GINv2.0: a comprehensive topological network integrating molecular interactions from multiple knowledge bases.

Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

Nomogram for the prediction of crescent formation in IgA nephropathy patients: a retrospective study.

BACKGROUND: The 2017 Oxford classification of immunoglobulin A nephropathy (IgAN) recently reported that crescents could predict a worse renal outcome. Early prediction of crescent formation can help physicians determine the appropriate intervention, and thus, improve the outcomes. Therefore, we aimed to establish a nomogram model for the prediction of crescent formation in IgA nephropathy patients. METHODS: We retrospectively analyzed 200 cases of biopsy-proven IgAN patients. Least absolute shrinkage and selection operator(LASSO) regression and multivariate logistic regression was applied to screen for influencing factors of crescent formation in IgAN patients. The performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index), calibration plot, and decision curve analysis. RESULTS: Multivariate logistic analysis showed that urinary protein ≥ 1 g (OR = 3.129, 95%CI = 1.454-6.732), urinary red blood cell (URBC) counts ≥ 30/ul (OR = 3.190, 95%CI = 1.590-6.402), mALBU ≥ 1500 mg/L(OR = 2.330, 95%CI = 1.008-5.386), eGFR < 60ml/min/1.73m2(OR = 2.295, 95%CI = 1.016-5.187), Serum IgA/C3 ratio ≥ 2.59 (OR = 2.505, 95%CI = 1.241-5.057), were independent risk factors for crescent formation. Incorporating these factors, our model achieved well-fitted calibration curves and a good C-index of 0.776 (95%CI [0.711-0.840]) in predicting crescent formation. CONCLUSIONS: Our nomogram showed good calibration and was effective in predicting crescent formation risk in IgAN patients.

Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning.

BACKGROUND AND OBJECTIVES: Immunoglobulin a nephropathy (IgAN) is the most common primary glomerular disease in the world, with different clinical manifestations, varying severity of pathological changes, common complications of crescent formation in different proportions, and great individual heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis <25%). MATERIALS: We retrospectively reviewed biopsy-proven IgAN patients in our hospital and cooperative hospital from 2005 to 2017. The method of feature importance of random forest (RF) was applied to conduct feature exploration of feature variables to establish the characteristic variables that are closely related to the prognosis of focal crescent IgAN. Multiple ML algorithms were attempted to establish the prediction models. The area under the precision-recall curve (AUPRC) and the area under the receiver operating characteristic curve (AUROC) were applied to evaluate the predictive performance via three-fold cross validation (namely 2 training sets and 1 validation set). RESULTS: RF was used to screen the important features, the top three of which were baseline estimated glomerular filtration rate (eGFR), serum creatine and triglyceride. Ten important features were selected as important predictors for modeling on the basis of data-driven and medical selection, predictors include: age, baseline eGFR, serum creatine, serum triglycerides, complement 3(C3), proteinuria, mean arterial pressure (MAP) and Hematuria, crescents proportion of glomeruli, Global crescent proportion of glomeruli. In a variety of ML algorithms, the support vector machine (SVM) algorithm displayed better predictive performance, with Precision of 0.77, Recall of 0.77, F1-score of 0.73, accuracy of 0.77, AUROC of 79.57%, and AUPRC of 76.5%. CONCLUSIONS: The SVM model is potentially useful for predicting the prognosis of IgAN patients with focal crescent shape and without obvious chronic renal lesions.

The role of urine IgG in the progression of IgA nephropathy with a high proportion of global glomerulosclerosis.

BACKGROUND: IgA nephropathy (IgAN), the most common glomerulonephritis in the world, is an important cause of end-stage renal disease (ESRD). It is necessary to explore new prognostic markers for predicting the activity and progress of IgAN. There are few studies on new prognostic markers in IgAN patients with high proportion of glomerulosclerosis. This study aims to explore the value of urine IgG in predicting the prognosis of IgAN patients. METHODS: The primary end point of this retrospective study was a composite event with a reduction in estimated glomerular filtration rate (eGFR) of ≥ 50% or ESRD or death. This study assessed the association between urinary IgG and clinicopathological parameters, as well as the prognosis of a high proportion of patients with global glomerulosclerotic IgAN. RESULTS: This study included 105 IgAN patients with high proportion of global glomerulosclerotic. The level of urinary protein IgG was significantly correlated with clinical prognostic factors. The level of urinary protein IgG was positively correlated with urinary protein excretion (rs = 0.834, P < 0.001), CRP (rs = 0.375, P < 0.001), and C4 (rs = 0.228, P = 0.019), and negatively correlated with eGFR (rs = - 0.307, P = 0.001). In addition, the level of urinary IgG increased with the increase of tubulointerstitial injury rate, which was positively correlated with endothelial cell proliferation and crescent (all P < 0.05). Prognostic analysis using the Cox proportional hazard regression model and Kaplan-Meier survival curve further determined that urine IgG is an independent risk factor for the prognosis of IgAN with high proportion of global glomerulosclerosis. CONCLUSIONS: This study determined that urine IgG can be used as a useful predictor of the prognosis of IgAN patients with high proportion global glomerulosclerosis. The mechanism of urine IgG trends in IgAN with high proportion of glomerulosclerosis needs further study.

Mesenchymal Stem Cell-Derived Exosomes Ameliorate Dermal Fibrosis in a Murine Model of Bleomycin-Induced Scleroderma.

Mesenchymal stem cells (MSCs) have become a promising therapeutic strategy for scleroderma. Exosomes derived from MSCs (MSC-exosomes) possess functional properties similar to those of their source cells. In this study, we aimed to explore the potential role of MSC-exosomes in the treatment of scleroderma. MSC-exosomes were isolated from human umbilical cords through ultracentrifugation and characterized. An experimental fibrosis model was established in BALB/c mice by a subcutaneous injection of bleomycin, followed by treatment with MSC-exosomes or MSC infusions once a week for a total of four doses. Using hematoxylin and eosin and Masson's trichrome staining and immunohistochemistry, hydroxyproline content, and quantitative real-time polymerase chain reaction analyses, we investigated the effects of MSC-exosomes on dermal fibrosis and explored the underlying mechanism. MSC-exosome treatment restored the dermal architecture, reduced dermal thickness, and partially increased subcutaneous adipose tissue thickness. In addition, MSC-exosomes inhibited the expression of collagen (COL)-I, COL-III, and α-smooth muscle actin. The transforming growth factor (TGF)-ß/Smad signaling pathway was also suppressed in MSC-exosome-treated mice. Taken together, our results suggest that MSC-exosomes can attenuate myofibroblast activation and collagen deposition in dermal fibrosis by downregulating the TGF-ß/Smad signaling pathway. Therefore, the use of MSC-exosomes may be a potential therapeutic approach for the treatment of scleroderma.

Volume of Crescents Affects Prognosis of IgA Nephropathy in Patients without Obvious Chronic Renal Pathology.

INTRODUCTION: A working group on the Oxford classification of IgA nephropathy (IgAN) recently reported that crescents detected in the kidney tissue predicted a worse renal outcome. However, the effect of C1 lesion (crescents in <1/4th of all glomeruli) and their volume on the prognosis of IgAN is still unclear. We explored the association of C1 lesion with the renal prognosis in IgAN patients without obvious chronic renal lesions (glomerulosclerosis <25%, T score <2). METHODS: We investigated 305 biopsy-proven IgAN patients without obvious chronic renal lesions. Clinicopathologic features and treatment modalities were recorded. The patients were divided into several groups according to the presence or absence of a global crescent: no crescent (NC) group, only segmental crescent (SC) group, and global crescent (GC) group. The outcome was the survival from a combined event defined by a ≥15% decline in the estimated glomerular filtration rate (eGFR) after 1 year or ≥30% decline in the eGFR after 2 years. RESULTS: Among all patients, 75.7% were in the NC group, 14.8% were in the SC group, and 9.5% were in the GC group. Compared with the NC group, patients in the SC group and the GC group had more urine protein, lower eGFR, and presented with more severe pathological change. During a median follow-up of 34.8 (26.16-57.95) months, the combined event occurred in 34 individuals (11.1%). In a multivariate model, the GC group (HR = 2.756, 95% CI = 1.068-7.109) was associated with an increased risk of the combined event. CONCLUSIONS: In IgAN patients without obvious chronic renal lesions, the GC group had more severe clinical and pathological manifestations than in the NC group. GC is an independent risk factor for the progression of IgAN renal function.

Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation.

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current highquality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floatingpoint operations (FLOPs) on 1024×2048 inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects.

[Three-dimensional spatial distribution patterns of Monochamus alternatus and its natural enemy Dastarcus helophoroides].

In 2003 to approximately 2005, the Monochamus alternatus-infected Pinus thunbergii, P. massoniana and P. elliotii in pure and mixed forests were selected as attractants and dissected with one meter section manner, and five aggregation indices were used to analyze the spatial distribution patterns of M. alternatus and its natural enemy Dastarcus helophoroides. The results showed that M. alternatus and D. helophoroides had the horizontal distribution indices of diffused coefficient C > 1, diffused index I delta > 1, Kuno index C(A) > 0, clump intensity index I > 1, and swarm index Iw > 1 in the two stands, suggesting their aggregated horizontal distribution patterns, and except D. helophoroides on the P. massoniana in mixed forest appeared assemble vertical distribution, these two insects all had an even vertical distribution, with their C < 1, I delta < 1, C(A) < 0, Iw < 1, and I < 0. The spatial distribution patterns of M. alternatus and D. helophoroides were consistent, and the latter was spatially following the former.

[Structural characteristics of arthropod community in grapery].

By the method of principal component analysis, this paper analyzed the characteristic parameters of arthropod community and its phytophagous and predacious sub-communities in grapery. The results showed that in the first principal component, the contribution of arthropod community, phytophagous sub-community and predacious sub-community was 66.70%, 73.39% and 54.17%, respectively. For arthropod community, the absolute values of normalized regressive coefficients of individuals' number N, Hill diversity index N1, Hill diversity index N2 and McIntosh index D(mc) were larger, indicating their greater contribution on the community. For phytophagous sub-community, N, N1, N2, N and S (total species) had greater contribution; while for predacious sub-community, the greater contribution was made by N1, N2, Shannon-Wiener diversity index H' and abundance R. Synthetically, N2 and N1 had greater contribution on the arthropod community and its phytophagous and predacious sub-communities in grapery, comparing with other characteristic parameters.

[Effects of management level on community characteristics of arthropod and on population numbers of target insect pest and its natural enemies in graperies].

In this paper, an investigation on the grape tree and ground vegetation was conducted in two graperies with intensive and extensive management, aimed to study the effects of different management level on the community characteristics of arthropod, and the population numbers of target pest Halticinae chalybca (Illiger) and its natural enemies Erigonidium gram inicolum and Tetragnathidae. The results showed that between the two graperies, the individual number, concentration value, evenness, and Hill diversity index of arthropod community had no significant difference, but its species number and abundance was significantly different (P < 0.05). The species number of arthropod on the grape trees in intensive management grapery was not significantly different from that in extensive management grapery, while on the ground vegetation, it was significantly different (P < 0.05). There was a little difference in the population numbers of H. chalybca and its natural enemies on the trees and ground vegetations of the two graperies.