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Hypokalemic periodic paralysis one of the channelopathy disorders with low serum potassium level, clinically presenting as acute onset extremity weakness. In most cases, the cause of the hypokalemia is familial, but rarely hypokalemic periodic paralysis occurs secondary to other diseases including endocrinopathies, renal disorders, gastrointestinal loss. We report a patient with no known underlying diseases, who were diagnosed with sporadic hypokalemic periodic paralysis accompanied by neurogenic diabetes insipidus.
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Hypokalemic periodic paralysis one of the channelopathy disorders with low serum potassium level, clinically presenting as acute onset extremity weakness. In most cases, the cause of the hypokalemia is familial, but rarely hypokalemic periodic paralysis occurs secondary to other diseases including endocrinopathies, renal disorders, gastrointestinal loss. We report a patient with no known underlying diseases, who were diagnosed with sporadic hypokalemic periodic paralysis accompanied by neurogenic diabetes insipidus.
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Background@#and Purpose: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that mainly affects the pyramidal motor system. However, recent studies have suggested that degeneration of the extramotor system plays a role in the disability experienced by patients with ALS. We investigated the local shape changes and mean volumes of the subcortical nuclei in sporadic ALS patients with preserved cognition. @*Methods@#The participants comprised 32 patients with ALS and 43 age- and sex-matched healthy controls. Three-dimensional T1-weighted structural images were acquired. Surfacebased vertex analysis was performed with fully automated segmentation of both amygdalae, hippocampi, caudate nuclei, nuclei accumbens, putamina, pallida, and thalami, and the brainstem. The scalar distances from the mean surfaces of the individual subcortical nuclei were compared between groups, and correlations of the local shape distances with initial Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALS-FRS-R) scores and the delta FRS-R and with the disease duration were analyzed. @*Results@#ALS patients showed regional shape contractions on the lateral surfaces of both pallida, the lateroposterior surface of the right putamen, and the anterior basal surface of the right accumbens. Delta FRS-R scores were negatively correlated with local shape distances in the right hippocampus and the putamina. However, the initial ALS-FRS-R score and disease duration were not correlated with local shape distances. @*Conclusions@#Subcortical gray-matter structures are involved in the neurodegenerative process of ALS before cognitive impairment becomes evident.
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Background@#and Purpose: Many elderly patients are unable to actively stand up by themselves and have contraindications to performing the head-up tilt test (HUTT). We aimed to develop screening algorithms for diagnosing orthostatic hypotension (OH) before performing the HUTT. @*Methods@#This study recruited 663 patients with orthostatic intolerance (78 with and 585 without OH, as confirmed by the HUTT) and compared their clinical characteristics. Univariate and multivariate analyses were performed to investigate potential predictors of an OH diagnosis. Machine-learning algorithms were applied to determine whether the accuracy of OH prediction could be used for screening OH without performing the HUTT. @*Results@#Differences between expiration and inspiration (E-I differences), expiration:inspiration ratios (E:I ratios), and Valsalva ratios were smaller in patients with OH than in those without OH. The univariate analysis showed that increased age and baseline systolic blood pressure (BP) as well as decreased E-I difference, E:I ratio, and Valsalva ratio were correlated with OH. In the multivariate analysis, increased baseline systolic BP and decreased Valsalva ratio were found to be independent predictors of OH. Using those variables as input features, the classification accuracies of the support vector machine, k-nearest neighbors, and random forest methods were 84.4%, 84.4%, and 90.6%, respectively. @*Conclusions@#We have identified clinical parameters that are strongly associated with OH. Machine-learning analysis using those parameters was highly accurate in differentiating OH from non-OH patients. These parameters could be useful screening factors for OH in patients who are unable to perform the HUTT.
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No abstract available.