Background: Arthrofibrosis is a debilitating complication after total knee arthroplasty (TKA) which becomes a considerable burden for both patients and clinical practitioners. Our study aimed to identify novel biomarkers and therapeutic targets for drug discovery.
Methods: Potential biomarker genes were identified based on bioinformatic analysis. Twelve male New Zealand white rabbits underwent surgical fixation of unilateral knees to mimics the joint immobilization of the clinical scenario after TKA surgery. Macroscopic assessment, hydroxyproline content determination, and histological analysis of tissue were performed separately after 3-days, 1-week, 2-weeks, and 4-weeks of fixation. We also enrolled 46 arthrofibrosis patients and 92 controls to test the biomarkers. Clinical information such as sex, age, range of motion (ROM), and visual analogue scale (VAS) was collected by experienced surgeons FINDINGS: Base on bioinformatic analysis, transforming growth factor-beta receptor 1 (TGFBR1) was identified as the potential biomarkers. The level of TGFBR1 was significantly raised in the rabbit synovial tissue after 4-weeks of fixation (p<0.05). TGFBR1 also displayed a highly positive correlation with ROM loss and hydroxyproline contents in the animal model. TGFBR1 showed a significantly higher expression level in arthrofibrosis patients with a receiver operating characteristic (ROC) area under curve (AUC) of 0.838. TGFBR1 also performed positive correlations with VAS baseline (0.83) and VAS after 1 year (0.76) while negatively correlated with ROM baseline (-0.76) in clinical patients.
Interpretation: Our findings provided novel biomarkers for arthrofibrosis diagnosis and uncovered the role of TGFBR1. This may contribute to arthrofibrosis prevention and therapeutic drug discovery.
Funding: None.
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http://dx.doi.org/10.1016/j.ebiom.2021.103486 | DOI Listing |
Medicine (Baltimore)
January 2025
Cardiovascular Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Chronic coronary artery disease (CAD) remains a significant global healthcare burden. Current risk assessment methods have notable limitations in early detection and risk stratification. Hence, there is an urgent need for innovative biomarkers that facilitate the premature CAD diagnosis, ultimately leading to reduction in associated morbidity and mortality rates.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
Background: The morbidity and mortality of sepsis remain high, and so far specific diagnostic and therapeutic means are lacking.
Objective: To screen novel biomarkers for sepsis.
Methods: Raw sepsis data were downloaded from the Chinese National Genebank (CNGBdb) and screened for differentially expressed RNAs.
Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers.
View Article and Find Full Text PDFCancer Rep (Hoboken)
January 2025
Department of Biology, College of Sciences, Shiraz University, Shiraz, Iran.
Background: The breakthrough discovery of novel biomarkers with prognostic and diagnostic value enables timely medical intervention for the survival of patients diagnosed with gastric cancer (GC). Typically, in studies focused on biomarker analysis, highly connected nodes (hubs) within the protein-protein interaction network (PPIN) are proposed as potential biomarkers. However, this study revealed an unexpected finding following the clustering of network nodes.
View Article and Find Full Text PDFAging Cell
January 2025
EPITERNA, Epalinges, Switzerland.
In this study, we investigated age-related changes in clinical laboratory data and their association with mortality in dogs from the Golden Retriever Lifetime Study. By analyzing complete blood count (CBC) and biochemistry data from 2'412 Golden Retrievers over 16,678 visits, we observed significant changes during the first 2 years of life and throughout aging. Based on these observations, we developed a biological aging clock using a LASSO model to predict age based on blood markers, achieving an accuracy of R = 0.
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