Quantifying structural features of native myocardium in engineered tissue is essential for creating functional tissue that can serve as a surrogate for in vitro testing or the eventual replacement of diseased or injured myocardium. We applied three-dimensional confocal imaging and image analysis to quantitatively describe the features of native and engineered cardiac tissue. Quantitative analysis methods were developed and applied to test the hypothesis that environmental cues direct engineered tissue toward a phenotype resembling that of age-matched native myocardium. The analytical approach was applied to engineered cardiac tissue with and without the application of electrical stimulation as well as to age-matched and adult native tissue. Individual myocytes were segmented from confocal image stacks and assigned a coordinate system from which measures of cell geometry and connexin-43 spatial distribution were calculated. The data were collected from 9 nonstimulated and 12 electrically stimulated engineered tissue constructs and 5 postnatal day 12 and 7 adult hearts. The myocyte volume fraction was nearly double in stimulated engineered tissue compared to nonstimulated engineered tissue (0.34 ± 0.14 vs 0.18 ± 0.06) but less than half of the native postnatal day 12 (0.90 ± 0.06) and adult (0.91 ± 0.04) myocardium. The myocytes under electrical stimulation were more elongated compared to nonstimulated myocytes and exhibited similar lengths, widths, and heights as in age-matched myocardium. Furthermore, the percentage of connexin-43-positive membrane staining was similar in the electrically stimulated, postnatal day 12, and adult myocytes, whereas it was significantly lower in the nonstimulated myocytes. Connexin-43 was found to be primarily located at cell ends for adult myocytes and irregularly but densely clustered over the membranes of nonstimulated, stimulated, and postnatal day 12 myocytes. These findings support our hypothesis and reveal that the application of environmental cues produces tissue with structural features more representative of age-matched native myocardium than adult myocardium. We suggest that the presented approach can be applied to quantitatively characterize developmental processes and mechanisms in engineered tissue.

Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.