Claudio Babiloni, Susanna Lopez, Giuseppe Noce+34 more · 2026 · Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · Elsevier · added 2026-04-24
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 Show more
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
Breast cancer (BC) progression to metastatic disease is the leading cause of death in women worldwide. Metastasis is driven by cancer stem cells (CSCs) and signals from their microenvironment. Interle Show more
Breast cancer (BC) progression to metastatic disease is the leading cause of death in women worldwide. Metastasis is driven by cancer stem cells (CSCs) and signals from their microenvironment. Interleukin (IL) 30 promotes BC progression, and its expression correlates with disease recurrence and mortality. Whether it acts by regulating BCSCs is unknown and could have significant therapeutic implications. Human (h) and murine (m) BCSCs were tested for their production of and response to IL30 by using flow cytometry, confocal microscopy, proliferation and sphere-formation assays, and PCR array. Immunocompetent mice were used to investigate the role of BCSC-derived IL30 on tumor development and host outcome. TCGA PanCancer and Oncomine databases provided gene expression data from 1084 and 75 hBC samples, respectively, and immunostaining unveiled the BCSC microenvironment. hBCSCs constitutively expressed IL30 as a membrane-anchored glycoprotein. Blocking IL30 hindered their proliferation and self-renewal efficiency, which were boosted by IL30 overexpression. IL30 regulation of immunity gene expression in human and murine BCSCs shared a significant induction of Constitutive expression of membrane-bound IL30 regulates BCSC viability by juxtacrine signals and Show less