education. Healthcare professionals and caregivers identified time-pressures within a hospital setting as a barrier for providing sufficient education. However, all participants felt they had adequate knowledge in their asthma management skills and inhaler technique. Technology-based innovations were viewed positively by all participant groups to improve asthma education., Conclusions: Several barriers and facilitators to current hospital-based asthma education delivery were reported by target end-users. Future programs should consider these findings when developing asthma inhaler educational interventions, particularly those using technology-enhanced information delivery., (© 2023 The Authors. Pediatric Pulmonology published by Wiley Periodicals LLC.)

Background: Smartphone and tablet apps that deliver health care education have been identified as effective in improving patient knowledge and treatment adherence in asthma populations. Despite asthma being the most common chronic disease in pediatrics, there are few apps that are targeted specifically for children. Only half of children with asthma have acceptable control of their symptoms, and 40%-98% do not use their inhalers correctly. With children being increasingly connected to technology, there is an opportunity to improve asthma inhaler technique education by delivery via smartphone or tablet apps. Augmented reality (AR) technology was used in this study to capitalize on growing technological innovations. Digital health interventions that use a co-design process for development have the highest likelihood of successful uptake and effectiveness on their intended outcomes. Perceived usability also has been shown to improve the effectiveness of education as well as the acceptance of the intervention., Objective: The aims of this study were to describe the co-design process, development, and design outcomes of a smartphone or tablet app that incorporates AR technology to deliver asthma inhaler technique education to children with asthma. This study also aimed to provide a usability evaluation, using the System Usability Scale to inform our work and future research, and recommendations for others performing similar work., Methods: The development of the AR asthma inhaler technique education app was based on an iterative co-design process with likely end users (children with asthma, their caregivers, and health care professionals). This involved multiple stages: recruitment of end users for qualitative interviews and usability testing with a previously designed educational intervention, which used an AR-embedded smartphone or tablet app; ideation of content for a specific asthma inhaler technique education intervention with end users; development of the specific asthma inhaler intervention; and 2 further rounds of interviews and usability testing with the redesign of the initial prototype., Results: We included 16 participants aged 9-45 years. Using the co-design process, the AR asthma inhaler technique education app was designed, incorporating the preferences of end users. After iteration 1, animation was included based on the feedback provided. Iteration 2 feedback resulted in increased AR experiences and the removal of the requirement of a paper-based resource to trigger AR in the third iteration. Throughout all rounds, the ease of use of the app and the novel nature of the intervention were frequently described. The usability of the intervention overall was perceived to be excellent, and the mean System Usability Scale score of the intervention was found to be highest in the final round of evaluation (90.14)., Conclusions: The results from this co-design process and usability evaluation will be used to develop a final AR asthma inhaler technique educational intervention, which will be evaluated in the clinical setting., International Registered Report Identifier (irrid): RR2-10.1177/16094069211042229., (©Antonia O'Connor, Andrew Tai, Malcolm Brinn, Amy Nguyen Thuc Hien Hoang, Daniele Cataldi, Kristin Carson-Chahhoud. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 25.07.2023.)

