SARS-CoV-2 mRNA vaccination induces stem cell memory T cells specific for the virus that persist up to 6 months.

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

Angiotensin-converting enzyme 2 (ACE2) has been recognized as the entry receptor of the novel severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). Structural and sequence variants in ACE2 gene may affect its expression in different tissues and determine a differential response to SARS-Cov-2 infection and the COVID-19-related phenotype. The present study investigated the genetic variability of ACE2 in terms of single nucleotide variants (SNVs), copy number variations (CNVs), and expression quantitative loci (eQTLs) in a cohort of 268 individuals representative of the general Italian population. The analysis identified five SNVs (rs35803318, rs41303171, rs774469453, rs773676270, and rs2285666) in the Italian cohort. Of them, rs35803318 and rs2285666 displayed a significant different frequency distribution in the Italian population with respect to worldwide population. The eQTLs analysis located in and targeting ACE2 revealed a high distribution of eQTL variants in different brain tissues, suggesting a possible link between ACE2 genetic variability and the neurological complications in patients with COVID-19. Further research is needed to clarify the possible relationship between ACE2 expression and the susceptibility to neurological complications in patients with COVID-19. In fact, patients at higher risk of neurological involvement may need different monitoring and treatment strategies in order to prevent severe, permanent brain injury.

Psoriasis and psoriatic arthritis are multifactorial chronic disorders whose etiopathogenesis essentially derives from the alteration of several signalling pathways and the co‐occurrence of genetic, epigenetic and non‐genetic susceptibility factors that altogether affect the functional and structural property of the skin. Although shared and differential susceptibility genes and molecular pathways are known to contribute to the onset of pathological phenotypes, further research is needed to dissect the molecular causes of psoriatic disease and its progression towards Psoriatic Arthritis. This review will therefore be addressed to explore differences and similarities in the etiopathogenesis and progression of both disorders, with a particular focus on genes involved in the maintenance of the skin structure and integrity (keratins and collagens), modulation of patterns of recognition (through Toll‐like receptors and dectin‐1) and immuno‐inflammatory response (by NLRP3‐dependent inflammasome) to microbial pathogens. In addition, special emphasis will be given to the contribution of epigenetic elements (methylation pattern, non‐coding RNAs, chromatin modifiers and 3D genome organization) to the etiopathogenesis and progression of psoriasis and psoriatic arthritis. The evidence discussed in this review highlights how the knowledge of patients' clinical and (epi)genomic make‐up could be helpful for improving the available therapeutic strategies for psoriasis and psoriatic arthritis treatment.

Far from being weakened, the COVID-19 pandemic has found new strength in another wave of infections with the Omicron variant of SARS-CoV-2.¹ Mutations in the receptor-binding domain region correlate with lower neutralization potency of sera collected from individuals with immunity from previous infection or vaccination,² setting the stage for immune evasion by the mutated virus. Conveniently, T-cell responses are characterized by vast cross-reactivity,³ and cellular immunity is maintained in the face of mutations that may escape antibody recognition.⁴ Still, infection in an immunized individual with a slightly different version of the immunizing pathogen occurs in a context of preexisting immunity, mostly mediated by the cellular component of the immune response. This raises the question of whether the intrinsic cross-reactivity of the spike-specific T cells induced by vaccination or infection will confer a broad enough repertoire for T cells to respond to emerging variants of SARS-CoV-2. Studies on the fine specificity and persistence of spike-specific T cells in individuals with previous SARS-CoV-2 infection have indicated that the cellular response to SARS-CoV-2 offers broad reactivity against spike epitopes,^5,6 and induction of broadly reactive CD4⁺ and CD8⁺ memory cells has been shown to occur after vaccination as well.⁷ In this study, we investigate the T-cell response to the mutated regions of the spike protein from the Omicron BA.1 variant in 61 individuals who received COVID-19 vaccinations and/or with immunity from previous SARS-CoV-2 infection. By also measuring reactivity to the equivalent regions from the ancestral vaccine strain and to the whole spike protein, we estimate the degree of remaining immunity to the SARS-CoV-2 spike protein.

In the big data era, artificial intelligence techniques have been applied to tackle traditional issues in the study of neurodegenerative diseases. Despite the progress made in understanding the complex (epi)genetics signatures underlying neurodegenerative disorders, performing early diagnosis and developing drug repurposing strategies remain serious challenges for such conditions. In this context, the integration of multi-omics, neuroimaging, and electronic health records data can be exploited using deep learning methods to provide the most accurate representation of patients possible. Deep learning allows researchers to find multi-modal biomarkers to develop more effective and personalized treatments, early diagnosis tools, as well as useful information for drug discovering and repurposing in neurodegenerative pathologies. In this review, we will describe how relevant studies have been able to demonstrate the potential of deep learning to enhance the knowledge of neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases through the integration of all sources of biomedical data.