288 research outputs found

    Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

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    Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys

    The investigation of metabolic profiling of human synovial fluid to provide joint disease analysis and the association with implant material wear

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    The goal of this thesis was to establish a biobank of human synovial fluid (HSF) samples. Furthermore, the methodology, conditions, fit-for-purpose criteria were needed to ensure validity and robustness. Once those conditions were met, the relationship between HSF and the behaviour of the joint implant material was to be evaluated. The current literature relating to the metabolism of osteoarthritis, synovial fluid, its storage and analysis was systematically reviewed. A biobank of HSF samples was collected. Metabolite identification of HSF was achieved using a combination of published NMR studies, the Human Metabolite Database (HMDB), 1D and 2D NMR spectra and STOCSY analysis. The stability of samples to handling, collection and long-term -80oC storage was investigated. All metabolite concentrations affected by storage were reported. This work has validated the systems and methodology to metabolically profile HSF. A variety of protein precipitation steps to maximise the metabolic information from the samples were evaluated. An acetonitrile liquid/liquid extraction performed well with additional recovery of unknown metabolites, albeit with increased variation and diminished lipid detection. To understand which metabolic components are important for mechanical wear, the metabolic profile of HSF and its wear model, BCS, were analysed for components of both fluids which correlate to measured wear in a bench-top testing rig. Wear analysis demonstrated variation in the HSF mechanical properties. This correlated to the presence of glycosaminoglycan (GAG) and proteoglycan molecules with binding to citrate and glucose. Furthermore, specific amino acids: lysine, glutamine, glycine, threonine, asparagine, proline, histidine and tyrosine, correlated with measured wear.The reported unstable metabolites must be considered for any HSF study. The acetonitrile liquid/liquid extraction method is recommended to maximise metabolite detection. The small molecule components of HSF contributing to the wear properties of implant materials has not been reported previously, is unique and opens a new field of study in implant survival.Open Acces

    Ti-6Al-4V ÎČ Phase Selective Dissolution: In Vitro Mechanism and Prediction

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    Retrieval studies document Ti-6Al-4V ÎČ phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H2O2) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur and (3) near field electrochemical impedance spectra (nEIS) would distinguish between dissolved and polished Ti-6Al-4V, allowing for deep neural network prediction. First, we show a combinatorial effect of cathodic activation and inflammatory species, degrading the oxide film’s polarization resistance (Rp) by a factor of 105 Ωcm2 (p = 0.000) and inducing selective dissolution. Next, we establish a potential range (-0.3 V to –1 V) where inflammatory species, cathodic activation and increasing solution temperatures (24 oC to 55 oC) synergistically affect the oxide film. Then, we evaluate the effect of solution temperature on the dissolution rate, documenting a logarithmic dependence. In our second aim, we show decreased AM Ti-6Al-4V Rp when compared with AM Ti-29Nb-21Zr in H2O2. AM Ti-6Al-4V oxide degradation preceded pit nucleation in the ÎČ phase. Finally, in our third aim, we identified gaps in the application of artificial intelligence to metallic biomaterial corrosion. With an input of nEIS spectra, a deep neural network predicted the surface dissolution state with 96% accuracy. In total, these results support the inclusion of inflammatory species and cathodic activation in pre-clinical titanium devices and biomaterial testing

    Orvosképzés 2023

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    Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

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    Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products

    3D printing in biomedicine: advancing personalized care through additive manufacturing

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    The integration of three-dimensional (3D) printing techniques into the domains of biomedical research and personalized medicine highlights the evolving paradigm shifts within contemporary healthcare. This technological advancement signifies potential breakthroughs in patient-specific therapeutic interventions and innovations. This systematic review offers a critical assessment of the existing literature, elucidating the present status, inherent challenges, and prospective avenues of 3D printing in augmenting biomedical applications and formulating tailored medical strategies. Based on an exhaustive literature analysis comprising empirical studies, case studies, and extensive reviews from the past decade, pivotal sectors including tissue engineering, prosthetic development, drug delivery systems, and customized medical apparatuses are delineated. The advent of 3D printing provides precision in the fabrication of patient-centric implants, bio-structures, and devices, thereby mitigating associated risks. Concurrently, it facilitates the ideation of individualized drug delivery paradigms to optimize therapeutic outcomes. Notwithstanding these advancements, issues concerning material biocompatibility, regulatory compliance, and the economic implications of avant-garde printing techniques persist. To fully harness the transformative potential of 3D printing in healthcare, collaborative endeavors amongst academicians, clinicians, industrial entities, and regulatory bodies are paramount. With continued research and innovation, 3D printing is poised to redefine the trajectories of biomedical science and patient-centric care. The paper aims to justify the research objective of whether to what extent the integration of 3D printing technology in biomedicine enhances patient-specific treatment and contributes to improved healthcare outcomes

