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Annotating the X-ray diffraction pattern of vertebrate striated muscle
Get PDFLow-angle X-ray diffraction is a powerful technique for analyzing the molecular structure of the myofilaments of striated muscle in situ. It has contributed greatly to our understanding of the relaxed, 430-Å-repeating organization of myosin heads in thick filaments in skeletal and cardiac muscle. Using X-ray diffraction, changes in filament structure can be detected on the Å length scale and millisecond time scale, leading to models that are the foundation of our understanding of the structural basis of contraction. As with all X-ray fiber diffraction studies, interpretation requires modeling, which has previously been based on low-resolution knowledge of thick filament structure and is complicated by the contributions of multiple filament components to most X-ray reflections. Here, we use an atomic model of the human cardiac thick filament C-zone, derived from cryo-EM in the presence of the myosin inhibitor, mavacamten, to compute objectively the contributions of myosin heads, tails, titin, and cMyBP-C to the diffraction pattern, by including/excluding these components in the calculations. Our results support some previous interpretations but contradict others. We confirm that the myosin heads are responsible for most of the intensity on the myosin layer-lines, including the M3 meridional. Contrary to expectation, we find that myosin tails contribute little to the pattern, including the M6 meridional; this reflection arises mainly from heads and other components. The M11 layer-line (39 Å spacing) arises mostly from the curved and kinked structure of titin, which allows eleven ∼42-Å-long domains to fit into the 430 Å repeat. The M11 spacing can be used as a measure of strain in the myosin filament backbone as there is negligible head contribution. The computed layer-lines account well for the experimentally determined pattern. These insights should aid future understanding of the X-ray pattern of intact muscle in different conditions such as contraction and drug treatment.No embarg
Engineered Nme2Cas9 Adenine Base Editors to Treat Rett Syndrome
Get PDFRett syndrome (RTT) is a severe X-linked progressive neurodevelopmental disorder that primarily affects females in early childhood. Although the FDA has approved the first drug treatment for RTT, no curative therapy is currently available. Precision genome editing technologies—particularly CRISPR-based adenine base editing (ABE)—offer a promising strategy for the permanent correction of pathogenic MECP2 mutations, the primary genetic cause of RTT. This thesis explores the use of an imperfect guide RNA (igRNA) strategy incorporating internal barcodes to reduce bystander editing while maintaining high on-target editing efficiency. The approach demonstrated robust editing performance and laid the groundwork for validation in patient-derived fibroblasts and relevant mouse models. These findings highlight a potentially curative genome editing strategy for RTT that can be extended to several of the most prevalent MECP2 mutations. Furthermore, this work supports the broader application of compact ABEs for the treatment of additional central nervous system (CNS) disorders.Interdisciplinary Graduate ProgramNo embarg
Travel Nurses’ Experience with Ethical Challenges in Practice: A Qualitative Study
No full textBackground: Nursing turnover rates are among the highest measured in recent years, contributing to financial and staffing challenges in the healthcare industry. Citing ethical challenges and subsequent moral distress, nurses have increasingly turned to travel nurse positions to continue their practice. Current literature regarding moral distress and ethical challenges has largely focused on staff nurses, and it is unknown how travel nurses experience ethical challenges and any associated moral distress.
Methods: Qualitative Descriptive Study with a purposive sampling strategy.
Results: Fifteen Interviews were completed with a sample of 12 women and 3 men, and a range of travel nurse experience between 1 and 13 years. Three categories emerged from the data to describe participant experiences with ethical challenges: “Varied Experiences Enhance Moral Agency,” “Calculating Acceptance Versus Advocacy,” and “Knowing Oneself and Crafting a Community.” Each category has 3 subthemes which highlights how their unique experiences affected their ethical awareness, professional attitudes and judgments, and community building.
Discussion: The contributions of this study will not only inform the practice models of travel nursing, which is predicted to continue to grow in the coming years but may help in developing strategies to support all nurses encountering ethical challenges in their practice. This study will provide data needed for future intervention studies that have important implications for the health and well-being of nurses and patients. Understanding how travel nurses experience and mitigate ethical challenges will help identify successful strategies that may be applied more broadly.6 months12/30/202
Disparities in MR enterography utilization stratified by social drivers of health (SDOH): a zip code-based analysis
No full textPURPOSE/OBJECTIVE: Social drivers of health (SDOH) are nonmedical factors that influence health outcomes. We aim to evaluate if there are differences in MR enterography (MRE) utilization among the various zip codes surrounding UMass Memorial Medical Center in Worcester, MA stratified by various SDOH.
