Quantitative Biology

We propose an interpretable 3D Grid-Attention deep neural network that can accurately predict a person's age and whether they have Alzheimer's disease (AD) from a structural brain MRI scan. Building on a 3D convolutional neural network, we added two attention modules at different layers of abstraction, so that features learned are spatially related to the global features for the task. The attention layers allow the network to focus on brain regions relevant to the task, while masking out irrelevant or noisy regions. In evaluations based on 4,561 3-Tesla T1-weighted MRI scans from 4 phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI), salience maps for age and AD prediction partially overlapped, but lower-level features overlapped more than higher-level features. The brain age prediction network also distinguished AD and healthy control groups better than another state-of-the-art method. The resulting visual analyses can distinguish interpretable feature patterns that are important for predicting clinical diagnosis. Future work is needed to test performance across scanners and populations.

There exists a large body of research on the lens of mammalian eye over the past several decades. The objective of the current work is to provide a link between the most recent computational models to some of the pioneering work in the 1970s and 80s. We introduce a general non-electro-neutral model to study the microcirculation in lens of eyes. It describes the steady state relationships among ion fluxes, water flow and electric field inside cells, and in the narrow extracellular spaces between cells in the lens. Using asymptotic analysis, we derive a simplified model based on physiological data and compare our results with those in the literature. We show that our simplified model can be reduced further to the first generation models while our full model is consistent with the most recent computational models. In addition, our simplified model captures the main features of the full model. Our results serve as a useful link intermediate between the computational models and the first generation analytical models.

Osteonecrosis occurs due to the loss of blood supply to the bone, leading to spontaneous death of the trabecular bone. Delayed treatment of the involved patients results in collapse of the femoral head, which leads to a need for total hip arthroplasty surgery. Core decompression, as the most popular technique for treatment of the osteonecrosis, includes removal of the lesion area by drilling a straight tunnel to the lesion, debriding the dead bone and replacing it with bone substitutes. However, there are two drawbacks for this treatment method. First, due to the rigidity of the instruments currently used during core decompression, lesions cannot be completely removed and/or excessive healthy bone may also be removed with the lesion. Second, the use of bone substitutes, despite its biocompatibility and osteoconductivity, may not provide sufficient mechanical strength and support for the bone. To address these shortcomings, a novel robot-assisted curved core decompression (CCD) technique is introduced to provide surgeons with direct access to the lesions causing minimal damage to the healthy bone. In this study, with the aid of finite element (FE) simulations, we investigate biomechanical performance of core decompression using the curved drilling technique in the presence of normal gait loading. In this regard, we compare the result of the CCD using bone substitutes and flexible implants with other conventional core decompression techniques. The study finding shows that the maximum principal stress occurring at the superior domain of the neck is smaller in the CCD techniques (i.e. 52.847 MPa) compared to the other core decompression methods.

In the present work, complex irregular bones and joints of the complete human arm were developed in a computer-aided design environment. Finite element analysis of an actual human arm was done to identify the distribution of stress using von-Mises stress and maximum principal stress measures. The results obtained from the present study revealed the region where, maximum stress was developed for different loading and boundary conditions with different joint rotations as obtained in the actual human arm. This subject specific analysis helps to analyse the region of the arm in which the risk is more.

Although cirrhosis is a key risk factor for the development of hepatocellular carcinoma (HCC), mounting evidence indicates that in a subset of patients presenting with non-alcoholic steatohepatitis (NASH), HCC manifests in the absence of cirrhosis. Given the sheer size of the non-alcoholic fatty liver disease (NAFLD) epidemic, and the dismal prognosis associated with late-stage primary liver cancer, there is an urgent need for HCC surveillance in the NASH patient. In the present study, adult male mice randomized to control diet or a fast food diet (FFD) were followed for up to 14 mo and serum level of a panel of HCC-relevant biomarkers was compared with liver biopsies at 3 and 14 mo. Both NAFLD Activity Score (NAS) and hepatic hydroxyproline content were elevated at 3 and 14 mo on FFD. Picrosirius red staining of liver sections revealed a filigree pattern of fibrillar collagen deposition with no cirrhosis at 14 mo on FFD. Nevertheless, 46% of animals bore one or more tumors on their livers confirmed as HCC in hematoxylin-eosin-stained liver sections. Receiver operating characteristic (ROC) curves analysis for serum levels of the HCC biomarkers osteopontin (OPN), alpha-fetoprotein (AFP) and Dickkopf-1 (DKK1) returned concordance-statistic/area under ROC curve of > 0.89. These data suggest that serum levels of OPN (threshold, 218 ng/mL; sensitivity, 82%; specificity, 86%), AFP (136 ng/mL; 91%; 97%) and DKK1 (2.4 ng/mL; 82%; 81%) are diagnostic for HCC in a clinically relevant model of NASH

Arterial tissue delamination, manifested as the failure between arterial layers, is a critical process in the rupture of atherosclerotic plaque, leading to potential life-threatening clinical consequences. Numerous models have been used to characterize the arterial tissue delamination. Few has investigated the effect of Cohesive Zone Model (CZM) shapes on predicting the delamination behavior of the arterial wall. In this study, four types of cohesive zone models (triangular, trapezoidal, linear-exponential and exponential-linear) were investigated to compare their predictability of the arterial wall failure. The Holzapfel-Gasser-Ogden (HGO) model was adopted for modelling the mechanical behavior of the aortic bulk material. The simulation results using CZM on the aortic media delamination were also compared with the results on mouse plaque delamination and human fibrous cap delamination. The results show that: 1) the simulation results based on the four shapes of CZMs match well with the experimental results, 2) the triangular and exponential-linear CZMs are in good agreement with the experimental force-displacement curves of mouse plaque delamination, 3) considering the viscoelastic effect of the arterial tissue, the triangular and exponential-linear CZMs match well with the experimental force-displacement curves of human fibrous cap delamination. Thus, triangular and exponential-linear CZMs can capture the arterial tissue failure response well.

This paper presents a new method for modeling the mechanics of the aortic valve, and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures, and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws, and gross morphology. These experiments suggest the following conclusions, which are directly applicable to the design of prosthetic aortic valves. (i) The loaded geometry, tensions and tangent moduli primarily determine model function. (ii) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (iii) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (iv) Valve performance is highly sensitive to free edge length and leaflet height. For future use, our aortic valve modeling framework offers flexibility in patient-specific models of cardiac flow.

Traumatic injuries are measured using the Abbreviated Injury Scale (AIS), which is a risk to life scale. New human computer models use stresses and strains to evaluate whether serious or fatal injuries are reached, unfortunately these tensors bear no direct relation to AIS. This paper proposes to overcome this deficiency and suggests a unique Organ Trauma Model (OTM) able to calculate the risk to life based on the severity on any organ injury, focussing on real-life pedestrian accidents. The OTM uses a power method, named Peak Virtual Power (PVP), and calculates the risk to life of brain white and grey matters as a function of impact direction and impact speed. The OTM firstly calibrates PVP against the medical critical AIS threshold observed in each part of the head as a function of speed. This base PVP critical trauma function is then scaled and banded across all AIS levels using the confirmed property that AIS and the probability of death is statistically and numerically a cubic one. The OTM model has been tested against four real-life pedestrian accidents and proven to be able to predict pedestrian head trauma severity. In some cases, the method did however under-estimate the head trauma by 1 AIS level, because of post-impact haemorrhage which cannot be captured with the employed Lagrangian Finite Element (FE) solver. It is also shown that the location of the injury predictions using PVP coincide with the post mortem reports and are different to the predictions made using maximum principal strain.

In the real world, the severity of traumatic injuries are measured using the Abbreviated Injury Scale (AIS). However the AIS scale cannot currently be computed by using finite element human computer models, which calculate a maximum principal strains (MPS). Further, MPS only establishes a threshold above which a serious or fatal injury occurs. In order to overcome these limitations, a unique Organ Trauma Model (OTM) able to calculate the threat to life of any organ injury is proposed. The focus, in this case is on real world pedestrian brain injuries. The OTM uses a power method, named Peak Virtual Power (PVP), and defines brain white and grey matters trauma responses as a function of impact location and impact speed extracted from the pedestrian collision kinematics. This research has included ageing in the injury severity computation by including soft tissue material degradation, as well as brain volume changes. Further, to account for the limitations of the Lagrangian formulation of the brain model in representing haemorrhage, an approach to include the effects of subdural hematoma is proposed and included as part of the OTM predictions in this study. The OTM model was tested against three real-life pedestrian accidents and has proven to reasonably predict the Post Mortem (PM) outcome. Its AIS predictions are closer to the real world injury severity than standard MPS methods currently recommended. This study suggests that the OTM has the potential to improve forensic predictions as well as contribute to the improvement in vehicle safety design through the ability to measure injury severity. This study concludes that future advances in trauma computing would require the development of a brain model which could predict haemorrhaging.

Understanding biological phenomena requires a systemic approach that incorporates different mechanisms acting on different spatial and temporal scales, since in organisms the workings of all components, such as organelles, cells, and organs interrelate. This inherent interdependency between diverse biological mechanisms, both on the same and on different scales, provides the functioning of an organism capable of maintaining homeostasis and physiological stability through numerous feedback loops. Thus, developing models of organisms and their constituents should be done within the overall systemic context of the studied phenomena. We introduce such a method for modeling growth and regeneration of livers at the organ scale, considering it a part of the overall multi-scale biochemical and biophysical processes of an organism. Our method is based on the earlier discovered general growth law, postulating that any biological growth process comprises a uniquely defined distribution of nutritional resources between maintenance needs and biomass production. Based on this law, we introduce a liver growth model that allows to accurately predicting the growth of liver transplants in dogs and liver grafts in humans. Using this model, we find quantitative growth characteristics, such as the time point when the transition period after surgery is over and the liver resumes normal growth, rates at which hepatocytes are involved in proliferation, etc. We then use the model to determine and quantify otherwise unobservable metabolic properties of livers.