Numerical wear predictions are gaining increasing interest in many engineering applications, as they allow to simulate complex operative conditions not easily replicable in the laboratory. As far as hip prostheses are concerned, most of the wear models in the literature are based on the simulation of gait (recommended also in experimental wear tests), since gait is considered the most frequent and important motor task to recover after arthroplasty. However, since joint prostheses have been increasingly implanted in younger people, high loads and potentially severe conditions, e.g. due to sporting activities, should also be considered for a more reliable wear assessment of these implants. In this study, we propose a profitable combination of musculoskeletal and analytical wear modelling for the prediction of wear caused by common daily activities in metal-on-plastic hip arthroplasties. Several motion analysis data available in the literature (walking, fast walking, lunge, squat, stair negotiation) were selected and the effects of such motor tasks on prosthesis wear were investigated, both separately and in combination. Additionally, for comparative purposes, wear prediction for simplified gait conditions prescribed by the ISO 14242 standard, were also considered. Results suggest that this latter case produces lower wear depth and volume with respect to a relatively demanding combination of the selected daily activities. The preliminary results of the present study represent a first step towards the auspicious goal of validating the proposed procedure for in silico trials of hip arthroplasties.

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned eighteen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads were assessed through quantitative error indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts, which are typically overestimated. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

The lolotte or drop-knee technique is a fundamental of rock climbing that particularly involves lower limbs, and especially knee joints. To the authors’ best knowledge, no biomechanical analysis of the lolotte seems to have ever been conducted, despite its widespread use. As a first contribution to this research topic, the present work deals with an athlete-specific kinematic analysis of the lolotte aimed at quantifying the hip and knee joint angle trajectories and knee ligament strains. A marker-based motion capture system was employed to track the execution of the lolotte on a purposely designed climbing structure. The marker trajectories were then used as input for a numerical simulation in the OpenSim program, where an athlete-specific musculoskeletal model was set up to perform an inverse kinematics analysis and obtain the joint angle trajectories as well as their ranges of motion. Further processing of the model allowed to estimate the strain of the knee medial collateral ligament. Such kinematic analysis revealed characteristic hip and knee joint angle patterns and highlighted a critical phase in which the knee is considerably abducted (increased valgus). As a consequence, the medial collateral ligament is remarkably recruited, thereby substantiating the claim diffused among climbers that drop-kneeing may cause ligament injury.

Predictive simulations of human motion are a precious resource for a deeper understanding of the motor control policies encoded by the central nervous system. They also have profound implications for the design and control of assistive and rehabilitation devices, for ergonomics, as well as for surgical planning. However, the potential of state-of-the-art predictive approaches is not fully realized yet, making it difficult to draw convincing conclusions about the actual optimality principles underlying human walking. In the present study we propose a novel formulation of a bilevel, inverse optimal control strategy based on a full-body three-dimensional neuromusculoskeletal model. In the lower level, prediction of walking is formulated as a principled multi-objective optimal control problem based on a weighted Chebyshev metric, whereas the contributions of candidate control objectives are systematically and efficiently identified in the upper level. Our framework has proved to be effective in determining the contributions of the selected objectives and in reproducing salient features of human locomotion. Nonetheless, some deviations from the experimental kinematic and kinetic trajectories have emerged, suggesting directions for future research. The proposed framework can serve as an inverse optimal control platform for testing multiple optimality criteria, with the ultimate goal of learning the control objectives that best explain observed human motion.

Introduction: In Local Health Unit 7, human papilloma virus (HPV) vaccination campaigns for 12-year-olds have long been implemented by the vaccination services of the Department of Prevention. Due to the pressure of the COVID-19 pandemic on these services, an emergency vaccination campaign was directly managed by primary care pediatricians (PCPs). An initial evaluation of this experience was conducted. Materials and methods: Data on 12-year-olds assisted by PCPs belonging to the 2006 (pre-pandemic) and 2008 (pandemic) birth cohorts were extracted, along with HPV vaccination data. Health district, gender, citizenship, socioeconomic status, and PCPs were evaluated as possible influencing factors in a two-level logistic regression (second level: single PCP). Results: The HPV vaccination gap between males and females increased significantly for the 2008 birth cohort compared to the 2006 birth cohort (11 vs. 4 percentage points). As for PCPs, the vaccination uptake range was 4–71% for the 2008 birth cohort vs. 32–85% for the 2006 cohort. The proportion of variance explained at the second level was overall equal to 9.7% for the 2008 cohort vs. 3.6% for the 2006 cohort. Conclusions: The vaccination campaign carried out during the peak of the COVID-19 pandemic increased the HPV vaccination gaps among Health Districts, genders, and individual PCPs, probably due to a lack of homogeneity in professional practices and attitudes toward HPV vaccination. Catch-up interventions are required in the immediate term, while an equity-lens approach should be taken for reprogramming the vaccination campaign. Greater involvement of schools and families could ensure a more equitable approach and a better uptake.