Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review

In Vitro Engineered ECM-incorporated Hydrogels for Osteochondral Tissue Repair: A Cell-Free Approach

Prevalence of osteoarthritis has been increasing in aging populations, which has necessitated the use of advanced biomedical treatments. These involve grafts or delivering drug molecules entrapped in scaffolds. However, such treatments often show suboptimal therapeutic effects due to poor half-life and off-target effects of drug molecules. As a countermeasure, a 3D printable robust hydrogel-based tissue-repair platform is developed containing decellularized extracellular matrix (dECM) from differentiated mammalian cells as the therapeutic cargo. Here, pre-osteoblastic and pre-chondrogenic murine cells are differentiated in vitro, decellularized, and incorporated into methacrylated gelatin (GelMA) solutions to form osteogenic (GelO) and chondrogenic (GelC) hydrogels, respectively. Integrating the bioactive dECM from differentiated cell sources allows GelO and GelC to induce differentiation in human adipose-derived stem cells (hASCs) toward osteogenic and chondrogenic lineages. Further, GelO and GelC can be covalently adhered using a carbodiimide coupling reaction, forming a multi-layered hydrogel with potential application as a bioactive osteochondral plug. The designed multi-layered hydrogel can also induce differentiation of hASCs in vitro. In conclusion, the bioactive dECM carrying 3D printed robust hydrogel offers a promising new drug and cell-free therapeutic strategy for bone and cartilage repair and future osteoarthritis management.

Flexible piezoelectric materials and strain sensors for wearable electronics and artificial intelligence applications

With the rapid development of artificial intelligence, the applications of flexible piezoelectric sensors in health monitoring and human-machine interaction have attracted increasing attention. Recent advances in flexible materials and fabrication technologies have promoted practical applications of wearable devices, enabling their assembly in various forms such as ultra-thin films, electronic skins and electronic tattoos. These piezoelectric sensors meet the requirements of high integration, miniaturization and low power consumption, while simultaneously maintaining their unique sensing performance advantages. This review provides a comprehensive overview of cutting-edge research studies on enhanced wearable piezoelectric sensors. Promising piezoelectric polymer materials are highlighted, including polyvinylidene fluoride and conductive hydrogels. Material engineering strategies for improving sensitivity, cycle life, biocompatibility, and processability are summarized and discussed focusing on filler doping, fabrication techniques optimization, and microstructure engineering. Additionally, this review presents representative application cases of smart piezoelectric sensors in health monitoring and human-machine interaction. Finally, critical challenges and promising principles concerning advanced manufacture, biological safety and function integration are discussed to shed light on future directions in the field of piezoelectrics.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Strontium chloride improves bone mass by affecting the gut microbiota in young male rats

Introduction

Bone mass accumulated in early adulthood is an important determinant of bone mass throughout the lifespan, and inadequate bone deposition may lead to associated skeletal diseases. Recent studies suggest that gut bacteria may be potential factors in boosting bone mass. Strontium (Sr) as a key bioactive element has been shown to improve bone quality, but the precise way that maintains the equilibrium of the gut microbiome and bone health is still not well understood.

Methods

We explored the capacity of SrCl2 solutions of varying concentrations (0, 100, 200 and 400 mg/kg BW) on bone quality in 7-week-old male Wistar rats and attempted to elucidate the mechanism through gut microbes.

Results

The results showed that in a Wistar rat model under normal growth conditions, serum Ca levels increased after Sr-treatment and showed a dose-dependent increase with Sr concentration. Three-point mechanics and Micro-CT results showed that Sr exposure enhanced bone biomechanical properties and improved bone microarchitecture. In addition, the osteoblast gene markers BMP, BGP, RUNX2, OPG and ALP mRNA levels were significantly increased to varying degrees after Sr treatment, and the osteoclast markers RANKL and TRAP were accompanied by varying degrees of reduction. These experimental results show that Sr improves bones from multiple angles. Further investigation of the microbial population revealed that the composition of the gut microbiome was changed due to Sr, with the abundance of 6 of the bacteria showing a different dose dependence with Sr concentration than the control group. To investigate whether alterations in bacterial flora were responsible for the effects of Sr on bone remodeling, a further pearson correlation analysis was done, 4 types of bacteria (Ruminococcaceae_UCG-014, Lachnospiraceae_NK4A136_group, Alistipes and Weissella) were deduced to be the primary contributors to Sr-relieved bone loss. Of these, we focused our analysis on the most firmly associated Ruminococcaceae_UCG-014.

