Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System

In recent years, different groups have developed algorithms to control the stiffness of a robotic device through the electromyographic activity collected from a human operator. However, the approaches proposed so far require an initial calibration, have a complex subject-specific muscle model, or consider the activity of only a few pairs of antagonist muscles. This study described and tested an approach based on a biomechanical model to estimate the limb stiffness of a multi-joint, multi-muscle system from muscle activations. The “virtual stiffness” method approximates the generated stiffness as the stiffness due to the component of the muscle-activation vector that does not generate any endpoint force. Such a component is calculated by projecting the vector of muscle activations, estimated from the electromyographic signals, onto the null space of the linear mapping of muscle activations onto the endpoint force. The proposed method was tested by using an upper-limb model made of two joints and six Hill-type muscles and data collected during an isometric force-generation task performed with the upper limb. The null-space projection of the muscle-activation vector approximated the major axis of the stiffness ellipse or ellipsoid. The model provides a good approximation of the voluntary stiffening performed by participants that could be directly implemented in wearable myoelectric controlled devices that estimate, in real-time, the endpoint forces, or endpoint movement, from the mapping between muscle activation and force, without any additional calibrations

Nano-HPLC-HRMS analysis to evaluate leptin level in milk samples: A pilot study

Leptin is a 16 kDa lipophilic protein hormone secreted by adipocytes and its most significant function is to inform the brain with negative feedback that regulates food intake. Recently the protein found in human breast milk was related to breast feeding and onset of obesity, and the evidence of a low probability to develop pediatric obesity in children fed with breast milk was also confirmed. Since leptin could have a critical role, its quantitation both in human breast, bovine milk and in infant formula products is interesting. For this reason, we developed an analytical method based on immunoaffinity purification followed by an analysis with nano-High Pressure Liquid Chromatography coupled with High Resolution Mass Spectrometry analyzer (nano-HPLC-HRMS) to identify and quantify leptin in milk samples and performed a pilot study using samples of human breast milk, bovine milk and infant formulas. With an obtained lower limit of quantitation (LLOQ) of 100 ng mL−1 we quantified leptin in human breast milk finding an average of 6.70 ng mL−1. Our results show that leptin was under LLOQ both in bovine milk and in infant formula products. In conclusion, the developed analytical method here described was suitable to quantify leptin in milk samples with a good sensitivity and selectivity, and without the use of radioactive reagents

Machine Learning approaches for the design of biomechanically compatible bone tissue engineering scaffolds

Triply-Periodic Minimal Surfaces (TPMS) analytical formulation does not provide a direct correlation between the input parameters (analytical) and the mechanical and morphological properties of the structure. In this work, we created a dataset with more than one thousand TPMS scaffolds for the training of Machine Learning (ML) models able to find such correlation. Finite Element Modeling and image analysis have been used to characterize the scaffolds. In particular, we trained three different ML models, exploring both a linear and non-linear approach, to select the features able to predict the input parameters. Furthermore, the features used for the prediction can be selected in three different modes: i) fully automatic, through a greedy algorithm, ii) arbitrarily, by the user and iii) in a combination of the two above methods: i.e. partially automatic and partially through a user-selection. The latter, coupled with the non-linear ML model, exhibits a median error less than 3% and a determination coefficient higher than 0.89 for each of the selected features, and all of them are accessible during the design phase. This approach has been applied to the design of a hydroxyapatite TPMS scaffolds with prescribed properties obtained from a real trabecular-like hydroxyapatite scaffold. The obtained results demonstrate that the ML model can effectively design a TPMS scaffold with prescribed features on the basis of biomechanical, mechanobiology and technological constraints

Growing e-waste management risk awareness points towards new recycling scenarios: The view of the Big Four's youngest consultants

The e-waste sector is characterised by a rapid growth at global level and therefore involves an area not yet sufficiently investigated in its risk management dimension. This research fills the gap of the absence of a holistic approach to risk identification and assessment in e-waste management, suggesting a new Risk Awareness Indicator (RAI). An integrated Multi-criteria decision analysis (MCDA)-Analytic Hierarchy Process (AHP) is proposed to calculate the new index. Weights and values will be proposed by twenty Big Four's youngest consultants (generation-Z and millennials). For e-waste, cyber risks related to personal data are critical in the collection phase, environmental risks in the transport phase, and financial and economic risks in the processing phase. Recycling scenarios pose less overall risk than landfill alternatives. The results can help policy makers to meet the circular economy targets set at the European Union level by implementing administrative and regulatory simplifications to support recycling supply chains and make them more efficient and resilient after the pandemic disruption. This work focuses on e-waste and the opinion of screenagers consultants, however the methodology used to design the RAI index makes it easy to replicate the analysis to other social settings and other waste supply chains

Tocilizumab in MOG-antibody spectrum disorder: a case report

Background: Myelin oligodendrocyte glycoprotein antibody-related spectrum disorders (MOG-SD) are a heterogeneous group of inflammatory demyelinating diseases of the central nervous system, usually responsive to conventional immunosuppressive therapies. However, knowledge about treatment of non-responder patients is scarce. Methods: We report on a 20-year-old MOG-SD patient who experienced clinical deterioration despite rituximab-induced B-cell depletion. Results: Rescue therapy with tocilizumab (TCZ) prevented further relapses, with reduction of spinal-cord load on MRI, and a remarkable reduction of disability at the two-year follow-up. Conclusion: Our observations suggest that TCZ could induce clinico-radiologic improvements, which make it as an option for the treatment of MOG-SD

Cannabinoid Formulations and Delivery Systems: Current and Future Options to Treat Pain

The field of Cannabis sativa L. research for medical purposes has been rapidly advancing in recent decades and a growing body of evidence suggests that phytocannabinoids are beneficial for a range of conditions. At the same time impressing development has been observed for formulations and delivery systems expanding the potential use of cannabinoids as an effective medical therapy. The objective of this review is to present the most recent results from pharmaceutical companies and research groups investigating methods to improve cannabinoid bioavailability and to clearly establish its therapeutic efficacy, dose ranges, safety and also improve the patient compliance. Particular focus is the application of cannabinoids in pain treatment, describing the principal cannabinoids employed, the most promising delivery systems for each administration routes and updating the clinical evaluations. To offer the reader a wider view, this review discusses the formulation starting from galenic preparation up to nanotechnology approaches, showing advantages, limits, requirements needed. Furthermore, the most recent clinical data and meta-analysis for cannabinoids used in different pain management are summarized, evaluating their real effectiveness, in order also to spare opioids and improve patients’ quality of life. Promising evidence for pain treatments and for other important pathologies are also reviewed as likely future directions for cannabinoids formulations

Contraction level, but not force direction or wrist position, affects the spatial distribution of motor unit recruitment in the biceps brachii muscle

Purpose: Different motor units (MUs) in the biceps brachii (BB) muscle have been shown to be preferentially recruited during either elbow flexion or supination. Whether these different units reside within different regions is an open issue. In this study, we tested wheter MUs recruited during submaximal isometric tasks of elbow flexion and supination for two contraction levels and with the wrist fixed at two different angles are spatially localized in different BB portions. Methods: The MUs’ firing instants were extracted by decomposing high-density surface electromyograms (EMG), detected from the BB muscle of 12 subjects with a grid of electrodes (4 rows along the BB longitudinal axis, 16 columns medio-laterally). The firing instants were then used to trigger and average single-differential EMGs. The average rectified value was computed separately for each signal and the maximal value along each column in the grid was retained. The center of mass, defined as the weighted mean of the maximal, average rectified value across columns, was then consdiered to assess the medio-lateral changes in the MU surface representation between conditions. Results: Contraction level, but neither wrist position nor force direction (flexion vs. supination), affected the spatial distribution of BB MUs. In particular, higher forces were associated with the recruitment of BB MUs whose action potentials were represented more medially. Conclusion: Although the action potentials of BB MUs were represented locally across the muscle medio-lateral region, dicrimination between elbow flexion or supination seems unlikely from the surface representation of MUs action potentials

Gluten contamination of canned and dry grain-free commercial pet foods determined by HPLC-HRMS

The aim was to determine the absence of gluten in pet food samples marked as ‘grain-free’ and ‘gluten-free’ diets, to assess the reliability of manufacturer labelling of such products. A total of 15 diets labelled as grain- or gluten-free and 2 commercial diets containing wheat were sampled. An analytical procedure using high-pressure liquid chromatography coupled with mass spectrometry with high power of resolution was developed and applied to determine specific markers of wheat gluten. The results are expressed as mg of wheat flour type ‘00’ present in 1 g of feed. The quantification limit (LOQ) obtained in the flour for ion m/z 894.5043, z = 2, is 4 mg of flour per gram. In 14 out of the 15 samples from a grain- or gluten-free diet the quantifier ion signal was < LOQ, while in 1 out of the 15 samples 10 mg of flour/g feed were measured.Highlights Adverse reaction to gluten in dogs have been documented in certain breeds Gluten is tricky to detect and measure in pet food Contamination of gluten in pet food samples marked as ‘grain-free’ and ‘gluten-free’ diets An analytical procedure was developed using HPLC coupled with HRM