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!
Journal of the Mechanical Behavior of Biomedical Materials: On the need of a scale-dependent material characterization to describe the mechanical behavior of 3D
printed Ti6Al4V custom prostheses using finite element models
Francesca Danielli et al
Abstract
The present study focuses on two patient-specific acetabular and hemipelvis prostheses, with the aim of experimentally characterizing and numerically describing the dependency of the mechanical behavior of 3D printed parts on their peculiar scale, therefore, overcoming one major limitation of current numerical models. Coupling experimental activities with finite element analyses, the authors initially characterized 3D printed Ti6Al4V dog-bone samples at different scales, representative of the main material components of the investigated prostheses. Afterwards, the authors implemented the characterized material behaviors into finite element models to compare the implications of adopting scale-dependent vs. conventional scale-independent approaches in predicting the experimental mechanical behavior of the prostheses in terms of their overall stiffness and the local strain distribution. The material characterization results highlighted the need for a scale- dependent reduction of the elastic modulus for thin samples compared to the conventional Ti6Al4V, which is fundamental to properly describe the overall stiffness and local strain distribution on the prostheses. The presented works demonstrate how an appropriate material characterization and a scale-dependent material description is needed to develop reliable FE models of 3D printed implants characterized by a complex material distribution at different scales.
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Computer Methods and Programs in Biomedicine: A finite element model of the embryonic zebrafish heart electrophysiology
Ludovica Cestariolo et al
Abstract
In the last 30 years, a growing interest has involved the study of zebrafish thanks to its physiological characteristics similar to those of humans. The aim of the following work is to create an electrophysiological computational model of the zebrafish heart and lay the foundation for the development of an in-silico model of the zebrafish heart that will allow to study the correlation between pathologies and drug administration with the main electrophysiological parameters as the ECG signal. The model considers a whole body and the two chambers of three days post fertilization (3 dpf) zebrafish. A four-variable phenomenological action potential model describes the action potential of different heart regions. Tissue conductivity was calibrated to reproduce the experimentally described activation sequence. The model is able to correctly reproduce the activation sequence and times found in literature, with activation of the atrium and ventricle that correspond to 36 and 59 ms, respectively, and a delay of 14 ms caused by the presence of the atrioventricular band (AV band). Moreover, the obtained in-silico ECG reflects the main characteristics of the zebrafish ECG in good agreement with experimental records, a P-wave with a duration of approximately the total atrial activation, followed by a QRS complex of approximately 109 ms corresponding to ventricle activation. The model allows the assessment of the main electrophysiological parameters in terms of activation sequence and timing, reproducing monopolar and bipolar ECG signals in line with experimental data.
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WIREs: From fetus to neonate: A review of cardiovascular modeling in early life
Robyn W. May et al
Abstract
Computational modeling has well-established utility in the study of cardiovascular hemodynamics, with applications in medical research and, increasingly, in clinical settings to improve the diagnosis and treatment of cardiovascular diseases. Most cardiovascular models developed to date have been of the adult circulatory system; however, the perinatal period is unique as cardiovascular physiology undergoes drastic changes from the fetal circulation, during the birth transition, and into neonatal life. There may also be further complications in this period: for example, preterm birth (defined as birth before 37 completed weeks of gestation) carries risks of short-term cardiovascular instability and is associated with increased lifetime cardiovascular risk. Here, we review computational models of the cardiovascular system in early life, their applications to date and potential improvements and enhancements of these models. We propose a roadmap for developing an open-source cardiovascular model that spans the fetal, perinatal, and postnatal periods.
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Nature Reviews Rheumatology: The potential contribution of in silico studies to improved treatment of osteoarthritis
Corrinus C. van Donkelaar
Abstract
Osteoarthritis has many appearances and can stabilize or progress aggressively. However, there is not yet an aetiological classification of osteoarthritis subtypes. Can in silico approaches, despite difficulties in validation, help with the identification of experimentally challenging subtypes? And if they can, will these approaches translate to clinical benefits?
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Osteoarthritis and cartilage: Contribution of collagen degradation and proteoglycan depletion to cartilage degeneration in primary and secondary osteoarthritis: an in silico study
S.A. Elahi et al
Abstract
Current experimental approaches cannot elucidate the effect of maladaptive changes on the main cartilage constituents during the degeneration process in osteoarthritis (OA). In silico approaches, however, allow creating âvirtual knock-outâ cases to elucidate these effects in a constituent-specific manner. We used such an approach to study the main mechanisms of cartilage degeneration in different mechanical loadings associated with the following OA etiologies: (1) physiological loading of degenerated cartilage, (2) injurious loading of healthy intact cartilage and (3) physiological loading of cartilage with a focal defect.
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THE LANCET Digital Health: Augmenting digital twins with federated learning in medicine
Divya Nagaraj et al
Abstract
Clinical digital twins are virtual representations of patients that throughout patient's treatment course, making them valuable for various applications for predicting patient's treatment outcomes. Hailed as a fundamental shift in medical treatment, digital twins face major challenges, particularly regarding privacy concerns before adoption of digital twins. We identify federated learning as a unique solution to this challenge that also enables proliferation and active sharing of digital twins technology without the necessity to reveal patient information.
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THE LANCET Digital Health: Automated machine learning as a partner in predictive modelling
Thomas Callender & Mihaela van der Schaar
Abstract
Machine learning promises to underpin personalised medicine. However, the expertise required to develop and deploy state-of-the-art machine learning algorithms has contributed to the inconsistent quality of model development, the shallow range of methods considered, and the relatively poor penetrance of machine learning models in clinical use. In this Comment, we discuss the emerging field of automated machine learning and propose that it could have a central role in the future of clinical risk prediction. We argue that automated machine learning can empower both modelling experts and non-experts, democratise access to machine learning methods, and encode better standards in model development. Finally, we advocate that such frameworks be an initial step in model development to support practitioners to find the most suitable modelling approach for their question and to understand if machine learning shows benefit.
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MDPI: Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma
Matteo Italia et al
Abstract
Neuroblastoma is a rare type of cancer that usually affects children. The high-risk patientsâ expected survival rate is less than 50%. One reason is the lack of precision in the standard treatment protocol: a one-size-fits-all multi-modal therapy. The study presented in this paper was designed to address this deficit by optimising the use of two chemotherapeutic agentsâvincristine and cyclophosphamideâduring induction chemotherapyâthe part of the protocol that shrinks the primary tumour before surgical removal. We combined a mathematical model and an optimisation algorithm to identify the best chemotherapy schedules for a cohort of virtual patients with different initial tumour compositions. Our results reveal novel strategies to exploit a pair of drugs with different levels of efficacy, provide a platform on which to individualise induction chemotherapy, and lay the foundation for a personalised therapy that leverages targeted therapies, multi-region sequencing, liquid biopsies, and modern computational methods to improve todayâs multi-modal therapy..
