Engineering a 3D in vitro model of human skeletal muscle at the single fiber scale

The reproduction of reliable in vitro models of human skeletal muscle is made harder by the intrinsic 3D structural complexity of this tissue. Here we coupled engineered hydrogel with 3D structural cues and specific mechanical properties to derive human 3D muscle constructs ("myobundles") at the scale of single fibers, by using primary myoblasts or myoblasts derived from embryonic stem cells. To this aim, cell culture was performed in confined, laminin-coated micrometric channels obtained inside a 3D hydrogel characterized by the optimal stiffness for skeletal muscle myogenesis. Primary myoblasts cultured in our 3D culture system were able to undergo myotube differentiation and maturation, as demonstrated by the proper expression and localization of key components of the sarcomere and sarcolemma. Such approach allowed the generation of human myobundles of ~10 mm in length and ~120 \u3bcm in diameter, showing spontaneous contraction 7 days after cell seeding. Transcriptome analyses showed higher similarity between 3D myobundles and skeletal signature, compared to that found between 2D myotubes and skeletal muscle, mainly resulting from expression in 3D myobundles of categories of genes involved in skeletal muscle maturation, including extracellular matrix organization. Moreover, imaging analyses confirmed that structured 3D culture system was conducive to differentiation/maturation also when using myoblasts derived from embryonic stem cells. In conclusion, our structured 3D model is a promising tool for modelling human skeletal muscle in healthy and diseases conditions

Clinical characteristics, management and in-hospital mortality of patients with COVID-19 In Genoa, Italy

To describe clinical characteristics, management and outcome of COVID-19 patients; and to evaluate risk factors for all-cause in-hospital mortality

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine