Click here to read some interesting recently published papers from our community. If you have published an article in the field of in silico medicine, send it to us: we will include it in this section of the newsletter!
Nature Communications: Predictive musculoskeletal simulations reveal the mechanistic link between speed, posture and energetics among extant mammals
Christofer J Clemente et al
Abstract
An unusual pattern among the scaling laws in nature is that the fastest animals are neither the largest, nor the smallest, but rather intermediately sized. Because of the enormous diversity in animal shape, the mechanisms underlying this have long been difficult to determine. To address this, we challenge predictive human musculoskeletal simulations, scaled in mass from the size of a mouse (0.1 kg) to the size of an elephant (2000 kg), to move as fast as possible. Our models replicate patterns observed across extant animals including: (i) an intermediate optimal body mass for speed; (ii) a reduction in the cost of transport with increasing size; and (iii) crouched postures at smaller body masses and upright postures at larger body masses. Finally, we use our models to determine the mechanical limitations of speed with size, showing larger animals may be limited by their ability to produce muscular force while smaller animals are likely limited by their ability to produce larger ground reaction forces. Despite their bipedal gait, our models replicate patterns observed across quadrupedal animals, suggesting these biological phenomena likely represent general rules and are not the result of phylogenetic or other ecological factors that typically hinder comparative studies.
---------------------------------------------------------
Medical Physics: Toward widespread use of virtual trials in medical imaging innovation and regulatory science
Ehsan Abadi et al
Abstract
The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.
---------------------------------------------------------
Medical & Biological Engineering & Computing: Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis
Roberta Scuoppo et al
Abstract
Purpose: In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI).
Methods: A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population.
Results: By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67 mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98).
Conclusion: The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.
---------------------------------------------------------
Cell: Multiscale drug screening for cardiac fibrosis identifies MD2 as a therapeutic target
Hao Zhang et al
Abstract
Cardiac fibrosis impairs cardiac function, but no effective clinical therapies exist. To address this unmet need, we employed a high-throughput screening for antifibrotic compounds using human induced pluripotent stem cell (iPSC)-derived cardiac fibroblasts (CFs). Counter-screening of the initial candidates using iPSC-derived cardiomyocytes and iPSC-derived endothelial cells excluded hits with cardiotoxicity. This screening process identified artesunate as the lead compound. Following profibrotic stimuli, artesunate inhibited proliferation, migration, and contraction in human primary CFs, reduced collagen deposition, and improved contractile function in 3D-engineered heart tissues. Artesunate also attenuated cardiac fibrosis and improved cardiac function in heart failure mouse models. Mechanistically, artesunate targeted myeloid differentiation factor 2 (MD2) and inhibited MD2/Toll-like receptor 4 (TLR4) signaling pathway, alleviating fibrotic gene expression in CFs. Our study leverages multiscale drug screening that integrates a human iPSC platform, tissue engineering, animal models, in silico simulations, and multiomics to identify MD2 as a therapeutic target for cardiac fibrosis.
---------------------------------------------------------
