vocal signal analysis in patients affected by multiple sclerosis

Abstract Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life

LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties

We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field.Comment: 26 pages and one figur

The Sant Pau Initiative on Neurodegeneration (SPIN) cohort : A data set for biomarker discovery and validation in neurodegenerative disorders

Altres ajuts: The SPIN cohort has received funding from CIBERNED; Instituto de Salud Carlos III; jointly funded by Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea, "Una manera de hacer Europa"; Generalitat de Catalunya; Fundació "La Marató TV3" Fundació Bancària Obra Social La Caixa; Fundación BBVA; Fundación Española para el Fomento de la Investigación de la Esclerosis Lateral Amiotrófica (FUNDELA); Global Brain Health Institute; Fundació Catalana Síndrome de Down; and Fundació Víctor Grífols i Lucas. These funding sources had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.The SPIN (Sant Pau Initiative on Neurodegeneration) cohort is a multimodal biomarker platform designed for neurodegenerative disease research following an integrative approach. Participants of the SPIN cohort provide informed consent to donate blood and cerebrospinal fluid samples, receive detailed neurological and neuropsychological evaluations, and undergo a structural 3T brain MRI scan. A subset also undergoes other functional or imaging studies (video-polysomnogram, 18 F-fluorodeoxyglucose PET, amyloid PET, Tau PET). Participants are followed annually for a minimum of 4 years, with repeated cerebrospinal fluid collection and imaging studies performed every other year, and brain donation is encouraged. The integration of clinical, neuropsychological, genetic, biochemical, imaging, and neuropathological information and the harmonization of protocols under the same umbrella allows the discovery and validation of key biomarkers across several neurodegenerative diseases. We describe our particular 10-year experience and how different research projects were unified under an umbrella biomarker program, which might be of help to other research teams pursuing similar approaches

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Using smartphone sensors for ataxia trials: consensus guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers

The current state of biomarker research for Friedreich's ataxia: a report from the 2018 FARA biomarker meeting

The 2018 FARA Biomarker Meeting highlighted the current state of development of biomarkers for Friedreich's ataxia. A mass spectroscopy assay to sensitively measure mature frataxin (reduction of which is the root cause of disease) is being developed. Biomarkers to monitor neurological disease progression include imaging, electrophysiological measures and measures of nerve function, which may be measured either in serum and/or through imaging-based technologies. Potential pharmacodynamic biomarkers include metabolic and protein biomarkers and markers of nerve damage. Cardiac imaging and serum biomarkers may reflect cardiac disease progression. Considerable progress has been made in the development of biomarkers for various contexts of use, but further work is needed in terms of larger longitudinal multisite studies, and identification of novel biomarkers for additional use cases

KONZO : the IBRO Africa Regional Committee (ARC) organizes its first Global Advocacy Workshop for Neuroscience in Kinshasa

Neurological diseases such as epilepsy, konzo, or neurolathyrism are not well understood or even accepted as major causes of disability. It is important that the public – from parents and children to politicians and policymakers – be informed about the importance of brain research and how it can help understand the causes and develop cures or, at least, alleviate the symptoms of neurological diseases

Network-Based Genome Wide Study of Hippocampal Imaging Phenotype In Alzheimer's Disease To Identify Functional Interaction Modules

Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases