Serum complement C4 is an important prognostic factor for IgA nephropathy: a retrospective study.

BACKGROUND: IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide and is an important cause of end-stage renal disease (ESRD). Exploring novel biomarkers is necessary for predicting the disease activity and progression of IgAN patients. The present study sought to investigate the value of serum C4 for predicting the prognosis of IgAN patients. METHODS: The primary endpoint of this retrospective study was a composite event of either a ≥ 50% reduction in estimated glomerular filtration rate (eGFR) or end-stage renal disease (ESRD) or death. The associations between serum C4 and clinicopathological parameters and prognosis of this cohort of IgAN patients were evaluated. RESULTS: The present study included 1356 IgAN patients. Serum C4 levels correlated significantly with clinical prognostic factors. Serum C4 levels correlated positively with urinary protein excretion (r = 0.307, P < 0.001), and negatively correlated with estimated glomerular filtration rate (r = - 0.281, P < 0.001). Furthermore, serum C4 levels increased with aggravation of tubulointerstitial injury, crescents and ratios of global sclerosis (all P < 0.05). Prognostic analyses with the Cox proportional hazards regression model and Kaplan-Meier survival curves further identified serum C4 as an independent risk factor for the prognosis of IgAN. CONCLUSIONS: The present study identified serum C4 as a useful predictor for the prognosis of IgAN patients. The mechanism of the trend of serum C4 in IgAN needs to be illustrated in further research.

A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning.

Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases. However, cutaneous vessels are highly variable in shape, size, color and architecture, which complicate the detection task. Considering the large variability of these structures, conventional vessel detection techniques lack the generalizability to detect different vessel types and require separate algorithms to be designed for each type. Furthermore, such techniques are highly dependent on precise hand-crafted features which are time-consuming and computationally inefficient. As a solution, we propose a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) for comprehensive detection of cutaneous vessels. Each training image is divided into small patches of either containing or non-containing vasculature. A multilayer SSAE is designed to learn hidden features of the data in hierarchical layers in an unsupervised manner. The high-level learned features are subsequently fed into a classifier which categorizes each patch into absence or presence of vasculature and localizes vessels within the lesion. Over a test set of 3095 patches derived from 200 images, the proposed framework demonstrated superior performance of 95.4% detection accuracy over a variety of vessel patterns; outperforming other techniques by achieving the highest positive predictive value of 94.7%. The proposed Computer-Aided Diagnosis (CAD) framework can serve as a decision support system assisting dermatologists for more accurate diagnosis, especially in teledermatology applications in remote areas.

Spatiotemporal Excitation Module-based CNN for Diagnosis of Hepatic Malignancy in Four-phase CT Images.

Liver cancer is a part of the common causes of cancer death worldwide, and the accurate diagnosis of hepatic malignancy is important for effective next treatment. In this paper, we propose a convolutional neural network (CNN) based on a spatiotemporal excitation (STE) module for identification of hepatic malignancy in four-phase computed tomography (CT) images. To enhance the display detail of lesion, we expand single-channel CT images into three channels by using the channel expansion method. Our proposed STE module consists of a spatial excitation (SE) module and a temporal interaction (TI) module. The SE module calculates the temporal differences of CT slices at the feature level, which is used to excite shape-sensitive channels of the lesion features. The TI module shifts a portion of the channels in the temporal dimension to exchange information among the current CT slice and adjacent CT slices. Four-phase CT images of 398 patients diagnosed with hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are used for experiments and five cross-validations are performed. Our model achieved average accuracy of 85.00% and average AUC of 88.91% for classifying HCC and ICC.Clinical Relevance- The proposed deep learning-based model can perform HCC and ICC recognition tasks based on four-phase CT images, assisting doctors to obtain better diagnostic performance.

Glomerular deposition of fibrinogen predicts good prognosis of IgA nephropathy: a single-center cohort study.

PURPOSE: It has been proven that fibrinogen deposition exists in IgA nephropathy (IgAN), but its clinical significance has not been identified. We aim to investigate the clinical implication of fibrinogen deposition in evaluating the activity and prognosis of IgA nephropathy. METHODS: In this cohort, 935 adult IgAN patients were divided into 3 groups according to the intensity of glomerular fibrinogen deposition. Primary outcome refers to a composite event of either a ≥ 50% reduction in eGFR or ESRD (eGFR < 15 ml/min/1.73m2, dialysis, or renal transplantation). Factors associated with fibrinogen deposition and prognosis were identified. RESULTS: The results showed that the intensity of fibrinogen deposition was positively correlated with eGFR (P < 0.001), serum albumin (P = 0.041), and hemoglobin levels (P < 0.05), but negatively correlated with age (P = 0.04), serum fibrinogen levels (P < 0.001), serum C4 (P = 0.023), the proportion of patients with hypertension (P = 0.003), and the percentage of glomeruli sclerosis (P < 0.001). The prognostic analyses identified that fibrinogen deposition was an independent predictor for the progression of IgAN (P = 0.033). CONCLUSION: Our study indicated that the deposition of renal fibrinogen can predict the prognosis of IgAN with high reliability.

Exploring the molecular mechanism of Sishen Decoction in the treatment of rheumatoid arthritis.

Background: To explore the potential targets and mechanism of action of Sishen Decoction in the treatment of rheumatoid arthritis (RA) using a network pharmacology approach. Methods: Firstly, we analyzed the differentially expressed genes in the Gene Expression Omnibus (GEO) database and constructed a protein-protein interaction (PPI) network using potential core target proteins determined by the STRING platform and Cytoscape software. We also performed gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, and gene set enrichment analysis (GSEA) on the potential targets, and then used the disease target database to download targets related to RA pathogenesis. The relevant targets were intersected with the action targets of Sishen Decoction to obtain the potential targets considered for the treatment of RA with Sishen Decoction. Results: The GSE55235 and GSE77298 datasets were downloaded from the GEO database for analysis, and 73 genes with abnormally high expression in RA and 26 genes with abnormally low expression in RA were obtained by taking the intersection of highly expressed genes and low expression genes in RA. The results of KEGG and Metascape showed that the differential genes were enriched in inflammation-related signaling pathways such as leukocyte migration, myeloid leukocyte-mediated immunity, and lymphocyte activation, as well as bone activation and bone development, which are closely related to RA. In the exploration of the drug targets of Sishen Decoction for the treatment of RA, it was found that Sishen Decoction may regulate the interleukin (IL)-17) signaling pathway, tumour necrosis factor (TNF) signaling pathway, and chemokine signaling pathway in RA by targeting MMP9 and CXCR4. Conclusions: This study explored the potential targets and signaling pathways of Sishen Decoction in the treatment of RA, which may help to illustrate the mechanisms involved in the action of Sishen Decoction and better understand its anti-RA role in the inhibition of angiogenesis, synovial proliferation, and bone destruction.

A highly sensitive and selective fluorescent Cu2+ sensor synthesized with silica nanoparticles.

A novel fluorescent nanosensor for the determination of Cu(2+) was synthesized with N-(quinoline-8-yl)-2-(3-triethoxysilyl-propylamino)-acetamide (QlOEt) grafted onto the surface of silica nanoparticles (SiNPs) using the reverse microemulsion method. Spherical SiNPs were used as substrate and QlOEt was used simultaneously as the binding and readout system for Cu(2+). This sensor has been realized as a highly sensitive and selective technique for the detection and quantification of trace amounts of Cu(2+). The probe exhibits a dynamic response range for Cu(2+) from 2.0 x 10(-6) to 2.0 x 10(-5) M, with a detection limit of 3.8 x 10(-7) M. Other alkali, alkaline earth, and transitional metal ions including Li(+), K(+), Mg(2+), Ca(2+), Sr(2+), Mn(2+), Zn(2+), Mo(6+), Pb(2+), Ag(+) had no significant interference on Cu(2+) determination. Poisonous and flammable reagents are avoided during the synthesis of this nanosensor. Therefore the strategy explored in this work can be extended to the synthesis of other chemo- and biosensors for direct detection of specific targets in an intracellular environment.

[Effect of Tongdu Shujin Decoction () plus Lijin () manipulation on degenerative lumbar spinal stenosis].

OBJECTIVE: To explore the clinical efficacy of Tongdu Shujin Decoction (, TSD) combined with manipulation for the treatment of lumbar spinal stenosis. METHODS: The clinical data of 50 patients with lumbar spinal stenosis diagnosed from October 2015 to October 2017 were retrospectively studied. Fifty patients were divided into a treatment group and a control group, 25 cases in each group. In the treatment group, 25 patients were treated by TSD plus combined manipulation, including 10 males and 15 females, with an average age of (53.43±5.26) years old, the course of disease was (6.9± 2.3) years. In the control group, 25 patients were treated by the adenosine cobalamin tablets and loxoprofen sodium tablets, including 12 males and 13 females, with an average age of (56.37±4.61) years old, the course of disease was (7.1±3.4) years. The two groups were treated for 4 weeks. After treatment, the clinical efficacy of the two groups were evaluated by the low back pain evaluation criteria (JOA) and visual analogue scale (VAS) . RESULTS: Fifty patients were followed up for 2.2 to 3.2 (2.62± 0.47) months. After treatment, JOA and VAS scores of the two groups were significantly improved compared with those before treatment, the difference was statistically significant (P<0.01) , and the improvement degree of the treatment group was better than that of the control group, the difference was statistically significant (P<0.01) . CONCLUSION: TSD combined with manipulation for the treatment of lumbar spinal stenosis has a good effect.

