RESUMEN
With the accelerated aging of the global population, the incidence of neurodegenerative diseases (NDDs) is increasing year by year. Because of the presence of the blood-brain barrier (BBB), the low concentration of the biomarkers in peripheral blood and the low penetration rate of the drugs through BBB into brain hinders the development of diagnosis and treatment of NDDs. As an effective mediator to penetrate through BBB in both directions, extracellular vesicles (EVs) have attracted much attention in the early diagnosis and treatment of NDDs because of their superior performance as drug carriers and detection biomarkers. Brain-derived EVs in body fluids contain disease-related biomolecules can be used as early diagnostic biomarkers for NDDs. In addition, as one of the subpopulations of EVs, exosomes, especially stem cell-derived exosomes, have great potential in the treatment of NDDs. The ability to cross the BBB, together with the feasibility of versatile functionalization of EV for NDDs pathogen targeting facilitate EVs a potential tool for targeted drug delivery systems for NDDs. In this review, the important role of EVs in the diagnosis and treatment of NDDs and the current research progress will be discussed. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease.
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Barrera Hematoencefálica , Vesículas Extracelulares , Enfermedades Neurodegenerativas , Nanomedicina Teranóstica , Humanos , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/tratamiento farmacológico , Vesículas Extracelulares/metabolismo , Animales , Barrera Hematoencefálica/metabolismo , Biomarcadores/metabolismo , Sistemas de Liberación de Medicamentos , RatonesRESUMEN
Degenerative diseases oftentimes occur within the continuous process of aging, and the corresponding clinical manifestations may be neurodegeneration, neoplastic diseases, or various human complex diseases. DNA methylation provides the opportunity to explore aging and degenerative diseases as epigenetic traits. It has already been applied to age prediction and disease diagnosis. It has been shown that various degenerative diseases share co-physiology mechanisms with each other, clues of which may be gained from studying the aging process. Here, we endeavor to predict the risk of degenerative diseases in an aging-relevant comorbid mechanism perspective. Firstly, an epigenetic clock method was implemented based on a multi-scale convolutional neural network, and a Shapley feature attribution analysis was applied to discover the aging-related CpG sites. Then, these sites were further screened to a smaller subset composed of 196 sites by using biomics analysis according to their biological functions and mechanisms. Finally, we constructed a multilayer perceptron (MLP)-based degenerative disease risk prediction model, Mlp-DDR, which was well trained and tested to accurately classify nine degenerative diseases. Recent studies also suggest that DNA methylation plays a significant role in conditions like osteoporosis and osteoarthritis, broadening the potential applications of our model. This approach significantly advances the ability to understand degenerative diseases and represents a substantial shift from traditional diagnostic methods. Despite the promising results, limitations regarding model complexity and dataset diversity suggest directions for future research, including the development of tissue-specific epigenetic clocks and the inclusion of a wider range of diseases.
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Metilación de ADN , Epigénesis Genética , Enfermedades Neurodegenerativas , Humanos , Enfermedades Neurodegenerativas/genética , Enfermedades Neurodegenerativas/diagnóstico , Islas de CpG , Envejecimiento/genética , Redes Neurales de la ComputaciónRESUMEN
Aims: Early detection and treatment of neurodegenerative Langerhans cell histiocytosis (ND-LCH) have been suggested to prevent neurodegenerative progression. The aim of the study is to validate a standardized multidisciplinary diagnostic work-up to monitor the intravenous immunoglobulins (IVIG) treatment response and the natural course of the disease in untreated patients. Methods: Patients with abnormal somatosensory evoked potentials (SEPs) received monthly 0.5 g/kg IVIG. The diagnostic protocol included structural 3T MRI, neurological examination, brainstem auditory evoked potentials (BAEPs) and SEPs. Results: Twenty-two patients were followed for 5.2 years (median) from the first MRI evidence of ND-LCH. Eleven patients received IVIG for 1.7 years (median). At treatment start neurological examination was abnormal in 10 patients, of whom two had severe clinical impairment and four had abnormal BAEPs. At last follow-up, 1/11 remained stable and 7/11 improved, while worsening of neurological or neurophysiological findings, or both, occurred in 3/11. Risk factors for worsening were a severe clinical or MRI ND-LCH at treatment initiation and prolonged exposure to LCH. Of the 11 untreated patients, none improved and three worsened. Conclusions: Using a standardized diagnostic protocol, we demonstrated that IVIG treatment can lead to clinical stabilization or improvement in all pauci-symptomatic patients with an MRI grading of less than 4.
