Introducción: promover autonomía significa transitar desde un modelo paternalista hacia uno que posi-cione en un rol activo a las personas durante el encuentro clínico, con participación en la toma de deci-siones relativas al cuidado de su salud. Este artículo describe la percepción de usuarios que viven con multimorbilidad respecto del ejercicio de su autonomía durante la atención clínica. Método: estudio de caso cualitativo en usuarios con multimorbilidad atendidos en un centro de salud familiar de Santiago (Chile).Se realizó análisis de contenido según Krippendorf. Resultados: la muestra quedó conformada por 19 participantes adultos con un promedio de 2.7 condiciones crónicas de salud. Del análisis de contenido de las entrevistas emergieron tres categorías: a) significado atribuido por los usuarios a la autonomía en la atención de salud, b) elementos que debe considerar una atención en salud que respete la autonomía del usuario y c) participación durante la atención clínica. Conclusiones: frente al aumento de las condicio-nes crónicas de salud es imperativo repensar la forma de brindar atención de salud, relevando el valor de la participación usuaria a través de la toma de decisiones compartida como expresión de respeto de su autonomía y una forma de fomentar el cuidado centrado en las personas
Aim: Promoting autonomy means changing from a paternalistic model to one in which individuals play an active role in their healthcare, which their participation in medical decision-making will reflect. This issue needs to be sufficiently explored in Chile, so this article aims to describe the perception of users liv-ing with multimorbidity regarding their ability to exercise autonomy in clinical care. Method: Qualitative case study conducted in a sample of patients with multimorbility from a family health center in Santiago de Chile. Content analysis was performed according to the Krippendorf method. Results: The sample com-prised 19 adult participants with an average of 2.7 chronic health conditions. Three categories emerged from the content analysis of the interviews: (a) Meaning attributed by users to autonomy in health care, (b) Elements that health care respecting user autonomy should consider, and (c) Participation during clinical care. Conclusions: Considering the sustained increase in chronic health conditions, it is impera-tive to rethink how health care is provided, highlighting the value of user participation through shared decision-making as an expression of respect for individuals' autonomy and the promotion of patient-cen-tered care
Objetivo: promover a autonomia significa passar de um modelo paternalista para um que posicione as pessoas num papel ativo durante o encontro clínico, com participação na tomada de decisões relaciona-das com os seus cuidados de saúde. Este manuscrito descreve a percepção de usuários que convivem com multimorbidade quanto ao exercício de sua autonomia durante o atendimento clínico. Método: estudo de caso qualitativo em usuários com multimorbidade atendidos em um Centro de Saúde da Família de Santiago, no Chile. A análise de conteúdo foi realizada segundo Krippendorf. Resultados: a amostra foi composta por 19 participantes adultos com média de 2.7 condições crônicas de saúde. Da análise de conteúdo das entrevistas emergem três categorias: a) Significado atribuído pelos usuários à autonomia no cuidado em saúde, b) Elementos que um cuidado de saúde que respeite a autonomia do usuário deve considerar, e c) Participação durante o atendimento clínico. Conclusões: face ao aumento das condições crónicas de saúde, é imperativo repensar a forma de prestar cuidados de saúde, destacando o valor da participação dos pacientes através da tomada de decisão partilhada como expressão de respeito pela sua autonomia e forma de promover o cuidado centrado nas pessoas
Humans , Chile , Disease
mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.
Disease , Microbiota , Humans , Statistics as Topic
Ankyrin repeat and LEM domain-containing 2 (ANKLE2) is a scaffolding protein with established roles in cell division and development, the dysfunction of which is increasingly implicated in human disease. ANKLE2 regulates nuclear envelope disassembly at the onset of mitosis and its reassembly after chromosome segregation. ANKLE2 dysfunction is associated with abnormal nuclear morphology and cell division. It regulates the nuclear envelope by mediating protein-protein interactions with barrier to autointegration factor (BANF1; also known as BAF) and with the kinase and phosphatase that modulate the phosphorylation state of BAF. In brain development, ANKLE2 is crucial for proper asymmetric division of neural progenitor cells. In humans, pathogenic loss-of-function mutations in ANKLE2 are associated with primary congenital microcephaly, a condition in which the brain is not properly developed at birth. ANKLE2 is also linked to other disease pathologies, including congenital Zika syndrome, cancer and tauopathy. Here, we review the molecular roles of ANKLE2 and the recent literature on human diseases caused by its dysfunction.
