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This study aims to investigate and assess salivary biomarkers and microbial profiles as a means of diagnosing periodontitis. A total of 121 subjects were included: 28 periodontally healthy subjects, 24 with Stage I periodontitis, 24 with Stage II, 23 with Stage III, and 22 with Stage IV. Salivary proteins (including active matrix metalloproteinase-8 (MMP-8), pro-MMP-8, total MMP-8, C-reactive protein, secretory immunoglobulin A) and planktonic bacteria (including , , , , , , , , , , , , , , and ) were measured from salivary samples. The performance of the diagnostic models was assessed by receiver operating characteristics (ROCs) and area under the ROC curve (AUC) analysis. The diagnostic models were constructed based on the subjects' proteins and/or microbial profiles, resulting in two potential diagnosis models that achieved better diagnostic powers, with an AUC value > 0.750 for the diagnosis of Stages II, III, and IV periodontitis (Model PA-I; AUC: 0.796, sensitivity: 0.754, specificity: 0.712) and for the diagnosis of Stages III and IV periodontitis (Model PA-II; AUC: 0.796, sensitivity: 0.756, specificity: 0.868). This study can contribute to screening for periodontitis based on salivary biomarkers.
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http://dx.doi.org/10.3390/diagnostics10100820 | DOI Listing |
Protein Cell
August 2025
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
Cardiovascular disease (CVD) research is hindered by limited comprehensive analyses of plasma proteome across disease subtypes. Here, we systematically investigated the associations between plasma proteins and cardiovascular outcomes in 53,026 UK Biobank participants over a 14-year follow-up. Association analyses identified 3,089 significant associations involving 892 unique protein analytes across 13 CVD outcomes.
View Article and Find Full Text PDFScand J Rheumatol
September 2025
The Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Frederiksberg, Denmark.
Objective: Pain hypersensitivity and hypersensitivity to other sensory modalities (visual, auditory, olfactory, and tactile) are considered defining features in nociplastic pain states. A self-report measure of sensory sensitivity may help to characterize sensory profiles across pain populations. This study aimed to evaluate the psychometric properties of a newly developed Danish nine-item Sensory Sensitivity Profile (SSP) questionnaire in patients with fibromyalgia.
View Article and Find Full Text PDFHum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFPediatr Transplant
November 2025
Division of Urology, University of Toronto, Toronto, Canada.
Introduction: Differentiating acute tubular necrosis (ATN) from rejection in pediatric kidney transplant (KT) recipients remains challenging and necessitates invasive biopsy. Doppler ultrasound-derived resistive index (RI) is a noninvasive modality to assess graft status, but its diagnostic utility in children is unclear. This study evaluates RI's ability to distinguish ATN and rejection in KT.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
September 2025
Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India.
Parkinson's disease (PD) is a neurodegenerative condition that impairs motor functions. Accurate and early diagnosis is essential for enhancing well-being and ensuring effective treatment. This study proposes a deep learning-based approach for PD detection using EEG signals.
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