Background: Inhaled medications or inhalers provide first-line pharmacotherapeutic treatment for patients with asthma for both acute symptomatic relief and long-term management to keep symptoms under control. A good technique requires only basic instruction and training; however, a recent study identified that 92% of children do not follow all correct steps when using inhalers. There is a growing interest in technology-enhanced asthma education, with evidence demonstrating improvements in knowledge and treatment adherence. Subsequently, there are calls to explore the role of technology-based solutions in improving asthma management and disease outcomes from public health experts, health professionals, and patients with asthma. Augmented reality (AR) technology is an information delivery mechanism with proven efficacy in educational settings. AR displays digital content in a real-world environment using the camera on a smartphone or tablet device to create an immersive learning experience., Objective: The study aimed to evaluate the acceptability of AR as a mechanism for delivering asthma inhaler technique education from the perspective of children with asthma and their parents and health professionals, examined through the theoretical framework of acceptability (TFA)., Methods: An asthma education resource enhanced with AR technology was created to provide inhaler technique education to children. An iterative co-design process was undertaken with target end users for a qualitative evaluation. The participants were 8 to 12 years old with asthma, their caregivers, and health professionals who had experience in managing asthma. Qualitative data were obtained through semistructured one-on-one interviews. Deductive thematic analysis using TFA was undertaken using NVivo software 2020 to assess the acceptability of AR as a delivery modality for asthma inhaler technique education., Results: Overall, 6 health care professionals, 5 asthmatic children, and 5 caregivers of children with asthma totaled a sample of 16. The use of AR in the asthma inhaler resource was found to be acceptable when responses were examined in accordance with TFA. Each of the 7 component constructs of TFA was coded throughout the 16 interviews, with perceived effectiveness (157 times) and affective attitude (63 times) coded most frequently. Positive responses included the intervention being accessible, easy to use, interesting, and fitting within the users' value systems. Negative responses included the need to maintain an interest in children and concerns about the loss of face-to-face interaction with health professionals., Conclusions: AR appears to be an acceptable modality for delivering asthma education to children when explored using TFA constructs. Although some challenges were identified with the use of AR, the results were predominantly positive. Future designs of asthma education interventions involving AR should consider the results of this study, and further research should focus on the feasibility, usability, and barriers and facilitators of behavior change to ensure the successful implementation and uptake of AR into clinical settings., International Registered Report Identifier (irrid): RR2-10.1177/16094069211042229., (©Antonia O'Connor, Andrew Tai, Malcolm Brinn, Amy Nguyen Thuc Hien Hoang, Daniele Cataldi, Kristin Carson-Chahhoud. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 13.01.2023.)

The French chemist Michel Eugène Chevreul discovered creatine in meat two centuries ago. Extensive biochemical and physiological studies of this organic molecule followed with confirmation that creatine is found within the cytoplasm and mitochondria of human skeletal muscles. Two groups of investigators exploited these relationships five decades ago by first estimating the creatine pool size in vivo with 14 C and 15 N labelled isotopes. Skeletal muscle mass (kg) was then calculated by dividing the creatine pool size (g) by muscle creatine concentration (g/kg) measured on a single muscle biopsy or estimated from the literature. This approach for quantifying skeletal muscle mass is generating renewed interest with the recent introduction of a practical stable isotope (creatine-(methyl-d 3 )) dilution method for estimating the creatine pool size across the full human lifespan. The need for a muscle biopsy has been eliminated by assuming a constant value for whole-body skeletal muscle creatine concentration of 4.3 g/kg wet weight. The current single compartment model of estimating creatine pool size and skeletal muscle mass rests on four main assumptions: tracer absorption is complete; tracer is all retained; tracer is distributed solely in skeletal muscle; and skeletal muscle creatine concentration is known and constant. Three of these assumptions are false to varying degrees. Not all tracer is retained with urinary isotope losses ranging from 0% to 9%; an empirical equation requiring further validation is used to correct for spillage. Not all tracer is distributed in skeletal muscle with non-muscle creatine sources ranging from 2% to 10% with a definitive value lacking. Lastly, skeletal muscle creatine concentration is not constant and varies between muscles (e.g. 3.89-4.62 g/kg), with diets (e.g. vegetarian and omnivore), across age groups (e.g. middle-age, ~4.5 g/kg; old-age, 4.0 g/kg), activity levels (e.g. athletes, ~5 g/kg) and in disease states (e.g. muscular dystrophies, <3 g/kg). Some of the variability in skeletal muscle creatine concentrations can be attributed to heterogeneity in the proportions of wet skeletal muscle as myofibres, connective tissues, and fat. These observations raise serious concerns regarding the accuracy of the deuterated-creatine dilution method for estimating total body skeletal muscle mass as now defined by cadaver analyses of whole wet tissues and in vivo approaches such as magnetic resonance imaging. A new framework is needed in thinking about how this potentially valuable method for measuring the creatine pool size in vivo can be used in the future to study skeletal muscle biology in health and disease., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Background: Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model., Objectives: We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models., Methods: Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99)., Results: Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW., Conclusions: Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)