Nuclear Proteins , Humans , Nuclear Proteins/metabolism , Animals , Disease , DNA-Binding Proteins/metabolism , DNA-Binding Proteins/genetics , Mutation/genetics
BACKGROUND: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications, and potential gene targets. Nevertheless, many disease annotations are incomplete, requiring laborious expert medical input. This challenge is especially pronounced for rare and orphan diseases, where resources are scarce. METHODS: We present a machine learning approach to identifying diseases with potential subtypes, using the approximately 23,000 diseases documented in OT. We derive novel features for predicting diseases with subtypes using direct evidence. Machine learning models were applied to analyze feature importance and evaluate predictive performance for discovering both known and novel disease subtypes. RESULTS: Our model achieves a high (89.4%) ROC AUC (Area Under the Receiver Operating Characteristic Curve) in identifying known disease subtypes. We integrated pre-trained deep-learning language models and showed their benefits. Moreover, we identify 515 disease candidates predicted to possess previously unannotated subtypes. CONCLUSIONS: Our models can partition diseases into distinct subtypes. This methodology enables a robust, scalable approach for improving knowledge-based annotations and a comprehensive assessment of disease ontology tiers. Our candidates are attractive targets for further study and personalized medicine, potentially aiding in the unveiling of new therapeutic indications for sought-after targets.
Machine Learning , Humans , Disease/classification , ROC Curve , Computational Biology/methods , Algorithms , Deep Learning
Intercellular mitochondrial transfer (MT) is a newly discovered form of cell-to-cell signalling involving the active incorporation of healthy mitochondria into stressed/injured recipient cells, contributing to the restoration of bioenergetic profile and cell viability, reduction of inflammatory processes and normalisation of calcium dynamics. Recent evidence has shown that MT can occur through multiple cellular structures and mechanisms: tunneling nanotubes (TNTs), via gap junctions (GJs), mediated by extracellular vesicles (EVs) and other mechanisms (cell fusion, mitochondrial extrusion and migrasome-mediated mitocytosis) and in different contexts, such as under physiological (tissue homeostasis and stemness maintenance) and pathological conditions (hypoxia, inflammation and cancer). As Mesenchimal Stromal/ Stem Cells (MSC)-mediated MT has emerged as a critical regulatory and restorative mechanism for cell and tissue regeneration and damage repair in recent years, its potential in stem cell therapy has received increasing attention. In particular, the potential therapeutic role of MSCs has been reported in several articles, suggesting that MSCs can enhance tissue repair after injury via MT and membrane vesicle release. For these reasons, in this review, we will discuss the different mechanisms of MSCs-mediated MT and therapeutic effects on different diseases such as neuronal, ischaemic, vascular and pulmonary diseases. Therefore, understanding the molecular and cellular mechanisms of MT and demonstrating its efficacy could be an important milestone that lays the foundation for future clinical trials.
Energy Metabolism , Mesenchymal Stem Cells , Mitochondria , Humans , Mesenchymal Stem Cells/metabolism , Mesenchymal Stem Cells/cytology , Mitochondria/metabolism , Animals , Mesenchymal Stem Cell Transplantation , Disease
Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of â¼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of â¼800 disease reactions in the context of â¼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.
Molecular Sequence Annotation , Phenotype , Humans , Databases, Genetic , Disease/genetics
MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/DiSMVC.
Computational Biology , Humans , Computational Biology/methods , Disease , Algorithms , MicroRNAs/genetics , Software , Machine Learning
N6-methyladenosine (m6A) stands as the most prevalent modified form of RNA in eukaryotes, pivotal in various biological processes such as regulating RNA stability, translation, and transcription. All members within the YT521-B homology (YTH) gene family are categorized as m6A reading proteins, capable of identifying and binding m6A modifications on RNA, thereby regulating RNA metabolism and functioning across diverse physiological processes. YTH domain-containing 2 (YTHDC2), identified as the latest member of the YTH family, has only recently started to emerge for its biological function. Numerous studies have underscored the significance of YTHDC2 in human physiology, highlighting its involvement in both tumor progression and non-tumor diseases. Consequently, this review aims to further elucidate the pathological mechanisms of YTHDC2 by summarizing its functions and roles in tumors and other diseases, with a particular focus on its downstream molecular targets and signaling pathways.
