👤 Fabio Castellana

Early diagnosis of Alzheimer's disease (AD) and related dementias remains challenging because no single biomarker sufficiently captures the complex and multifactorial nature of the underlying pathology. In recent years, multimodal artificial intelligence (AI) models capable of integrating heterogeneous data sources-such as neuroimaging, fluid biomarkers, genetics, and cognitive assessments-have emerged as a promising strategy to improve early detection and risk stratification. We performed a PRISMA-guided systematic review (PROSPERO: CRD420251049848) of studies published from 2010 to 2025. We included 27 peer-reviewed studies applying AI/ML to ≥2 biomarker modalities for diagnostic classification or prognostic prediction (e.g., MCI-to-AD conversion), with an explicit emphasis on multimodal designs that incorporated at least one minimally invasive and/or widely deployable modality (e.g., cognitive tests, blood-based biomarkers, APOE/genetics, retinal imaging, or routine clinical features). Risk of bias was assessed using QUADAS-2. Across the 27 included studies, multimodal AI models generally outperformed the best unimodal baselines, particularly when combining complementary biological information (e.g., imaging with molecular or clinical features). Diagnostic tasks more often achieved high discrimination (frequently AUCs in the ~0.85-0.95 range under internal validation), whereas prognostic prediction-especially MCI-to-AD conversion-remained more challenging (typically ~0.75-0.85 AUC in the best-performing models). However, evidence for generalizability was limited, as external validation was uncommon and QUADAS-2 frequently highlighted concerns in the Index Test domain related to overfitting risk and incomplete validation. Overall, multimodal AI provides a more comprehensive representation of AD/MCI-related pathology than unimodal approaches and can improve early diagnostic classification and, to a lesser extent, prognostic prediction. However, translation to clinical practice is still constrained by limited external validation and heterogeneous reporting, which hamper generalizability and clinical trust. Future work should prioritize prospective multi-center studies, robust external validation, and transparent reporting (including interpretability analyses) to support real-world deployment. Show less

Cardiomyopathies caused by double gene mutations are rare but conferred a remarkably increased risk of end-stage progression, arrhythmias, and poor outcome. Compound genetic mutations leading to complex phenotype in the setting of cardiomyopathies represent an important challenge in clinical practice, and genetic tests allow risk stratification and personalized clinical management of patients. We report a case of a 50-year-old woman with congestive heart failure characterized by dilated cardiomyopathy, diffuse coronary disease, complete atrioventricular block, and missense mutations in cardiac myosin-binding protein C (MYBPC3) and myopalladin (MYPN). We discuss the plausible role of genetic profile in phenotype determination. Show less

To orchestrate the genomic response to cellular stress signals, p53 recognizes and binds to DNA containing specific and well-characterized p53-responsive elements (REs). Differences in RE sequences can strongly affect the p53 transactivation capacity and occur even between closely related species. Therefore, the identification and characterization of a species-specific p53 Binding sistes (BS) consensus sequence and of the associated target genes may help to provide new insights into the evolution of the p53 regulatory networks across different species. Although p53 functions were studied in a wide range of species, little is known about the p53-mediated transcriptional signature in Danio rerio. Here, we designed and biochemically validated a computational approach to identify novel p53 target genes in Danio rerio genome. Screening all the Danio rerio genome by pattern-matching-based analysis, we found p53 RE-like patterns proximal to 979 annotated Danio rerio genes. Prioritization analysis identified a subset of 134 candidate pattern-related genes, 31 of which have been investigated in further biochemical assays. Our study identified runx1, axin1, traf4a, hspa8, col4a5, necab2, and dnajc9 genes as novel direct p53 targets and 12 additional p53-controlled genes in Danio rerio genome. The proposed combinatorial approach resulted to be highly sensitive and robust for identifying new p53 target genes also in additional animal species. Show less