An approach to model the effect of exercise on the growth of mammal long bones is described. A Ginzburg-Landau partial differential equation system is utilised to study the change of size and shape of a cross-section caused by mechanically enhanced bone growth. The concept is based on a phase variable that keeps track of the material properties during the evolution of the bone. The relevant free energies are assumed to be elastic strain energy, concentration gradient energy and a double well chemical potential. The equation governing the evolution of the phase is derived from the total free energy and put on a non-dimensional form, which reduces all required information regarding load, material and cross-section size to one single parameter. The partial differential equation is solved numerically for the geometry of a cross-section using a finite element method. Bending in both moving and fixed directions is investigated regarding reshaping and growth rates. A critical non-zero load is found under which the bone is resorbed. The result for bending around a fixed axis can be compared with experiments made on turkeys. Three loading intervals are identified, I) low load giving resorption of bone on the external periosteum and the internal endosteum, II) intermediate load with growth at the periosteum and resorption at endosteum and III) large loads with growth at both periosteum and endosteum. In the latter case the extent of the medullary cavity decreases.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.mbs.2019.04.005 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.
J Environ Manage
January 2025
School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin, 300401, China. Electronic address:
Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, Stacking, and Categorical Boosting were applied as artificial intelligence methods to predict chemical oxygen demand (COD) removal efficiency, biomass productivity, biomass yield, and energy yield. Among these, the Stacking model demonstrated superior predictive performance across all targets.
View Article and Find Full Text PDFPhysics-informed Neural Implicit Flow neural network for parametric PDEs.
Neural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
View Article and Find Full Text PDFProfiling Exosomal Metabolomics as a Means for Diagnosis and Researching Early-Stage Hypertensive Nephropathy.
Br J Hosp Med (Lond)
January 2025
Department of Cardiology, The Second Affiliated Hospital of Chengdu Medical College, Nuclear Industry 416 Hospital, Chengdu, Sichuan, China.
Hypertension (HT) is a prevalent medical condition showing an increasing incidence rate in various populations over recent years. Long-term hypertension increases the risk of the occurrence of hypertensive nephropathy (HTN), which is also a health-threatening disorder. Given that very little is known about the pathogenesis of HTN, this study was designed to identify disease biomarkers, which enable early diagnosis of the disease, through the utilization of high-throughput untargeted metabolomics strategies.
View Article and Find Full Text PDFDifferentiation of and by Metabolite Profiles Obtained Using Gold Nanoparticles-Based Surface-Assisted Laser Desorption/Ionization Mass Spectrometry.
Pathogens
December 2024
Department of Inorganic and Analytical Chemistry, Faculty of Chemistry, Rzeszów University of Technology, Powstańców Warszawy 6, 35-959 Rzeszów, Poland.
and are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying and by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from and cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system.
View Article and Find Full Text PDFFour-Dimensional Printing of β-Tricalcium Phosphate-Modified Shape Memory Polymers for Bone Scaffolds in Osteochondral Regeneration.
Materials (Basel)
January 2025
Department of Medical Chemistry and Biochemistry, Faculty of Medicine and Dentistry, Palacký University Olomouc, 779 00 Olomouc, Czech Republic.
The use of scaffolds for osteochondral tissue regeneration requires an appropriate selection of materials and manufacturing techniques that provide the basis for supporting both cartilage and bone tissue formation. As scaffolds are designed to replicate a part of the replaced tissue and ensure cell growth and differentiation, implantable materials have to meet various biological requirements, e.g.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!