Ageing and Disability According to the Perspective of Clinical Psychology of Disability

The progressive ageing of the global population is an important anthropological and social phenomenon, and it is due to the overall increasing of life expectancy and the overall increasing of health and living conditions, even if with various trends and speeds in various countries all over the world [...]

Exploring physical and psychosocial well-being and self-awareness as a new frontier in active aging

The knowledge about the effects of exercise, physical and sport activities on general well-being has been advanced thanks to pioneering studies in several medical conditions and in rehabilitation from the 1980s onwards [1]. However, a noteworthy contribution to improving standard tools hallowing to measure of how much exercise, physical and sport activities could affect the quality of life (QoL) of the elderly and adults came mainly from the studies on their effects on depression and mental health

Teaching in Times of the COVID-19 Pandemic: A Pilot Study on Teachers’ Self-Esteem and Self-Efficacy in an Italian Sample

The aim of this research was to investigate the impact of the COVID-19 pandemic on teachers, particularly on their self-esteem and self-efficacy, their difficulty in the transition to distance learning, the difficulty of students, and specially of students with learning disabilities (LDs students), as perceived by teachers. 226 teachers were invited to complete an online questionnaire. Our results showed lower self-esteem and lower self-efficacy by the teachers compared with the normative sample. Self-esteem and self-efficacy also decrease in teachers with greater service seniority at work. Teachers perceived a greater difficulty in students than in their own difficulty. The concentration of the school system’s efforts on the massive and, for long periods, exclusive organisation of distance learning risks favouring only cognitive aspects to the detriment of affective dynamics. This aspect could make teaching more complex for teachers and learning poorer for students, impoverishing the complex relational process that forms the basis of the learning process

The use of distance learning and e-learning in students with learning disabilities: A review on the effects and some hint of analysis on the use during covid-19 outbreak

Even if the use of distance learning and E-learning has a long tradition all over the world and both have been used to keep in contact with students and to provide lessons, support and learning materials, there is an open debate on the balance between advantages and disadvantages in the use of distance learning. This debate is even more central in their use to support students with Learning Disabilities (LDs), an overarching group of neurodevelopmental disorders that affect more than 5% of students. The current COVID-19 outbreak caused school closures and the massive use of E-learning all over the world and it put higher attention on the debate of the effects of E-learning. This paper aims to review papers that investigated the positive and negative effects of the use of Distance Learning and E-learning in students with LDs. We conducted a literature review on the relationship between Distance Learning, E-learning and Learning Disabilities, via Scopus, Eric and Google Scholar electronic database, according to Prisma Guidelines. The findings are summarized using a narrative, but systematic, approach. According to the data resulting from the papers, we also discuss issues to be analyzed in future research and in the use of E-learning during the current pandemic of COVID-19

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Intra- and inter-individual genetic differences in gene expression

Genetic variation is known to influence the amount of mRNA produced by a gene. Given that the molecular machines control mRNA levels of multiple genes, we expect genetic variation in the components of these machines would influence multiple genes in a similar fashion. In this study we show that this assumption is correct by using correlation of mRNA levels measured independently in the brain, kidney or liver of multiple, genetically typed, mice strains to detect shared genetic influences. These correlating groups of genes (CGG) have collective properties that account for 40-90% of the variability of their constituent genes and in some cases, but not all, contain genes encoding functionally related proteins. Critically, we show that the genetic influences are essentially tissue specific and consequently the same genetic variations in the one animal may up-regulate a CGG in one tissue but down-regulate the same CGG in a second tissue. We further show similarly paradoxical behaviour of CGGs within the same tissues of different individuals. The implication of this study is that this class of genetic variation can result in complex inter- and intra-individual and tissue differences and that this will create substantial challenges to the investigation of phenotypic outcomes, particularly in humans where multiple tissues are not readily available.

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Moving toward a system genetics view of disease

Testing hundreds of thousands of DNA markers in human, mouse, and other species for association to complex traits like disease is now a reality. However, information on how variations in DNA impact complex physiologic processes flows through transcriptional and other molecular networks. In other words, DNA variations impact complex diseases through the perturbations they cause to transcriptional and other biological networks, and these molecular phenotypes are intermediate to clinically defined disease. Because it is also now possible to monitor transcript levels in a comprehensive fashion, integrating DNA variation, transcription, and phenotypic data has the potential to enhance identification of the associations between DNA variation and diseases like obesity and diabetes, as well as characterize those parts of the molecular networks that drive these diseases. Toward that end, we review methods for integrating expression quantitative trait loci (eQTLs), gene expression, and clinical data to infer causal relationships among gene expression traits and between expression and clinical traits. We further describe methods to integrate these data in a more comprehensive manner by constructing coexpression gene networks that leverage pairwise gene interaction data to represent more general relationships. To infer gene networks that capture causal information, we describe a Bayesian algorithm that further integrates eQTLs, expression, and clinical phenotype data to reconstruct whole-gene networks capable of representing causal relationships among genes and traits in the network. These emerging network approaches, aimed at processing high-dimensional biological data by integrating data from multiple sources, represent some of the first steps in statistical genetics to identify multiple genetic perturbations that alter the states of molecular networks and that in turn push systems into disease states. Evolving statistical procedures that operate on networks will be critical to extracting information related to complex phenotypes like disease, as research goes beyond a single-gene focus. The early successes achieved with the methods described herein suggest that these more integrative genomics approaches to dissecting disease traits will significantly enhance the identification of key drivers of disease beyond what could be achieved by genetic association studies alone

Loss of the Wnt/β-catenin pathway in microglia of the developing brain drives pro-inflammatory activation leading to white matter injury

Microglia-mediated neuroinflammation is key in numerous brain diseases including encephalopathy of the preterm born infant. Microglia of the still-developing brain have unique properties but little is known of how they regulate their inflammatory activation. This is important information as every year 9 million preterm born infants acquire persisting neurological injuries associated with encephalopathy and we lack strategies to prevent and treat these injuries. Our study of activation state regulators in immature brain microglia found a robust down-regulation of Wnt/β-catenin pathway receptors, ligands and intracellular signalling members in pro-inflammatory microglia. We undertook our studies initially in a mouse model of microglia-mediated encephalopathy including the clinical hallmarks of oligodendrocyte injury and hypomyelination. We purified microglia from this model and applied a genome-wide transcriptomics analysis validated with quantitative profiling. We then verified that down-regulation of the Wnt/β-catenin signalling cascade is sufficient and necessary to drive microglia into an oligodendrocyte-damaging phenotype using multiple pharmacological and genetic approaches in vitro and in vivo in mice and in humans and zebrafish. We also demonstrated that genomic variance in the WNT/β-catenin pathway is associated with the anatomical connectivity phenotype of the human preterm born infant. This integrated analysis of genomics and connectivity, as a surrogate for oligodendrocyte function/myelination, is agnostic to cell type. However, this data indicates that the WNT pathway is relevant to human brain injury and specifically that WNT variants may be useful clinically for injury stratification and prognosis. Finally, we performed a translational experiment using a BBB penetrant microglia-specific targeting 3DNA nanocarrier to deliver a Wnt agonist specifically and directly to microglia in vivo. Increasing the activity of the Wnt/β-catenin pathway specifically in microglia in our model of microglia-mediated encephalopathy was able to reduce microglial pro-inflammatory activation, prevent the typical hypomyelination and also prevent the long-term memory deficit associated with this hypomyelination. In summary, the canonical Wnt/β-catenin pathway regulates microglial activation and up-regulation of this pathway could be a viable neurotherapeutic strategy

Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes