Anti-LGI1 Autoimmune Encephalitis in a Patient with Rheumatoid Arthritis and MGUS.

Background: Anti-leucine-rich glioma inactivated 1 limbic encephalitis (anti-LGI1 LE) is one of the most frequent autoimmune encephalitis, commonly coexisting with other autoimmune diseases. Rheumatoid arthritis (RA) and monoclonal gammopathy of unknown significance (MGUS) are commonly associated with autoimmune phenomena. However, neither RA nor MGUS have been described in the literature to date as coexisting with anti-LGI1 LE.

Progression of subclinical atherosclerosis in ankylosing spondylitis: a 10-year prospective study.

Chronic systemic inflammation contributes to increased CVD burden in Ankylosing Spondylitis (AS). Since long-term follow-up data on subclinical atherosclerosis acceleration are lacking, we examined its progression in contemporary AS patients during 10 years. Fifty-three (89% male, aged 50.

Alterations in Retinal Vessel Diameters in Patients with Retinal Vein Occlusion before and after Treatment with Intravitreal Ranibizumab.

: To investigate the alterations of retinal vessel diameters in patients with macular edema secondary to retinal vein occlusion (RVO), before and after treatment with intravitreal ranibizumab. : Digital retinal images were obtained from 16 patients and retinal vessel diameters were measured before and three months after treatment with intravitreal ranibizumab with validated software to determine central retinal arteriolar and venular equivalents, as well as arteriolar to venular ratio. : In 17 eyes of 16 patients with macular edema secondary to RVO (10 with branch RVO and 6 with central RVO) aged 67 ± 10.

Time of blood pressure in target range in acute ischemic stroke.

Objective: The purpose of this study was to investigate the association of blood pressure (BP) time-in-target range (TTR) derived from 24-h ambulatory BP monitoring (ABPM) during the acute phase of ischemic stroke (AIS), with the severity of stroke and its predictive value for the 3 months outcome.

Methods: A total of 228 AIS patients (prospective multicenter follow-up study) underwent ABPM every 20 min within 48 h from stroke onset using an automated oscillometric device. Clinical and laboratory findings were recorded.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.

: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models.

COVLIAS 1.0 vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods.

Inpatient Cost of Stroke Care in Greece: Preliminary Results of the Web-Based "SUN4P" Registry.

Unlabelled: The aim of this study was to calculate the average operational cost per sub-type of stroke patient and to investigate cost drivers (e.g. ALoS, NIHSS score, age) correlated to cost.

Assessing Staff's and Stroke Patients' Experiences in 8 Hospitals in Greece: Results from a Prospective Multi-Center Study ("SUN4Patients").

Unlabelled: To assess stroke patient-reported experiences and hospital staff experiences, during hospital stay.

Methods: Stroke patient-reported experiences (n=387) were recorded using the translated and culturally adapted NHS-Stroke Questionnaire into Greek and staff experiences (n=236) were investigated using the Compassion Satisfaction and Burnout subscales of the ProQOL questionnaire.

Results: Staff's mean compassion satisfaction score was 39.

Stroke Units Necessity for Patients, Web-Based "SUN4P" Registry: Descriptive Characteristics of the Population.

Unlabelled: The aim of this study was to present the descriptive characteristics of the Stroke Units Necessity for Patients (SUN4P) registry.

Methods: The study population derived from the web-based SUN4P registry included 823 patients with first-ever acute stroke. Descriptive statistics were used to present patients' characteristics.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020-2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review.

: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.

: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias.

PCSK9/LDLR System and Rheumatoid Arthritis-Related Atherosclerosis.

Rheumatoid arthritis (RA) is associated with the emergence of cardiovascular disease, while chronic inflammation is considered a common denominator for their parallel progression. The Proprotein convertase subtilisin/kexin type 9 (PCSK9)/LDL-Receptor (LDLR) system is of high importance during atherogenesis, via regulating the clearance of LDL from the circulation; nevertheless the role of this molecular mechanism during RA-related atheromatosis is not known. Herein, high-resolution ultrasound measurements for arterial hypertrophy, atheromatosis and arterial stiffness as well as comprehensive biochemical profiling were performed in 85 RA patients.