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Histiocitosis de Células de Langerhans , Inmunoglobulinas Intravenosas , Imagen por Resonancia Magnética , Humanos , Inmunoglobulinas Intravenosas/uso terapéutico , Inmunoglobulinas Intravenosas/administración & dosificación , Histiocitosis de Células de Langerhans/tratamiento farmacológico , Histiocitosis de Células de Langerhans/diagnóstico , Masculino , Femenino , Potenciales Evocados Somatosensoriales , Resultado del Tratamiento , Preescolar , Niño , Adolescente , Enfermedades Neurodegenerativas/tratamiento farmacológico , Enfermedades Neurodegenerativas/diagnóstico , Lactante , Adulto , Potenciales Evocados Auditivos del Tronco EncefálicoRESUMEN
Neurodegenerative diseases are a group of complex diseases characterized by a progressive loss of neurons and degeneration in different areas of the nervous system. They share similar mechanisms, such as neuroinflammation, oxidative stress, and mitochondrial injury, resulting in neuronal loss. One of the biggest challenges in diagnosing neurodegenerative diseases is their heterogeneity. Clinical symptoms are usually present in the advanced stages of the disease, thus it is essential to find optimal biomarkers that would allow early diagnosis. Due to the development of ultrasensitive methods analyzing proteins in other fluids, such as blood, huge progress has been made in the field of biomarkers for neurodegenerative diseases. The application of protein biomarker measurement has significantly influenced not only diagnosis but also prognosis, differentiation, and the development of new therapies, as it enables the recognition of early stages of disease in individuals with preclinical stages or with mild symptoms. Additionally, the introduction of biochemical markers into routine clinical practice may improve diagnosis and allow for a stratification group of people with higher risk, as well as an extension of well-being since a treatment could be started early. In this review, we focus on blood biomarkers, which could be potentially useful in the daily medical practice of selected neurodegenerative diseases.
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Biomarcadores , Enfermedades Neurodegenerativas , Humanos , Biomarcadores/sangre , Enfermedades Neurodegenerativas/sangre , Enfermedades Neurodegenerativas/diagnósticoRESUMEN
Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.
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Enfermedades Neurodegenerativas , Humanos , Enfermedades Neurodegenerativas/diagnóstico , Masculino , Femenino , Persona de Mediana Edad , Anciano , Esclerosis Amiotrófica Lateral/diagnóstico , Máquina de Vectores de Soporte , Enfermedad de Parkinson/diagnóstico , Redes Neurales de la Computación , Enfermedad de Huntington/diagnóstico , Enfermedad de Huntington/fisiopatología , Procesamiento de Señales Asistido por Computador , Marcha/fisiología , AlgoritmosRESUMEN
BACKGROUND: Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. RESEARCH QUESTION: Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? METHODS: GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. RESULTS: The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91â¯%, while GoogLeNet and SqueezeNet achieved accuracies of 96.49â¯% and 92.73â¯%, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20â¯%, while GoogLeNet and SqueezeNet achieved accuracies of 96.75â¯% and 95.00â¯%, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. SIGNIFICANCE: The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved.
