Assessment of swallowing performance in patients with neurodegenerative disease: A hierarchical cluster analysis.

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.

Validity and Reliability Study of Online Cognitive Tracking Software (BEYNEX).

Background: Detecting cognitive impairment such as Alzheimer's disease early and tracking it over time is essential for individuals at risk of cognitive decline. Objective: This research aimed to validate the Beynex app's gamified assessment tests and the Beynex Performance Index (BPI) score, which monitor cognitive performance across seven categories, considering age and education data. Methods: Beynex test cut-off scores of participants (n = 91) were derived from the optimization function and compared to the Montreal Cognitive Assessment (MoCA) test. Validation and reliability analyses were carried out with data collected from an additional 214 participants. Results: Beynex categorization scores showed a moderate agreement with MoCA ratings (weighted Cohen's Kappa = 0.48; 95% CI: 0.38-0.60). Calculated Cronbach's Alpha indicates good internal consistency. Test-retest reliability analysis using a linear regression line fitted to results yielded R∧2 of 0.65 with a 95% CI: 0.58, 0.71. Discussion: Beynex's ability to reliably detect and track cognitive impairment could significantly impact public health, early intervention strategies and improve patient outcomes.

Demyelinating Disease of the Central Nervous System Concurrent With COVID-19.

Neurological diseases related to coronavirus disease-2019 (COVID-19) are increasingly reported. We report here three cases that presented with subtle neurologic findings manifesting within a range of 15 days to four months after their COVID-19 diagnoses. Magnetic resonance imaging showed acute multifocal periventricular and subcortical demyelinating lesions. Some of the lesions showed contrast enhancement and diffusion restriction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR was found in the cerebrospinal fluid of just one patient. All patients received intravenous methylprednisolone therapy. In this report, we aim to discuss the aspects of possible COVID-19-related demyelination that support a diagnosis of multiple sclerosis (MS) or acute disseminated encephalomyelitis (ADEM).