Signs and symptoms

Résumé

Signs and symptoms are the observed or detectable signs, and experienced symptoms of an illness, injury, or condition. Signs are objective and externally observable; symptoms are a person's reported subjective experiences. A sign for example may be a higher or lower temperature than normal, raised or lowered blood pressure or an abnormality showing on a medical scan. A symptom is something out of the ordinary that is experienced by an individual such as feeling feverish, a headache or other pains in the body. Signs and symptoms Signs A medical sign is an objective observable indication of a disease, injury, or medical condition that may be detected during a physical examination. These signs may be visible, such as a rash or bruise, or otherwise detectable such as by using a stethoscope or taking blood pressure. Medical signs, along with symptoms, help in forming a diagnosis. Some examples of signs are nail clubbing of either the fingernails or toenails, an abnorm

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https://en.wikipedia.org/wiki/Signs_and_symptoms

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Parkinson's disease (PD), the second most common progressive neurodegenerative disease, develops and progresses for 10-15 years before the clinical diagnostic symptoms of the disease are manifested. Furthermore, several aspects of PD pathology overlap with other neurodegenerative diseases (NDDs) linked to alpha-synuclein (aSyn) aggregation, also called synucleinopathies. Therefore, there is an urgent need to discover and validate early diagnostic and prognostic markers that reflect disease pathophysiology, progression, severity, and potential differences in disease mechanisms between PD and other NDDs. The close association between aSyn and the development of pathology in synucleinopathies, along with the identification of aSyn species in biological fluids, has led to increasing interest in aSyn species as potential biomarkers for early diagnosis of PD and differentiate it from other synucleinopathies. In this review, we (1) provide an overview of the progress toward mapping the distribution of aSyn species in the brain, peripheral tissues, and biological fluids; (2) present comparative and critical analysis of previous studies that measured total aSyn as well as other species such as modified and aggregated forms of aSyn in different biological fluids; and (3) highlight conceptual and technical gaps and challenges that could hinder the development and validation of reliable aSyn biomarkers; and (4) outline a series of recommendations to address these challenges. Finally, we propose a combined biomarker approach based on integrating biochemical, aggregation and structure features of aSyn, in addition to other biomarkers of neurodegeneration. We believe that capturing the diversity of aSyn species is essential to develop robust assays and diagnostics for early detection, patient stratification, monitoring of disease progression, and differentiation between synucleinopathies. This could transform clinical trial design and implementation, accelerate the development of new therapies, and improve clinical decisions and treatment strategies.

NATURE PORTFOLIO2022

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On Modeling Glottal Source Information for Phonation Assessment in Parkinson's Disease

Julian David Fritsch, Mathew Magimai Doss

Parkinson's disease produces several motor symptoms, including different speech impairments that are known as hypokinetic dysarthria. Symptoms associated to dysarthria affect different dimensions of speech such as phonation, articulation, prosody, and intelligibility. Studies in the literature have mainly focused on the analysis of articulation and prosody because they seem to be the most prominent symptoms associated to dysarthria severity. However, phonation impairments also play a significant role to evaluate the global speech severity of Parkinson's patients. This paper proposes an extensive comparison of different methods to automatically evaluate the severity of specific phonation impairments in Parkinson's patients. The considered models include the computation of perturbation and glottal-based features, in addition to features extracted from a zero frequency filtered signals. We consider as well end-to-end models based on 1D CNNs, which are trained to learn features from the raw speech waveform, reconstructed glottal signals, and zero-frequency filtered signals. The results indicate that it is possible to automatically classify between speakers with low versus high phonation severity due to the presence of dysarthria and at the same time to evaluate the severity of the phonation impairments on a continuous scale, posed as a regression problem.

ISCA-INT SPEECH COMMUNICATION ASSOC2021

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Outcome Prediction of Consciousness Disorders in the Acute Stage Based on a Complementary Motor Behavioural Tool

Bogdan Draganski, Richard Frackowiak, Djalel Eddine Meskaldji, Maria Giulia Preti, Dimitri Nestor Alice Van De Ville

Introduction Attaining an accurate diagnosis in the acute phase for severely brain-damaged patients presenting Disorders of Consciousness (DOC) is crucial for prognostic validity; such a diagnosis determines further medical management, in terms of therapeutic choices and end-of-life decisions. However, DOC evaluation based on validated scales, such as the Revised Coma Recovery Scale (CRS-R), can lead to an underestimation of consciousness and to frequent misdiagnoses particularly in cases of cognitive motor dissociation due to other aetiologies. The purpose of this study is to determine the clinical signs that lead to a more accurate consciousness assessment allowing more reliable outcome prediction. Methods From the Unit of Acute Neurorehabilitation (University Hospital, Lausanne, Switzerland) between 2011 and 2014, we enrolled 33 DOC patients with a DOC diagnosis according to the CRS-R that had been established within 28 days of brain damage. The first CRS-R assessment established the initial diagnosis of Unresponsive Wakefulness Syndrome (UWS) in 20 patients and a Minimally Consciousness State (MCS) in the remaining13 patients. We clinically evaluated the patients over time using the CRS-R scale and concurrently from the beginning with complementary clinical items of a new observational Motor Behaviour Tool (MBT). Primary endpoint was outcome at unit discharge distinguishing two main classes of patients (DOC patients having emerged from DOC and those remaining in DOC) and 6 subclasses detailing the outcome of UWS and MCS patients, respectively. Based on CRS-R and MBT scores assessed separately and jointly, statistical testing was performed in the acute phase using a non-parametric Mann-Whitney U test; longitudinal CRS-R data were modelled with a Generalized Linear Model. Results Fifty-five per cent of the UWS patients and 77% of the MCS patients had emerged from DOC. First, statistical prediction of the first CRS-R scores did not permit outcome differentiation between classes; longitudinal regression modelling of the CRS-R data identified distinct outcome evolution, but not earlier than 19 days. Second, the MBT yielded a significant outcome predictability in the acute phase (p0.81). Third, a statistical comparison of the CRS-R subscales weighted by MBT became significantly predictive for DOC outcome (p

Public Library of Science2016

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NATURE PORTFOLIO2022

Outcome Prediction of Consciousness Disorders in the Acute Stage Based on a Complementary Motor Behavioural Tool

Bogdan Draganski, Richard Frackowiak, Djalel Eddine Meskaldji, Maria Giulia Preti, Dimitri Nestor Alice Van De Ville

Public Library of Science2016