Int J Med Sci
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The paper displayed the pathological changes and relationships of the modified Mankin score, tidemark roughness and calcified cartilage (CC) thickness by extracorporeal shockwave therapy (ESWT) (0.25 mJ/ mm2 with 800 impulses) on different positions of the medial and lateral rat knee OA joint. After the experiments, the articular cartilage was assessed using histomorphometry, image analysis and statistical method. In the micro-CT analysis, ESWT on medial groups were better than lateral groups in the trabecular volume and trabecular number. ⋯ In terms of the relationship of tidemark roughness with CC thickness, the medial and Sham groups showed a significant negative correlation (r = -0.788, P = 0.022). Additionally, the Euclidean distance derived from 3D scatter plot analysis was an indicator of chondropathic conditions, exhibiting a strong correlation with OA stage in the articular cartilage of the femur (r = 0.911, P < 0.001) and tibia (r = 0.890, P < 0.001) after ESWT. Principle component analysis (PCA) further demonstrated that ESWT applied to medial locations had a better outcome than treatment at lateral locations for knee OA by comparing with Sham and OA groups, and CC thickness was the most important factor affecting hyaline cartilage repair after ESWT.
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Introduction: Early detection of lung cancer is one way to improve outcomes. Improving the detection of nodules on chest CT scans is important. Previous artificial intelligence (AI) modules show rapid advantages, which improves the performance of detecting lung nodules in some datasets. ⋯ Conclusions: Detection of lung nodules is important for lung cancer treatment. When facing a large number of CT scans, error-prone nodules are a great challenge for doctors. The AI-assisted program improved the performance of detecting lung nodules, especially for error-prone nodules.
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Background: A comprehensive understanding of phenotypes related to CKD will facilitate the identification and management of CKD. We aimed to panoramically test and validate associations between multiple phenotypes and CKD using a phenotype-wide association study (PheWAS). Methods: 15,815 subjects from cross-sectional cohorts of the National Health and Nutrition Examination Survey (1999-2006) were randomly 50:50 split into training and testing sets. ⋯ AUROC of the RF model was 0.951 (full model) and 0.914 (top 5 phenotypes). Conclusion: Our study demonstrated associations between multiple phenotypes with CKD from a holistic view, including 3 novel phenotypes: retinol, RDW, and C-peptide. Our findings provided valid evidence for the identification of novel biomarkers for CKD.
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[This corrects the article DOI: 10.7150/ijms.8128.].
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Purpose: Distinguishing between high-grade and low-grade meningiomas might be difficult but has high clinical value in deciding precise treatment and prognostic factors. Magnetic resonance imaging (MRI) using apparent diffusion coefficient (ADC) values and dynamic contrast enhancement (DCE) may have a significant role in capturing such complexities. Methods: Data from our hospital database on meningioma patients from January 2020 to December 2021 were obtained. ⋯ Type IV TIC had a sensitivity of 80% and specificity of 89.3% in distinguishing high-grade meningiomas from low-grade meningiomas. Optimal cut-offs of 940.2 for SImax (AUC = 0.98, sensitivity = 80%, specificity = 96.4%), 245% for MCER (AUC = 0.94, sensitivity = 80%, specificity = 85.7%), and 5% per second for slope (AUC = 0.97, sensitivity = 80%, specificity = 96.4%) were estimated. Conclusion: The ADC value and DCE-MRI parameters (TIC, SImax, Tmax, MCER, and slope) are potential predictors for separating high-grade from low-grade meningiomas.