Worldwide epidemic scale of Diabetes mellitus (DM) has been underestimated for a long time. Currently every 10 seconds one patient dies of diabetes-related pathologies. Given the high risk and prevalence of secondary complications as well as individual predisposition to target organ injury, DM is one of the best examples for the application of predictive diagnostics aimed at preventive measures and personalized treatment approaches. Generally there are three levels in desirable pre- and Diabetes care: 1st level: prediction of the predisposition early in childhood. 2nd level: prediction of early/premature aging and prestages of Diabetes. 3rd level: prediction of Diabetes-related complications - cardiovascular, neurodegenerative and cancer diseases frequently developed in Diabetics. Predictive diagnosis is considered as the basis for targeted preventive measures and consequent creation of individualized treatment approaches. Communication among the professionals - healthcare providers, policy-makers, educators, etc., obligatory involved in the overall process to improving (pre)Diabetes care is of paramount importance.
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http://dx.doi.org/10.2174/157339910790442637 | DOI Listing |
Curr Pharm Biotechnol
January 2025
Department of Intensive Care Unit, Affiliated Hospital of Guangdong Medical University, 524000 Zhanjiang, China.
Objectives: This study aimed to comprehensively investigate the molecular landscape of gastric cancer (GC) by integrating various bioinformatics tools and experimental validations.
Methodology: GSE79973 dataset, limma package, STRING, UALCAN, GEPIA, OncoDB, cBioPortal, DAVID, TISIDB, Gene Set Cancer Analysis (GSCA), tissue samples, RT-qPCR, and cell proliferation assay were employed in this study.
Results: Analysis of the GSE79973 dataset identified 300 differentially expressed genes (DEGs), from which COL1A1, COL1A2, CHN1, and FN1 emerged as pivotal hub genes using protein-protein interaction network analysis.
Endocr Metab Immune Disord Drug Targets
January 2025
Department of Stomatology, The Affiliated Huaian No.1 People's Hospital, Nanjing Medical University, No.1 Huanghe West Road, Huaian, 223300, Jiangsu Province, China.
Background: Crohn's Disease (CD) is a chronic inflammatory gastrointestinal disease. Ustekinumab (UST) has been utilized as a therapeutic option for CD patients. However, approximately 40-60% of patients exhibit an inadequate response to UST.
View Article and Find Full Text PDFDementia (London)
January 2025
Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Australia.
Dementia is one of the fastest emerging global public health concerns today, as the World Health Organisation has predicted that the number of cases will triple from 55 million in 2023 to 152 million by 2050. Current evidence indicates that approximately 45% of dementia cases can be prevented or delayed by acting on potentially modifiable risk factors. However, public knowledge regarding this remains unknown in numerous poorly resourced countries, including Nepal, where the prevalence of dementia continues to increase.
View Article and Find Full Text PDFAddiction
January 2025
Department of Psychology, York University, Toronto, Canada.
Aims: To establish the feasibility of using ecological momentary assessment (EMA) to estimate total quantities of Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) used across different forms of cannabis, and to assess the predictive validity of THC estimates for predicting acute cannabis-related consequences.
Design: 14-day EMA using a smartphone application to assess cannabis use in real time.
Setting: Canada.
Cancer Med
January 2025
Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, China.
Background: Distinctive heterogeneity characterizes diffuse large B-cell lymphoma (DLBCL), one of the most frequent types of non-Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL.
Objective: The purposes of this study were to identify the prognostic mitochondria-related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms.
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