Objective: To delineate temporal and spatial dynamics of vascular smooth muscle cell (SMC) transcriptomic changes during aortic aneurysm development in Marfan syndrome (MFS). Approach and Results: We performed single-cell RNA sequencing to study aortic root/ascending aneurysm tissue from Fbn1 C1041G/ + (MFS) mice and healthy controls, identifying all aortic cell types. A distinct cluster of transcriptomically modulated SMCs (modSMCs) was identified in adult Fbn1 C1041G/ + mouse aortic aneurysm tissue only. Comparison with atherosclerotic aortic data (ApoE −/− mice) revealed similar patterns of SMC modulation but identified an MFS-specific gene signature, including plasminogen activator inhibitor-1 ( Serpine1 ) and Kruppel-like factor 4 ( Klf4 ). We identified 481 differentially expressed genes between modSMC and SMC subsets; functional annotation highlighted extracellular matrix modulation, collagen synthesis, adhesion, and proliferation. Pseudotime trajectory analysis of Fbn1 C1041G/ + SMC/modSMC transcriptomes identified genes activated differentially throughout the course of phenotype modulation. While modSMCs were not present in young Fbn1 C1041G/ + mouse aortas despite small aortic aneurysm, multiple early modSMCs marker genes were enriched, suggesting activation of phenotype modulation. modSMCs were not found in nondilated adult Fbn1 C1041G/ + descending thoracic aortas. Single-cell RNA sequencing from human MFS aortic root aneurysm tissue confirmed analogous SMC modulation in clinical disease. Enhanced expression of TGF-β (transforming growth factor beta)-responsive genes correlated with SMC modulation in mouse and human data sets. Conclusions: Dynamic SMC phenotype modulation promotes extracellular matrix substrate modulation and aortic aneurysm progression in MFS. We characterize the disease-specific signature of modSMCs and provide temporal, transcriptomic context to the current understanding of the role TGF-β plays in MFS aortopathy. Collectively, single-cell RNA sequencing implicates TGF-β signaling and Klf4 overexpression as potential upstream drivers of SMC modulation.

Assessing the efficacy of cancer therapeutics in mouse models is a critical step in treatment development. However, low-resolution measurement tools and small sample sizes make determining drug efficacy in vivo a difficult and time-intensive task. Here, we present a commercially scalable wearable electronic strain sensor that automates the in vivo testing of cancer therapeutics by continuously monitoring the micrometer-scale progression or regression of subcutaneously implanted tumors at the minute time scale. In two in vivo cancer mouse models, our sensor discerned differences in tumor volume dynamics between drug- and vehicle-treated tumors within 5 hours following therapy initiation. These short-term regression measurements were validated through histology, and caliper and bioluminescence measurements taken over weeklong treatment periods demonstrated the correlation with longer-term treatment response. We anticipate that real-time tumor regression datasets could help expedite and automate the process of screening cancer therapies in vivo.

IntroductionIt is well documented that on entering college, students experience a multitude of changes in sleep habits. Very few studies have been conducted that explore sleep quality in Indian undergraduate students; fewer still study the effects of burnout in the same population. Medical students, in particular, are believed to be more stressed, sleep deprived, and burnt out than their non-medical peers.MethodsA cross-sectional study was conducted to study sleep disturbances and burnout in a sample of 214 Indian undergraduate students (112 medical, 102 non-medical). The instruments used to measure the sleep quality and burnout were the PSQI (Pittsburgh Sleep Quality Index) and OLBI (Oldenburg Burnout Inventory), respectively. Differences between continuous variables were analysed using Wilcox Mann Whitney U-tests. Bivariate Spearman’s rho correlations were done to identify correlations between the individual burnout components and the PSQI sleep quality components.ResultsOf the students surveyed, 62.6% were found to be poor sleepers with an average score of 6.45 ± 2.85. It was seen that 20% of the students (n = 43) slept less than five hours a day. Medical students, in particular, were found to have more poor sleep (72.9%) than their non-medical peers (51.9%; p < 0.001). Of the sampled women, 65.8% were poor sleepers, as compared to 62.1% of the sampled men, but the difference was not statistically significant. The average scores of the burnout dimensions were 2.43 ± 0.57 for exhaustion and 2.32 ± 0.53 for disengagement. Both exhaustion and disengagement correlated with PSQI sleep scores (Rho 0.21, p 0.001) and (Rho = 0.18, p = 0.008), respectively. The exhaustion dimension of burnout was higher in medical students (2.46 ± 0.55) than in non-medical students (2.38 ± 0.59), but was seen to correlate more with the PSQI sleep score in the non-medical group (Rho = 0.62, p < 0.001). The PSQI scores showed a weak but significant correlation with academic year (rho = -0.19, p = 0.004). Unlike the sleep scores, the burnout dimensions did not correlate well with the academic year.ConclusionsBurnout and sleep quality are both uncommonly studied topics in India. Fostering a healthier and more proactive approach to tackling burnout and poor sleep quality may help unearth culture specific causes for some of the results we have demonstrated.

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design – automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

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