A multiscale orchestrated computational framework to reveal emergent phenomena in neuroblastoma.

Neuroblastoma is a complex and aggressive type of cancer that affects children. Current treatments involve a combination of surgery, chemotherapy, radiotherapy, and stem cell transplantation. However, treatment outcomes vary due to the heterogeneous nature of the disease. Computational models have been used to analyse data, simulate biological processes, and predict disease progression and treatment outcomes. While continuum cancer models capture the overall behaviour of tumours, and agent-based models represent the complex behaviour of individual cells, multiscale models represent interactions at different organisational levels, providing a more comprehensive understanding of the system. In 2018, the PRIMAGE consortium was formed to build a cloud-based decision support system for neuroblastoma, including a multi-scale model for patient-specific simulations of disease progression. In this work we have developed this multi-scale model that includes data such as patient's tumour geometry, cellularity, vascularization, genetics and type of chemotherapy treatment, and integrated it into an online platform that runs the simulations on a high-performance computation cluster using Onedata and Kubernetes technologies. This infrastructure will allow clinicians to optimise treatment regimens and reduce the number of costly and time-consuming clinical trials. This manuscript outlines the challenging framework's model architecture, data workflow, hypothesis, and resources employed in its development.

The EurValve model execution environment.

The goal of this paper is to present a dedicated high-performance computing (HPC) infrastructure which is used in the development of a so-called reduced-order model (ROM) for simulating the outcomes of interventional procedures which are contemplated in the treatment of valvular heart conditions. Following a brief introduction to the problem, the paper presents the design of a model execution environment, in which representative cases can be simulated and the parameters of the ROM fine-tuned to enable subsequent deployment of a decision support system without further need for HPC. The presentation of the system is followed by information concerning its use in processing specific patient cases in the context of the EurValve international collaboration.

Estimation of valvular resistance of segmented aortic valves using computational fluid dynamics.

Aortic valve stenosis is associated with an elevated left ventricular pressure and transaortic pressure drop. Clinicians routinely use Doppler ultrasound to quantify aortic valve stenosis severity by estimating this pressure drop from blood velocity. However, this method approximates the peak pressure drop, and is unable to quantify the partial pressure recovery distal to the valve. As pressure drops are flow dependent, it remains difficult to assess the true significance of a stenosis for low-flow low-gradient patients. Recent advances in segmentation techniques enable patient-specific Computational Fluid Dynamics (CFD) simulations of flow through the aortic valve. In this work a simulation framework is presented and used to analyze data of 18 patients. The ventricle and valve are reconstructed from 4D Computed Tomography imaging data. Ventricular motion is extracted from the medical images and used to model ventricular contraction and corresponding blood flow through the valve. Simplifications of the framework are assessed by introducing two simplified CFD models: a truncated time-dependent and a steady-state model. Model simplifications are justified for cases where the simulated pressure drop is above 10 mmHg. Furthermore, we propose a valve resistance index to quantify stenosis severity from simulation results. This index is compared to established metrics for clinical decision making, i.e. blood velocity and valve area. It is found that velocity measurements alone do not adequately reflect stenosis severity. This work demonstrates that combining 4D imaging data and CFD has the potential to provide a physiologically relevant diagnostic metric to quantify aortic valve stenosis severity.

Prolactin--not only lactotrophin. A "new" view of the "old" hormone.

Prolactin (PRL) is a hormone mainly secreted by the anterior pituitary. Recent studies have shown that it may also be produced by many extrapituitary cells. The PRL gene expression is controlled by two independent promoter regions, which may be differentially regulated in the pituitary and extrapituitary organs. Proteolytic modifications of PRL generate variants of the hormone. A16 kDa PRL fragment, acting through a specific receptor, has both an antiangiogenic activity as well as an inhibitory effect on tumor growth. Stimulation of the PRL receptor involves many signal transduction pathways, for example JAK2/STAT, MAPK, c-src and Fyn kinase cascade, and these pathways may vary in different tissues. PRL synthesis and secretion is mainly regulated by the inhibitory influence of dopamine but other hormones are also involved in these mechanisms. The essential biological action of PRL is the stimulation of lactogenesis and galactopoesis. Apart from its classical functions, PRL affects other aspects of human body function including osmoregulation, metabolism and regulation of the immune and the central nervous system. Hyperprolactinemia is a common syndrome affecting both men and women. It is manifested by the presence of galactorrhoea and through the symptoms of hypogonadotrophic hypogonadism. Following on from the fact that PRL has so many pleiotropic tissue specific effects it is not surprising to learn that hyperprolactinaemia is a systemic condition which may predispose to numerous cardiovascular and immune-mediated reactions. The exact effects of PRL on both immune and cardiovascular systems are being currently unraveled and may lead to the introduction of novel therapeutic approaches in the future.