William N. Martin

Osteoarthritis (OA) lacks disease-modifying therapies, in part because key features of the joint microenvironment remain underappreciated. One such feature is localized acidosis, characterized by sustained reductions in extracellular pH within the cartilage, meniscus, and the osteochondral interface despite near-neutral bulk synovial fluid. We synthesize current evidence on the origins, sensing, and consequences of joint acidosis in OA. Metabolic drivers include hypoxia-biased glycolysis in avascular cartilage, cytokine-driven reprogramming in the synovium, and limits in proton/lactate extrusion (e.g., monocarboxylate transporters (MCTs)), with additional contributions from fixed-charge matrix chemistry and osteoclast-mediated acidification at the osteochondral junction. Acidic niches shift proteolysis toward cathepsins, suppress anabolic control, and trigger chondrocyte stress responses (calcium overload, autophagy, senescence, apoptosis). In the nociceptive axis, protons engage ASIC3 and sensitize TRPV1, linking acidity to pain. Joint cells detect pH through two complementary sensor classes: proton-sensing GPCRs (GPR4, GPR65/TDAG8, GPR68/OGR1, GPR132/G2A), which couple to G_s, G_q/11, and G_12/13 pathways converging on MAPK, NF-κB, CREB, and RhoA/ROCK; and proton-gated ion channels (ASIC1a/3, TRPV1), which convert acidity into electrical and Ca²⁺ signals. Therapeutic implications include inhibition of acid-enabled proteases (e.g., cathepsin K), pharmacologic modulation of pH-sensing receptors (with emerging interest in GPR68 and GPR4), ASIC/TRPV1-targeted analgesia, metabolic control of lactate generation, and pH-responsive intra-articular delivery systems. We outline research priorities for pH-aware clinical phenotyping and imaging, cell-type-resolved signaling maps, and targeted interventions in ‘acidotic OA’ endotypes. Framing acidosis as an actionable component of OA pathogenesis provides a coherent basis for mechanism-anchored, locality-specific disease modification.

Background: Osteoarthritis (OA) is a debilitating joints disease affecting millions of people worldwide. As OA progresses, chondrocytes experience heightened catabolic activity, often accompanied by alterations in the extracellular environment’s osmolarity and acidity. Nevertheless, the precise mechanism by which chondrocytes perceive and respond to acidic stress remains unknown. Recently, there has been growing interest in pH-sensing G protein-coupled receptors (GPCRs), such as GPR68, within musculoskeletal tissues. However, function of GPR68 in cartilage during OA progression remains unknown. This study aims to identify the role of GPR68 in regulation of catabolic gene expression utilizing an in vitro model that simulates catabolic processes in OA. Methods: We examined the expression of GPCR by analyzing high throughput RNA-Seq data in human cartilage isolated from healthy donors and OA patients. De-identified and discarded OA cartilage was obtained from joint arthroplasty and chondrocytes were prepared by enzymatic digestion. Chondrocytes were treated with GPR68 agonist, Ogerin and then stimulated IL1β and RNA isolation was performed using Trizol method. Reverse transcription was done using the cDNA synthesis kit and the expression of GPR68 and OA related catabolic genes was quantified using SYBR® green assays. Results: The transcriptome analysis revealed that pH sensing GPCR were expressed in human cartilage with a notable increase in the expression of GPR68 in OA cartilage which suggest a potential role for GPR68 in the pathogenesis of OA. Immunohistochemical (IHC) and qPCR analyses in human cartilage representing various stages of OA indicated a progressive increase in GPR68 expression in cartilage associated with higher OA grades, underscoring a correlation between GPR68 expression and the severity of OA. Furthermore, IHC analysis of Gpr68 in murine cartilage subjected to surgically induced OA demonstrated elevated levels of GPR68 in knee cartilage and meniscus. Using IL1β stimulated in vitro model of OA catabolism, our qPCR analysis unveiled a time-dependent increase in GPR68 expression in response to IL1β stimulation, which correlates with the expression of matrix degrading proteases suggesting the role of GPR68 in chondrocytes catabolism and matrix degeneration. Using pharmacological activator of GPR68, our results further showed that GPR68 activation repressed the expression of MMPs in human chondrocytes. Conclusions: Our results demonstrated that GPR68 was robustly expressed in human cartilage and mice and its expression correlates with matrix degeneration and severity of OA progression in human and surgical model. GPR68 activation in human chondrocytes further repressed the expression of MMPs under OA pathological condition. These results identify GPR68 as a possible therapeutic target in the regulation of matrix degradation during OA.

More people invest their money in the stock market. However, this kind of investment possesses a lot of risks. Therefore, many works have been done to build a model using Machine Learning algorithm to try to predict the stock price values. In this work, Support Vector Regression (SVR) and Long-Short Term Memory (LSTM) techniques are used to predict the closing price from five different companies. The models are evaluated using one of the common error metrics, which is RMSE (Root Mean Squared Error). The results of the experiment indicate that LSTM has better performance than SVR.

Objective: There has been a debate as to the alignment of the collagen fibers. Using a hand lens, Sir William Hunter demonstrated that the collagen fibers ran perpendicular and later aspects were supported by Benninghoff. Despite these 2 historical studies, modern technology has conflicting data on the collagen alignment. Design: Ten mature New Zealand rabbits were used to obtain 40 condyle specimens. The specimens were passed through ascending grades of alcohol, subjected to critical point drying (CPD), and viewed in the scanning electron microscope. Specimens revealed splits from the dehydration process. When observing the fibers exposed within the opening of the splits, parallel fibers were observed to run in a radial direction, normal to the surface of the articular cartilage, radiating from the deep zone and arcading as they approach the surface layer. After these observations, the same samples were mechanically fractured and damaged by scalpel. Results: The splits in the articular surface created deep fissures, exposing parallel bundles of collagen fibers, radiating from the deep zone and arcading as they approach the surface layer. On higher magnification, individual fibers were observed to run parallel to one another, traversing radially toward the surface of the articular cartilage and arcading. Mechanical fracturing and scalpel damage induced on the same specimens with the splits showed randomly oriented fibers. Conclusion: Collagen fiber orientation corroborates aspects of Hunter’s findings and compliments Benninghoff. Investigators must be aware of the limits of their processing and imaging techniques in order to interpret collagen fiber orientation in cartilage.

Health monitoring of aerospace structures can be done passively by listening for acoustic waves generated by cracks, impact damage and delaminations, or actively by propagating diagnostic stress waves and interpreting the parameters that characterize the wave travel. This paper investigates modeling of flexural wave propagation in a plate and the design of sensors to detect damage in plates based on stress wave parameters. To increase understanding of the actual physical process of wave propagation, a simple model is developed to simulate wave propagation in a plate with boundaries. The waves can be simulated by applied forces and moments in the model either to represent passive damage growth or active wave generation using piezoceramic actuators. For active wave generation, the model considers a piezoceramic patch bonded perfectly to a quasi-isotropic glass-epoxy composite plate. Distributed sensors are used on the plate and are modeled as being constructed using active fiber composite and piezoceramic materials. For active wave generation, a moment impulse is generated by the actuation of a piezoceramic patch. The waves generated from the patch are detected by the distributed sensor. For passive sensing of acoustic waves, a step function is used to simulate an acoustic emission from a propagating damage. The resulting acoustic wave is measured by the distributed sensor and produces micro-strains in the sensor nodes. The strains produce a single voltage signal output from the distributed sensor. Computational simulations and animations of acoustic wave propagation in a plate are discussed in the paper. A new method to locate the source of an acoustic emission using the time history of the dominant lower frequency components of the flexural wave mode detected by continuous sensors is also presented.