Motor skill learning modulates striatal extracellular vesicles' content in a mouse model of Huntington's disease.

Huntington's disease (HD) is a neurological disorder caused by a CAG expansion in the Huntingtin gene (HTT). HD pathology mostly affects striatal medium-sized spiny neurons and results in an altered cortico-striatal function. Recent studies report that motor skill learning, and cortico-striatal stimulation attenuate the neuropathology in HD, resulting in an amelioration of some motor and cognitive functions. During physical training, extracellular vesicles (EVs) are released in many tissues, including the brain, as a potential means for inter-tissue communication. To investigate how motor skill learning, involving acute physical training, modulates EVs crosstalk between cells in the striatum, we trained wild-type (WT) and R6/1 mice, the latter with motor and cognitive deficits, on the accelerating rotarod test, and we isolated their striatal EVs. EVs from R6/1 mice presented alterations in the small exosome population when compared to WT. Proteomic analyses revealed that striatal R6/1 EVs recapitulated signaling and energy deficiencies present in HD. Motor skill learning in R6/1 mice restored the amount of EVs and their protein content in comparison to naïve R6/1 mice. Furthermore, motor skill learning modulated crucial pathways in metabolism and neurodegeneration. All these data provide new insights into the pathogenesis of HD and put striatal EVs in the spotlight to understand the signaling and metabolic alterations in neurodegenerative diseases. Moreover, our results suggest that motor learning is a crucial modulator of cell-to-cell communication in the striatum.

IT-Related Barriers and Facilitators to the Implementation of a New European eHealth Solution, the Digital Survivorship Passport (SurPass Version 2.0): Semistructured Digital Survey.

BACKGROUND: To overcome knowledge gaps and optimize long-term follow-up (LTFU) care for childhood cancer survivors, the concept of the Survivorship Passport (SurPass) has been invented. Within the European PanCareSurPass project, the semiautomated and interoperable SurPass (version 2.0) will be optimized, implemented, and evaluated at 6 LTFU care centers representing 6 European countries and 3 distinct health system scenarios: (1) national electronic health information systems (EHISs) in Austria and Lithuania, (2) regional or local EHISs in Italy and Spain, and (3) cancer registries or hospital-based EHISs in Belgium and Germany. OBJECTIVE: We aimed to identify and describe barriers and facilitators for SurPass (version 2.0) implementation concerning semiautomation of data input, interoperability, data protection, privacy, and cybersecurity. METHODS: IT specialists from the 6 LTFU care centers participated in a semistructured digital survey focusing on IT-related barriers and facilitators to SurPass (version 2.0) implementation. We used the fit-viability model to assess the compatibility and feasibility of integrating SurPass into existing EHISs. RESULTS: In total, 13/20 (65%) invited IT specialists participated. The main barriers and facilitators in all 3 health system scenarios related to semiautomated data input and interoperability included unaligned EHIS infrastructure and the use of interoperability frameworks and international coding systems. The main barriers and facilitators related to data protection or privacy and cybersecurity included pseudonymization of personal health data and data retention. According to the fit-viability model, the first health system scenario provides the best fit for SurPass implementation, followed by the second and third scenarios. CONCLUSIONS: This study provides essential insights into the information and IT-related influencing factors that need to be considered when implementing the SurPass (version 2.0) in clinical practice. We recommend the adoption of Health Level Seven Fast Healthcare Interoperability Resources and data security measures such as encryption, pseudonymization, and multifactor authentication to protect personal health data where applicable. In sum, this study offers practical insights into integrating digital health solutions into existing EHISs.

Li-Fraumeni: ¿la detección de familias aumentaría la supervivencia entre sus miembros? / Li-Fraumeni: Will the detection in families increase the survival of its members?

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