Medicine
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Obesity, a multifactorial and complex health condition, has emerged as a significant global public health concern. Integrating machine learning techniques into obesity research offers great promise as an interdisciplinary field, particularly in the screening, diagnosis, and analysis of obesity. Nevertheless, the publications on using machine learning methods in obesity research have not been systematically evaluated. Hence, this study aimed to quantitatively examine, visualize, and analyze the publications concerning the use of machine learning methods in obesity research by means of bibliometrics. ⋯ Utilizing bibliometrics as a research tool and methodology, this study, for the first time, reveals the intrinsic relationship and developmental pattern among obesity research using machine learning methods, which provides academic references for clinicians and researchers in understanding the hotspots and cutting-edge issues as well as the developmental trend in this field to detect patients' obesity problems early and develop personalized treatment plans.
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To more accurately diagnose and treat patients with different subtypes of thyroid cancer, we constructed a diagnostic model related to the iodine metabolism of THCA subtypes. THCA expression profiles, corresponding clinicopathological information, and single-cell RNA-seq were downloaded from TCGA and GEO databases. Genes related to thyroid differentiation score were obtained by GSVA. ⋯ In addition, the diagnostic model was significantly negatively correlated with immune scores. Finally, the results of qRT-PCR corresponded with bioinformatics results. This diagnostic model has good diagnostic and prognostic value for THCA patients, and can be used as an independent prognostic indicator for THCA patients.
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Previous research has shown a strong correlation between sepsis and brain structure. However, whether this relationship represents a causality remains elusive. In this study, we employed Mendelian randomization (MR) to probe the associations of genetically predicted sepsis and sepsis-related death with structural changes in specific brain regions. ⋯ We also indicated a possible bidirectional causal association between genetic liability to sepsis-related death and the thickness of the transverse temporal gyrus. Sensitivity analyses verified the robustness of the above associations. These findings suggested that genetically determined liability to sepsis might influence the specific brain structure in a causal way, offering new perspectives to investigate the mechanism of sepsis-related neuropsychiatric disorders.
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Exosomes, which are extracellular vesicles secreted and released from specific cells, exist widely in cell culture supernatants and various body fluids. This study aimed to analyze the research status of exosomes in stroke, and predict developmental trends via bibliometric analyses. The related literature from January 1, 2008 to January 1, 2024 was searched in the Web of Science Core Collection and 943 articles were retrieved. ⋯ In the keyword cluster "Exosomes and the Mechanism of Stroke: Inflammation and Apoptosis," exosomes and inflammation were identified as hotspots. "Functional recovery" was a new trend in the keyword cluster of "Angiogenesis and Functional Recovery after Stroke." China and the United States are the main forces in this field, and both countries focusing on drug treatments. The studies have been published mainly in China and United States. The findings of our bibliometric analyses of the literature may enable researchers to choose appropriate institutions, collaborators, and journals.
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Pyroptosis-related genes have great potential for prognosis, an accurate prognostic model based on pyroptosis genes has not been seen in Colorectal adenocarcinoma (COAD). Furthermore, understanding the mechanisms of gene expression characteristics and the Tumor Immune Microenvironment associated with the prognosis of COAD is still largely unknown. Constructing a prognostic model based on pyroptosis-related genes, and revealing prognosis-related mechanisms associated with the gene expression characteristics and tumor microenvironment. 59 pyroptosis-related genes were collected. ⋯ This study constructed a prognostic model for COAD using 10 pyroptosis-related genes with prognostic value. This study also revealed significant differences in specific pathways and the tumor immune microenvironment (TME) between the high-risk group and the low-risk group, highlighted the roles of ALDH5A1 and Wnt signaling in promoting COAD and the suppressive effects of the IL-4/IL-13 pathway and RORC on COAD. The study will be helpful for precision therapy.