👤 Matteo Pardini

We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patients with mild cognitive impairment (ADMCI) using resting-state electroencephalographic (rsEEG) biomarkers enriched with APOE genotype, sex, age, and educational attainment data. The study analyzed datasets from 63 ADMCI patients obtained from an international archive. The ML algorithms included Simple Logistic Regression, Model Trees, Logistic Regression, K-nearest neighbor, and Support Vector Machine. Input features comprised lobar rsEEG source activities across delta (<4 Hz) to alpha (≈10-12 Hz) bands, cerebrospinal fluid (CSF Aβ1-42/p-tau), and structural magnetic resonance imaging (sMRI) biomarkers. Cognitive decline was assessed over a 1-year follow-up ("stable" vs. "decliner") based on Mini-Mental State Examination (MMSE) scores. The four independent ML algorithms accurately predicted changes in the MMSE score over a 1-year follow-up, with accuracies of 77-78% in ADMCI participants aged ≥ 70 years and 74-77% in those aged < 70 years. These findings suggest that rsEEG biomarkers in ADMCI patients may not only reveal underlying pathophysiological mechanisms affecting cortical arousal and vigilance but also hold predictive value for cognitive outcomes. Show less

Dementia with Lewy Bodies (DLB) is a neurodegenerative disorder characterised by α-synuclein pathology, causing cognitive decline, motor and non-motor symptoms. This review explores the clinical and neuropathological heterogeneity of DLB, which complicates its early diagnosis, prognosis, and treatment. The staging of Lewy body (LB) pathology varies, with both brain-first and body-first hypotheses suggesting different origins and pathways for disease progression. Co-pathologies, such as amyloid-β plaques, tau tangles, and cerebrovascular changes, further influence the clinical presentation and rate of disease progression in DLB patients, contributing to significant variability. In this review, the role of genetic factors, particularly APOE ε4 and GBA mutations, in shaping DLB's clinical and pathological diversity is also emphasized. Heterogeneous manifestations, including REM sleep behavior disorder (RBD), mild cognitive impairment, and psychiatric-onset DLB, highlight the need for improved biomarkers to guide early diagnosis. Neuroimaging techniques such as [ Overall, the paper explores the complexity of DLB's heterogeneous nature, advocating for deeper exploration of its diverse pathological pathways, genetic predispositions, and clinical profiles to improve diagnosis and treatment outcomes. Understanding this heterogeneity is crucial for the development of personalized therapeutic strategies and effective management of the disease. Show less

The aim of our study is to investigate in vitro and in vivo MC4R as a novel target in melanoma using the selective antagonist ML00253764 (ML) alone and in combination with vemurafenib, a B-rafV600E inhibitor. The human melanoma B-raf mutated A-2058 and WM 266-4 cell lines were used. An MC4R null A-2058 cell line was generated using a CRISPR/Cas9 system. MC4R protein expression was analysed by western blotting, immunohistochemistry, and immunofluorescence. Proliferation and apoptotic assays were performed with ML00253764, whereas the synergism with vemurafenib was evaluated by the combination index (CI) and Loewe methods. ERK1/2 phosphorylation and BCL-XL expression were quantified by western blot. In vivo experiments were performed in Athymic Nude-Foxn1 Show less

Restless legs syndrome (RLS) is a frequent neurological disorder which is presented in idiopathic and secondary form. Idiopathic RLS is associated with common genetic variants in four chromosomal regions. Recently, multiple sclerosis (MS) was identified as a common cause for secondary RLS. The aim of our study was to evaluate the prevalence of RLS among Czech patients with MS and to further analyze the impact of known genetic risk factors for RLS in patients with MS. Each patient underwent a semi-structured interview. A patient was considered to be affected by RLS if all four standard criteria had ever been met in their lifetime. The sample was genotyped using 12 single nucleotide polymorphisms within the four genomic regions, which were selected according to the results of previous genome-wide association studies. A total of 765 subjects with MS were included in the study and the diagnosis of RLS was confirmed in 245 subjects (32.1%, 95%CI 28.7-35.4%). The genetic association study included 642 subjects; 203 MS patients with RLS were compared to 438 MS patients without RLS. No significant association with MEIS 1, BTBD9, and PTPRD gene variants was found despite sufficient statistical power for the first two loci. There was a trend for association with the MAP2K5/SCOR1 gene - the best model for the risk allele was the recessive one (p nominal=0.0029, p corrected for four loci and two models=0.023, odds ratio=1.60). We confirmed that RLS prevalence was high in patients with multiple sclerosis, but this form did not share all genetic risk variants with idiopathic RLS. Show less