Sickle cell anemia: hierarchical cluster analysis and clinical profile in a cohort in Brazil

Dutra, Valéria de Freitas; Biassi, Thaís Priscila; Figueiredo, Maria Stella

doi:10.1016/j.htct.2021.08.015

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…This cluster analysis shows the heterogeneity that MetS could present depending on patients' characteristics and morbidity. Similar analysis has demonstrated their utility in other cardiometabolic diseases such as Atrial Fibrillation (AF) (70) or Sickle cell anemia (71).…”

Section: Discussionmentioning

confidence: 99%

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

et al. 2022

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Metabolic syndrome (MetS) is one of the most important medical problems around the world. Identification of patient´s singular characteristic could help to reduce the clinical impact and facilitate individualized management. This study aimed to categorize MetS patients using phenotypical and clinical variables habitually collected during health check-ups of individuals considered to have high cardiovascular risk. The selected markers to categorize MetS participants included anthropometric variables as well as clinical data, biochemical parameters and prescribed pharmacological treatment. An exploratory factor analysis was carried out with a subsequent hierarchical cluster analysis using the z-scores from factor analysis. The first step identified three different factors. The first was determined by hypercholesterolemia and associated treatments, the second factor exhibited glycemic disorders and accompanying treatments and the third factor was characterized by hepatic enzymes. Subsequently four clusters of patients were identified, where cluster 1 was characterized by glucose disorders and treatments, cluster 2 presented mild MetS, cluster 3 presented exacerbated levels of hepatic enzymes and cluster 4 highlighted cholesterol and its associated treatments Interestingly, the liver status related cluster was characterized by higher protein consumption and cluster 4 with low polyunsaturated fatty acid intake. This research emphasized the potential clinical relevance of hepatic impairments in addition to MetS traditional characterization for precision and personalized management of MetS patients.

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Section: Discussionmentioning

confidence: 99%

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

et al. 2022

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“…The use of AI has drastically increased in clinical genomics. It has been applied in a wide range of conditions and approaches, such as patient photography analysis (facial analysis for disease identification, radiologic studies, microscopy data) [ 164 ], cardiology predictions (hypertension incident, atrial fibrillation, aortic stenosis) [ 165 ] blood biomarkers (mantle cell lymphoma [ 166 ], anemia [ 167 ]), interpretation of copy number variants [ 168 ], or classification of non-coding variants [ 169 ]. Regarding variant pathogenicity predictions, AI has revolutionized the field, providing advanced tools for accurate assessment.…”

Section: Ai-driven Enhancement Of Predictive Models In Bioinformaticsmentioning

confidence: 99%

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

Larrea-Sebal,

Jebari-Benslaiman,

Galicia-Garcia

et al. 2023

Curr Atheroscler Rep

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Purpose of Review Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. Recent Findings In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. Summary This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

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“…Given the significant interindividual variabilities of presentation and the clinical course among patients with sickle cell anemia, Dutra et al [61] proposed an AI method for better understanding this disease. By using a cluster analysis, the authors identified five clusters differentiated by unconjugated bilirubin, reticulocytes, lactate dehydrogenase, leukocytes, lymphocytes, and monocytes.…”

Section: Hematologymentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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Sickle cell anemia: hierarchical cluster analysis and clinical profile in a cohort in Brazil

Cited by 5 publications

References 42 publications

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

Morbid liver manifestations are intrinsically bound to metabolic syndrome and nutrient intake based on a machine-learning cluster analysis

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

Clinical Applications of Artificial Intelligence—An Updated Overview

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