Dementia is a major issue for healthcare systems worldwide, necessitating the development of creative and effective strategies for its management. This paper examines the use of Artificial Intelligence (AI) technologies in caring for and managing people with dementia. AI-driven solutions can improve diagnosis, personalize care, optimize medication management, and reduce the burden on caregivers. This paper discusses the implementation of an AI-based framework for creating videos related to people’s memories to support train-therapy or travel- therapy, a non-pharmacological intervention for Alzheimer’s disease patients

Dyslipidemia (Dys) is a disease characterised by abnormally high levels of lipids in the 1 blood. It causes the deposits of lipids on the arterial wall, leading to the insurgence of atherosclerosis, 2 i.e. the increased thickness of arterial walls, which finally increases the risk of cardiovascular diseases. 3 Dyslipidemia is as comorbidity of Type 2 Diabetes Mellitus (T2DM). A set of genes has been associated 4 with dyslipidemia, but examining the regulation of such genes in age and sex is still an open research 5 field. In this study, starting from publicly available databases, we select genes associated with 6 dyslipidemia and we analyse their basal level changes by means of age and sex. Also studying the 7 networks associated to the genes, we identify the rewiring changes showing that there exists a set of 8 genes whose expression changes with age in: artery tibial, artery aorta and blood. Finally, we report 9 about changes associated to gender atributes in the extracted data

Recently, many independent research project focus on the study of the molecular basis of aging processes. I parallel, the different progression of many diseaes between sex is a hot topic research area. These studies require many data, models and tools for inferring aging and sex specific molecular machinaries. Among the others, the Genotype-Tissue Expression (GTEx) database is one of the preminent resources for the analysis of expression data related to tissues, sex and age. The current version of the database has a lot of querying interface that enable many analysis centered on the expression of genes on tissues. Despite this the database lacks on the analysis at sex/age level, thus the researcher has to download data and then write queries by hand. Nevertheless it lacks on the integration with existing protein interaction data. Therefore, the need for the introduction of tools enabling easy access and powerful analysis capabilities (i.e. state of the art network based analysis and integration), arises. We here present NOMA-DB a framework for the analysis of age related genes based on the GTEx database that enable easy querying at sex/age level, network based analysis. The framework is based on wrapping the GTEx database and on building an application logic level on top of existing data. The current version enables the analysis of genes by tissue, gene and age, thus it may be used in potentially future directions of analysis towards better comprehension of aging/sex-related molecular machineries based on the analysis of expression dat

Dyslipidemia (Dys) is a disease characterised by abnormally high levels of lipids in the blood. It causes the deposits of lipids on the arterial wall leading to the insurgence of atherosclerosis, i.e. the increased thickness of arterial walls, which finally increases the risk of cardiovascular diseases. Dyslipidemia is a comorbidity of Type 2 Diabetes Mellitus (T2DM). A set of genes has been associated with dyslipidemia, but examining the regulation of such genes in age and sex is still an open research field. In this study, we select from publicly available databases the genes associated with dyslipidemia and analyse changes in the basal level of such genes by age and sex. Moreover, we also distinct differential rewiring of the related gene association network in males and females. Our results show that there exist a set of genes whose expression changes with age in artery tibial, artery aorta and blood. Moreover, there exist significant differences between sex.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

Protein Contact Network (PCN) is an emerging paradigm for modelling protein structure. A common approach to interpreting such data is through network-based analyses. It has been shown that clustering analysis may discover allostery in PCN. Nevertheless Network Embedding has shown good performances in discovering hidden communities and structures in network. In this work, we compare some approaches for graph embedding with respect to some classical clustering approaches for annotating protein structures.

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal per- formances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients at- tributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

Vaccination has been the most effective way to control the outbreak of the Covid pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We learn the necessity of defining a vaccination distribution strategies with regard to Covid that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, spread of infections, symptoms, hospitalization and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of Covid variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences which can be useful in other pandemic or epidemiological contexts.

There is increasing evidence that many molecular processes exhibit differences with age 1 and sex. Such differences produces also differences in the insurgence and progression of many 2 complex diseases. For instances, demographic data on insurgence of comorbidities of mellitus 3 diabetes, on lethality of COVID-19, and on some cancers, shows differences between sex and age 4 groups. Therefore, the growing interest on such area requires the management of related data as 5 well as the development of algorithms and tool for the analysis. The availability of omics data 6 annotated with metadata related to age and sex is mandatory for building pipeline of the analysis. 7 The number of databases containing data related to age and sex is hencefort growing. We here show 8 some databases and tools storing such data. Finally, future research direction are highlighte

Acquired immunodeficiency syndrome (AIDS) has transitioned from a progressive, fatal disease to a chronic, manageable disease thanks to better defining of antiretroviral therapy, hence contributing to increased life expectancy. In parallel, a growing number of subjects without overt clinical signs of disease but living with chronic HIV infection (people living with HIV, PLWH) are experiencing early cardiovascular disease worldwide. However, a progressive increase in the prevalence of multiple comorbid diseases has been reported as these patients age, including cardiovascular disease (CVD). Cardiovascular mortality can be either directly related to the viral infection, a progressive reduction of response to the antiretroviral therapy, chronic inflammation or lifestyle-related. Cardiovascular ageing represents a relevant issue in the management of HIV-infected patients. Although the exact pathophysiological mechanism that leads PLWH to develop cardiovascular disease is not entirely understood, there is substantial evidence that they accumulate age-related conditions earlier than the general population. Furthermore, since the proportion of PLWH growing older than 50 years has progressively increased, this results in a complex interaction between disease-related pathophysiology and the exposition of a growing burden of cardiovascular risk factors. For this reason, we performed a systematic review of the genes most frequently associated with ageing in HIV-infected subjects, followed by a bioinformatic analysis to explore the biological impact of the genes related to ageing.