The alarming increase in type 2 diabetes mellitus (T2DM) underscores the need for efficient screening and preventive strategies. Select protein biomarker profiles emerge over time during T2DM development. Periodic evaluation of these markers will increase the predictive ability of diabetes risk scores. Noninvasive methods for frequent measurements of biomarkers are increasingly being investigated. Application of salivary diagnostics has gained importance with the establishment of significant similarities between the salivary and serum proteomes. The objective of this study is to identify T2DM-specific salivary biomarkers by literature-based discovery. A serial interrogation of the PubMed database was performed using MeSH terms of specific T2DM pathological processes in primary and secondary iterations to compile cohorts of T2DM-specific serum markers. Subsequent search consisted of mining for the identified serum markers in human saliva. More than 60% of T2DM-associated serum proteins have been measured in saliva. Nearly half of these proteins have been reported in diabetic saliva. Measurements of salivary lipids and oxidative stress markers that can exhibit correlated saliva plasma ratio could constitute reliable factors for T2DM risk assessment. We conclude that a high percentage of T2DM-associated serum proteins can be measured in saliva, which offers an attractive and economical strategy for T2DM screening.
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http://dx.doi.org/10.4137/BMI.S22177 | DOI Listing |
Methods
January 2025
School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Compound-protein interaction (CPI) prediction is critical in the early stages of drug discovery, narrowing the search space for CPIs and reducing the cost and time required for traditional high-throughput screening. However, CPI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. Constructing a scheme that enhances multi-institutional collaboration to improve prediction accuracy while protecting data privacy is essential.
View Article and Find Full Text PDFTalanta
January 2025
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China. Electronic address:
Conventionally, gas sensors are studied based on functional materials, case by case, using experimental methods. In this study, 872 datasets with 34 features of doped oxides, extracted from the literature, were used to analyze the key features of gas-sensing reactions and understand gas-sensing mechanisms from a global perspective using a genetic algorithm-optimized artificial neural network. Shapley additive explanations were employed to determine the importance and relationships of the features.
View Article and Find Full Text PDFAlzheimers Dement (Amst)
January 2025
Alzheimer Center Amsterdam, Neurology Amsterdam University Medical Center Amsterdam the Netherlands.
Introduction: We examined semantic and phonemic fluency in individuals with subjective cognitive decline (SCD) in relation to amyloid status and clinical progression.
Methods: A total of 490 individuals with SCD (62 ± 8 years, 42% female, 28% amyloid-positive, 17% clinical progression) completed annual fluency assessments (mean ± SD follow-up 4.3 ± 2.
Acta Physiol (Oxf)
February 2025
Department of Molecular Medicine, Cardiovascular and Renal Research Unit, University of Southern Denmark, Odense M, Denmark.
The Renin-Angiotensin System (RAS) is a complex neuroendocrine system consisting of a single precursor protein, angiotensinogen (AGT), which is processed into various peptide hormones, including the angiotensins [Ang I, Ang II, Ang III, Ang IV, Ang-(1-9), Ang-(1-7), Ang-(1-5), etc] and Alamandine-related peptides [Ang A, Alamandine, Ala-(1-5)], through intricate enzymatic pathways. Functionally, the RAS is divided into two axes with opposing effects: the classical axis, primarily consisting of Ang II acting through the AT receptor (ATR), and in contrast the protective axis, which includes the receptors Mas, ATR and MrgD and their respective ligands. A key area of RAS research is to gain a better understanding how signaling cascades elicited by these receptors lead to either "classical" or "protective" effects, as imbalances between the two axes can contribute to disease.
View Article and Find Full Text PDFCurr Pharm Des
January 2025
Center of Bioinformatics, College of Life Sciences, Northwest Agriculture and Forestry University, Yangling, Shaanxi, 712100, China.
Introduction: The COVID-19 pandemic has necessitated rapid advancements in therapeutic discovery. This study presents an integrated approach combining machine learning (ML) and network pharmacology to identify potential non-covalent inhibitors against pivotal proteins in COVID-19 pathogenesis, specifically B-cell lymphoma 2 (BCL2) and Epidermal Growth Factor Receptor (EGFR).
Method: Employing a dataset of 13,107 compounds, ML algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) were utilized for screening and predicting active inhibitors based on molecular features.
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