Rethinking running biomechanics: a critical review of ground reaction forces, tibial bone loading, and the role of wearable sensors.

This study presents a comprehensive review of the correlation between tibial acceleration (TA), ground reaction forces (GRF), and tibial bone loading, emphasizing the critical role of wearable sensor technology in accurately measuring these biomechanical forces in the context of running. This systematic review and meta-analysis searched various electronic databases (PubMed, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect) to identify relevant studies. It critically evaluates existing research on GRF and tibial acceleration (TA) as indicators of running-related injuries, revealing mixed findings. Intriguingly, recent empirical data indicate only a marginal link between GRF, TA, and tibial bone stress, thus challenging the conventional understanding in this field. The study also highlights the limitations of current biomechanical models and methodologies, proposing a paradigm shift towards more holistic and integrated approaches. The study underscores wearable sensors' potential, enhanced by machine learning, in transforming the monitoring, prevention, and rehabilitation of running-related injuries.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review.

Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.

Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors.

The ankle joint plays a crucial role in gait, facilitating the articulation of the lower limb, maintaining foot-ground contact, balancing the body, and transmitting the center of gravity. This study aimed to implement long short-term memory (LSTM) networks for predicting ankle joint angles, torques, and contact forces using inertial measurement unit (IMU) sensors. Twenty-five healthy participants were recruited. Two IMU sensors were attached to the foot dorsum and the vertical axis of the distal anteromedial tibia in the right lower limb to record acceleration and angular velocity during running. We proposed a LSTM-MLP (multilayer perceptron) model for training time-series data from IMU sensors and predicting ankle joint biomechanics. The model underwent validation and testing using a custom nested k-fold cross-validation process. The average values of the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) for ankle dorsiflexion joint and moment, subtalar inversion joint and moment, and ankle joint contact forces were 0.89 ± 0.04, 0.75 ± 1.04, and 2.96 ± 4.96 for walking, and 0.87 ± 0.07, 0.88 ± 1.26, and 4.1 ± 7.17 for running, respectively. This study demonstrates that IMU sensors, combined with LSTM neural networks, are invaluable tools for evaluating ankle joint biomechanics in lower limb pathological diagnosis and rehabilitation, offering a cost-effective and versatile alternative to traditional experimental settings.

Differences in intra-foot movement strategies during locomotive tasks among chronic ankle instability, copers and healthy individuals.

Individuals with chronic ankle instability (CAI) suffer from the resulting sequela of repetitive lateral ankle sprains (LAS), whilst copers appear to cope with initial LAS successfully. Therefore, the aim of this study was to explore the intra-foot biomechanical differences among CAI, copers, and healthy individuals during dynamic tasks. Twenty-two participants per group were included and required to perform cutting and different landing tasks (DL: drop landing; FL: forward jump followed a landing). A five-segment foot model with 8 degrees of freedom was used to explore the intra-foot movement among these three groups. Smaller dorsiflexion angles were found in copers (DL tasks and prelanding task) and CAI (DL and FL task) compared to healthy participants. Copers presented a more eversion position compared to others during these dynamic tasks. During the descending phase of DL task, greater dorsiflexion angles in the metatarsophalangeal joint were found in copers compared to the control group. Joint moment difference was only found in the subtalar joint during the descending phase of FL task, presenting more inversion moments in copers compared to healthy participants. Copers rely on more eversion positioning to prevent over-inversion of the subtalar joint compared to CAI. Further, the foot became more unstable when conducting sport-related movements, suggesting that foot stability seems to be sensitive to the task types. These findings may help in designing and implementing interventions to restore functions of the ankle joint in CAI individuals.

Understanding the form and function in Chinese bound foot from last-generation cases.

Purpose: Foot adaptation in the typically developed foot is well explored. In this study, we aimed to explore the form and function of an atypical foot, the Chinese bound foot, which had a history of over a thousand years but is not practised anymore. Methods: We evaluated the foot shape and posture via a statistical shape modelling analysis, gait plantar loading distribution via gait analysis, and bone density adaptation via implementing finite element simulation and bone remodelling prediction. Results: The atypical foot with binding practice led to increased foot arch and vertically oriented calcaneus with larger size at the articulation, apart from smaller metatarsals compared with a typically developed foot. This shape change causes the tibia, which typically acts as a load transfer beam and shock absorber, to extend its function all the way through the talus to the calcaneus. This is evident in the bound foot by i) the reduced center of pressure trajectory in the medial-lateral direction, suggesting a reduced supination-pronation; ii) the increased density and stress in the talus-calcaneus articulation; and iii) the increased bone growth in the bound foot at articulation joints in the tibia, talus, and calcaneus. Conclusion: Knowledge from the last-generation bound foot cases may provide insights into the understanding of bone resorption and adaptation in response to different loading profiles.

Computer simulation on the cueing movements in cue sports: a validation study.

Background: Simulation models have been applied to analyze daily living activities and some sports movements. However, it is unknown whether the current upper extremity musculoskeletal models can be utilized for investigating cue sports movements to generate corresponding kinematic and muscle activation profiles. This study aimed to test the feasibility of applying simulation models to investigate cue sports players' cueing movements with OpenSim. Preliminary muscle forces would be calculated once the model is validated. Methods: A previously customized and validated unimanual upper extremity musculoskeletal model with six degrees of freedom at the scapula, shoulder, elbow, and wrist, as well as muscles was used in this study. Two types of cueing movements were simulated: (1) the back spin shot, and (2) 9-ball break shot. Firstly, kinematic data of the upper extremity joints were collected with a 3D motion capture system. Using the experimental marker trajectories of the back spin shot on 10 male cue sports players, the simulation on the cueing movements was executed. The model was then validated by comparing the model-generated joint angles against the experimental results using statistical parametric mapping (SPM1D) to examine the entire angle-time waveform as well as t-tests to compare the discrete variables (e.g., joint range of motion). Secondly, simulation of the break shot was run with the experimental marker trajectories and electromyographic (EMG) data of two male cue sports players as the model inputs. A model-estimated muscle activation calculation was performed accordingly for the upper extremity muscles. Results: The OpenSim-generated joint angles for the back spin shot corresponded well with the experimental results for the elbow, while the model outputs of the shoulder deviated from the experimental data. The discrepancy in shoulder joint angles could be due to the insufficient kinematic inputs for the shoulder joint. In the break shot simulation, the preliminary findings suggested that great shoulder muscle forces could primarily contribute to the forward swing in a break shot. This suggests that strengthening the shoulder muscles may be a viable strategy to improve the break shot performance. Conclusion: It is feasible to cater simulation modeling in OpenSim for biomechanical investigations of the upper extremity movements in cue sports. Model outputs can help better understand the contributions of individual muscle forces when performing cueing movements.

Dose-response effect of incremental lateral-wedge hardness on the lower limb Biomechanics during typical badminton footwork.

Badminton footwork has been characterised with jump-landing, cross step, side side and lunges, which requires movement agility to facilitate on-court performance. A novel badminton shoe design with systematic increase of lateral wedge hardness (Asker C value of 55, 60, 65, and 70) was developed and investigated in this study, aiming to analyse the dose-response effect of incremental wedge hardness on typical badminton footwork. Stance time and joint stiffness were employed to investigate the footwork performance, and the factorial Statistical non-Parametric Mapping and Principal Component Analysis (PCA) were used to quantify the biomechanical responses over the stance. As reported, shorter contact times (decreased by 8.9%-13.5%) and increased joint stiffness (in side step) of foot-ankle complex were found, suggesting improved footwork stability and agility from increased hardness. Time-varying differences were noted during the initial landing and driving-off phase of cross and side steps and drive-off returning of lunges, suggesting facilitated footwork performance. The reconstructed modes of variations from PCA further deciphered the biomechanical response to the wedge dosage, especially during drive-off, to understand the improved footwork agility and stability.

Machine Learning for Medical Image Translation: A Systematic Review.

BACKGROUND: CT scans are often the first and only form of brain imaging that is performed to inform treatment plans for neurological patients due to its time- and cost-effective nature. However, MR images give a more detailed picture of tissue structure and characteristics and are more likely to pick up abnormalities and lesions. The purpose of this paper is to review studies which use deep learning methods to generate synthetic medical images of modalities such as MRI and CT. METHODS: A literature search was performed in March 2023, and relevant articles were selected and analyzed. The year of publication, dataset size, input modality, synthesized modality, deep learning architecture, motivations, and evaluation methods were analyzed. RESULTS: A total of 103 studies were included in this review, all of which were published since 2017. Of these, 74% of studies investigated MRI to CT synthesis, and the remaining studies investigated CT to MRI, Cross MRI, PET to CT, and MRI to PET. Additionally, 58% of studies were motivated by synthesizing CT scans from MRI to perform MRI-only radiation therapy. Other motivations included synthesizing scans to aid diagnosis and completing datasets by synthesizing missing scans. CONCLUSIONS: Considerably more research has been carried out on MRI to CT synthesis, despite CT to MRI synthesis yielding specific benefits. A limitation on medical image synthesis is that medical datasets, especially paired datasets of different modalities, are lacking in size and availability; it is therefore recommended that a global consortium be developed to obtain and make available more datasets for use. Finally, it is recommended that work be carried out to establish all uses of the synthesis of medical scans in clinical practice and discover which evaluation methods are suitable for assessing the synthesized images for these needs.

Understanding foot conditions, morphologies and functions in children: a current review.

This study provided a comprehensive updated review of the biological aspects of children foot morphology across different ages, sex, and weight, aiming to reveal the patterns of normal and pathological changes in children feet during growth and development. This review article comprised 25 papers in total that satisfied the screening standards. The aim was to investigate how weight changes, age and sex affect foot type, and gain a deeper understanding of the prevalent foot deformities that occur during children growth. Three different foot morphological conditions were discussed, specifically including the effect of sex and age differences, the effect of weight changes, and abnormal foot morphologies commonly documented during growth. This review found that sex, age, and weight changes would affect foot size, bony structure, foot posture, and plantar pressures during child growth. As a result of this biological nature, the children's feet generally exhibit neutral and internally rotated foot postures, which frequently lead to abnormal foot morphologies (e.g., flat foot, pronated foot, etc.). In the future, attention shall be paid to the causal factors leading to specific foot morphologies during the growth and development of children. However, sufficient evidence could not be provided due to a relatively short period of investigation and non-uniformed research methodology in the current literature. A more comprehensive and in-depth exploration is recommended to provide scientific evidence for the discovery of children foot development and personalized growth pattern.

Regional plantar forces and surface geometry variations of a chronic ankle instability population described by statistical shape modelling.

BACKGROUND: Understanding detailed foot morphology as well as regional plantar forces could provide insight into foot function and provide recommendation for footwear design for chronic ankle instability (CAI) people. RESEARCH QUESTION: This study presented 3-dimensional statistical shape models of feet from three different populations including CAI, copers and healthy individuals, with regional plantar forces also acquired. METHODS: Sixty-six males (22 participants per group) were included in this study to capture 3-dimensional foot shapes under a standing condition and regional plantar forces during a cutting maneuver. Principal component analysis was performed to generate a mean foot shape of each group as well as modes of variations. A generalized procrustes analysis was used to achieve rapid registration of mean shapes. Besides, regional plantar forces and contact duration among these three populations were compared. RESULTS: For 3-dimensional foot shapes, although no significant differences of the average distance between each mode and mean shape were found among three populations, there were subtle variations in mean shapes. The CAI population presented a more bulging of the lateral malleolus; copers were characterized by the flexion of the lesser toes, a more bulging of the medial foot in the sagittal plane; and healthy individuals showed a greater heel width and a more bulging of the heel in the sagittal plane. In terms of plantar forces, healthy individuals had significantly greater summated plantar forces and greater plantar forces in the lateral heel area during the early contact phase compared to copers and CAI participants. SIGNIFICANCE: Overall, this study suggested that repetitive ankle sprains may lead to the bulging of the lateral malleolus. Further, CAI and copers seem to stabilize the ankle joint by medially shifting the center of pressure compared to healthy individuals under the static and less challenging dynamic conditions.

Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court.

Introduction: Playing badminton has been reported with extensive health benefits, while main injuries were documented in the lower extremity. This study was aimed to investigate and predict the knee- and ankle-joint loadings of athletes who play badminton, with "gold standard" facilities. The axial impact acceleration from wearables would be used to predict joint moments and contact forces during sub-maximal and maximal lunge footwork. Methods: A total of 25 badminton athletes participated in this study, following a previously established protocol of motion capture and musculoskeletal modelling techniques with the integration of a wearable inertial magnetic unit (IMU). We developed a principal component analysis (PCA) statistical model to extract features in the loading parameters and a multivariate partial least square regression (PLSR) machine learning model to correlate easily collected variables, such as the stance time, approaching velocity, and peak accelerations, with knee and ankle loading parameters (moments and contact forces). Results: The key variances of joint loadings were observed from statistical principal component analysis modelling. The promising accuracy of the partial least square regression model using input parameters was observed with a prediction accuracy of 94.52%, while further sensitivity analysis found a single variable from the ankle inertial magnetic unit that could predict an acceptable range (93%) of patterns and magnitudes of the knee and ankle loadings. Conclusion: The attachment of this single inertial magnetic unit sensor could be used to record and predict loading accumulation and distribution, and placement would exhibit less influence on the motions of the lower extremity. The intelligent prediction of loading patterns and accumulation could be integrated to design training and competition schemes in badminton or other court sports in a scientific manner, thus preventing fatigue, reducing loading-accumulation-related injury, and maximizing athletic performance.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects.

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

Foot Pronation Prediction with Inertial Sensors during Running: A Preliminary Application of Data-Driven Approaches.

Abnormal foot postures may affect foot movement and joint loading during locomotion. Investigating foot posture alternation during running could contribute to injury prevention and foot mechanism study. This study aimed to develop feature-based and deep learning algorithms to predict foot pronation during prolonged running. Thirty-two recreational runners have been recruited for this study. Nine-axial inertial sensors were attached to the right dorsum of the foot and the vertical axis of the distal anteromedial tibia. This study employed feature-based machine learning algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest, and deep learning, i.e., one-dimensional convolutional neural networks (CNN1D), to predict foot pronation. A custom nested k-fold cross-validation was designed for hyper-parameter tuning and validating the model's performance. The XGBoot classifier achieved the best accuracy using acceleration and angular velocity data from the foot dorsum as input. Accuracy and the area under curve (AUC) were 74.7 ± 5.2% and 0.82 ± 0.07 for the subject-independent model and 98 ± 0.4% and 0.99 ± 0 for the record-wise method. The test accuracy of the CNN1D model with sensor data at the foot dorsum was 74 ± 3.8% for the subject-wise approach with an AUC of 0.8 ± 0.05. This study found that these algorithms, specifically for the CNN1D and XGBoost model with inertial sensor data collected from the foot dorsum, could be implemented into wearable devices, such as a smartwatch, for monitoring a runner's foot pronation during long-distance running. It has the potential for running shoe matching and reducing or preventing foot posture-induced injuries.

Predicting ankle and knee sagittal kinematics and kinetics using an ankle-mounted inertial sensor.

The purpose of this study was to develop a machine learning model to reconstruct time series kinematic and kinetic profiles of the ankle and knee joint across six different tasks using an ankle-mounted IMU. Four male collegiate basketball players performed repeated tasks, including walking, jogging, running, sidestep cutting, max-height jumping, and stop-jumping, resulting in a total of 102 movements. Ankle and knee flexion-extension angles and moments were estimated using motion capture and inverse dynamics and considered 'actual data' for the purpose of model fitting. Synchronous acceleration and angular velocity data were collected from right ankle-mounted IMUs. A time-series feature extraction model was used to determine a set of features used as input to a random forest regression model to predict the ankle and knee kinematics and kinetics. Five-fold cross-validation was performed to verify the model accuracy, and statistical parametric mapping was used to determine the difference between the predicted and experimental time series. The random forest regression model predicted the time-series profiles of the ankle and knee flexion-extension angles and moments with high accuracy (Kinematics: R2 ranged from 0.782 to 0.962, RMSE ranged from 2.19° to 11.58°; Kinetics: R2 ranged from 0.711 to 0.966, RMSE ranged from 0.10 Nm/kg to 0.41 Nm/kg). There were differences between predicted and actual time series for the knee flexion-extension moment during stop-jumping and walking. An appropriately trained feature-based regression model can predict time series knee and ankle joint angles and moments across a wide range of tasks using a single ankle-mounted IMU.

Evaluating the risk of knee osteoarthritis following unilateral ACL reconstruction based on an EMG-assisted method.

Objective: Anterior cruciate ligament reconstruction (ACLR) cannot decrease the risk of knee osteoarthritis after anterior cruciate ligament rupture, and tibial contact force is associated with the development of knee osteoarthritis. The purpose of this study was to compare the difference in bilateral tibial contact force for patients with unilateral ACLR during walking and jogging based on an EMG-assisted method in order to evaluate the risk of knee osteoarthritis following unilateral ACLR. Methods: Seven unilateral ACLR patients participated in experiments. The 14-camera motion capture system, 3-Dimension force plate, and wireless EMG test system were used to collect the participants' kinematics, kinetics, and EMG data during walking and jogging. A personalized neuromusculoskeletal model was established by combining scaling and calibration optimization. The inverse kinematics and inverse dynamics algorithms were used to calculate the joint angle and joint net moment. The EMG-assisted model was used to calculate the muscle force. On this basis, the contact force of the knee joint was analyzed, and the tibial contact force was obtained. The paired sample t-test was used to analyze the difference between the participants' healthy and surgical sides of the participants. Results: During jogging, the peak tibial compression force on the healthy side was higher than on the surgical side (p = 0.039). At the peak moment of tibial compression force, the muscle force of the rectus femoris (p = 0.035) and vastus medialis (p = 0.036) on the healthy side was significantly higher than that on the surgical side; the knee flexion (p = 0.042) and ankle dorsiflexion (p = 0.046) angle on the healthy side was higher than that on the surgical side. There was no significant difference in the first (p = 0.122) and second (p = 0.445) peak tibial compression forces during walking between the healthy and surgical sides. Conclusion: Patients with unilateral ACLR showed smaller tibial compression force on the surgical side than on the healthy side during jogging. The main reason for this may be the insufficient exertion of the rectus femoris and vastus medialis.

Delayed skeletal muscle repair following inflammatory damage in simulated agent-based models of muscle regeneration.

Healthy skeletal muscle undergoes repair in response to mechanically localised strains during activities such as exercise. The ability of cells to transduce the external stimuli into a cascade of cell signalling responses is important to the process of muscle repair and regeneration. In chronic myopathies such as Duchenne muscular dystrophy and inflammatory myopathies, muscle is often subject to chronic necrosis and inflammation that perturbs tissue homeostasis and leads to non-localised, widespread damage across the tissue. Here we present an agent-based model that simulates muscle repair in response to both localised eccentric contractions similar to what would be experienced during exercise, and non-localised widespread inflammatory damage that is present in chronic disease. Computational modelling of muscle repair allows for in silico exploration of phenomena related to muscle disease. In our model, widespread inflammation led to delayed clearance of tissue damage, and delayed repair for recovery of initial fibril counts at all damage levels. Macrophage recruitment was delayed and significantly higher in widespread compared to localised damage. At higher damage percentages of 10%, widespread damage led to impaired muscle regeneration and changes in muscle geometry that represented alterations commonly observed in chronic myopathies, such as fibrosis. This computational work offers insight into the progression and aetiology of inflammatory muscle diseases, and suggests a focus on the muscle regeneration cascade in understanding the progression of muscle damage in inflammatory myopathies.

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review.

BACKGROUND: Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise. OBJECTIVE: This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions. METHOD: This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications. RESULTS: a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6). CONCLUSION: Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

Cannabinoid CB2 receptors modulate alcohol induced behavior, and neuro-immune dysregulation in mice.

