Search

Background The diagnosis of myasthenia gravis (MG) in children remains difficult. Circulating small extracellular vesicle (sEV)-derived miRNAs (sEV-miRNAs) have been recognized as biomarkers of various diseases and can be excreted by different cell types. These biomarker candidates also play a vital role in autoimmune diseases via intercellular communication. Methods In the present study, we used sEV isolation and purification methods to extract the plasma-derived sEV-miRNAs from children with MG and healthy controls. A small RNA sequencing analysis confirmed the miRNA expression features in plasma-derived sEVs from MG patients. The miRNA expression analysis in vitro was determined using microarray analysis. The enrichment and network analyses of altered sEV-miRNAs were performed using miRNA databases and Database for Annotation, Visualization, and Integrated Discovery website. Quantitative real-time polymerase chain reaction was performed for validation of sEV-miRNA. The diagnostic power of altered sEV-miRNAs was evaluated using receiver operating characteristic curve analyses. Results Twenty-four sEV-miRNAs with altered expression level were identified between groups by DESeq2 method. The miRNAs were extracted from the sEVs, which were isolated from human primary skeletal muscle cell culture treated with mAb198. The target genes and enriched pathways of sEV-miRNAs partially overlapped between cell supernatant and plasma samples. The significantly downregulated miR-143-3p was validated in quantitative real-time polymerase chain reaction analysis. Conclusions For the first time, we report that plasma-derived sEV-miRNAs may act as novel circulating biomarkers and therapeutic targets in pediatric MG. What is already known on this subject: The miRNA expression signature can potentially be used as a biomarker to diagnose a wide variety of diseases. Small extracellular vesicles (sEVs) can provide a stable environment for miRNA cargo to avoid degradation. What this study adds: Twenty-four sEV-miRNA candidates were differentially expressed in pediatric myasthenia gravis (MG) patients compared to healthy controls. Plasma-derived sEV-miR-143-3p has potential value in the diagnosis of pediatric autoantibody seronegative myasthenia gravis. How this study might affect research, practice, or policy: Our findings provide a set of plasma-derived sEV-miRNAs candidates for the diagnosis of pediatric myasthenia gravis, especially autoantibody seronegative patients. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC, and accurate grading is crucial for prognosis and treatment selection. Biopsy is the reference standard for grading, but MRI methods can improve and complement the grading procedure. Purpose: Assess the performance of diffusion relaxation correlation spectroscopic imaging (DR‐CSI) in grading ccRCC. Study Type: Prospective. Subjects: 79 patients (age: 58.1 +/− 11.5 years; 55 male) with ccRCC confirmed by histopathology (grade 1, 7; grade 2, 45; grade 3, 18; grade 4, 9) following surgery. Field Strength/Sequence: 3.0 T MRI scanner. DR‐CSI with a diffusion‐weighted echo‐planar imaging sequence and T2‐mapping with a multi‐echo spin echo sequence. Assessment: DR‐CSI results were analyzed for the solid tumor regions of interest using spectrum segmentation with five sub‐region volume fraction metrics (VA, VB, VC, VD, and VE). The regulations for spectrum segmentation were determined based on the D‐T2 spectra of distinct macro‐components. Tumor size, voxel‐wise T2, and apparent diffusion coefficient (ADC) values were obtained. Histopathology assessed tumor grade (G1–G4) for each case. Statistical Tests: One‐way ANOVA or Kruskal–Wallis test, Spearman's correlation (coefficient, rho), multivariable logistic regression analysis, receiver operating characteristic curve analysis, and DeLong's test. Significance criteria: P < 0.05. Results: Significant differences were found in ADC, T2, DR‐CSI VB, and VD among the ccRCC grades. Correlations were found for ccRCC grade to tumor size (rho = 0.419), age (rho = 0.253), VB (rho = 0.553) and VD (rho = −0.378). AUC of VB was slightly larger than ADC in distinguishing low‐grade (G1‐G2) from high‐grade (G3‐G4) ccRCC (0.801 vs. 0.762, P = 0.406) and G1 from G2 to G4 (0.796 vs. 0.647, P = 0.175), although not significant. Combining VB, VD, and VE had better diagnostic performance than combining ADC and T2 for differentiating G1 from G2‐G4 (AUC: 0.814 vs 0.643). Data Conclusion: DR‐CSI parameters are correlated with ccRCC grades, and may help to differentiate ccRCC grades. Evidence Level: 2 Technical Efficacy Stage: 2 [ABSTRACT FROM AUTHOR]

Objectives To differentiate high-grade from low-grade clear cell renal cell carcinoma (ccRCC) using diffusion-relaxation correlation spectroscopic imaging (DR-CSI) spectra in an equal separating analysis. Methods Eighty patients with 86 pathologically confirmed ccRCCs who underwent DR-CSI were enrolled. Two radiologists delineated the region of interest. The spectrum was derived based on DR-CSI and was further segmented into multiple equal subregions from 2*2 to 9*9. The agreement between the 2 radiologists was assessed by the intraclass correlation coefficient (ICC). Logistic regression was used to establish the regression model for differentiation, and 5-fold cross-validation was used to evaluate its accuracy. McNemar's test was used to compare the diagnostic performance between equipartition models and the traditional parameters, including the apparent diffusion coefficient (ADC) and T2 value. Results The inter-reader agreement decreased as the divisions in the equipartition model increased (overall ICC ranged from 0.859 to 0.920). The accuracy increased from the 2*2 to 9*9 equipartition model (0.68 for 2*2, 0.69 for 3*3 and 4*4, 0.70 for 5*5, 0.71 for 6*6, 0.78 for 7*7, and 0.75 for 8*8 and 9*9). The equipartition models with divisions >7*7 were significantly better than ADC and T2 (vs ADC: P  = .002-.008; vs T2: P  = .001-.004). Conclusions The equipartition method has the potential to analyse the DR-CSI spectrum and discriminate between low-grade and high-grade ccRCC. Advances in knowledge The evaluation of DR-CSI relies on prior knowledge, and how to assess the spectrum derived from DR-CSI without prior knowledge has not been well studied. [ABSTRACT FROM AUTHOR]