R534C mutation in hERG causes a trafficking defect in iPSC-derived cardiomyocytes from patients with type 2 long QT syndrome

Patient-specific cardiomyocytes obtained from induced pluripotent stem cells (CM-iPSC) offer unprecedented mechanistic insights in the study of inherited cardiac diseases. The objective of this work was to study a type 2 long QT syndrome (LQTS2)-associated mutation (c.1600C > T in KCNH2, p.R534C in hERG) in CM-iPSC. Peripheral blood mononuclear cells were isolated from two patients with the R534C mutation and iPSCs were generated. In addition, the same mutation was inserted in a control iPSC line by genome editing using CRISPR/Cas9. Cells expressed pluripotency markers and showed spontaneous differentiation into the three embryonic germ layers. Electrophysiology demonstrated that action potential duration (APD) of LQTS2 CM-iPSC was significantly longer than that of the control line, as well as the triangulation of the action potentials (AP), implying a longer duration of phase 3. Treatment with the IKr inhibitor E4031 only caused APD prolongation in the control line. Patch clamp showed a reduction of IKr on LQTS2 CM-iPSC compared to control, but channel activation was not significantly affected. Immunofluorescence for hERG demonstrated perinuclear staining in LQTS2 CM-iPSC. In conclusion, CM-iPSC recapitulated the LQTS2 phenotype and our findings suggest that the R534C mutation in KCNH2 leads to a channel trafficking defect to the plasma membrane.Fil: Mesquita, Fernanda C. P.. Universidade Federal do Rio de Janeiro; BrasilFil: Arantes, Paulo C.. Universidade Federal do Rio de Janeiro; BrasilFil: Kasai Brunswick, Tais H.. Universidade Federal do Rio de Janeiro; BrasilFil: Araujo, Dayana S.. Universidade Federal do Rio de Janeiro; BrasilFil: Gubert, Fernanda. Universidade Federal do Rio de Janeiro; BrasilFil: Monnerat, Gustavo. Universidade Federal do Rio de Janeiro; BrasilFil: Silva dos Santos, Danúbia. Universidade Federal do Rio de Janeiro; BrasilFil: Neiman, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Leitão, Isabela C.. Universidade Federal do Rio de Janeiro; BrasilFil: Barbosa, Raiana A. Q.. Universidade Federal do Rio de Janeiro; BrasilFil: Coutinho, Jorge L.. National Institute Of Cardiology; BrasilFil: Vaz, Isadora M.. Pontificia Universidad Catolica de Parana; BrasilFil: dos Santos, Marcus N.. Universidade Federal do Rio de Janeiro; BrasilFil: Borgonovo, Tamara. Pontificia Universidad Catolica de Parana; BrasilFil: Cruz, Fernando E. S.. National Institute of Cardiology; BrasilFil: Miriuka, Santiago Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia; ArgentinaFil: Medei, Emiliano H.. Universidade Federal do Rio de Janeiro; BrasilFil: Campos de Carvalho, Antonio C.. Universidade Federal do Rio de Janeiro; Brasil. National Institute of Cardiology; Brasil. National Institute for Science and Technology in Regenerative Medicine; BrasilFil: Carvalho, Adriana B.. Universidade Federal do Rio de Janeiro; Brasil. National Institute for Science and Technology in Regenerative Medicine; Brasi

Classification of schizophrenic traits in transcriptions of audio spectra from patient literature: artificial intelligence models enhanced by geometric properties

Schizophrenia is a severe mental illness that affects approximately 1% of the global population and presents significant challenges for patients, families, and healthcare professionals. Characterized by symptoms such as delusions, hallucinations, disorganized speech or behavior, and cognitive impairment, this condition has an early onset and chronic trajectory, making it a debilitating challenge. Schizophrenia also imposes a substantial burden on society, exacerbated by the stigma associated with mental disorders. Technological advancements, such as computerized semantic, linguistic, and acoustic analyses, are revolutionizing the understanding and assessment of communication alterations, a significant aspect in various severe mental illnesses. Early and accurate diagnosis is crucial for improving prognosis and implementing appropriate treatments. In this context, the advancement of Artificial Intelligence (AI) has provided new perspectives for the treatment of schizophrenia, with machine learning techniques and natural language processing allowing a more detailed analysis of clinical, neurological, and behavioral data sets. The present article aims to present a proposal for computational models for the identification of schizophrenic traits in texts.  The database used in this article was created with 139 excerpts of patients' speeches reported in the book “Memories of My Nervous Disease” by German judge Daniel Paul Schreber, classifying them into three categories: 1 - schizophrenic, 2 - with schizophrenic traits and 3 - without any relation to the disorder. Of these speeches, 104 were used for training the models and the others 35 for validation.Three classification models were implemented using features based on geometric properties of graphs (number of vertices, number of cycles, girth, vertex of maximum degree, maximum clique size) and text entropy. Promising results were observed in the classification, with the Decision Tree-based model [1] achieving 100% accuracy, the KNN- k-Nearest Neighbor model observed with 62.8% accuracy, and the 'centrality-based' model with 59% precision. The high precision rates, observed when geometric properties are incorporated into Artificial Intelligence Models, suggest that the models can be improved to the point of capturing the language deviation traits that are indicative of schizophrenic disorders. In summary, this study paves the way for significant advances in the use of geometric properties in the field of psychiatry, offering a new data-based approach to the understanding and therapy of schizophrenia

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost