Sex differences in mitochondrial free-carnitine levels in subjects at-risk and with Alzheimer's disease in two independent study cohorts.

A major challenge in the development of more effective therapeutic strategies for Alzheimer's disease (AD) is the identification of molecular mechanisms linked to specific pathophysiological features of the disease. Importantly AD has a two-fold higher incidence in women than men and a protracted prodromal phase characterized by amnestic mild-cognitive impairment (aMCI) suggesting that biological processes occurring early can initiate vulnerability to AD. Here, we used a sample of 125 subjects from two independent study cohorts to determine the levels in plasma (the most accessible specimen) of two essential mitochondrial markers acetyl-L-carnitine (LAC) and its derivative free-carnitine motivated by a mechanistic model in rodents in which targeting mitochondrial metabolism of LAC leads to the amelioration of cognitive function and boosts epigenetic mechanisms of gene expression.

Compensating elastic faults in a torque-assisted knee exoskeleton: functional evaluation and user perception study.

Wearable robots are often powered by elastic actuators, which can mimic the intrinsic compliance observed in human joints, contributing to safe and seamless interaction. However, due to their increased complexity, when compared to direct drives, elastic actuators are susceptible to faults, which pose significant challenges, potentially compromising user experience and safety during interaction. In this article, we developed a fault-tolerant control strategy for torque assistance in a knee exoskeleton and investigated user experience during a walking task while emulating faults.

A shared robot control system combining augmented reality and motor imagery brain-computer interfaces with eye tracking.

: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device control through mental imagery. Despite their potential, decoding neural activity in real-world conditions poses significant challenges, making BCIs currently impractical compared to traditional interaction methods. This study introduces a novel motor imagery (MI) BCI control strategy for operating a physically assistive robotic arm, addressing the difficulties of MI decoding from electroencephalogram (EEG) signals, which are inherently non-stationary and vary across individuals.

User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.

This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy.