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Aging is an important medical and social problem. Excessive angiopoietin-like protein (ANGPTL)-2 signaling causes chronic tissue inflammation, promoting development and progression of aging-related diseases. Moreover, circulating ANGPTL2 levels reportedly predict the risk of some aging-related diseases and subsequent death. However, there are, as yet, no reports of whether circulating ANGPTL2 levels predict vital prognosis in younger-old, community-dwelling populations. This study investigated associations between plasma ANGPTL2 levels and all-cause and specific-cause mortality in this population. The case–cohort study was abstracted from an ongoing, age-specific prospective cohort study: the New Integrated Suburban Seniority Investigation Project. This project enrolled 3 073 participants aged 64 years at the beginning of the investigation from 1996 through 2005. A subcohort of 714 randomly sampled participants plus 387 cases representing deceased participants followed through 2015 underwent survival analysis. Plasma ANGPTL2 concentrations were positively associated with >80% and 100% higher risk of all-cause mortality and cancer mortality, respectively, after adjustment for gender, smoking, alcohol consumption, walking time, sleep duration, caloric intake, medical status, disease history, BMI, and triglyceride, creatinine, uric acid, and high sensitivity C-reactive protein levels. A more robust association between ANGPTL2 levels and all-cause and cancer mortality was seen in participants with either frailties or with lifestyles of heavier drinking or current smoking. Elevated plasma ANGPTL2 levels are associated with high all-cause and cancer mortality in a community-dwelling sample of younger-old adults. These findings expand our knowledge of human aging and associated diseases.

The breakage and deep plugging law of deformable gel particle (DGP) in porous media are not clear. In this study, the flow experiments of DGP were carried out, the changes of residual resistance coefficient (RRC), DGP size and concentration along the way were measured, and the breakage and plugging law of DGP was quantitatively characterized. The results show that the passage after breakage is the main migration behaviors of DGP, and DGP are reduced by half in front of the porous media. Breakage is an important reason for the continuous decrease of concentration, particle size and RRC. However, the DGP can plug in deep of the porous media after breaking. The higher the injection rate or the larger the DGP size, the faster the breakage along the injection direction, and the worse the deep plugging ability. The empirical model can be used to optimize the DGP parameters in oil field.

Anoikis is a term used to describe the programmed cell death that takes place when cells become disconnected from the extracellular matrix[1]. Numerous long non-coding RNAs (lncRNAs) have been found to be linked to anoikis resistance in various tumors, including glioma[2], breast cancer[3], and bladder cancer[4]. There is a lack of studies examining the relationship between ARLs and HCC prognosis. Further research is needed to explore this potential link and understand the role of ARLs in HCC progression. Patients and methods: In this study, we acquired 36 genes associated with anoikis through the GO and GSEA databases, and identified 22 differentially expressed lncrnas that were correlated with these genes based on data from The Cancer Genome Atlas (TCGA). Using Cox regression analyses, we created an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which we then validated in both a testing cohort and a combined cohort composed of data from both cohorts. Then, we collected 8 pairs of liver cancer tissues and adjacent tissues from Affiliated Tumor Hospital of Nantong University. Results: Five ARLs were developed as a risk stratification system to categorize patients into low- and high-risk groups. The high-risk group exhibited a notably lower overall survival (OS) rate in comparison to the low-risk group. The model's predictive performance is supported by the receiver operating characteristic curve, calibration of nomogram, clinical correlation analysis, and clinical decision curve. Furthermore, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations indicated significant discrepancies between the high and low-risk groups. IC50 analysis determined that commonly prescribed drugs were more effective for patients in the high-risk group. Lastly, quantitative real-time PCR validated the expression levels of the five lncRNAs in specific drug-resistant cell lines. In conclusion, our ARlncSig model possesses critical predictive value in the prognosis of HCC patients, and it may provide clinical guidance for personalized immunotherapy.

Associations between NAMPT-AS level and clinico-pathological characteristics in TCGA breast cancer patients

Detailed information of the materials and methods referenced in this manuscript is shown as supplementary files.

