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Age Stratification
The Experts below are selected from a list of 41214 Experts worldwide ranked by ideXlab platform
Maria Luz Cuadrado – One of the best experts on this subject based on the ideXlab platform.
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The clinical, histologic, and genotypic spectrum of SEPN1 -related myopathy
Neurology, 2020Co-Authors: Rocio Villar-quiles, Maja Von Der Hagen, Corinne Métay, Victoria Gonzalez, Sandra Donkervoort, Enrico Bertini, Claudia Castiglioni, Denys Chaigne, Jaume Colomer, Maria Luz CuadradoAbstract:Objective: To clarify the prevalence, long-term natural history, and severity determinants of SEPN1-related myopathy (SEPN1-RM), we analyzed a large international case series. Methods: Retrospective clinical, histologic, and genetic analysis of 132 pediatric and adult patients (2-58 years) followed up for several decades. Results: The clinical phenotype was marked by severe axial muscle weakness, spinal rigidity, and scoliosis (86.1%, from 8.9 ± 4 years), with relatively preserved limb strength and previously unreported ophthalmoparesis in severe cases. All patients developed respiratory failure (from 10.1±6 years), 81.7% requiring ventilation while ambulant. Histopathologically, 79 muscle biopsies showed large variability, partly determined by site of biopsy and Age. Multi-minicores were the most common lesion (59.5%), often associated with mild dystrophic features and occasionally with eosinophilic inclusions. Identification of 65 SEPN1 mutations, including 32 novel ones and the first pathogenic copy number variation, unveiled exon 1 as the main mutational hotspot and revealed the first genotype-phenotype correlations, bi-allelic null mutations being significantly associated with disease severity (p = 0.017). SEPN1-RM was more severe and progressive than previously thought, leading to loss of ambulation in 10% of cases, systematic functional decline from the end of the third decade, and reduced lifespan even in mild cases. The main prognosis determinants were scoliosis/respiratory manAgement, SEPN1 mutations, and body mass abnormalities, which correlated with disease severity. We propose a set of severity criteria, provide quantitative data for outcome identification, and establish a need for Age Stratification. Conclusion: Our results inform clinical practice, improving diagnosis and manAgement, and represent a major breakthrough for clinical trial readiness in this not so rare disease.
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the clinical histologic and genotypic spectrum of sepn1 related myopathy a case series
Neurology, 2020Co-Authors: Rocio Nur Villarquiles, Maja Von Der Hagen, Corinne Métay, Victoria Gonzalez, Sandra Donkervoort, Enrico Bertini, Claudia Castiglioni, Denys Chaigne, Jaume Colomer, Maria Luz CuadradoAbstract:Objective To clarify the prevalence, long-term natural history, and severity determinants of SEPN1-related myopathy (SEPN1-RM), we analyzed a large international case series. Methods Retrospective clinical, histologic, and genetic analysis of 132 pediatric and adult patients (2–58 years) followed up for several decades. Results The clinical phenotype was marked by severe axial muscle weakness, spinal rigidity, and scoliosis (86.1%, from 8.9 ± 4 years), with relatively preserved limb strength and previously unreported ophthalmoparesis in severe cases. All patients developed respiratory failure (from 10.1±6 years), 81.7% requiring ventilation while ambulant. Histopathologically, 79 muscle biopsies showed large variability, partly determined by site of biopsy and Age. Multi-minicores were the most common lesion (59.5%), often associated with mild dystrophic features and occasionally with eosinophilic inclusions. Identification of 65 SEPN1 mutations, including 32 novel ones and the first pathogenic copy number variation, unveiled exon 1 as the main mutational hotspot and revealed the first genotype–phenotype correlations, bi-allelic null mutations being significantly associated with disease severity (p = 0.017). SEPN1-RM was more severe and progressive than previously thought, leading to loss of ambulation in 10% of cases, systematic functional decline from the end of the third decade, and reduced lifespan even in mild cases. The main prognosis determinants were scoliosis/respiratory manAgement, SEPN1 mutations, and body mass abnormalities, which correlated with disease severity. We propose a set of severity criteria, provide quantitative data for outcome identification, and establish a need for Age Stratification. Conclusion Our results inform clinical practice, improving diagnosis and manAgement, and represent a major breakthrough for clinical trial readiness in this not so rare disease.
Lu Shen – One of the best experts on this subject based on the ideXlab platform.
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multiple visual rating scales based on structural mri and a novel prediction model combining visual rating scales and Age Stratification in the diagnosis of alzheimer s disease in the chinese population
Frontiers in Neurology, 2019Co-Authors: Zhenhua Yuan, Chuzheng Pan, Tingting Xiao, Menghui Liu, Weiwei Zhang, Bin Jiao, Xinxiang Yan, Beisha Tang, Lu ShenAbstract:Objective: To explore the value of multiple visual rating scales based on structural MRI in the diagnosis of Alzheimer’s disease (AD) in the Chinese population. Materials and Methods: One hundred patients with AD and 100 Age– and gender- matched cognitively normal controls were enrolled in this study. All the participants underwent neuropsychological tests and a structural MRI scan of the brain, among them, 42 AD cases and 47 cognitively normal controls also underwent 3D-T1 weighted sequence used for the analysis of voxel-based morphometry (VBM). The AD cases were divided into mild and moderate–severe groups according to the mini-mental state examination. Each participant was evaluated by two trained radiologists who were blind to the clinical information, according to the six visual rating scales, including for medial temporal lobe atrophy (MTA), posterior atrophy (PA), anterior temporal (AT), orbitofrontal (OF) cortex, anterior cingulate (AC), and fronto-insula (FI). Finally, we estimated the relationship between the visual rating scales and the volume of corresponding brain regions, using correlation analysis, and evaluated the specificity and sensitivity of every single scale and combination of multiple scales in the diagnosis of AD, using a receiver operating characteristic (ROC) curve and establishing a logistic regression model. Results: The optimal cutoff of all six visual rating scales for distinguishing AD cases from normal controls was 1.5. Using automated classification based on all six rating scales, the accuracy for distinguishing AD cases from healthy controls ranged from 0.68-0.80 (for mild AD) and 0.77-0.90 (for moderate–severe AD), respectively. A diagnostic prediction model with a combination of MTA and OF results was made as follows: Score = BMTA(score) + BOF(score) –1.58 (Age <65 years); Score = BMTA(score) + BOF(score) –4.09 (Age ≥65 years). The model was superior to any single visual rating scale in the diagnosis of mild AD (P <0.05). Conclusion: Each of the six visual rating scales could be applied to the diagnosis of moderate-severe AD alone in the Chinese population. A prediction model of the combined usAge of MTA, OF, and Age Stratification for the early diagnosis of AD was preliminarily established.
Zhenhua Yuan – One of the best experts on this subject based on the ideXlab platform.
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multiple visual rating scales based on structural mri and a novel prediction model combining visual rating scales and Age Stratification in the diagnosis of alzheimer s disease in the chinese population
Frontiers in Neurology, 2019Co-Authors: Zhenhua Yuan, Chuzheng Pan, Tingting Xiao, Menghui Liu, Weiwei Zhang, Bin Jiao, Xinxiang Yan, Beisha Tang, Lu ShenAbstract:Objective: To explore the value of multiple visual rating scales based on structural MRI in the diagnosis of Alzheimer’s disease (AD) in the Chinese population. Materials and Methods: One hundred patients with AD and 100 Age– and gender- matched cognitively normal controls were enrolled in this study. All the participants underwent neuropsychological tests and a structural MRI scan of the brain, among them, 42 AD cases and 47 cognitively normal controls also underwent 3D-T1 weighted sequence used for the analysis of voxel-based morphometry (VBM). The AD cases were divided into mild and moderate–severe groups according to the mini-mental state examination. Each participant was evaluated by two trained radiologists who were blind to the clinical information, according to the six visual rating scales, including for medial temporal lobe atrophy (MTA), posterior atrophy (PA), anterior temporal (AT), orbitofrontal (OF) cortex, anterior cingulate (AC), and fronto-insula (FI). Finally, we estimated the relationship between the visual rating scales and the volume of corresponding brain regions, using correlation analysis, and evaluated the specificity and sensitivity of every single scale and combination of multiple scales in the diagnosis of AD, using a receiver operating characteristic (ROC) curve and establishing a logistic regression model. Results: The optimal cutoff of all six visual rating scales for distinguishing AD cases from normal controls was 1.5. Using automated classification based on all six rating scales, the accuracy for distinguishing AD cases from healthy controls ranged from 0.68-0.80 (for mild AD) and 0.77-0.90 (for moderate–severe AD), respectively. A diagnostic prediction model with a combination of MTA and OF results was made as follows: Score = BMTA(score) + BOF(score) –1.58 (Age <65 years); Score = BMTA(score) + BOF(score) –4.09 (Age ≥65 years). The model was superior to any single visual rating scale in the diagnosis of mild AD (P <0.05). Conclusion: Each of the six visual rating scales could be applied to the diagnosis of moderate-severe AD alone in the Chinese population. A prediction model of the combined usAge of MTA, OF, and Age Stratification for the early diagnosis of AD was preliminarily established.