Objective. Muscle activation patterns in the muscle-to-force null space, i.e. patterns that do not generate task-relevant forces, may provide an opportunity for motor augmentation by allowing to control additional end-effectors simultaneously to natural limbs. Here we tested the feasibility of muscular null space control for augmentation by assessing simultaneous control of natural and extra degrees of freedom. Approach. We instructed eight participants to control translation and rotation of a virtual 3D end-effector by simultaneous generation of isometric force at the hand and null space activity extracted in real-time from the electromyographic signals recorded from 15 shoulder and arm muscles. First, we identified the null space components that each participant could control more naturally by voluntary co-contraction. Then, participants performed several blocks of a reaching and holding task. They displaced an ellipsoidal cursor to reach one of nine targets by generating force, and simultaneously rotated the cursor to match the target orientation by activating null space components. We developed an information-theoretic metric, an index of difficulty defined as the sum of a spatial and a temporal term, to assess individual null space control ability for both reaching and holding. Main results. On average, participants could reach the targets in most trials already in the first block (72%) and they improved with practice (maximum 93%) but holding performance remained lower (maximum 43%). As there was a high inter-individual variability in performance, we performed a simulation with different spatial and temporal task conditions to estimate those for which each individual participants would have performed best. Significance. Muscular null space control is feasible and may be used to control additional virtual or robotics end-effectors. However, decoding of motor commands must be optimized according to individual null space control ability.

Estimation of the force exerted by muscles from their electromyographic (EMG) activity may be useful to control robotic devices. Approximating end-point forces as a linear combination of the activities of multiple muscles acting on a limb may lead to an inaccurate estimation because of the dependency between the EMG signals, i.e., multi-collinearity. This study compared the EMG-to-force mapping estimation performed with standard multiple linear regression and with three other algorithms designed to reduce different sources of the detrimental effects of multi-collinearity: Ridge Regression, which performs an L2 regularization through a penalty term; linear regression with constraints from foreknown anatomical boundaries, derived from a musculoskeletal model; linear regression of a reduced number of muscular degrees of freedom through the identification of muscle synergies. Two datasets, both collected during the exertion of submaximal isometric forces along multiple directions with the upper limb, were exploited. One included data collected across five sessions and the other during the simultaneous exertion of force and generation of different levels of co-contraction. The accuracy and consistency of the EMG-to-force mappings were assessed to determine the strengths and drawbacks of each algorithm. When applied to multiple sessions, Ridge Regression achieved higher accuracy (R² = 0.70) but estimations based on muscle synergies were more consistent (differences between the pulling vectors of mappings extracted from different sessions: 67%). In contrast, the implementation of anatomical constraints was the best solution, both in terms of consistency (R² = 0.64) and accuracy (74%), in the case of different co-contraction conditions. These results may be used for the selection of the mapping between EMG and force to be implemented in myoelectrically controlled robotic devices.

(1) Background: Physical stimuli may activate peripheral blood mononuclear cells (PBMCs) to secrete cytokines, which may favor pro-inflammatory responses or trigger reparative phenomena. The purpose of this study is to evaluate the action of Polarized Polychromatic Incoherent Low Energy Radiation (PILER) on human in vitro PBMCs, by detection of the possible effects on cytokine production; (2) Methods: isolated PBMCs were irradiated with a PILER lamp at different exposure times, at a distance of 10 cm, before incubation. The supernatants were collected after 24 h and 48 h and cytokines evaluated by ELISA; (3) Results: Our results showed a decrease in the levels of pro-inflammatory IL-12p70, IL-17A, IFN-γ, and TNF-α cytokines, whereas IL-10 and TGF-β1 with regulatory activity increased; (4) Conclusions: PILER irradiation affected the cytokine production by isolated PBMCs driving the immune response toward an anti-inflammatory/reparative profile.

Meibomitis is an ocular disease which leads to a dysfunction of the meibomian glands. This ophthalmologic disease may cause severe pain and obvious vision loss. The therapeutic protocol used in the treatment of this pathology consists in local and systemic antibiotic therapy. The results obtained using this approach are scarce and, in many cases, result in adverse events. In this study, we propose an alternative and original approach using TECAR therapy in the treatments of meibomitis disease. The endogenous heat produced by the TECAR device produced beneficial effects from both a histological and anatomical point of view. Different parameters (TBUT, interferometry, tear meniscus height, meibography and OCTA) were evaluated before the TECAR treatments, immediately afterwards, and 15 days after the end of the treatments. The obtained results suggest a new possible use of TECAR therapy on ophthalmological patients, opening an innovative scenario in a non-invasive manner.

Learned movements are thought to be represented in both extrinsic and intrinsic coordinate systems. Intrinsic representations have traditionally been characterized using joint-based coordinates, although the relationship between joint movements and muscle activation depends strongly on limb configuration. Consequently, movement directions aligned in joint space may not be aligned in muscle space, but the implications of this mismatch for motor learning have remained largely unexplored. We addressed this question by combining electromyographic (EMG) analysis with a visuomotor adaptation experiment. In Experiment 1, participants performed planar reaching movements in two workspaces separated by a 45° shoulder rotation while EMG activity was recorded from nine upper-limb muscles. Muscle-pattern similarity analysis revealed that movement directions aligned in joint space were not always aligned in muscle space and that the degree of misalignment varied systematically across movement directions. Based on these results, we predicted that visuomotor adaptation to clockwise (CW) and counterclockwise (CCW) rotations would produce different patterns of motor generalization, contrary to the prediction of conventional joint-space accounts. Experiment 2 confirmed this prediction, revealing a systematic shift between the CW and CCW generalization patterns that was consistent with the muscle-space prediction. These findings suggest that intrinsic representations of learned movements are not fully captured by joint-based coordinates alone and that muscle-based coordinates contribute to motor learning and its generalization. Together, these findings highlight the importance of considering underlying biomechanics when interpreting motor representations using generalization paradigms.