Advanced Data Mining Research and Bioinformatics Learning (ADMIRABLE)
http://hdl.handle.net/10259/4219
2024-03-29T07:07:09ZComputer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping
http://hdl.handle.net/10259/8860
Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping
Amo Salas, Javier; Olivares Gil, Alicia; García Bustillo, Álvaro; García García, David; Arnaiz González, Álvar; Cubo Delgado, Esther
Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has
steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients
with PD, although the diagnostic process is time-consuming and various symptoms take years to appear,
which means that the diagnosis is prone to human error. The partial automatization of PD assessment
and diagnosis through computational processes has therefore been considered for some time. One
well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer
vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the
area of CV, have made it possible to develop computer systems that can help specialists assess and
diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as
to offer insight into future research lines that technological advances are now opening up.
2024-02-01T00:00:00ZCómo estimar la composición corporal en la enfermedad de Huntington. Estudio transversal y observacional con bioimpedancia de múltiples frecuencias
http://hdl.handle.net/10259/8859
Cómo estimar la composición corporal en la enfermedad de Huntington. Estudio transversal y observacional con bioimpedancia de múltiples frecuencias
Rivadeneyra Posadas, Jéssica Jannett; Simón Vicente, Lucía; Castillo, Daniel; Raya-González, Javier; Soto Célix, María .; Rodríguez Fernández, Alejandro; García Bustillo, Álvaro; Saiz Rodríguez, Miriam; Vázquez Sánchez, Fernando; Aguado, Laura; Leyva-Hernández, Gonzalo Gámez; Cubo Delgado, Esther
Introducción: La enfermedad de Huntington (EH) es un trastorno raro neurodegenerativo. La información fiable del estado nutricional, especialmente de la composición corporal, es crítica en clínica y en investigación. La facilidad de aplicación
y portabilidad del análisis de la bioimpedancia de múltiples frecuencias (mfBIA) la convierten en una herramienta atractiva para medirla, pero se desconoce su precisión en la EH.
Objetivo: Evaluar la precisión del mfBIA frente a la absorciometría dual de rayos X (DEXA) en la EH.
Pacientes y métodos: Estudio transversal, observacional y unicéntrico. La EH se midió con la subescala motora de la escala
unificada de valoración de la EH y con la capacidad funcional total. La composición corporal se valoró según la masa libre
de grasa (MLG), la masa grasa (MG), el índice de masa libre de grasa (IMLG) y el índice de masa grasa (IMG). Se utilizó el
coeficiente de correlación intraclase con intervalos de confianza al 95% y estimaciones de sesgo mediante gráficos de
Bland-Altman.
Resultados: Se incluyó a 16 pacientes, siete hombres y nueve mujeres, con edad media de 58,5 (32-68) años, capacidad
funcional total de 10 (3-13) y escala unificada de valoración de la EH de 31 (7-85). La fiabilidad era alta entre el mfBIA y la
DEXA para el IMLG en hombres, 0,88 (intervalo de confianza al 95%: 0,17-0,98), y mujeres, 0,9 (intervalo de confianza al
95%: 0,61-0,98); y para el IMG en hombres, 0,97 (intervalo de confianza al 95%: 0,83-0,99), y mujeres, 0,91 (intervalo
de confianza al 95%: 0,68-0,98). El mfBIA sobreestimó ligeramente la MLG, la MG, el IMG y el IMLG en los hombres, pero
subestimó el IMLG en las mujeres.
Conclusiones: El mfBIA es un método fácil de usar, seguro, no invasivo y preciso para medir la composición corporal y el
estado nutricional en pacientes con EH leve-moderada.; Introduction: Huntington´s disease (HD) is a rare neurodegenerative disorder. Reliable information about nutritional
status, especially body composition from individuals with HD is critical for clinical care and research. The ease of application
and portability of multiple frequencies bioelectrical impedance analysis (mfBIA) make it an attractive tool for measuring
body composition, but its accuracy in HD is unknown.
Aim: To evaluate the accuracy of mfBIA vs. Dual X-ray absorptiometry (DEXA) in HD.
Patients and methods: Cross-sectional, observational, and single-center study. HD severity was measured using motor
subscale of the unified Huntington´s disease rating scale (m-UHDRS) and the total functional capacity (TFC). Body
composition was measured in terms of fat-free mass (FFM), fat mass (FM), fat-free mass index (FFMI), and fat mass index
(FMI). Using Bland-Altman plots, we analyzed reliability between DEXA and mfBIA using the Intraclass Correlation
Coefficient with 95% confidence intervals (CI) and bias estimates for all.
Results: We included 16 patients with HD, 7 men, and 9 women, median age of 58.5 (32;68) years, TFC: 10 (3;13), and
m-UHDRS: 31 (7;85). The reliability between mfBIA and DEXA were high for FFMI in men: 0.88 (95% CI 0.17-0.98), and
women: 0.90 (95% CI 0.61- 0.98); for FMI, men: 0.97 (95% CI 0.83-0.99), and women: 0.91 (95% CI 0.68-0.98).
Compared to DEXA, mfBIA slightly overestimated FFM, FM, FMI and FFMI in men and underestimated FFMI in women.
Conclusions: mfBIA is an easy-to-use, safe, non-invasive, accurate method for measuring body composition and nutritional
status in patients with mild-moderate HD.
2024-01-01T00:00:00ZMulti-ancestry genome-wide association meta-analysis of Parkinson’s disease
http://hdl.handle.net/10259/8858
Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease
Kim, Jonggeol Jeffrey; Vitale, Dan; Véliz Otani, Diego; Lian, Michelle Mulan; Heilbron, Karl; 23andMe Research Team; Iwaki, Hirotaka; Lake, Julie; Warly Solsberg, Caroline; Leonard, Hampton; Makarious, Mary B.; Tan, Eng-King; Singleton, Andrew B.; Bandres-Ciga, Sara; Noyce, Alastair J.; Cubo Delgado, Esther; Global Parkinson's Genetics Program; Blauwendraat, Cornelis; Nalls, Mike A.; Nee Foo, Jia; Mata, Ignacio
Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations.
2023-12-01T00:00:00ZValidation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease
http://hdl.handle.net/10259/8857
Validation of ActiGraph and Fitbit in the assessment of energy expenditure in Huntington's disease
Simón Vicente, Lucía; Rodríguez Fernández, Alejandro; Rivadeneyra Posadas, Jéssica Jannett; Soto Célix, María .; Raya-González, Javier; Castillo, Daniel; Calvo Simal, Sara; Mariscal, Natividad; García Bustillo, Álvaro; Aguado, Laura; Cubo Delgado, Esther
Background: Consumer and research activity monitors have become popular because of their ability to quantify
energy expenditure (EE) in free-living conditions. However, the accuracy of activity trackers in determining EE in
people with Huntington’s Disease (HD) is unknown.
Research question:
Can the ActiGraph wGT3X-B or the Fitbit Charge 4 accurately measure energy expenditure during physical
activity, in people with HD compared to Indirect Calorimetry (IC) (Medisoft Ergo Card)?
Methods: We conducted a cross-sectional, observational study with fourteen participants with mild-moderate HD
(mean age 55.7 ± 11.4 years). All participants wore an ActiGraph and Fitbit during an incremental test, running
on a treadmill at 3.2 km/h and 5.2 km/h for three minutes at each speed. We analysed and compared the accuracy of EE estimates obtained by Fitbit and ActiGraph against the EE estimates obtained by a metabolic cart,
using with Intra-class correlation (ICC), Bland-Altman analysis and correlation tests.
Results: A significant correlation and a moderate reliability was found between ActiGraph and IC for the incremental test (r = 0.667)(ICC=0.633). There was a significant correlation between Fitbit and IC during the incremental test (r = 0.701), but the reliability was poor at all tested speeds in the treadmill walk. Fitbit
significantly overestimated EE, and ActiGraph underestimated EE compared to IC, but ActiGraph estimates were
more accurate than Fitbit in all tests.
Significance: Compared to IC, Fitbit Charge 4 and ActiGraph wGT3X-BT have reduced accuracy in estimating EE
at slower walking speeds. These findings highlight the need for population-specific algorithms and validation of
activity trackers.
2024-03-01T00:00:00Z