Adenosine , Neoplasms , RNA-Binding Proteins , Humans , Adenosine/analogs & derivatives , Adenosine/metabolism , Neoplasms/metabolism , Neoplasms/genetics , Neoplasms/pathology , RNA-Binding Proteins/metabolism , Animals , Disease , Signal Transduction , RNA Helicases
Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.
Genetics, Population , Genomics , Humans , Genomics/methods , Pluripotent Stem Cells , Genetic Variation , Phenotype , Single-Cell Analysis/methods , Disease/genetics
Studying mechanisms of development and the causes of various human diseases continues to be the focus of attention of various researchers [...].
Genetic Predisposition to Disease , Humans , Disease/genetics
Morganella morganii is a bacterium belonging to the normal intestinal microbiota and the environment; however, in immunocompromised individuals, this bacterium can become an opportunistic pathogen, causing a series of diseases, both in hospitals and in the community, being urinary tract infections more prevalent. Therefore, the objective of this study was to evaluate the prevalence, virulence profile, and resistance to antimicrobials and the clonal relationship of isolates of urinary tract infections (UTI) caused by M. morganii, both in the hospital environment and in the community of the municipality of Londrina-PR, in southern Brazil, in order to better understand the mechanisms for the establishment of the disease caused by this bacterium. Our study showed that M. morganii presents a variety of virulence factors in the studied isolates. Hospital strains showed a higher prevalence for the virulence genes zapA, iutA, and fimH, while community strains showed a higher prevalence for the ireA and iutA genes. Hospital isolates showed greater resistance compared to community isolates, as well as a higher prevalence of multidrug-resistant (MDR) and extended-spectrum beta lactamase (ESBL)-producing isolates. Several M. morganii isolates from both sources showed high genetic similarity. The most prevalent plasmid incompatibility groups detected were FIB and I1, regardless of the isolation source. Thus, M. morganii isolates can accumulate virulence factors and antimicrobial resistance, making them a neglected opportunistic pathogen. (AU)
Humans , Morganella morganii , Bacteria , Gastrointestinal Microbiome , Environment , Disease , Hospitals
Air Pollutants , Air Pollution , Environmental Exposure , Hospitalization , Humans , Air Pollutants/adverse effects , Air Pollutants/analysis , Air Pollution/adverse effects , Air Pollution/analysis , Environmental Exposure/adverse effects , Environmental Exposure/statistics & numerical data , Hospitalization/statistics & numerical data , Particulate Matter/adverse effects , Particulate Matter/analysis , Disease
As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.
RNA, Long Noncoding , RNA, Long Noncoding/genetics , Humans , Computational Biology/methods , Genetic Predisposition to Disease , Disease/genetics , Machine Learning
In recent years, there has been a lot of research interest in the growing use of artificial intelligence (AI) in health and medicine. This study attempts to provide a global, verified picture of research on AI in medicine and health. There are vast informational resources available, but there are also devices that can't decide examples precisely or predict the future. The conventional methods for diagnosing illnesses are manual and prone to error. When compared to elite human ability, the use of artificial intelligence's predictive approaches improves auto determination and reduces identification errors. A thorough analysis of those articles convinced the ordering party to order the most complex AI processes for clinical symptomatic frameworks. This research report seeks to unearth some key information on the flow and pastof many AI techniques in the clinical setting used in the current clinical investigation, particularly in the areas of coronary disease prediction, brain illness, prostate, liver illness, and kidney infection. In order to ensure that Childs are well-informed and guided, this study uses the coordination examination calculation to distinguish Childs' mental health difficulties and applies the reconciliation examination calculation to Childs' mental health inquiry. A thorough analysis and exploration of children's mental health is completed in light of the framework design approach and information mining grouping technique. (AU)
Artificial Intelligence , Allied Health Personnel , Diagnosis , Disease , Brain Diseases , Prostate
