Spinal muscular atrophy (SMA) is caused by mutations of the survival of motor neuron (SMN1) gene and deficiency of full-length SMN protein (FL-SMN). All SMA patients retain one or more copies of the SMN2 gene, but the principal protein product of SMN2 lacks exon 7 (SMNDelta7) and is unable to compensate for a deficiency of FL-SMN. SMN is known to oligomerize and form a multimeric protein complex; however, the mechanisms regulating stability and degradation of FL-SMN and SMNDelta7 proteins have been largely unexplored. Using pulse-chase analysis, we characterized SMN protein turnover and confirmed that SMN was ubiquitinated and degraded by the ubiquitin proteasome system (UPS). The SMNDelta7 protein had a twofold shorter half-life than FL-SMN in cells despite similar intrinsic rates of turnover by the UPS in a cell-free assay. Mutations that inhibited SMN oligomerization and complex formation reduced the FL-SMN half-life. Furthermore, recruitment of SMN into large macromolecular complexes as well as increased association with several Gemin proteins was regulated in part by protein kinase A. Together, our data indicate that SMN protein stability is modulated by complex formation. Promotion of the SMN complex formation may be an important novel therapeutic strategy for SMA.

Background: Patients with Duchenne muscular dystrophy exhibit progressive cardiac and skeletal muscle dysfunction. Based on prior data, cardiac dysfunction in Duchenne muscular dystrophy patients may be influenced by myocardial fibrosis and steroid therapy. We examined the longitudinal relationship of myocardial fibrosis and ventricular dysfunction using cardiac magnetic resonance in a large Duchenne muscular dystrophy cohort. Methods and Results: We reviewed 465 serial cardiac magnetic resonance studies (98 Duchenne muscular dystrophy patients with ≥4 cardiac magnetic resonance studies) for left ventricular ejection fraction ( LVEF ) and presence of late gadolinium enhancement ( LGE ), a marker for myocardial fibrosis. LVEF was modeled by examining LGE status, myocardial fibrosis burden (as assessed by the number of LGE ‐positive left ventricular segments), patient age, and steroid treatment duration. An age‐only model demonstrated that LVEF declined 0.58±0.10% per year. In patients with both LGE ‐negative and LGE ‐positive studies (n=51), LVEF did not decline significantly over time if LGE was absent but declined 2.2±0.31% per year when LGE was present. Univariate modeling showed significant associations between LVEF and steroid treatment duration, presence of LGE , and number of LGE ‐positive left ventricular segments; multivariate modeling showed that LVEF declined by 0.93±0.09% for each LGE ‐positive left ventricular segment, whereas age and steroid treatment duration were not significant. The number of LGE ‐positive left ventricular segments increased with age, and longer steroid treatment duration was associated with lower age‐related increases. Conclusion: Progressive myocardial fibrosis, as detected by LGE , was strongly correlated with the LVEF decline in Duchenne muscular dystrophy patients. Longer steroid treatment duration was associated with a lower age‐related increase in myocardial fibrosis burden.

Background: Shortcomings in existing methods of image segmentation preclude the widespread adoption of patient-specific 3D printing as a routine decision-making tool in the care of those with congenital heart disease. We sought to determine the range of cardiovascular segmentation methods and how long each of these methods takes. Methods: A systematic review of literature was undertaken. Medical imaging modality, segmentation methods, segmentation time, segmentation descriptive quality (SDQ) and segmentation software were recorded. Results: Totally 136 studies met the inclusion criteria (1 clinical trial; 80 journal articles; 55 conference, technical and case reports). The most frequently used image segmentation methods were brightness thresholding, region growing and manual editing, as supported by the most popular piece of proprietary software: Mimics (Materialise NV, Leuven, Belgium, 1992–2015). The use of bespoke software developed by individual authors was not uncommon. SDQ indicated that reporting of image segmentation methods was generally poor with only one in three accounts providing sufficient detail for their procedure to be reproduced. Conclusions and implication of key findings: Predominantly anecdotal and case reporting precluded rigorous assessment of risk of bias and strength of evidence. This review finds a reliance on manual and semi-automated segmentation methods which demand a high level of expertise and a significant time commitment on the part of the operator. In light of the findings, we have made recommendations regarding reporting of 3D printing studies. We anticipate that these findings will encourage the development of advanced image segmentation methods.

We propose the addition of usability validation to the extended V3 framework, now “V3+”, and describe a pragmatic approach to ensuring that sensor-based digital health technologies can be used optimally at scale by diverse users. Alongside the original V3 components (verification; analytical validation; clinical validation), usability validation will ensure user-centricity of digital measurement tools, paving the way for more inclusive, reliable, and trustworthy digital measures within clinical research and clinical care.

Background: Patients with congenital heart disease (CHD) are at risk for the development of low cardiac output and other physiologic derangements, which could be detected early through continuous stroke volume (SV) measurement. Unfortunately, existing SV measurement methods are limited in the clinic because of their invasiveness (eg, thermodilution), location (eg, cardiac magnetic resonance imaging), or unreliability (eg, bioimpedance). Multimodal wearable sensing, leveraging the seismocardiogram, a sternal vibration signal associated with cardiomechanical activity, offers a means to monitoring SV conveniently, affordably, and continuously. However, it has not been evaluated in a population with significant anatomical and physiological differences (ie, children with CHD) or compared against a true gold standard (ie, cardiac magnetic resonance). Here, we present the feasibility of wearable estimation of SV in a diverse CHD population (N=45 patients). Methods and Results: We used our chest‐worn wearable biosensor to measure baseline ECG and seismocardiogram signals from patients with CHD before and after their routine cardiovascular magnetic resonance imaging, and derived features from the measured signals, predominantly systolic time intervals, to estimate SV using ridge regression. Wearable signal features achieved acceptable SV estimation (28% error with respect to cardiovascular magnetic resonance imaging) in a held‐out test set, per cardiac output measurement guidelines, with a root‐mean‐square error of 11.48 mL and R ² of 0.76. Additionally, we observed that using a combination of electrical and cardiomechanical features surpassed the performance of either modality alone. Conclusions: A convenient wearable biosensor that estimates SV enables remote monitoring of cardiac function and may potentially help identify decompensation in patients with CHD.