Socrates Dokos

Hill's force-velocity relationship is a fundamental property of myocardial contraction, yet its mechanical role in patient-specific left ventricular hypertrophy (LVH) remains underexplored. This study systematically evaluated the consequences of including versus omitting Hill-type velocity dependence in magnetic resonance imaging (MRI)-derived three-dimensional (3D) finite element (FE) models of three hypertrophic left ventricles subjected to stage 3 hypertensive loading. Incorporating velocity dependence moderated peak systolic pressure, tempered early fibre shortening, sustained mid-systolic deformation, and yielded smoother pressure-volume behaviour with improved energetic efficiency. In contrast, omitting velocity dependence amplified systolic pressure generation, accelerated contraction prematurely, and increased stroke work by 15%. Regional analysis across all three patient-specific LVH models consistently demonstrated that the absence of velocity dependence elevated fibre stress by 15-25%, most prominently in subendocardial and high-curvature regions, accompanied by reduced mid-systolic strain. A biophysical damping formulation was implemented to ensure numerical stability across heterogeneous LVH geometries. Together, these findings provide mechanistic insight into how sarcomere shortening dynamics govern regional ventricular adaptation and advance the physiological fidelity of cardiac simulations under severe hypertensive conditions.

Cell adhesion is a key property of cancer cells, as it relates to their potential for dissemination and metastasis. The in vitro assays used to measure it and, however, are characterized by several drawbacks, including low temporal resolution and limited procedural standardization, which reduce their usefulness and accuracy. In this work, we propose an alternative analytical approach, based on live-cell imaging, which enables the study of cell adhesion dynamics at the single-cell level. The increased resolution afforded by this method was instrumental for the identification of cell division prior to attachment and the co-existence of markedly different proliferation rates across the culture, previously unidentified patterns of behavior in the adhesion process. Finally, we generalize our method by substituting the segmentation algorithm and showing that this approach can be integrated within routine laboratory analytical procedures and does not require high-performance microscopy and imaging setups. Our new analytical approach improves the in vitro quantification of cell adhesion, enabling the study of this process with high temporal resolution and increased level of detail. The extension of the analysis to the single-cell level, additionally, uncovered the role of population variability and proliferation in this process. The simple and cost-effective procedure here described enables the accurate characterization of cell adhesion. In addition to improving our understanding of adhesion dynamics, its results could support the development of treatments targeting the ability of cancer cells to adhere to surrounding tissues by allowing detailed quantification of cell adhesion metrics.

Heart failure is a cardiovascular disease that causes changes in heart structure and function, reducing cardiac output. This disease can be categorized as either diastolic or systolic, and is a worldwide growing problem. In diastolic heart failure, the thickness of ventricular walls is increased and is associated with concentric hypertrophy. Systolic failure is characterized by dilation of the ventricles and is associated with eccentric hypertrophy. In this study, concentric and eccentric hypertrophies are deemed to be caused by pressure and volume overloads, respectively. The equations of concentric and eccentric hypertrophic growth have been implemented in COMSOL Multiphysics software using an idealized biventricular geometry and defining the microstructure with a rule-based approach. Simulation results of diastolic heart failure indicated that the ventricular wall thickness increased due to the excessive increase in pressure, with ventricular size remaining almost unchanged. Results for systolic heart failure demonstrated that the ventricular chamber volume increased dramatically, with wall thickness remaining relatively constant. The utilized approach for implementing growth equations can also be used in the future for coupling with electromechanical models of the myocardium.

Hill's force-velocity relationship is integrated into a 3D left-ventricular finite element model to enhance myocardial contraction dynamics alongside the Frank-Starling mechanism. Hypertensive conditions were simulated by adjusting arterial resistance and compliance to mimic varying afterload, with damping applied on velocity as an alternative stabilization method for numerical stability. Hill's relationship reduced peak systolic pressures (6.3%-7.2%) as hypertension progressed, moderated shortening velocities (1.61-1.69 s^-1), and prevented velocity overshoot. Cardiac output was preserved, and ejection fraction remained unaffected. These findings highlight Hill's role in regulating systolic function, optimizing myocardial response under elevated afterload, and improving predictive capabilities for hypertensive heart disease.

