Alzheimer's disease (AD) is considered one of the most spouting elderly diseases. In 2015, AD is reported the US's sixth cause of death. Substantially, non-invasive imaging is widely employed to provide biomarkers supporting AD screening, diagnosis, and progression. In this study, Gaussian descriptors-based features are proposed to be efficient new biomarkers using Magnetic Resonance Imaging (MRI) T-weighted images to differentiate between Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and Normal controls (NC). Several Gaussian map-based features are extracted such as Gaussian shape operator, Gaussian curvature, and mean curvature. The aforementioned features are then introduced to the Support Vector Machine (SVM). They were, first, calculated separately for the Hippocampus and Amygdala. Followed by the fusion of the features. Moreover, Fusion of the regions before feature extraction was also employed. Alzheimer's disease Neuroimaging Initiative (ADNI) dataset, formed of 45, 55, and 65 cases for AD, MCI, and NC respectively, is appointed in this study. The shape operator feature outperformed the other features, with 74.6%, and 98.9% accuracy in the case of normal vs. abnormal, and AD vs. MCI classification respectively.

Idiopathic inflammatory myopathies (IIMs) confer a significant risk of disability and poor quality of life, though fatigue, an important contributing factor, remains under-reported in these individuals. We aimed to compare and analyze differences in visual analog scale (VAS) scores (0-10 cm) for fatigue (VAS-F) in patients with IIMs, non-IIM systemic autoimmune diseases (SAIDs), and healthy controls (HCs). We performed a cross-sectional analysis of the data from the COVID-19 Vaccination in Autoimmune Diseases (COVAD) international patient self-reported e-survey. The COVAD survey was circulated from December 2020 to August 2021, and details including demographics, COVID-19 history, vaccination details, SAID details, global health, and functional status were collected from adult patients having received at least one COVID-19 vaccine dose. Fatigue experienced 1 week prior to survey completion was assessed using a single-item 10 cm VAS. Determinants of fatigue were analyzed in regression models. Six thousand nine hundred and eighty-eight respondents (mean age 43.8 years, 72% female; 55% White) were included in the analysis. The overall VAS-F score was 3 (IQR 1-6). Patients with IIMs had similar fatigue scores (5, IQR 3-7) to non-IIM SAIDs [5 (IQR 2-7)], but higher compared to HCs (2, IQR 1-5; P < 0.001), regardless of disease activity. In adjusted analysis, higher VAS-F scores were seen in females (reference female; coefficient -0.17; 95%CI -0.21 to -13; P < 0.001) and Caucasians (reference Caucasians; coefficient -0.22; 95%CI -0.30 to -0.14; P < 0.001 for Asians and coefficient -0.08; 95%CI -0.13 to 0.30; P = 0.003 for Hispanics) in our cohort. Our study found that patients with IIMs exhibit considerable fatigue, similar to other SAIDs and higher than healthy individuals. Women and Caucasians experience greater fatigue scores, allowing identification of stratified groups for optimized multidisciplinary care and improve outcomes such as quality of life.