MATERIALS AND METHODS: In this HIPAA compliant IRB approved retrospective population-based study, MRE utilization for patient zip codes within a 10 mile radius of UMass Memorial Medical Center was determined by dividing the number of MREs performed from 01/08/2018 to 07/27/2022 by the population for each zip code. Data on median household income (MHI), population in poverty (PIP), health insurance status, race and ethnicity, and transportation access was collected from the United Census Bureau and data on social vulnerability index (SVI) was collected from the Centers of Disease Control for each zip code. Disadvantaged zip codes for each SDOH were defined as zip codes in the bottom quartile for MHI and the top quartile for the remaining SDOH. MRE utilization was compared between the two groups for each SDOH by one-way analysis.
RESULTS: The mean MRE utilization for the included zip codes was 1.1 MRE per 1000 capita over the study period of 55 months. The MRE utilization was lower for the disadvantaged zip codes stratified by each studied SDOH. The largest difference in MRE utilization was identified between disadvantaged zip codes and non-disadvantaged zip codes stratified by SVI and percentage of population without a car, at 0.5 MRI per 1000 capita.
CONCLUSION: Individuals living in disadvantaged areas have lower rates of MRE utilization, which is commonly used in the evaluation of inflammatory bowel disease (IBD). Disparate utilization of MRE could lead to disparities in outcome for those with IBD.No embarg
Night Sky Darkness Immersion: An Exploratory Study with Veterans
No full textBackground: Nature exposure has well documented health benefits, but little is known about the effects of nighttime nature experiences. Veterans, due to their unique life and service-related experiences may benefit from restorative, nature-based interventions.
Purpose: To explore the feasibility, acceptability, and preliminary efficacy of a novel nighttime nature-based intervention, night sky darkness immersion (NSDI), among U.S. military Veterans.
Specific Aims:
• Assess the feasibility and acceptability of the NSDI.
• Explore NSDI’s effect on anxiety.
• Understand participant’s experiences through qualitative feedback.
Framework: This study was guided by Florence Nightingale’s Theory of the Environment which emphasizes the role of environment in supporting healing.
Design: A mixed methods pilot study was conducted with three Veteran groups (N = 11) between October 2023 and March 2024. Each group received a 15-minute night sky educational session and participated in a 45-minute immersive experience under a dark sky. Anxiety was measured pre- and post-intervention using the State-Trait Anxiety Inventory for Adults (STAI-A). Post-activity surveys and focus groups were thematically analyzed using conventional content analysis.
Results: A statistically and clinically significant reduction in anxiety was observed. No adverse events occurred. Qualitative findings yielded three themes: (1) Learning cool stuff, (2) A calming experience, and (3) Looking up under familiar skies.
Conclusion: NSDI was feasible, well-received, and potentially effective in reducing anxiety in Veterans. The findings support future research on NSDI in broader populations.1 year2026-06-3
The urinary microbiome distinguishes symptomatic urinary tract infection from asymptomatic older adult patients presenting to the emergency department
Get PDFOlder adults suffer from a high rate of asymptomatic bacteriuria (ASB), in which urinalysis may appear positive (presence of bacteria, white blood cells, and nitrates), often triggering initiation of antibiotics in acute care settings, without actual urinary tract infection (UTI) present. To investigate the urinary microbiome of older adults being tested for UTI, we enrolled a convenience sample of 250 older adult Emergency Department patients who had microscopic urinalysis ordered as part of their routine clinical care. Urinalysis results were classified as positive or negative, and patients were classified as being symptomatic or asymptomatic based on established diagnostic guidelines. We sought to determine if features of the urinary microbiome differed between positive and negative urinalysis (UAs) and symptomatic and asymptomatic patients with positive UAs. The same urine sample used for clinical testing was sequenced and analyzed for bacterial taxa, metabolic pathways, and known bacterial virulence factors. After exclusion of anatomical abnormalities and filtering for sequencing quality, 152 samples were analyzed (5 negative UAs, 147 positive UAs, among which 68 were asymptomatic, and 79 symptomatic). Positive UA samples showed significantly lower alpha diversity (2.29 versus 0.086, < 0.01) and distinct community composition based on beta-diversity (PERMANOVA on Bray-Curtis distance < 0.01). Alpha and beta diversity did not significantly differ between asymptomatic and symptomatic patients. Machine learning classifiers combining clinical covariates other than specific signs and symptoms and microbiome features (taxa, metabolic pathways, or virulence factors) revealed mostly microbiome features as predictive of symptomatic UTI over clinical features.No embarg
The Global Neuroanatomy Network: A new repository of open educational resources
No full textMany parts of the world, especially low- and middle-income countries, lack access to supplemental, time-efficient, and engaging teaching resources. Additionally, many anatomy educators may feel ill-equipped to teach in neuro-related fields. To address these issues, the Global Neuroanatomy Network (GNN) is a new repository of open educational resources (ROER) developed for neuroanatomy educators worldwide. The GNN expands on existing ROERs within health professions and anatomical sciences education while filling the neuroanatomy gap through peer-reviewed, multilingual teaching resources and clinical cases. Funded by the American Association for Anatomy, the GNN is freely available to neuroanatomy educators at all academic institutions. GNN members can submit their teaching resources or clinical cases for peer review and view or download content that global colleagues have submitted. The GNN aims to enhance neuroanatomy education by creating and supporting the expansion of a novel repository and further growing a community of neuroanatomy educators.No embarg
Patient-Centered mHealth Intervention to Improve Self-Care in Patients With Chronic Heart Failure: Phase 1 Randomized Controlled Trial
Get PDFBackground: Heart failure (HF) is one of the most common causes of hospital readmission in the United States. These hospitalizations are often driven by insufficient self-care. Commercial mobile health (mHealth) technologies, such as consumer-grade apps and wearable devices, offer opportunities for improving HF self-care, but their efficacy remains largely underexplored.
Objective: The objective of this study was to examine the feasibility, acceptability, safety, and preliminary efficacy of a patient-centered mHealth intervention (iCardia4HF) that integrates 3 consumer mHealth apps and devices (Heart Failure Health Storylines, Fitbit, and Withings) with a program of individually tailored SMS text messages to improve HF self-care.
Methods: We conducted a phase 1 randomized controlled trial. Eligible patients had stage C HF, were aged ≥40 years, and had New York Heart Association (NYHA) class I, II, or III HF. Patients were randomly assigned to either iCardia4HF plus usual care or to usual care only and were observed for 8 weeks. Key feasibility measures were recruitment and retention rates. The primary efficacy outcome was change in HF self-care subscale scores (maintenance, symptom perception, and self-care management) at 8 weeks, assessed with the Self-Care Heart Failure Index (SCHFI; version 7.2). Key secondary outcomes were modifiable behaviors targeted by the intervention (health beliefs, self-efficacy, and HF knowledge), health status, and adherence to daily self-monitoring of 2 core vital signs (body weight and blood pressure).
Results: A total of 27 patients were enrolled in the study and randomly assigned to iCardia4HF (n=13, 48%) or usual care (n=14, 52%). Of these 27 patients, 11 (41%) in the intervention group (iCardia4HF) and 14 (52%) in the usual care group started their assigned care and were included in the full analysis. Patients' mean age was 56 (SD 8.3) years, 44% (11/25) were female, 92% (23/25) self-reported race as Black, 76% (19/25) had NYHA class II or III HF, and 60% (15/25) had HF with reduced left ventricular ejection fraction. Participant retention, completion of study visits, and adherence to using the mHealth apps and devices for daily self-monitoring were high (>80%). At 8 weeks, the mean group differences in changes in the SCHFI subscale scores favored the intervention over the control group: maintenance (Cohen d=0.19, 95% CI -0.65 to 1.02), symptom perception (Cohen d=0.33, 95% CI -0.51 to 1.17), and self-care management (Cohen d=0.25, 95% CI -0.55 to 1.04). The greatest improvements in terms of effect size were observed in self-efficacy (Cohen d=0.68) and health beliefs about medication adherence (Cohen d=0.63) and self-monitoring adherence (Cohen d=0.94). There were no adverse events due to the intervention.