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Análisis de la Marcha , Enfermedades Neurodegenerativas , Humanos , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/fisiopatología , Análisis de la Marcha/métodos , Trastornos Neurológicos de la Marcha/clasificación , Trastornos Neurológicos de la Marcha/diagnóstico , Trastornos Neurológicos de la Marcha/fisiopatología , Trastornos Neurológicos de la Marcha/etiología , Esclerosis Amiotrófica Lateral/diagnóstico , Esclerosis Amiotrófica Lateral/fisiopatología , Esclerosis Amiotrófica Lateral/clasificación , Análisis de Ondículas , Masculino , Femenino , Persona de Mediana Edad , Enfermedad de Parkinson/diagnóstico , Enfermedad de Parkinson/fisiopatología , Enfermedad de Parkinson/clasificación , Aprendizaje Profundo , Procesamiento de Señales Asistido por Computador , Estudios de Casos y Controles , Enfermedad de Huntington/fisiopatología , Enfermedad de Huntington/diagnóstico , Enfermedad de Huntington/clasificación , AncianoRESUMEN
BACKGROUND: Protein biomarkers have been broadly investigated in cerebrospinal fluid and blood for the detection of neurodegenerative diseases, yet a clinically useful diagnostic test to detect early, pre-symptomatic Alzheimer's disease (AD) remains elusive. We conducted this study to quantify Aß40, Aß42, total Tau (t-Tau), hyperphosphorylated Tau (ptau181), glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) in eye fluids relative to blood. METHODS: In this cross-sectional study we collected vitreous humor, aqueous humor, tear fluid and plasma in patients undergoing surgery for eye disease. All six biomarkers were quantitatively measured by digital immunoassay. Spearman and Bland-Altman correlation analyses were performed to assess the agreement of levels between ocular fluids and plasma. RESULTS: Seventy-nine adults underwent pars-plana vitrectomy in at least one eye. Of the 79, there were 77 vitreous, 67 blood, 56 tear fluid, and 51 aqueous samples. All six biomarkers were quantified in each bio-sample, except GFAP and NfL in tear fluid due to low sample volume. All six biomarkers were elevated in vitreous humor compared to plasma samples. T-Tau, ptau181, GFAP and NfL were higher in aqueous than in plasma, and t-Tau and ptau181 concentrations were higher in tear fluid than in plasma. Significant correlations were found between Aß40 in plasma and tears (r = 0.5; p = 0.019), t-Tau in plasma and vitreous (r = 0.4; p = 0.004), NfL in plasma and vitreous (r = 0.3; p = 0.006) and plasma and aqueous (r = 0.5; p = 0.004). No significant associations were found for Aß42, ptau181 and GFAP among ocular fluids relative to plasma. Bland-Altman analysis showed aqueous humor had the closest agreement to plasma across all biomarkers. Biomarker levels in ocular fluids revealed statistically significant associations between vitreous and aqueous for t-Tau (r = 0.5; p = 0.001), GFAP (r = 0.6; p < 0.001) and NfL (r = 0.7; p < 0.001). CONCLUSION: AD biomarkers are detectable in greater quantities in eye fluids than in plasma and show correlations with levels in plasma. Future studies are needed to assess the utility of ocular fluid biomarkers as diagnostic and prognostic markers for AD, especially in those at risk with eye disease.
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Péptidos beta-Amiloides , Humor Acuoso , Biomarcadores , Proteína Ácida Fibrilar de la Glía , Proteínas de Neurofilamentos , Lágrimas , Cuerpo Vítreo , Proteínas tau , Humanos , Femenino , Masculino , Biomarcadores/sangre , Proteínas tau/sangre , Proteínas tau/líquido cefalorraquídeo , Proteínas tau/metabolismo , Anciano , Estudios Transversales , Péptidos beta-Amiloides/sangre , Péptidos beta-Amiloides/líquido cefalorraquídeo , Péptidos beta-Amiloides/metabolismo , Persona de Mediana Edad , Humor Acuoso/metabolismo , Humor Acuoso/química , Proteínas de Neurofilamentos/sangre , Proteínas de Neurofilamentos/líquido cefalorraquídeo , Lágrimas/química , Lágrimas/metabolismo , Cuerpo Vítreo/metabolismo , Proteína Ácida Fibrilar de la Glía/sangre , Proteína Ácida Fibrilar de la Glía/metabolismo , Fragmentos de Péptidos/líquido cefalorraquídeo , Fragmentos de Péptidos/sangre , Anciano de 80 o más Años , Enfermedades Neurodegenerativas/sangre , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/metabolismo , AdultoRESUMEN
BACKGROUND: Swallowing is a complex process that alters with age and neurological diseases; swallowing disorders can be a consequence of both of them. As an advanced multivariate statistical method, hierarchical cluster analysis (HCA) was utilized to make the dendrograms, which was used to find the relationship between the variables. The purpose of this study is to ascertain the type of clustering exhibited by the variables using HCA and to evaluate the approach to major neurodegenerative diseases (MND) with swallowing disorders based on the results obtained. METHODS: Data were collected from a total of 173 patients from various neurological diagnoses, such as dementia, Parkinson's disease, stroke and polyneuropathy, aging between 42 and 104 (mean of age 72.85) by using the Montreal Cognitive Assessment, the Edinburgh Feeding Evaluation Scale (EdFED), the Eating Assessment Tool (EAT-10), and the Modified Mann Swallowing Ability test. From the collected data, dendrograms were formed by using HCA with Ward linkage method. RESULTS: Based on cluster analysis results, clusters demonstrate statistical significance. They center around EdFED, EAT-10, and age in each MND. In healthy individuals, variables are not clustered as in the patient group. This study holds importance as it can give clinicians a different perspective on determining and managing the elderly population's swallowing problems. CONCLUSIONS: The HCA method explicitly proposes which variables should be examined concurrently in the clinic for MND. This research is one of the pioneering studies conducted by using the HCA method.
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Trastornos de Deglución , Enfermedades Neurodegenerativas , Humanos , Enfermedades Neurodegenerativas/fisiopatología , Enfermedades Neurodegenerativas/diagnóstico , Análisis por Conglomerados , Anciano , Masculino , Femenino , Trastornos de Deglución/fisiopatología , Trastornos de Deglución/diagnóstico , Trastornos de Deglución/etiología , Persona de Mediana Edad , Anciano de 80 o más Años , Adulto , Deglución/fisiologíaRESUMEN
It has been widely established that the characterization of extracellular vesicles (EVs), particularly small EVs (sEVs), shed by different cell types into biofluids, helps to identify biomarkers and therapeutic targets in neurological and neurodegenerative diseases. Recent studies are also exploring the efficacy of mesenchymal stem cell-derived extracellular vesicles naturally enriched with therapeutic microRNAs and proteins for treating various diseases. In addition, EVs released by various neural cells play a crucial function in the modulation of signal transmission in the brain in physiological conditions. However, in pathological conditions, such EVs can facilitate the spread of pathological proteins from one brain region to the other. On the other hand, the analysis of EVs in biofluids can identify sensitive biomarkers for diagnosis, prognosis, and disease progression. This review discusses the potential therapeutic use of stem cell-derived EVs in several central nervous system diseases. It lists their differences and similarities and confers various studies exploring EVs as biomarkers. Further advances in EV research in the coming years will likely lead to the routine use of EVs in therapeutic settings.
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Biomarcadores , Enfermedades del Sistema Nervioso Central , Vesículas Extracelulares , Humanos , Vesículas Extracelulares/metabolismo , Enfermedades del Sistema Nervioso Central/metabolismo , Enfermedades del Sistema Nervioso Central/terapia , Enfermedades del Sistema Nervioso Central/diagnóstico , Animales , MicroARNs/genética , MicroARNs/metabolismo , Células Madre Mesenquimatosas/metabolismo , Enfermedades Neurodegenerativas/terapia , Enfermedades Neurodegenerativas/metabolismo , Enfermedades Neurodegenerativas/diagnósticoRESUMEN
The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.
