Study on the biomechanical responses of the loaded bone in macroscale and mesoscale by multiscale poroelastic FE analysis

Multiscale interstitial fluid computation modeling of cortical bone to characterize the hydromechanical stimulation of lacunar-canalicular network

Bone tissue is a biological composite material with a complex hierarchical structure that could continuously adjust its internal structure to adapt to the alterations in the external load environment. The fluid flow within bone is the main route of osteocyte metabolism, and the pore pressure as well as the fluid shear stress generated by it are important mechanical stimuli perceived by osteocytes. Owing to the irregular multiscale structure of bone tissue, the fluid stimulation that lacunar-canalicular network (LCN) in different regions of the tissue underwent remained unclear. In this study, we constructed a multiscale conduction model of fluid flow stimulus signals in bone tissue based on the poroelasticity theory. We analyzed the fluid flow behaviors at the macro-scale (whole bone tissue), macro-meso scale (periosteum, interstitial bone, osteon and endosteum), and micro-scale (lacunar-osteocyte-canalicular) levels. We explored how fluid stimulation at the tissue level correlated with that at the cellular level in cortical bone and characterized the distributions of the pore pressure, fluid velocity and fluid shear stress that the osteocytes experienced across the entire tissue structure. The results showed that the initial conditions of intramedullary pressure had a significant impact on the pore pressure of Haversian systems, but had a relatively small influence on the fluid velocity. The osteocyte which were located at different positions in the bone tissue received very distinct fluid stimuli. Osteocytes in the vicinity of the Haversian Canals experienced higher fluid shear stress stimulation. When the permeability of the LCN was within the range from 10-21 m2 to 10-18 m2, the distribution of pressure, fluid velocity and fluid shear stress within the osteon near the periosteum and endosteum was significantly different from that in other parts of the bone. However, when the permeability was less than 10-22 m2, such a difference did not exist. Particularly, the flow velocity at the lacunae was markedly higher than that in the canaliculi. Meanwhile, the pore pressure and fluid shear stress were conspicuously lower than those in the canaliculi. In this study, we considered the interconnections of different biofunctional units at different scales of bone tissue, construct a more complete multiscale model of bone tissue, and propose that osteocytes at different locations receive different fluid stimuli, which provides a reference for a deeper understanding of bone mechanotransduction.

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

BACKGROUND

Increasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data-rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.

METHODS

We conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.

RESULTS

Our study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision-making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.

CONCLUSION

Advanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

Using Finite Element Modeling in Bone Mechanoadaptation

PURPOSE OF THE REVIEW

Bone adapts structure and material properties in response to its mechanical environment, a process called mechanoadpatation. For the past 50 years, finite element modeling has been used to investigate the relationships between bone geometry, material properties, and mechanical loading conditions. This review examines how we use finite element modeling in the context of bone mechanoadpatation.

RECENT FINDINGS

Finite element models estimate complex mechanical stimuli at the tissue and cellular levels, help explain experimental results, and inform the design of loading protocols and prosthetics. FE modeling is a powerful tool to study bone adaptation as it complements experimental approaches. Before using FE models, researchers should determine whether simulation results will provide complementary information to experimental or clinical observations and should establish the level of complexity required. As imaging technics and computational capacity continue increasing, we expect FE models to help in designing treatments of bone pathologies that take advantage of mechanoadaptation of bone.

A lumped model for long bone behavior based on poroelastic deformation and Darcy flow

The present paper provides a simplified model for compact bone behavior by accounting for bone fluid flow coupled to the elasticity of the porous structure. The lumped model considers the bone material as a layered poroelastic structure and predicts normal pressure versus displacement, i.e, a stress-strain curve. There is a parametric dependency on porosity and permeability but, in addition, on pressure history. Specifically, the pressure impulse (the integral of pressure versus time) plays a key role. This factor is alluded to in several past studies, but not highlighted in a simplified fashion. Based on a global flow balance, bone displacement depends on the fluid flow in a channel according to the classical Darcy model of 1856, and on the rate of change of fluid within the porous solid according to the 1941 classical model of Biot. The present results agree with those of Perrin et al. which, in turn, agree with results of a detailed numerical simulation.

Effects of Osteocyte Shape on Fluid Flow and Fluid Shear Stress of the Loaded Bone

This study was conducted to better understand the specific behavior of the intraosseous fluid flow. We calculated the number and distribution of bone canaliculi around the osteocytes based on the varying shapes of osteocytes. We then used these calculated parameters and other bone microstructure data to estimate the anisotropy permeability of the lacunar-canalicular network. Poroelastic finite element models of the osteon were established, and the influence of the osteocyte shape on the fluid flow properties of osteons under an axial displacement load was analyzed. Two types of boundary conditions (BC) that might occur in physiological environments were considered on the cement line of the osteon. BC1 allows free fluid passage from the outer elastic restraint boundary, and BC2 is impermeable and allows no free fluid passage from outer displacement constrained boundary. They both have the same inner boundary conditions that allow fluid to pass through. Changes in the osteocyte shape altered the maximum value of pressure gradient (PG), pore pressure (PP), fluid velocity (FV), and fluid shear stress (FSS) relative to the reference model (spherical osteocytes). The maximum PG, PP, FV, and FSS in BC2 were nearly 100% larger than those in BC1, respectively. It is found that the BC1 was closer to the real physiological environment. The fluid flow along different directions in the elongated osteocyte model was more evident than that in other models, which may have been due to the large difference in permeability along different directions. Changes in osteocyte shape significantly affect the degrees of anisotropy of fluid flow and porous media of the osteon. The model presented in this study can accurately quantify fluid flow in the lacunar-canalicular network.

Interactive effects of various loading parameters on the fluid dynamics within the lacunar-canalicular system for a single osteocyte

The osteocyte lacunar-canalicular system (LCS) serves as a mechanotransductive core where external loading applied to the skeleton is transduced into mechanical signals (e.g., fluid shear) that can be sensed by mechanosensors (osteocytes). The fluid velocity and shear stress within the LCS are affected by various loading parameters. However, the interactive effect of distinct loading parameters on the velocity and shear stress in the LCS remains unclear. To address this issue, we developed a multiscale modeling approach, combining a poroelastic finite element (FE) model with a single osteocytic LCS unit model to calculate the flow velocity and shear stress within the LCS. Next, a sensitivity analysis was performed to investigate individual and interactive effects of strain magnitude, strain rate, number of cycles, and intervening short rests between loading cycles on the velocity and shear stress around the osteocyte. Lastly, we developed a relatively simple regression model to predict those outcomes. Our results demonstrated that the strain magnitude or rate alone were the main factors affecting the velocity and shear stress; however, the combination of these two was not directly additive, and addition of a short rest between cycles could enhance the combination of these two related factors. These results show highly interactive effects of distinct loading parameters on fluid velocity and shear stress in the LCS. Specifically, our results suggest that an enhanced fluid dynamics environment in the LCS can be achieved with a brief number of load cycles combined with short rest insertion and high strain magnitude and rate.

Closing the osteon: Do osteocytes sense strain rate rather than fluid flow?

Osteons are cylindrical structures of bone created by matrix resorbing osteoclasts, followed by osteoblasts that deposit new bone. Osteons align with the principal loading direction and it is thought that the osteoclasts are directed by osteocytes, the mechanosensitive cells that reside inside the bone matrix. These osteocytes are presumably controlled by interstitial fluid flow, induced by the physiological loading of bones. Here I consider the stimulation of osteocytes while the osteon is closed by osteoblasts. In a conceptual finite element model, bone is considered a poro-elastic material and subjected to locomotion-induced loading conditions. It appears that the magnitude of flow is constant along the closing cone, while shear strain rate in the bone matrix diminishes linearly with the deposition of bone. This suggests that shear strain rate, rather than fluid flow, is the physical cue that controls osteocytes and bone deposition in newly formed osteons.