10 supplementary figures are referenced in this manuscript. Fig.S1. The expression of NOP14 is positively correlated with the capacity of PDAC cells. Fig. S2. The expression of NOP14 is determined in PANC-1 cells. Fig. S3. NOP14 promotes MIA PaCa-2 cell migration and invasion in vitro. Fig. S4. The expression of NOP14 is determined in MIA PaCa-2 cells. Fig. S5. NOP14 inhibition leads to dampened capacity of invasion in PANC-1 cells. Fig. S6. The suppressive impact of si-p53 is analyzed in PDAC cells. Fig. S7. NOP14 promotes PDAC metastasis by regulating miR-17-5p expression. Fig. S8. miR-17-5p inhibits the luciferase activity of P21 reporter in MIA PaCa-2 cells. Fig. S9. The RNA stability of pri-miR-17-5p is evaluated in PDAC cells. Fig. S10. The impact of NOP14 on c-Myc expression is determined in PDAC cells.

6 tables referenced in this manuscript is shown as supplementary files. Table S1. Characterization of 20 aired of PDAC patients. Table S2. Sequences of miRNAs and U6 snRNA primers. Table S3. Primer sequences of protein-coding genes. Table S4. NOP14 expression in primary PDAC and metastases. Table S5. Whole transcriptome RNA-sequencing data. Table S6. Comparison of NOP14 expression levels with clinico-pathological features in patients with pancreatic cancer.

The file contains 9 supplementary Figures indicating the bioinformatics analysis, transfection efficiency analysis, rescue experiments and clinical sample analysis.

Triple-negative breast cancer (TNBC) is highly heterogeneous and has a poor prognosis. It is therefore important to identify the underlying molecular mechanisms in order to develop novel therapeutic strategies. Although emerging research has revealed long noncoding RNAs (lncRNA) as vital to carcinogenesis and cancer progression, their functional involvement in TNBC has not been well defined. In this study, we utilized the The Cancer Genome Atlas (TCGA) database and analyzed clinical samples to show that the long noncoding antisense transcript of nicotinamide phosphoribosyltransferase (NAMPT), NAMPT-AS, is upregulated in TNBC and is associated with poor prognosis, lymph node involvement, metastasis, and advanced stage. NAMPT-AS was cotranscribed with NAMPT from a bidirectional promoter, where the distributions of H3K4me3 and H3K27Ac chromatin modifications were enriched based on ENCODE and FANTOM5, suggesting the potential enhancer-RNA characteristics of NAMPT-AS. NAMPT-AS epigenetically regulated the expression of NAMPT in two divergent ways: NAMPT-AS recruited POU2F2 to activate the transcription of NAMPT, and NAMPT-AS acted as a competing endogenous RNA to rescue NAMPT degradation from miR-548b-3p. NAMPT-AS/NAMPT promoted tumor progression and regulated autophagy through the mTOR pathway in vitro and in vivo. In a cohort of 480 breast cancer patients, NAMPT was associated with breast cancer–specific survival and overall survival. These results demonstrate that NAMPT-AS is an oncogenic lncRNA in TNBC that epigenetically activates NAMPT to promote tumor progression and metastasis. Furthermore, these data identify NAMPT-AS/NAMPT as promising therapeutic targets in patients with TNBC.Significance:Upregulation of the long noncoding antisense RNA of NAMPT gene (NAMPT-AS) is associated with metastasis and poor prognosis in TNBC.

Mutant p53 (mutp53) proteins promote tumor invasion and metastasis in pancreatic ductal adenocarcinoma (PDAC). However, the mechanism underlying sustained activation of mutp53 oncogenic signaling is currently unclear. In this study, we report that NOP14 nucleolar protein (NOP14) expression is upregulated in PDAC tumors and metastatic tissue specimens. NOP14 overexpression promoted cell motility, whereas NOP14 inhibition decreased invasive capacity of PDAC cells. In vivo invasion assays conducted on established subcutaneously, orthotopically, and intravenously injected tumor mouse models also indicated NOP14 as a promoter of PDAC metastasis. Mechanistically, mutp53 was validated as a functional target of NOP14; NOP14 primed tumor invasion and metastasis by increasing the stability of mutp53 mRNA. The NOP14/mutp53 axis suppressed p21 expression at both the transcriptional and posttranscriptional levels via induction of miR-17-5p in PDAC cells. In vivo, high NOP14 expression in PDAC patient tumors correlated with local metastasis and lymph invasion. Overall, our findings define a novel mechanism for understanding the function of NOP14 in the metastatic cascade of PDAC. Targeting NOP14 allows for effective suppression of tumor invasion in a mutp53-dependent manner, implicating NOP14 inhibition as a potential approach for attenuating metastasis in p53-mutant tumors. Cancer Res; 77(10); 2661–73. ©2017 AACR.

To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs.We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC).The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness.The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.

Isotropic gold nanoparticles (AuNPs) can generate a plasma-plasma interaction when aggregating and can also produce ideal photothermal effects. Some studies have designed ATP-responsive nanodrug delivery systems by taking advantage of the differences between internal and external ATP in tumor cells, but few studies have focused on the photothermal effects of ATP-induced AuNP aggregation in tumors. Here, a triple-helix probe (THP) molecular switch and MUC1 aptamer-functionalized AuNPs were constructed for fluorescence imaging analysis and photothermal therapy (PTT). The MUC1 aptamer guides THP-AuNP targeting in tumor cells, followed by the high concentration of ATP inducing structural changes in triple-helix probes and causing the intracellular aggregation of AuNPs, which cannot escape from the tumor site, enabling tumor imaging while performing PTT. Therefore, the designed THP-AuNPs have promising applications in fluorescence imaging and PTT.

Low-abundance biomarker amplification detection systems have been widely used to detect miRNAs; however, "always active" systems are insufficient for high spatial and temporal control of miRNAs. Here, we constructed a light-activated nanodevice (LAN) based on DNA nanotechnology for high spatial and temporal precision detection of low-abundance miRNA. Light-activated hairpin probes and triple-helix molecular switches were modified on the surface of gold nanoparticles (AuNPs) to trigger miRNA on-demand imaging analysis by UV light activation. In the presence of both UV light and miRNA, the LAN releases hairpin DNA and completes the hybridization chain reaction (HCR) with the conformation-altered triple-helix molecular switch, enabling fluorescence imaging of low-abundance miRNAs in living cells. The current work provides an opportunity to develop light-activated signal amplification sensors that can accurately image miRNAs on-demand in both temporal and spatial dimensions.

BACKGROUND: The current COVID-19 pandemic is posing a major challenge to public health on a global scale. While it is generally believed that severe COVID-19 results from over-expression of inflammatory mediators (i.e., a “cytokine storm”), it is still unclear whether and how co-infecting pathogens contribute to disease pathogenesis. To address this, we followed the entire course of the disease in cases with severe or critical COVID-19 to determine the presence and abundance of all potential pathogens present—the total “infectome”—and how they interact with the host immune system in the context of severe COVID-19. METHODS: We examined one severe and three critical cases of COVID-19, as well as a set of healthy controls, with longitudinal samples (throat swab, whole blood, and serum) collected from each case. Total RNA sequencing (meta-transcriptomics) was performed to simultaneously investigate pathogen diversity and abundance, as well as host immune responses, in each sample. A Bio-Plex method was used to measure serum cytokine and chemokine levels. RESULTS: Eight pathogens, SARS-CoV-2, Aspergillus fumigatus (A. fumigatus), Mycoplasma orale (M. orale), Myroides odoratus (M. odoratus), Acinetobacter baumannii (A. baumannii), Candida tropicalis, herpes simplex virus (HSV) and human cytomegalovirus (CMV), identified in patients with COVID-19 appeared at different stages of the disease. The dynamics of inflammatory mediators in serum and the respiratory tract were more strongly associated with the dynamics of the infectome compared with SARS-CoV-2 alone. Correlation analysis revealed that pulmonary injury was directly associated with cytokine levels, which in turn were associated with the proliferation of SARS-CoV-2 and co-infecting pathogens. CONCLUSIONS: For each patient, the cytokine storm that resulted in acute lung injury and death involved a dynamic and highly complex infectome, of which SARS-CoV-2 was a component. These results indicate the need for a precision medicine approach to investigate both the infection and host response as a standard means of infectious disease characterization.

The replacement of small cations with bulkier organic cations containing long alkyl chains or benzene rings to form a thin two-dimensional (2D) perovskite passivation layer on three-dimensional (3D) perovskite (2D/3D) has become a promising strategy for improving both the efficiency and stability of perovskite solar cells (PSCs). The 2D layer defines the interfacial chemistry and physics at the 2D/3D bilayer and endows the 2D/3D structure with better chemical and thermal stability. Herein, 2D/3D (CF3-PEA)2FA0.85MA0.15Pb2I7/FA0.85MA0.15PbI3 planar heterojunction perovskite was produced using a facile interfacial ion exchange process. The 2D (CF3-PEA)2FA0.85MA0.15Pb2I7 capping layer can not only passivate the FA0.85MA0.15PbI3 film but also act as super-hydrophobic layer to inhibit water diffusion and significantly enhance the stability. The 2D capping layer can also establish a unique graded band structure at the perovskite/Spiro-OMeTAD interface and lead to p-type doping for Spiro-OMeTAD layer which is beneficial for efficient charge transport. Optimized PSCs based on this 2D/3D heterojunction yield a champion power conversion efficiency (PCE) of 23.1% and improved stability. The device maintains 84% output for 2400 h aging under ambient environmental conditions without encapsulation, and maintains 81% for 200 h under illumination with encapsulation. This work will inspire the design of more fluorinated 2D perovskite interfaces for advanced photovoltaics and beyond.

Nowadays, echo state networks (ESNs) with a variety of model structures are available for industrial process time series predictions. However, most existing ESNs employ a uniform timescale for data knowledge learning, which obviously ignores the influences of multivariate status at different timescales to the prediction target, usually leading to unsatisfactory model approximation performances. In response to this problem, this paper proposes a multi-parallel cycle reservoir with jumps (MP-CRJ) which is embedded with the feature knowledge of different timescales contained in the multivariate data. The MP-CRJ uses a more concise and superior circular jump reserve pool and a more memorable leaky integral neuron-filled parallel structure able to reduce the spatial complexity resulted from parallel ESNs and relatively improve the dynamic diversity of reserve pools. In addition, grey relational analysis algorithms are used to select relevant variables contributing to the prediction in filtering unnecessary data information. Applying to practical plant data for methanol productions, it shows that the MP-CRJ can help increase the prediction accuracy while maintain prediction speeds, as well as enjoy better adaptions to dynamics of complex industrial processes.

Over the last decade, there has been a steady increase in the number of cities in China and around the world adopting License Plate-based Restriction (LPR) policies, whereby vehicles are permitted to enter a defined road or area on the basis of a specific digit on the registration, license or number plate, to help mitigate traffic congestion and its associated impacts. Although the costs and benefits of implementing a LPR policy have been recognized, the public acceptance of a LPR policy has been less well studied. To better understand the factors affecting the public acceptance of a LPR policy, we improved the theoretical model by adding two factors related to attitude and new supposed deeper level effects between different factors. Accordingly, the theoretical model on exploring the factors affecting the public acceptance of such a scheme were explored in a questionnaire completed by 619 local residents in Xi’an, China and analyzed using a partial least square structural equation model (PLS-SEM). In addition, we carried out heterogeneity analysis to compare the differences between residents with various socio-economic attributes in factors with respect to affecting the acceptance of a LPR policy. The results show that problem perception, perceived effectiveness, perceived value and social norms exert a significant direct and indirect impact on the acceptance of a LPR policy. Important aims to reach can only indirectly affect the acceptance of a LPR policy by perceived effectiveness and perceived value, while policy cognition, attribution of responsibility have no significant influence on the acceptance of a LPR policy. Important aims and perceived effectiveness directly and direct affect the acceptance of a LPR policy by residents who are young, high-income and car-owning. The findings of this study can assist policy makers to design a more acceptable policy to further mitigate congestion situations.