Discussion

To summarize, our current research explores changes in bone mass following Sr intervention in young individuals, and the connection between Sr-altered intestinal flora and potentially beneficial bacteria in the attenuation of bone loss. These discoveries underscore the importance of the "gut-bone" axis, contributing to an understanding of how Sr affects bone quality, and providing a fresh idea for bone mass accumulation in young individuals and thereby preventing disease due to acquired bone mass deficiency.

Knee Osteoarthritis: Epidemiology, Pathogenesis, and Mesenchymal Stem Cells: What Else Is New? An Update

Osteoarthritis (OA) is a chronic disease and the most common orthopedic disorder. A vast majority of the social OA burden is related to hips and knees. The prevalence of knee OA varied across studies and such differences are reflected by the heterogeneity of data reported by studies conducted worldwide. A complete understanding of the pathogenetic mechanisms underlying this pathology is essential. The OA inflammatory process starts in the synovial membrane with the activation of the immune system, involving both humoral and cellular mediators. A crucial role in this process is played by the so-called “damage-associated molecular patterns” (DAMPs). Mesenchymal stem cells (MSCs) may be a promising option among all possible therapeutic options. However, many issues are still debated, such as the best cell source, their nature, and the right amount. Further studies are needed to clarify the remaining doubts. This review provides an overview of the most recent and relevant data on the molecular mechanism of cartilage damage in knee OA, including current therapeutic approaches in regenerative medicine.

OA-Pain-Sense: Machine Learning Prediction of Hip and Knee Osteoarthritis Pain from IMU Data

Joint pain is a prominent symptom of Hip and Knee Osteoarthritis (OA), impairing patients’ movements and affecting the joint mechanics of walking. Self-report questionnaires are currently the gold standard for Hip OA and Knee OA pain assessment, presenting several problems, including the fact that older individuals often fail to provide accurate self-pain reports. Passive methods to assess pain are desirable. This study aims to explore the feasibility of OA-Pain-Sense, a passive, automatic Machine Learning-based approach that predicts patients’ self-reported pain levels using SpatioTemporal Gait features extracted from the accelerometer signal gathered from an anterior-posterior wearable sensor. To mitigate inter-subject variability, we investigated two types of data rescaling: subject-level and dataset-level. We explored six different binary machine learning classification models for discriminating pain in patients with Hip OA or Knee OA from healthy controls. In rigorous evaluation, OA-Pain-Sense achieved an average accuracy of 86.79% using the Decision Tree and 83.57% using Support Vector Machine classifiers for distinguishing Hip OA and Knee OA patients from healthy subjects, respectively. Our results demonstrate that OA-Pain-Sense is feasible, paving the way for the development of a pain assessment algorithm that can support clinical decision-making and be used on any wearable device, such as smartphones.

Detection and Classification of Knee Osteoarthritis

Osteoarthritis (OA) affects nearly 240 million people worldwide. Knee OA is the most common type of arthritis, especially in older adults. Physicians measure the severity of knee OA according to the Kellgren and Lawrence (KL) scale through visual inspection of X-ray or MR images. We propose a semi-automatic CADx model based on Deep Siamese convolutional neural networks and a fine-tuned ResNet-34 to simultaneously detect OA lesions in the two knees according to the KL scale. The training was done using a public dataset, whereas the validations were performed with a private dataset. Some problems of the imbalanced dataset were solved using transfer learning. The model results average of the multi-class accuracy is 61%, presenting better performance results for classifying classes KL-0, KL-3, and KL-4 than KL-1 and KL-2. The classification results were compared and validated using the classification of experienced radiologists.

Osteoarthritis Risks and Sports: An Evidence-based Systematic Review

PURPOSE

Regular sports activities are associated with multiple physical and psychological health benefits. However, sports also may lead to injuries and the development of osteoarthritis (OA). This systematic review investigated the association between sports activity, sports type, and the risk of developing OA.

METHODS

A systematic review was performed by assessing studies that have investigated the risk of OA development in sports. Data extracted included general information, study design, number of participants, related body mass index, sports type, and assessment of OA. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale.

RESULTS

A total of 63 studies were included in this systematic review. The overall Newcastle-Ottawa Scale score was 6.46±1.44 demonstrating a good methodological quality of the articles included in the present study. A total of 628,036 participants were included, with a mean follow-up of 8.0±8.4 years. The mean age of the included athletes was 45.6±15.8, with a mean body mass index of 24.9±2.3 kg/m 2 .

CONCLUSION

Football and soccer players seem to be at higher risk for the development of OA, although the injury status of the joint should be considered when assessing the risk of OA. High equipment weight and increased injury risk also put military personnel at a higher risk of OA, although elite dancing leads to more hip labral tears. Femoroacetabular impingement was also often diagnosed in ice-hockey players and ballet dancers.