The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data. Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675. Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects.

Neurodegenerative diseases (NDs), including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis/motor neuron disease, and multiple sclerosis, are characterized by progressive loss of neuronal structure and function, leading to severe cognitive, motor, and behavioral impairments. They pose a significant and growing challenge due to their rising prevalence and impact on global health systems. The societal and emotional toll on patients, caregivers, and healthcare infrastructures is considerable. While significant progress has been made in elucidating the pathological hallmarks of these disorders, the underlying cellular and molecular mechanisms remain incompletely understood. Increasing evidence implicates oligodendrocytes and their progenitors—oligodendrocyte progenitor cells (OPCs)—in the pathogenesis of several NDs, beyond their traditionally recognized role in demyelinating conditions such as MS. Oligodendrocytes are essential for axonal myelination, metabolic support, and neural circuit modulation in the central nervous system. Disruptions in oligodendrocyte function and myelin integrity—manifesting as demyelination, hypomyelination, or dysmyelination—have been associated with disease progression in various neurodegenerative contexts. This review consolidates recent findings on the role of OPCs in NDs, explores the concept of myelin plasticity, and discusses therapeutic strategies targeting oligodendrocyte dysfunction. By highlighting emerging research in oligodendrocyte biology, this review aims to provide a short overview of its relevance to neurodegenerative disease progression and potential therapeutic advances.

Exposure to global warming can be dangerous for health and can lead to an increase in the prevalence of neurological diseases worldwide. Such an effect is more evident in populations that are less prepared to cope with enhanced environmental temperatures. In this work, we extend our previous research on the link between climate change and Parkinson’s disease (PD) to also include Alzheimer’s Disease and other Dementias (AD/D) and Amyotrophic Lateral Sclerosis/Motor Neuron Diseases (ALS/MND). One hundred and eighty-four world countries were clustered into four groups according to their climate indices (warming and annual average temperature). Variations between 1990 and 2016 in the diseases’ indices (prevalence, deaths, and disability-adjusted life years) and climate indices for the four clusters were analyzed. Unlike our previous work on PD, we did not find any significant correlation between warming and epidemiological indices for AD/D and ALS/MND patients. A significantly lower increment in prevalence in countries with higher temperatures was found for ALS/MND patients. It can be argued that the discordant findings between AD/D or ALS/MND and PD might be related to the different features of the neuronal types involved and the pathophysiology of thermoregulation. The neurons of AD/D and ALS/MND patients are less vulnerable to heat-related degeneration effects than PD patients. PD patients’ substantia nigra pars compacta (SNpc), which are constitutively frailer due to their morphology and function, fall down under an overwhelming oxidative stress caused by climate warming.

The ageing population has become an increasing phenomenon world-wide, leading to a growing need for specialised help. Improving the quality of life of older people can lower the risk of depression and social isolation, but it requires a multi-dimensional approach through continuous monitoring and training of the main health domains (e.g., cognitive, motor, nutritional and behavioural). To this end, the use of mobile and e-health services tailored to the user’s needs can help stabilise their health conditions, in terms of physical, mental, and social capabilities. In this context, the INTESA project proposes a set of personalised monitoring and rehabilitation services for older people, based on mobile and wearable technologies ready to be used either at home or in residential long-term care facilities. We evaluated the proposed solution by deploying a suite of services in a nursing home and defining customised protocols to involve both guests (primary users) and nursing care personnel (secondary users). In this paper, we present the extended results obtained after the one-year period of experimentation in terms of technical reliability of the system, Quality of Experience, and user acceptance for both the user categories.

Alzheimer’s disease (AD) is the leading cause of dementia, but the pathogenetic factors are not yet well known, and the relationships between brain and systemic biochemical derangements and disease onset and progression are unclear. We aim to focus on blood biomarkers for an accurate prognosis of the disease. We used a dataset characterized by longitudinal findings collected over the past 10 years from 90 AD patients. The dataset included 277 observations (both clinical and biochemical ones, encompassing blood analytes encompassing routine profiles for different organs, together with immunoinflammatory and oxidative markers). Subjects were grouped into four severity classes according to the Clinical Dementia Rating (CDR) Scale: mild (CDR = 0.5 and CDR = 1), moderate (CDR = 2), severe (CDR = 3) and very severe (CDR = 4 and CDR = 5). Statistical models were used for the identification of potential blood markers of AD progression. Moreover, we employed the Pathfinder tool of the Reactome database to investigate the biological pathways in which the analytes of interest could be involved. Statistical results reveal an inverse significant relation between four analytes (high-density cholesterol, total cholesterol, iron and ferritin) with AD severity. In addition, the Reactome database suggests that such analytes could be involved in pathways that are altered in AD progression. Indeed, the identified blood markers include molecules that reflect the heterogeneous pathogenetic mechanisms of AD. The combination of such blood analytes might be an early indicator of AD progression and constitute useful therapeutic targets.

The Track-Hold System (THS) project, developed in a healthcare facility and therefore in a controlled and protected healthcare environment, contributes to the more general and broad context of Robotic-Assisted Therapy (RAT). RAT represents an advanced and innovative rehabilitation method, both motor and cognitive, and uses active, passive, and facilitating robotic devices. RAT devices can be equipped with sensors to detect and track voluntary and involuntary movements. They can work in synergy with multimedia protocols developed ad hoc to achieve the highest possible level of functional re-education. The THS is based on a passive robotic arm capable of recording and facilitating the movements of the upper limbs. An operational interface completes the device for its use in the clinical setting. In the form of a case study, the researchers conducted the experimentation in the former Tabarracci hospital (Viareggio, Italy). The case study develops a motor and cognitive rehabilitation protocol. The chosen subjects suffered from post-stroke outcomes affecting the right upper limb, including strength deficits, tremors, incoordination, and motor apraxia. During the first stage of the enrolment, the researchers worked with seven patients. The researchers completed the pilot with four patients because three of them got a stroke recurrence. The collaboration with four patients permitted the generation of an enlarged case report to collect preliminary data. The preliminary clinical results of the Track-Hold System Project demonstrated good compliance by patients with robotic-assisted rehabilitation; in particular, patients underwent a gradual path of functional recovery of the upper limb using the implemented interface.