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Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects’ motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments.

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.

Videogames are emerging as a promising experimental paradigm in neuroimaging. Acquiring gameplay in a scanner remains challenging due to the lack of a scanner-compatible videogame controller that provides a similar experience to standard, commercial devices. In this paper, we introduce a videogame controller designed for use in the functional magnetic resonance imaging as well as magnetoencephalography. The controller is made exclusively of 3D-printed and commercially available parts. We evaluated the quality of our controller by comparing it to a non-MRI compatible controller that was kept outside the scanner. The comparison of response latencies showed reliable button press accuracies of adequate precision. Comparison of the subjects' motion during fMRI recordings of various tasks showed that the use of our controller did not increase the amount of motion produced compared to a regular MR compatible button press box. Motion levels during an ecological videogame task were of moderate amplitude. In addition, we found that the controller only had marginal effect on temporal SNR in fMRI, as well as on covariance between sensors in MEG, as expected due to the use of non-magnetic building materials. Finally, the reproducibility of the controller was demonstrated by having team members who were not involved in the design build a reproduction using only the documentation. This new videogame controller opens new avenues for ecological tasks in fMRI, including challenging videogames and more generally tasks with complex responses. The detailed controller documentation and build instructions are released under an Open Source Hardware license to increase accessibility, and reproducibility and enable the neuroimaging research community to improve or modify the controller for future experiments.

Psoriasis is a chronic skin disorder marked by fast skin cell growth, leading to thick, red, scaly patches. MicroRNAs are small, non-coding RNA molecules that play a crucial role in post-transcriptional gene regulation. This study investigates miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis EVs and assesses their biomarker potential, exploring associated target genes and pathways via bioinformatics. A cross-sectional and case-control study included 40 psoriasis patients, with blood samples collected in EDTA tubes. RNA from extracellular vesicles was isolated using Qiagen kits, and miRNAs were quantified via RT-qPCR. Bioinformatic analysis predicted target genes using databases like miRDB and TargetScan. Gene expression data from GEO was processed, and differentially expressed genes were identified. This study assessed miR-16-5p, miR-21-5p, and miR-155-5p expression in psoriasis patients’ circulating vesicles versus controls, finding significantly lower levels in patients. ROC analysis confirmed their diagnostic potential. A positive correlation of miR-16-5p with the Psoriasis Area Severity Index (PASI) suggests severity marker potential. Bioinformatics identified 378 common dysregulated genes, revealing key pathways and gene interactions in psoriasis. A heat map confirmed miRNA-mediated gene suppression in the disease. This study identifies miR-16-5p, miR-21-5p, and miR-155-5p as potential psoriasis biomarkers, in addition to finding significant gene interactions and pathways involved in psoriasis pathophysiology.

ABSTRACT Objectives: to validate a scenario for clinical simulation: prenatal nursing consultation for adolescents. Methods: methodological study developed from January to December 2019, in five stages (overview, scenario, scenario design, progression, debriefing and assessment). The validation involved four volunteer students, a teacher as a facilitator and four judges. The judges filled out a Likert scale with four responses. Data was analyzed using Microsoft Excel® software, version 2016. Absolute and relative frequencies and the content validity index were calculated, considering a minimum acceptable value of 1.0. Results: the preparation of the scenario was based on the proposed learning objectives. The scenario was validated with a global content validity index equal to 1.0. Final Considerations: the study achieved the proposed objective. This scenario can contribute to preparing nurses to work in the care of pregnant teenagers, a representative public in Brazil that requires specific care. RESUMO Objetivos: validar um cenário para simulação clínica, no ensino de enfermagem, sobre primeira consulta de pré-natal à gestante adolescente. Métodos: estudo metodológico desenvolvido de janeiro a dezembro de 2019, em cinco etapas (overview, scenario, scenario design progression, debriefing e assessment). A validação envolveu quatro alunos voluntários, uma docente como facilitadora e quatro juízes. Os juízes preencheram uma escala Likert com quatro respostas. Os dados foram analisados no software Microsoft Excel®, versão 2016. Calcularam-se as frequências absolutas e relativas e o índice de validade de conteúdo, considerando valor mínimo aceitável de 1,0. Resultados: a elaboração do cenário partiu dos objetivos de aprendizagem propostos. O cenário foi validado com índice de validade de conteúdo global igual a 1,0. Considerações Finais: o estudo alcançou o objetivo proposto. Este cenário pode contribuir para preparar enfermeiros para atuação na atenção às gestantes adolescentes, um público representativo no Brasil e que requer cuidados específicos. RESUMEN Objetivos: validar un escenario para simulado clínico, en enseñanza de enfermería, sobre primera consulta de prenatal a gestante adolescente. Métodos: estudio metodológico desarrollado de enero a diciembre de 2019, en cinco etapas (overview, scenario, scenario design progression, debriefing y assessment). La validación involucró cuatro alumnos voluntarios, una docente como facilitadora y cuatro jueces. Los jueces rellenaron una escala Likert con cuatro respuestas. Datos fueran analizados en el software Microsoft Excel®, versión 2016. Calculadas las frecuencias absolutas y relativas y el índice de validez de contenido, considerando valor mínimo aceptable de 1,0. Resultados: la elaboración del escenario partió de los objetivos de aprendizaje propuestos. El escenario fue validado con índice de validez de contenido global igual a 1,0. Consideraciones Finales: el estudio alcanzó el objetivo propuesto. Este escenario puede contribuir para preparar enfermeros para actuación en la atención a gestantes adolescentes, un público representativo en Brasil y que requiere cuidados específicos.

Colorectal cancer (CRC) remains a leading global health challenge, being a highly prevalent cancer and a major cause of cancer-related deaths worldwide. The incidence of CRC varies significantly between high-income countries (HICs) and low- and middle-income countries (LMICs), with higher rates of incidence but lower mortality in HICs. Factors such as genetic predisposition, lifestyle, and dietary habits play significant roles in CRC development, with the Western diet and limited access to screening contributing to increased incidence. This review highlights disparities in CRC screening, management, and outcomes between HICs and LMICs, with HICs benefiting from advanced screening methods like colonoscopy and sigmoidoscopy, while LMICs face challenges due to limited healthcare infrastructure and resources. Tailored strategies, including low-cost screening options and community-based initiatives, are critical in LMICs to improve early detection and outcomes. Future directions for improving CRC care globally include telemedicine, artificial intelligence, and mobile health technologies to bridge access gaps, as well as personalized medicine to enhance treatment efficacy. Global collaboration and investment in healthcare infrastructure are necessary to reduce CRC-related mortality, particularly in resource-limited settings.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzing changes in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learning and natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimately improving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

The shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora.