Osteoarthritis (OA) involves the degeneration of articular cartilage and subchondral bone. The capacity of articular cartilage to repair and regenerate is limited. A biodegradable, fibrous scaffold containing zinc oxide (ZnO) was fabricated and evaluated for osteochondral tissue engineering applications. ZnO has shown promise for a variety of biomedical applications but has had limited use in tissue engineering. Composite scaffolds consisted of ZnO nanoparticles embedded in slow degrading, polycaprolactone to allow for dissolution of zinc ions over time. Zinc has well‐known insulin‐mimetic properties and can be beneficial for cartilage and bone regeneration. Fibrous ZnO composite scaffolds, having varying concentrations of 1–10 wt.% ZnO, were fabricated using the electrospinning technique and evaluated for human mesenchymal stem cell (MSC) differentiation along chondrocyte and osteoblast lineages. Slow release of the zinc was observed for all ZnO composite scaffolds. MSC chondrogenic differentiation was promoted on low percentage ZnO composite scaffolds as indicated by the highest collagen type II production and expression of cartilage‐specific genes, while osteogenic differentiation was promoted on high percentage ZnO composite scaffolds as indicated by the highest alkaline phosphatase activity, collagen production, and expression of bone‐specific genes. This study demonstrates the feasibility of ZnO‐containing composites as a potential scaffold for osteochondral tissue engineering.
Articular cartilage has a limited ability to heal. Mesenchymal stem cells (MSCs) derived from the bone marrow have shown promise as a cell type for cartilage regeneration strategies. In this study, sodium tungstate (Na 2 WO 4 ), which is an insulin mimetic, was evaluated for the first time as an inductive factor to enhance human MSC chondrogenesis. MSCs were seeded onto three-dimensional electrospun scaffolds in growth medium (GM), complete chondrogenic induction medium (CCM) containing insulin, and CCM without insulin. Na 2 WO 4 was added to the media leading to final concentrations of 0, 0.01, 0.1, and 1 mM. Chondrogenic differentiation was assessed by biochemical analyses, immunostaining, and gene expression. Cytotoxicity using human peripheral blood mononuclear cells (PBMCS) was also investigated. The chondrogenic differentiation of MSCs was enhanced in the presence of low concentrations of Na 2 WO 4 compared to control, without Na 2 WO 4 . In the induction medium containing insulin, cells in 0.01 mM Na 2 WO 4 produced significantly higher sulfated glycosaminoglycans, collagen type II, and chondrogenic gene expression than all other groups at day 28. Cells in 0.1 mM Na 2 WO 4 had significantly higher collagen II production and significantly higher sox-9 and aggrecan gene expression compared to control at day 28. Cells in GM and induction medium without insulin containing low concentrations of Na 2 WO 4 also expressed chondrogenic markers. Na 2 WO 4 did not stimulate PBMC proliferation or apoptosis. The results demonstrate that Na 2 WO 4 enhances chondrogenic differentiation of MSCs, does not have a toxic effect, and may be useful for MSC-based approaches for cartilage repair.
Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.
Many studies have studied the relationships between handgrip strength and different Anthropometric variables. However, the hand anatomical position when measuring the handgrip strength was not clear in many studies. This study aims to investigate the relationship between the anthropometric measurements and the handgrip strength at different anatomical positions of the arm among young individuals. 59 young males and 41 females were asked to squeeze the hand dynamometer to their maximum capacity. The maximum handgrip force was recorded for 7 different arm anatomical positions. Using SPSS, an Independent student's t-test was used to compare male and female groups. Pearson’s correlation coefficients were used to determine the correlations between handgrip strength and the anthropometric measurements, weight, and BMI at different arm anatomical positions. Furthermore, the dominance weight was computed to determine the most important predictors of grip strength. Significant correlation between handgrip strength and height and weight at all positions and with hand length for all positions except when the arm was abducted and extended 180 ͦ at the shoulder joint and 180 ͦ at the elbow joint. Arm length, forearm length and handbreadth were also correlated to handgrip strength at three positions, when the arm was adducted with 90 ͦ forward at the elbow joint, when the Arm was abducted with 90 ͦ at the shoulder joint and 180 ͦ at the elbow, and when the arm was abducted with 90 ͦ at the shoulder joint and 90 ͦ at the elbow joint with the forearm perpendicular to the frontal plane. However, these correlations were different when males and females were considered separately. Furthermore, the results showed that the height followed by hand length had the highest prediction power of handgrip strength among young adults. The current results showed the importance of considering the different anatomical positions of the arm when studying the relationship between anthropometric measurements and hand grip strength.
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