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Biología Computacional , Aprendizaje Automático , Enfermedades Neurodegenerativas , Neuroimagen , Humanos , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/diagnóstico por imagen , Biología Computacional/métodos , Neuroimagen/métodos , Algoritmos , Inteligencia Artificial , Encéfalo/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , Imagen por Resonancia Magnética/métodosRESUMEN
The central nervous system (CNS) is integrated by glial and neuronal cells, and both release extracellular vesicles (EVs) that participate in CNS homeostasis. EVs could be one of the best candidates to operate as nanosized biological platforms for analysing multidimensional bioactive cargos, which are protected during systemic circulation of EVs. Having a window into the molecular level processes that are happening in the CNS could open a new avenue in CNS research. This raises a particular point of interest: can CNS-derived EVs in blood serve as circulating biomarkers that reflect the pathological status of neurological diseases? L1 cell adhesion molecule (L1CAM) is a widely reported biomarker to identify CNS-derived EVs in peripheral blood. However, it has been demonstrated that L1CAM is also expressed outside the CNS. Given that principal data related to neurodegenerative diseases, such as multiple sclerosis, amyotrophic lateral sclerosis, Parkinson's disease and Alzheimer's disease were obtained using L1CAM-positive EVs, efforts to overcome present challenges related to its specificity are required. In this sense, other surface biomarkers for CNS-derived EVs, such as glutamate aspartate transporter (GLAST) and myelin oligodendrocyte glycoprotein (MOG), among others, have started to be used. Establishing a panel of EV biomarkers to analyse CNS-derived EVs in blood could increase the specificity and sensitivity necessary for these types of studies. This review covers the main evidence related to CNS-derived EVs in cerebrospinal fluid and blood samples of patients with neurological diseases, focusing on the reported biomarkers and the technical possibilities for their isolation. EVs are emerging as a mirror of brain physiopathology, reflecting both localized and systemic changes. Therefore, when the technical hindrances for EV research and clinical applications are overcome, novel disease-specific panels of EV biomarkers would be discovered to facilitate transformation from traditional medicine to personalized medicine.
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Biomarcadores , Sistema Nervioso Central , Vesículas Extracelulares , Enfermedades Neurodegenerativas , Humanos , Vesículas Extracelulares/metabolismo , Biomarcadores/sangre , Sistema Nervioso Central/metabolismo , Enfermedades Neurodegenerativas/sangre , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/metabolismo , AnimalesRESUMEN
Neurological damage is the pathological substrate of permanent disability in various neurodegenerative disorders. Early detection of this damage, including its identification and quantification, is critical to preventing the disease's progression in the brain. Tau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), as brain protein biomarkers, have the potential to improve diagnostic accuracy, disease monitoring, prognostic assessment, and treatment efficacy. These biomarkers are released into the cerebrospinal fluid (CSF) and blood proportionally to the degree of neuron and astrocyte damage in different neurological disorders, including stroke, traumatic brain injury, multiple sclerosis, neurodegenerative dementia, and Parkinson's disease. Here, we review how Tau, GFAP, and NfL biomarkers are detected in CSF and blood as crucial diagnostic tools, as well as the levels of these biomarkers used for differentiating a range of neurological diseases and monitoring disease progression. We also discuss a biosensor approach that allows for the real-time detection of multiple biomarkers in various neurodegenerative diseases. This combined detection system of brain protein biomarkers holds significant promise for developing more specific and accurate clinical tools that can identify the type and stage of human neurological diseases with greater precision.
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Biomarcadores , Proteína Ácida Fibrilar de la Glía , Enfermedades Neurodegenerativas , Proteínas de Neurofilamentos , Proteínas tau , Humanos , Biomarcadores/líquido cefalorraquídeo , Biomarcadores/sangre , Proteínas de Neurofilamentos/líquido cefalorraquídeo , Proteínas de Neurofilamentos/sangre , Proteína Ácida Fibrilar de la Glía/líquido cefalorraquídeo , Proteína Ácida Fibrilar de la Glía/sangre , Proteínas tau/líquido cefalorraquídeo , Proteínas tau/sangre , Enfermedades Neurodegenerativas/líquido cefalorraquídeo , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/sangre , Encéfalo/metabolismo , Encéfalo/patologíaRESUMEN
BACKGROUND: Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study. METHODS: Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning. RESULTS: Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis. CONCLUSIONS: Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.
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Fatiga , Estudios de Factibilidad , Marcha , Fatiga Mental , Enfermedades Neurodegenerativas , Caminata , Humanos , Masculino , Femenino , Persona de Mediana Edad , Fatiga/diagnóstico , Fatiga/fisiopatología , Fatiga/etiología , Caminata/fisiología , Anciano , Fatiga Mental/fisiopatología , Fatiga Mental/diagnóstico , Enfermedades Neurodegenerativas/complicaciones , Enfermedades Neurodegenerativas/fisiopatología , Enfermedades Neurodegenerativas/diagnóstico , Marcha/fisiología , Dispositivos Electrónicos Vestibles , Enfermedades del Sistema Inmune/complicaciones , Enfermedades del Sistema Inmune/diagnóstico , Adulto , Acelerometría/instrumentación , Acelerometría/métodosRESUMEN
BACKGROUND: Cognitive impairment is prevalent in patients with heart failure with reduced ejection fraction (HFrEF), affecting self-care and outcomes. Novel blood-based biomarkers have emerged as potential diagnostic tools for neurodegeneration. OBJECTIVES: This study aimed to assess neurodegeneration in HFrEF by measuring neurofilament light chain (NfL), total tau (t-tau), amyloid beta 40 (Aß40), and amyloid beta 42 (Aß42) in a large, well-characterized cohort. METHODS: The study included 470 patients with HFrEF from a biobank-linked prospective registry at the Medical University of Vienna. High-sensitivity single-molecule assays were used for measurement. Unplanned heart failure (HF) hospitalization and all-cause death were recorded as outcome parameters. RESULTS: All markers, but not the Aß42:Aß40 ratio, correlated with HF severity, ie, N-terminal pro-B-type natriuretic peptide and NYHA functional class, and comorbidity burden and were significantly associated with all-cause death and HF hospitalization (crude HR: all-cause death: NfL: 4.44 [95% CI: 3.02-6.53], t-tau: 5.04 [95% CI: 2.97-8.58], Aß40: 3.90 [95% CI: 2.27-6.72], and Aß42: 5.14 [95% CI: 2.84-9.32]; HF hospitalization: NfL: 2.48 [95% CI: 1.60-3.85], t-tau: 3.44 [95% CI: 1.95-6.04], Aß40: 3.13 [95% CI: 1.84-5.34], and Aß42: 3.48 [95% CI: 1.93-6.27]; P < 0.001 for all). These associations remained statistically significant after multivariate adjustment including N-terminal pro-B-type natriuretic peptide. The discriminatory accuracy of NfL in predicting all-cause mortality was comparable to the well-established risk marker N-terminal pro-B-type natriuretic peptide (C-index: 0.70 vs 0.72; P = 0.225), whereas the C-indices of t-tau, Aß40, Aß42, and the Aß42:Aß40 ratio were significantly lower (P < 0.05 for all). CONCLUSIONS: Neurodegeneration is directly interwoven with the progression of HF. Biomarkers of neurodegeneration, particularly NfL, may help identify patients potentially profiting from a comprehensive neurological work-up. Further research is necessary to test whether early diagnosis or optimized HFrEF treatment can preserve cognitive function.
Asunto(s)
Péptidos beta-Amiloides , Biomarcadores , Insuficiencia Cardíaca , Proteínas de Neurofilamentos , Fragmentos de Péptidos , Índice de Severidad de la Enfermedad , Proteínas tau , Humanos , Insuficiencia Cardíaca/sangre , Insuficiencia Cardíaca/mortalidad , Insuficiencia Cardíaca/diagnóstico , Masculino , Femenino , Biomarcadores/sangre , Péptidos beta-Amiloides/sangre , Anciano , Fragmentos de Péptidos/sangre , Proteínas tau/sangre , Proteínas de Neurofilamentos/sangre , Persona de Mediana Edad , Péptido Natriurético Encefálico/sangre , Hospitalización/estadística & datos numéricos , Volumen Sistólico/fisiología , Estudios Prospectivos , Enfermedades Neurodegenerativas/sangre , Enfermedades Neurodegenerativas/diagnóstico , Disfunción Cognitiva/sangre , Disfunción Cognitiva/diagnósticoRESUMEN
Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.
Asunto(s)
Espectrometría Raman , Espectrometría Raman/métodos , Humanos , Biopsia Líquida/métodos , Encefalopatías/diagnóstico , Encefalopatías/patología , Lesiones Traumáticas del Encéfalo/diagnóstico , Lesiones Traumáticas del Encéfalo/patología , Lesiones Traumáticas del Encéfalo/metabolismo , Lesiones Traumáticas del Encéfalo/diagnóstico por imagen , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/metabolismo , Encéfalo/patología , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagenRESUMEN
BACKGROUND: Red blood cells (RBCs) are usually considered simple cells and transporters of gases to tissues. HYPOTHESIS: However, recent research has suggested that RBCs may have diagnostic potential in major neurodegenerative disorders (NDDs). RESULTS: This review summarizes the current knowledge on changes in RBC in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and other NDDs. It discusses the deposition of neuronal proteins like amyloid-ß, tau, and α-synuclein, polyamines, changes in the proteins of RBCs like band-3, membrane transporter proteins, heat shock proteins, oxidative stress biomarkers, and altered metabolic pathways in RBCs during neurodegeneration. It also highlights the comparison of RBC diagnostic markers to other in-market diagnoses and discusses the challenges in utilizing RBCs as diagnostic tools, such as the need for standardized protocols and further validation studies. SIGNIFICANCE STATEMENT: The evidence suggests that RBCs have diagnostic potential in neurodegenerative disorders, and this study can pave the foundation for further research which may lead to the development of novel diagnostic approaches and treatments.
Asunto(s)
Biomarcadores , Eritrocitos , Enfermedades Neurodegenerativas , Humanos , Eritrocitos/metabolismo , Enfermedades Neurodegenerativas/diagnóstico , Enfermedades Neurodegenerativas/metabolismo , Enfermedades Neurodegenerativas/sangre , Biomarcadores/metabolismo , Biomarcadores/sangre , Estrés Oxidativo , Animales , Enfermedad de Alzheimer/diagnóstico , Enfermedad de Alzheimer/metabolismo , Enfermedad de Alzheimer/sangreRESUMEN
Neurodegenerative diseases that include Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), and multiple sclerosis (MS) that arise due to numerous causes like protein accumulation and autoimmunity characterized by neurologic depletion which lead to incapacity in normal physiological function such as thinking and movement in these patients. Glial cells perform an important role in protective neuronal function; in the case of neuroinflammation, glial cell dysfunction can promote the development of neurodegenerative diseases. miRNA that participates in gene regulation and plays a vital role in many biological processes in the body; in the central nervous system (CNS), it can play an essential part in neural maturation and differentiation. In neurodegenerative diseases, miRNA dysregulation occurs, enhancing the development of these diseases. In this review, we discuss neurodegenerative disease (Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS)) and how miRNA is preserved as a diagnostic biomarker or therapeutic agent in these disorders. Finally, we highlight miRNA as therapy.