    Applied AI/ML for automatic customisation of medical implants

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    Most knee replacement surgeries are performed using ‘off-the-shelf’ implants, supplied with a set number of standardised sizes. X-rays are taken during pre-operative assessment and used by clinicians to estimate the best options for patients. Manual templating and implant size selection have, however, been shown to be inaccurate, and frequently the generically shaped products do not adequately fit patients’ unique anatomies. Furthermore, off-the-shelf implants are typically made from solid metal and do not exhibit mechanical properties like the native bone. Consequently, the combination of these factors often leads to poor outcomes for patients. Various solutions have been outlined in the literature for customising the size, shape, and stiffness of implants for the specific needs of individuals. Such designs can be fabricated via additive manufacturing which enables bespoke and intricate geometries to be produced in biocompatible materials. Despite this, all customisation solutions identified required some level of manual input to segment image files, identify anatomical features, and/or drive design software. These tasks are time consuming, expensive, and require trained resource. Almost all currently available solutions also require CT imaging, which adds further expense, incurs high levels of potentially harmful radiation, and is not as commonly accessible as X-ray imaging. This thesis explores how various levels of knee replacement customisation can be completed automatically by applying artificial intelligence, machine learning and statistical methods. The principal output is a software application, believed to be the first true ‘mass-customisation’ solution. The software is compatible with both 2D X-ray and 3D CT data and enables fully automatic and accurate implant size prediction, shape customisation and stiffness matching. It is therefore seen to address the key limitations associated with current implant customisation solutions and will hopefully enable the benefits of customisation to be more widely accessible.Open Acces

    3D shape reconstruction of the femur from planar X-ray images using statistical shape and appearance models

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    Major trauma is a condition that can result in severe bone damage. Customised orthopaedic reconstruction allows for limb salvage surgery and helps to restore joint alignment. For the best possible outcome three dimensional (3D) medical imaging is necessary, but its availability and access, especially in developing countries, can be challenging. In this study, 3D bone shapes of the femur reconstructed from planar radiographs representing bone defects were evaluated for use in orthopaedic surgery. Statistical shape and appearance models generated from 40 cadaveric X-ray computed tomography (CT) images were used to reconstruct 3D bone shapes. The reconstruction simulated bone defects of between 0% and 50% of the whole bone, and the prediction accuracy using anterior–posterior (AP) and anterior–posterior/medial–lateral (AP/ML) X-rays were compared. As error metrics for the comparison, measures evaluating the distance between contour lines of the projections as well as a measure comparing similarities in image intensities were used. The results were evaluated using the root-mean-square distance for surface error as well as differences in commonly used anatomical measures, including bow, femoral neck, diaphyseal–condylar and version angles between reconstructed surfaces from the shape model and the intact shape reconstructed from the CT image. The reconstructions had average surface errors between 1.59 and 3.59 mm with reconstructions using the contour error metric from the AP/ML directions being the most accurate. Predictions of bow and femoral neck angles were well below the clinical threshold accuracy of 3°, diaphyseal–condylar angles were around the threshold of 3° and only version angle predictions of between 5.3° and 9.3° were above the clinical threshold, but below the range reported in clinical practice using computer navigation (i.e., 17° internal to 15° external rotation). This study shows that the reconstructions from partly available planar images using statistical shape and appearance models had an accuracy which would support their potential use in orthopaedic reconstruction

    Identification of factors associated with non-responders to total joint replacement and sustained knee pain in primary osteoarthritis patients by epidemiological and multi-omic studies

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    Osteoarthritis (OA) is among the most common rheumatic diseases, affecting 30% of the world’s population over 60 years. Currently, total joint replacement (TJR) is considered the most effective treatment for end-stage OA. However, up to 20% of patients do not see clinically significant improvement in pain or function after the surgery. This thesis aims to identify epidemiological, metabolic, and genetic factors which are significantly associated with non-responders to TJR and patients with sustained, treatment-resistant pain in a large cohort from Newfoundland and Labrador (NL), Canada. First, we identified a number of epidemiological factors significantly associated with non-responders to TJR including clinical depression, younger age, and multisite musculoskeletal pain (MSMP). This highlighted potential roles for altered pain perception and pain sensitization in non-responders. Subsequently, we used a targeted metabolomic approach which profiled 186 metabolites in plasma and identified three metabolite ratios and two metabolite networks which were significantly associated with pain or function non-responders. Our findings highlighted phosphatidylcholines (PCs), branched chain amino acids (BCAAs), and acylcarnitines, all of which are involved in inflammatory processes, as metabolites of interest for further study in non-responders. Next, we used the same metabolomic approach to assess metabolites and metabolite ratios associated with sustained knee pain in two independent cohorts, one from NL and the other from Ontario, Canada. We identified one metabolite and three metabolite ratios to be associated with sustained pain, further highlighting roles for PCs, acylcarnitines, and sphingomyelins (SMs) in OA knee pain. We then investigated mechanisms underlying sustained pain in the NL cohort using a multi-omic approach which identified KALRN as a candidate gene and a significant role for central pain sensitization in sustained knee pain. Finally, we developed and evaluated a method to profile eicosanoids and endocannabinoids, a large group of inflammatory mediators involved in pain generation, in plasma for use in future studies on non-responders and patients with sustained knee pain. Overall, our findings highlighted potential roles for inflammation and pain sensitization in OA pain and non-response to TJR and offer interesting routes for future studies in this area and could have potential utility in predicting surgical outcome or as druggable targets to modify outcomes
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