Background: ROAST (Realistic vOlumetric-Approach-based Simulator for Transcranial electric stimulation) has been increasingly utilized to inform studies of transcranial electrical stimulation (tES). The precision of ROAST is dependent on anatomical accuracy. Objectives: The aims of the study were to compare using only T1 magnetic resonance images in ROAST (T1 model), with a combination of T1 and T2 magnetic resonance images (T1 and T2 model) and to model the electrical fields generated by tES with commonly used ECT montages: bitemporal, bifrontal, and right unilateral and varying the skull conductivities (based on the electrode position) and including cranial sutures. Methods: The “T1 model” was selected for computational modeling. The skull conductivity was varied from the “default setting in ROAST” (0.01S/m) to that of temporal bone (0.0038S/m) and frontal bone (0.0126S/m). “Disc” electrodes (5 cm in diameter) were applied with 1 mA of current. “Pad” electrodes, 0.1 mm high and 40 mm wide, were positioned over the squamous suture, coronal suture and sagittal suture and the skull conductivity changed to approximate suture conductivity (0.32S/m). Results: The “T1 model” differed from the “T1 and T2 model,” which resulted in variable electric fields reaching individual tissue layers. Changing skull conductivity and simulating the cranial sutures resulted in changes to the electric current reaching the cortical and subcortical structures with the later having a greater impact. Conclusions: This study demonstrates the importance of anatomically accurate head models in ROAST computational modeling of tES. Varying the skull conductivity based on studies in vivo and including cranial sutures is imperative for more realistic predictions of electric field and reproducibility.

Coronary bifurcation lesions present complex challenges in interventional cardiology, necessitating effective stenting techniques to achieve optimal results. This literature review comprehensively examines the application of computational and bench testing methods in coronary bifurcation stenting, offering insights into procedural aspects, stent design considerations, and patient‐specific characteristics. Structural mechanics finite element analysis, computational fluid dynamics, and multi‐objective optimization are valuable tools for evaluating stenting strategies, including provisional side branch stenting and two‐stenting techniques. We highlight the impact of procedural factors, such as balloon positioning and rewiring techniques, and stent design features on the outcome of percutaneous coronary interventions with stents. We discuss the importance of patient‐specific characteristics in deployment strategies, such as bifurcation angle and plaque properties. This understanding informs present and future research and clinical practice on bifurcation stenting. Computational simulations are a continuously maturing advance that has significantly enhanced stenting devices and techniques for coronary bifurcation lesions over the years. However, the accurate account of patient‐specific vessel and lesion characteristics, both in terms of anatomical and accurate physiological behavior, and their large variation between patients, remains a significant challenge in the field. In this context, advancements in multi‐objective optimization offer significant opportunities for refining stent design and procedural practices.

Background: Computational modeling is used to optimize transcranial electrical stimulation (tES) approaches, and the precision of these models is dependent on their anatomical accuracy. We are unaware of any computational modeling of tES that has included cranial sutures. Objectives: The aims of the study were to review the literature on the timing of closure of the coronal and squamous sutures, which are situated under electrode placements used in tES; to review the literature regarding differences in skull and suture conductivity and to determine a more accurate conductivity for sutures; and to identify magnetic resonance image (MRI) techniques that could be used to detect cranial sutures. Methods: A scoping review of medical literature was conducted. We conducted computational modeling of a cranial bone plug using COMSOL Multiphysics finite element software, utilizing methodology and results from a previous study. We assessed use of the “3D Slicer” software to identify sutures in routine T1-weighted MRI scans. Results: Reports from forensic examinations and computed tomography (CT) scans showed suture closure does not correlate with age. Our computational modeling determined a cranial suture conductivity of 0.32 S/m, which is much higher than for skull (compact skull 0.004 S/m, standard trilayer 0.013 S/m). 3D slicer enabled rapid and precise identification of the anatomy and location of cranial sutures. Conclusions: Cranial sutures persist throughout the lifespan and have a far higher conductivity than skull bone. Cranial sutures can be localized quickly and precisely using a combination of MRI and readily available modeling software. Sutures should be included in tES computational modeling and electroencephalography source imaging to improve the accuracy of results.