Conclusions: iCardia4HF was found to be feasible, acceptable, and safe. A larger trial with a longer follow-up duration is warranted to examine its efficacy among patients with HF.
Trial registration: ClinicalTrials.gov NCT03642275; https://clinicaltrials.gov/study/NCT03642275.No embarg
A Method for Imaging the Ischemic Penumbra with MRI using IVIM
No full textBackground and purpose: In acute ischemic stroke, the amount of "local" CBF distal to the occlusion, i.e. all blood flow within a region whether supplied antegrade or delayed and dispersed through the collateral network, may contain valuable information regarding infarct growth rate and treatment response. DSC CBF using a local arterial input function (AIF) is one method of quantifying local CBF (local-qCBF) and correlates with collaterals. Similarly, intravoxel incoherent motion MRI (IVIM) is "local", with excitation and readout in the same plane, and a potential alternative way to measure local-qCBF. The purpose of this work was to compare IVIM local-qCBF against DSC local-qCBF in the ischemic penumbra, compare measurement of perfusion-diffusion mismatch (PWI/DWI), and examine if local-qCBF may improve prediction of final infarct.
Materials and methods: Eight experiments in a pre-clinical canine model of middle cerebral artery occlusion were performed; native collateral circulation was quantified via x-ray DSA 30 minutes post-occlusion, and collateralization was subsequently enhanced in a subset of experiments with simultaneous pressor and vasodilator. IVIM and DSC MRI were acquired 2.5hr post-occlusion. IVIM was post-processed to return local-qCBF from fD*, water transport time (WTT) from D*, diffusion from D, and the PWI/DWI mismatch. These were compared with DSC parameters processed first with a standard global-AIF and then with a local-AIF. These DSC parameters included time-to-maximum, local MTT, standard-qCBF, local-qCBF and PWI/DWI mismatch. Infarct volume was measured with DWI at 2.5hrs and 4hrs post-occlusion.
Results: 2.5hr post-occlusion, IVIM local-qCBF in the non-infarcted ipsilateral territory strongly correlated with DSC local-qCBF (slope=1.00, R2=0.69, Lin's CCC=0.71). Correlation was weaker between IVIM local-qCBF and DSC standard-qCBF (R2=0.13). DSC localqCBF and IVIM local-qCBF in the non-infarcted ipsilateral territory both returned strong prediction of final infarct volume (R2=0.78, R2=0.61 respectively). DSC standard-qCBF was a weaker predictor (R2=0.12). The hypoperfused lesion from DSC local-qCBF and from IVIM local-qCBF both predicted final infarct volume with good sensitivity and correlation (slope=2.08, R2=0.67, slope=2.50, R2=0.68 respectively). The IVIM PWI/DWI ratio was correlated with infarct growth (R2=0.70) and WTT correlated with DSC MTT (R2=0.60).
Conclusions: Non-contrast IVIM measurement of local-qCBF and PWI/DWI mismatch may include collateral circulation and improve prediction of infarct growth.No embarg
Combined application of deep learning and conventional computer vision for kidney ultrasound image classification in chronic kidney disease: preliminary study
Get PDFPurpose: This study evaluates the feasibility of combining deep learning (DL) and conventional computer vision techniques to classify kidney ultrasound (US) images for the presence or absence of chronic kidney disease (CKD).
Methods: A retrospective analysis was conducted on 258 kidneys (124 normal and 134 with CKD). A DL model was trained using midsagittal US images of the right kidney and corresponding contour maps to automate measurements of parenchymal thickness and parenchyma-to-sinus ratios. These features were integrated with a convolutional neural network for classification. The ground truth was determined based on clinical CKD diagnosis and laboratory data.
Results: The combined DL and conventional feature extraction model achieved an accuracy of 82%, with a specificity of 93% and a negative predictive value of 97%. This approach outperformed models that relied solely on raw US images using DL, which achieved an accuracy of 64%. The inclusion of contour-based parenchymal measurements enhanced classification performance.
Conclusion: The integration of DL with automated feature extraction enables accurate classification of CKD using minimal user input. This proof-of-concept study highlights the potential of combining artificial intelligence-driven analysis with traditional metrics to serve as a noninvasive adjunct for CKD diagnosis and monitoring.No embarg