The role of the brain in financial decisions : a viewpoint on neuroeconomics

In this article, we explain the important role neuroscience plays in economic and financial environments. Hence, we present neuroeconomics as a way to describe how decision-making processes affect brain activity, focusing especially on the importance of economic and financial decisions. We answer some questions regarding the role of emotions in finance, the psychological factors present in financial markets, and how neuropsychological stimuli affect our economic decisions. We conclude by citing the main research in the area of neuroscience in financial decision-making processes, and highlight further research projects in these areas

Sistema de gestió de informació per a grups de recerca mitjançant web 2.0.

L’objectiu d’aquest projecte és crear una aplicació que gestioni la informació d’un grup de recerca: els membres del grup, el cercle d’usuaris amb volen interactuar amb el grup, i els projectes que porten a terme el grup. La idea general és que un usuari validat pel grup o un membre del grup, pugui interactuar amb els membres del grup, fer propostes per a projectes, seguir els projectes propis. En definitiva un mitja per poder interactuar entre tots els membres del cercle i facilitar el seguiment i el desenvolupament de projectes. L’aplicació utilitza el servidor HTTP Apache i el sistema de gestió de bases de dades PostgreSQL Els llenguatges emprats han sigut: PHP per la part del servidor; i JavaScript i Xhtml per la part del client. Cal destacar: l’ utilització d’una llibreria gratuïta de Javascript anomenada JQuery, la utilització del xhtml per mostrar els continguts mentre el CSS s’encarrega de la presentació dels continguts aconseguint una web mes semàntica ,finalment, l’ utilització d’AJAX per aconseguir connexions asíncrones, i així, no haver de refrescar la pagina cada cop que s’enviï un formulari

Improving Reading Skills Using a Computerized Phonological Training Program in Early Readers with Reading Difficulties

In the last years, there has been a big effort to identify risk factors for reading difficulties and to develop new methodologies to help struggling readers. It has been shown that early intervention is more successful than late intervention, and that intensive training programs can benefit children with reading difficulties. The aim of our study is to investigate the effectiveness of an intensive computerized phonological training program designed to improve reading performance in a sample of children with reading difficulties at the early stages of their reading learning process. Thirty-two children with reading difficulties were randomly assigned to one of the two intervention groups: RDIR (children with reading difficulties following a computerized intensive remediation strategy) (n = 20) (7.01 +/- 0.69 years), focused on training phonemic awareness, decoding and reading fluency through the computational training; and RDOR (children with reading difficulties following an ordinary remediation strategy) (n = 12) (6.92 +/- 0.82 years), which consisted of a reinforcement of reading with a traditional training approach at school. Normal readers (NR) were assigned to the control group (n = 24) (7.32 +/- 0.66 years). Our results indicate that both the RDIR and RDOR groups showed an increased reading performance after the intervention. However, children in the RDIR group showed a stronger benefit than the children in the RDOR group, whose improvement was weaker. The control group did not show significant changes in reading performance during the same period. In conclusion, results suggest that intensive early intervention based on phonics training is an effective strategy to remediate reading difficulties, and that it can be used at school as the first approach to tackle such difficulties

The burden of long COVID: a multinational cohort analysis of Spanish and UK data including SARS-CoV-2 infections, reinfections, and matched contemporaneous test negative controls

As limited data was available on the effect of persisting COVID-19 symptoms, we characterised long COVID and identified key symptoms associated with persistent disease. Using primary care data from Spain and UK, we estimated incidence rates of long COVID in the population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential for long COVID by comparing their frequency in COVID-19 patients vs matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections. Fortunately, the proportion of COVID-19 cases resulting in long COVID declined over the study period. Risk for altered smell/taste, dyspnoea, and fatigue were consistently higher in long COVID patient vs controls [RR between 5.97-1.09]. All persistent symptoms were less common after reinfection than first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms

Tissue engineering by decellularization and 3D bioprinting

Discarded human donor organs have been shown to provide decellularized extracellular matrix (dECM) scaffolds suitable for organ engineering. The quest for appropriate cell sources to satisfy the need of multiple cells types in order to fully repopulate human organ-derived dECM scaffolds has opened new venues for the use of human pluripotent stem cells (hPSCs) for recellularization. In addition, three-dimensional (3D) bioprinting techniques are advancing towards the fabrication of biomimetic cell-laden biomaterial constructs. Here, we review recent progress in decellularization/recellularization and 3D bioprinting technologies, aiming to fabricate autologous tissue grafts and organs with an impact in regenerative medicine

The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms

“The burden of post-acute COVID-19 symptoms in a multinational network cohort analysis”

Persistent symptoms following the acute phase of COVID-19 present a major burden to both the affected and the wider community. We conducted a cohort study including over 856,840 first COVID-19 cases, 72,422 re-infections and more than 3.1 million first negative-test controls from primary care electronic health records from Spain and the UK (Sept 2020 to Jan 2022 (UK)/March 2022 (Spain)). We characterised post-acute COVID-19 symptoms and identified key symptoms associated with persistent disease. We estimated incidence rates of persisting symptoms in the general population and among COVID-19 patients over time. Subsequently, we investigated which WHO-listed symptoms were particularly differential by comparing their frequency in COVID-19 cases vs. matched test-negative controls. Lastly, we compared persistent symptoms after first infections vs. reinfections.Our study shows that the proportion of COVID-19 cases affected by persistent post-acute COVID-19 symptoms declined over the study period. Risk for altered smell/taste was consistently higher in patients with COVID-19 vs test-negative controls. Persistent symptoms were more common after reinfection than following a first infection. More research is needed into the definition of long COVID, and the effect of interventions to minimise the risk and impact of persistent symptoms.</p

Predicting Opioid Use Outcomes in Minoritized Communities

Machine learning algorithms can sometimes exacerbate health disparities based on ethnicity, gender, and other factors. There has been limited work at exploring potential biases within algorithms deployed on a small scale, and/or within minoritized communities. Understanding the nature of potential biases may improve the prediction of various health outcomes. As a case study, we used data from a sample of 539 young adults from minoritized communities who engaged in nonmedical use of prescription opioids and/or heroin. We addressed the indicated issues through the following contributions: 1) Using machine learning techniques, we predicted a range of opioid use outcomes for participants in our dataset; 2) We assessed if algorithms trained only on a majority sub-sample (e.g., Non-Hispanic/Latino, male), could accurately predict opioid use outcomes for a minoritized sub-sample (e.g., Latino, female). Results indicated that models trained on a random sample of our data could predict a range of opioid use outcomes with high precision. However, we noted a decrease in precision when we trained our models on data from a majority sub-sample, and tested these models on a minoritized sub-sample. We posit that a range of cultural factors and systemic forms of discrimination are not captured by data from majority sub-samples. Broadly, for predictions to be valid, models should be trained on data that includes adequate representation of the groups of people about whom predictions will be made. Stakeholders may utilize our findings to mitigate biases in models for predicting opioid use outcomes within minoritized communities