Predicting Surgical Outcome in Patients with Recurrent Patellar Dislocation

Introduction Lateral dislocation of the patella is a common injury in active adolescents and young adults. Patients who are ultimately managed surgically have a significantly lower risk of recurrent dislocation. However, determining the optimal surgical treatment remains a challenge, with patients sometimes undergoing multiple surgeries prior to successful stabilization. The aim of this study is to computationally evaluate patients that have undergone multiple surgeries to correct for recurrent lateral patellar dislocation and predict their clinical outcome. Methods Our patient cohort consisted of 16 patients with patella dislocation. Patient-specific imaging were used to create three-dimensional (3D) finite element (FE) models of the knee joint and evaluate patellofemoral (PF) stability at multiple time points pre- and post-surgery for each patient. We applied these models to predict the clinical success or failure of each surgery. Specifically, the FE model simulated a knee extension activity while a tibia external torsion, a recognized cause of patellofemoral pain and instability, was applied to assess PF stability. A healthy control group of 12 participants was also included to assess the ability of the model to identify successful outcomes. In addition, five anatomic factors of risk were measured, and statistical analysis was performed to establish if significant differences exist among pre-surgery, post-surgery and healthy control groups. Lastly, a logistic regression model was implemented, trained with anatomic values, and used to classify subjects into likelihood of dislocation categories in order to differentiate between successful and unsuccessful surgical outcomes. Feature scaling and feature combination (namely, principal component analysis (PCA)) was applied to improve the predictive performance of the regression model. Results Of 12 control participants, 12 pre-surgery subjects (8 patients after an initial unsuccessful MRPLR and 4 without any), and 9 post-surgery subjects (5 after a successful trochleoplasty and 4 patients after MPFLR), the FE model correctly predicted 29 out of 33 surgery outcomes (87.9% accuracy). Post-surgery simulations predicted patellofemoral stability metrics similar to the healthy control group. Particularly, post-trochleoplasty subjects were associated with an increased ability to provide constraint force on the patella lateral facet, and a lower involvement of the medial patellofemoral ligament, particularly close to full extension. A one-way ANOVA showed that four out of five anatomic factors were significantly different between the pre-surgery and the control group, and three of them also between the pre- and post- surgery group, suggesting that the surgery was able to restore a physiological condition. Lastly, logistic regression classification performance demonstrated 72.2% and 78.9% accuracy before and after PCA, respectively. Conclusion The overall aim of this study is to provide surgeons with a useful and validated computational tool that can predict the likelihood of patellar dislocation and differentiate, prior to clinical intervention, between a successful versus unsuccessful surgery, to determine the optimal treatment pathways for individual patients. Preliminary results are promising, but an improvement of the model and a larger clinical dataset are necessary to improve accuracy and comprehensively validate model performance

Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ETa, the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm

The kinematics of fixed-seat rowing : a structured synthesis

Olympic-style sliding-seat rowing is a sport that has been extensively researched, with studies investigating aspects related to the physiology, biomechanics, kinematics, and the performance of rowers. In contrast, studies on the more classic form of fixed-seat rowing are sparse. The aim of this study is to address this lacuna by analysing for the first time the specific kinematics of fixed-seat rowing as practised by able-bodied athletes, thus (i) documenting how this technique is performed in a manner that is replicable by others and (ii) showing how this technique compares and contrasts with the more standard sliding-seat technique. Fixed-seat rowing was replicated in a biomechanics laboratory where experienced fixed-seat rowers, marked with reflective markers following the modified Helen–Hayes model, were asked to row in a manner that mimics rowing on a fixed-seat boat. The findings from this study, complimented with data gathered through the observation of athletes rowing on water, were compared to sliding-seat ergometer rowing and other control experiments. The results show that, in fixed-seat rowing, there is more forward and backward thoracic movement than in sliding-seat rowing (75–77° vs. 44–52°, p < 0.0005). Tilting of the upper body stems was noted to result from rotations around the pelvis, as in sliding-seat rowing, rather than from spinal movements. The results also confirmed knee flexion in fixed-seat rowing with a range of motion of 30–35°. This is less pronounced than in standard-seat rowing, but not insignificant. These findings provide a biomechanical explanation as to why fixed-seat rowers do not have an increased risk of back injuries when compared with their sliding-seat counterparts. They also provide athletes, coaches, and related personnel with precise and detailed information of how fixed-seat rowing is performed so that they may formulate better and more specific evidence-based training programs to meliorate technique and performance.peer-reviewe

On the kinematics of the forward-facing Venetian-style rowing technique

This work presents a qualitative and quantitative pilot study which explores the kinematics of Venetian style forward-facing standing rowing as practised by able-bodied competitive athletes. The technique, made famous by the gondoliers, was replicated in a biomechanics laboratory by a cohort of four experienced rowers who compete in this style at National Level events in Malta. Athletes were marked with reflective markers following the modified Helen Hayes model and asked to row in a manner which mimics their on-water practise and recorded using a Vicon optoelectronic motion capture system. Data collected were compared to its equivalent using a standard sliding-seat ergometer as well as data collated from observations of athletes rowing on water, thus permitting the documentation of the manner of how this technique is performed. It was shown that this rowing style is characterised by rather asymmetric and complex kinematics, particularly upper-body movements which provides the athlete with a total-body workout involving all major muscle groups working either isometrically, to provide stability, or actively.peer-reviewe

Muscle spindles of the rat sternomastoid muscle

The sternomastoid (SM) muscle in rodents presents a peculiar distribution of fiber types with a steep gradient from the ventral, superficial, white portion to the dorsal, deep, red region, where muscle spindles are restricted. Cross section of the medial longitudinal third of the rat SM contains around 10,000 muscle fibers with a mean diameter of 51.28±12.62 (μm +/- SD). Transverse sections stained by Succinate Dehydrogenase (SDH) reaction clearly presents two distinct regions: the dorsal deep red portion encompassing a 40% cross section area contains a high percentage of packed SDH-positive muscle fibers, and the ventral superficial region which contains mainly SDH-negative muscle fibers. Indeed, the ventral superficial region of the rat SM muscle contains mainly fast 2B muscle fibers. These acidic ATPase pH 4.3-negative and SDH-negative 2B muscle fibers are the largest of the SM muscle, while the acidic ATPase pH 4.3-positive and SDH-positive Type 1 muscle fibers are the smallest. Here we show that in thin transverse cryosections only 2 or 3 muscle spindle are observed in the central part of the dorsal deep red portion of the SM muscle. Azan Mallory stained sections allow at the same time to count the spindles and to evaluate aging fibrosis of the skeletal muscle tissue. Though restricted in the muscle red region, SM spindles are embedded in perimysium, whose changes may influence their reflex activity. Our findings confirm that any comparisons of changes in number and percentage of muscle spindles and muscle fibers of the rat SM muscle will require morphometry of the whole muscle cross-section. Muscle biopsies of SM muscle from large mammals will only provide partial data on the size of the different types of muscle fibers biased by sampling. Nonetheless, histology of muscle tissue continue to provide practical and low-cost quantitative data to follow-up translational studies in rodents and beyond

On the Kinematics of the Forward-Facing Venetian-Style Rowing Technique

This work presents a qualitative and quantitative pilot study which explores the kinematics of Venetian style forward-facing standing rowing as practised by able-bodied competitive athletes. The technique, made famous by the gondoliers, was replicated in a biomechanics laboratory by a cohort of four experienced rowers who compete in this style at National Level events in Malta. Athletes were marked with reflective markers following the modified Helen Hayes model and asked to row in a manner which mimics their on-water practise and recorded using a Vicon optoelectronic motion capture system. Data collected were compared to its equivalent using a standard sliding-seat ergometer as well as data collated from observations of athletes rowing on water, thus permitting the documentation of the manner of how this technique is performed. It was shown that this rowing style is characterised by rather asymmetric and complex kinematics, particularly upper-body movements which provides the athlete with a total-body workout involving all major muscle groups working either isometrically, to provide stability, or actively

Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks

Throughout the last decade, many assistive robots for people with disabilities have been developed; however, researchers have not fully utilized these robotic technologies to entirely create independent living conditions for people with disabilities, particularly in relation to activities of daily living (ADLs). An assistive system can help satisfy the demands of regular ADLs for people with disabilities. With an increasing shortage of caregivers and a growing number of individuals with impairments and the elderly, assistive robots can help meet future healthcare demands. One of the critical aspects of designing these assistive devices is to improve functional independence while providing an excellent human–machine interface. People with limited upper limb function due to stroke, spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis, and other conditions find the controls of assistive devices such as power wheelchairs difficult to use. Thus, the objective of this research was to design a multimodal control method for robotic self-assistance that could assist individuals with disabilities in performing self-care tasks on a daily basis. In this research, a control framework for two interchangeable operating modes with a finger joystick and a chin joystick is developed where joysticks seamlessly control a wheelchair and a wheelchair-mounted robotic arm. Custom circuitry was developed to complete the control architecture. A user study was conducted to test the robotic system. Ten healthy individuals agreed to perform three tasks using both (chin and finger) joysticks for a total of six tasks with 10 repetitions each. The control method has been tested rigorously, maneuvering the robot at different velocities and under varying payload (1–3.5 lb) conditions. The absolute position accuracy was experimentally found to be approximately 5 mm. The round-trip delay we observed between the commands while controlling the xArm was 4 ms. Tests performed showed that the proposed control system allowed individuals to perform some ADLs such as picking up and placing items with a completion time of less than 1 min for each task and 100% success

NEMO-SN1 Abyssal Cabled Observatory in the Western Ionian Sea

The NEutrinoMediterranean Observatory—Submarine Network 1 (NEMO-SN1) seafloor observatory is located in the central Mediterranean Sea, Western Ionian Sea, off Eastern Sicily (Southern Italy) at 2100-m water depth, 25 km from the harbor of the city of Catania. It is a prototype of a cabled deep-sea multiparameter observatory and the first one operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of the European Multidisciplinary Seafloor Observatory (EMSO), one of the incoming European large-scale research infrastructures included in the Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI) since 2006. EMSO will specifically address long-term monitoring of environmental processes related to marine ecosystems, marine mammals, climate change, and geohazards