Pairwise domain adaptation module for CNN-based 2-D/3-D registration.

Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.

Efficacy and safety of mycophenolate mofetil for IgA nephropathy: An updated meta-analysis of randomized controlled trials.

The efficacy and safety of mycophenolate mofetil (MMF) for immunoglobulin A nephropathy (IgAN) remains debatable. Therefore, the present meta-analysis was conducted with randomized controlled trials (RCTs). PubMed/MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials were analyzed to identify eligible trials. The pooled risk ratio (RR) with 95% confidence interval (CI) was estimated for all the dichotomous outcome measures. A total of eight RCTs with nine publications (n=510 patients) were included. No significant difference was noted between therapeutic regimens with and without MMF for renal remission and end stage renal disease (ESRD) of patients with IgAN (seven trials; RR, 1.250; 95% CI, 0.993-1.574; P=0.057; and four trials; RR, 0.728; 95% CI, 0.164-3.236; P=0.676). To further define the effects of MMF for renal remission, subgroup analysis was performed, demonstrating that MMF was significantly more effective compared with the placebo (three trials; RR, 2.152; 95% CI, 1.198-3.867; P=0.010), although the immunosuppressive regimens with MMF had no significantly different effects compared with those without MMF (four trials; RR, 1.140; 95% CI, 0.955-1.361; P=0.146), indicating that MMF was superior to placebo and had a similar efficacy to other immunosuppressants for renal remission. In addition, subgroup analysis for ESRD revealed no significant differences between MMF and placebo and between the immunosuppressive regimens with and without MMF (three trials; RR, 0.957; 95% CI, 0.160-5.726; P=0.962; and one trial; RR, 0.205; 95% CI, 0.010-4.200; P=0.303). Furthermore, there were no significant differences between the therapeutic regimens with and without MMF in terms of the risk of adverse events. The present meta-analysis demonstrated that MMF was more effective compared with the placebo, may have similar efficacy to other immunosuppressants in terms of inducing renal remission of IgAN and may not increase the risk of adverse events. The long-term effects of MMF on the prognosis of patients with IgAN require verification in further studies.

Development and assessment of a predictive nomogram for the progression of IgA nephropathy.

The present study is to establish a nomogram for predicting the prognosis of IgA nephropathy (IgAN). Of the 869 IgAN patients, four-fifths were randomly assigned to the development cohort and one-fifth to the validation cohort. The primary outcome was a composite event of either a ≥ 50% reduction in estimated glomerular filtration rate (eGFR), end-stage renal disease or death. The mean follow-up time was 44 months. The Cox regression model identified urinary protein excretion (1-3.5 g/d, HR 11.639, 95% CI 3.601-37.625; ≥ 3.5 g/d, HR 32.435, 95% CI 10.079-104.380), eGFR (G2, HR 5.293, 95% CI 2.011-13.932; G3, HR 15.797, 95% CI 6.584-37.905; G4, HR 34.619, 95% CI 13.887-86.301; G5, HR 217.651, 95% CI 83.807-565.248), hyperuricaemia (HR 7.031, 95% CI 4.126-11.980), mesangial proliferation (HR 36.667, 95% CI 5.098-263.711), segmental glomerulosclerosis (HR 5.122, 95% CI 3.114-8.425), tubular atrophy/interstitial fibrosis (T1, HR 33.351, 95% CI 7.831-142.044; T2, HR 213.888, 95% CI 51.048-896.182), crescents (C1, HR 3.123, 95% CI 1.771-5.510; C2, HR 7.353, 95% CI 3.590-15.062) and glomerulosclerosis (25-49%, HR 3.123, 95% CI 1.771-5.510; ≥ 50%, HR 14.384, 95% CI 8.813-23.479) for developing the nomogram. The C-index was 0.945 (95% CI 0.914-0.976) in both the development and validation cohorts, showing good agreement between the nomogram-predicted probability and actual free-of-progression probability. Thus, our nomogram could accurately predict the progression of IgAN patients.