The identification of additional lipid mediators, enzymes, and receptors revealed an expanded endocannabinoid system (ECS) called the endocannabinoidome (eCBome). Furthermore, eCBome research using wild type and genetically modified mice indicate the involvement of this system in modulating alcohol induced neuroinflammatory alterations associated with behavioral impairments and the release of proinflammatory cytokines. We investigated the role of cannabinoid type 2 receptors (CB2Rs) in modulating behavioral and neuro-immune changes induced by alcohol using conditional knockout (cKO) mice with selective deletion of CB2Rs in dopamine neurons (DAT-Cnr2) and in microglia (Cx3Cr1-Cnr2) cKO mice. We used a battery of behavioral tests including locomotor and wheel running activity, rotarod performance test, and alcohol preference tests to evaluate behavioral changes induced by alcohol. ELISA assay was used, to detect alterations in IL-6, IL-1α, and IL-1ß in the prefrontal cortex, striatum, and hippocampal regions of mice to investigate the role of CB2Rs in neuroinflammation induced by alcohol in the brain. The involvement of cannabinoid receptors in alcohol-induced behavior was also evaluated using the non-selective cannabinoid receptor mixed agonist WIN 55,212-2. The results showed that cell-type specific deletion of CB2Rs in dopamine neurons and microglia significantly and differentially altered locomotor activity and rotarod performance activities. The result also revealed that cell-type specific deletion of CB2Rs enhanced alcohol-induced inflammation, and WIN significantly reduced alcohol preference in all genotypes compared to the vehicle controls. These findings suggest that the involvement of CB2Rs in modulating behavioral and neuroinflammatory alterations induced by alcohol may be potential therapeutic targets in the treatment of alcohol use disorder.

Enhanced bone formation in locally-optimised, low-stiffness additive manufactured titanium implants: An in silico and in vivo tibial advancement study.

A tibial tuberosity advancement (TTA), used to treat lameness in the canine stifle, provides a framework to investigate implant performance within an uneven loading environment due to the dominating patellar tendon. The purpose of this study was to reassess how we design orthopaedic implants in a load-bearing model to investigate potential for improved osseointegration capacity of fully-scaffolded mechanically-matched additive manufactured (AM) implants. While the mechanobiological nature of bone is well known, we have identified a lower limit in the literature where investigation into exceedingly soft scaffolds relative to trabecular bone ceases due to the trade-off in mechanical strength. We developed a finite element model of the sheep stifle to assess the stresses and strains of homogeneous and locally-optimised TTA implant designs. Using additive manufacturing, we printed three different low-stiffness Ti-6Al-4 V TTA implants: 0.8 GPa (Ti1), 0.6 GPa (Ti2) and an optimised design with a 0.3 GPa cortex and 0.1 GPa centre (Ti3), for implantation in a 12-week in vivo ovine pilot study. Static histomorphometry demonstrated uniform bone ingrowth in optimised low-modulus Ti3 samples compared to homogeneous designs (Ti1 and Ti2), and greater bone-implant contact. Mineralising surfaces were apparent in all implants, though mineral apposition rate was only consistent throughout Ti3. The greatest bone formation scores were seen in Ti3, followed by Ti2 and Ti1. Results from our study suggest lower stiffnesses and higher strain ranges improve early bone formation, and that by accounting for loading environments through rational design, implants can be optimised to improve uniform osseointegration. STATEMENT OF SIGNIFICANCE: The effect of different strain ranges on bone healing has been traditionally investigated and characterised through computational models, with much of the literature suggesting higher strain ranges being favourable. However, little has been done to incorporate strain-optimisation into porous orthopaedic implants due to the trade-off in mechanical strength required to induce these microenvironments. In this study, we used finite element analysis to optimise the design of additive manufactured (AM) titanium orthopaedic implants for different strain ranges, using a clinically-relevant surgical model. Our research suggests that there is potential for locally-optimised AM scaffolds in the use of orthopaedic devices to induce higher strains, which in turn encourages de novo bone formation and uniform osseointegration.