The corroded microstructures of the Cr2O3–Al2O3–ZrO2 refractory brick in an industrial opposed multi-burner (OMB) coal water slurry gasifier were investigated. It was found that the chromite spinel isolation layer rich in iron and magnesium was formed on the surface of the corroded brick body. By comparing and analyzing the corrosion of different zones in the gasifier, the results indicated that the molten slag entered the brick through the gaps and generated the spinel layer along the edge of the gaps, causing the dense structures of the refractory surface destroyed. In addition, in the high chrome brick corrosion experiment conducted by HTSM, 3 μm sized spinel were observed covering on the surface. The FactSage calculation results showed that the amount of spinel produced would drop significantly as the slag penetrated.

Encapsulation of copper nanoparticles by the electrochemical catalyst MoS2 effectively improved its HER performance.

The protein-coding ability of circular RNAs (circRNAs) has recently been a hot topic, but the expression and roles of protein-coding circRNAs in triple-negative breast cancer (TNBC) remain uncertain. By intersecting circRNA sequencing data from clinical samples and cell lines, we identified a circRNA, termed circ-EIF6, which predicted a poorer prognosis and correlated with clinicopathological characteristics in a cohort of TNBC patients. Functionally, we showed that circ-EIF6 promoted the proliferation and metastasis of TNBC cells in vitro and in vivo. Mechanistically, we found that circ-EIF6 contains a 675-nucleotide (nt) open reading frame (ORF) and that the -150-bp sequence from ATG functioned as an internal ribosome entry site (IRES), which is required for translation initiation in 5' cap-independent coding RNAs. circ-EIF6 encodes a novel peptide, termed EIF6-224 amino acid (aa), which is responsible for the oncogenic effects of circ-EIF6. The endogenous expression of EIF6-224aa was further examined in TNBC cells and tissues by specific antibody. Moreover, EIF6-224aa directly interacted with MYH9, an oncogene in breast cancer, and decreased MYH9 degradation by inhibiting the ubiquitin-proteasome pathway and subsequently activating the Wnt/beta-catenin pathway. Our study provided novel insights into the roles of protein-coding circRNAs and supported circ-EIF6/EIF6-224aa as a novel promising prognostic and therapeutic target for tailored therapy in TNBC patients.

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called α-sum and α-product of maximum and minimum eigenvalues (α-SMME, α-PMME) are further designed, in which α is a weight coefficient. Furthermore, the theoretical performance analysis of the α-SMME and α-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

A DNA tetrahedron-based intramolecular catalytic hairpin self-assembly platform that uses fluorescence signals to image miRNAs in live cells for accurate tumor cell identification.

In this letter, the correlation structure inherent in the received data is exploited to improve detection performance for sea-surface small floating targets in short observation time. Three detectors are devised resorting to the eigenvalues of covariance matrix that are versatile statistics reflecting the signal correlation. Specifically, the proposed detectors respectively exploit the maximum eigenvalue to arithmetic mean (MAM) of all eigenvalues, the maximum eigenvalue to geometric mean (MGM) of all eigenvalues, and the maximum eigenvalue to minimum eigenvalue (MME) to form test statistics. At the analysis stage, the three-parameter Burr function is exploited to approximate the statistical distributions of the test statistics under null hypothesis and alternative hypothesis. Besides, the analytic expressions of false alarm probability, detection probability, and thresholds of the proposed detectors are derived. Finally, simulation results on real sea clutter show that the proposed detectors provide effective solutions to the problem of target detection in sea clutter.

The emergence of multidrug-resistant bacterial pathogens is a growing threat to global public health. Here, we report the development and characterization of a panel of nine–amino acid residue synthetic peptides that display potent antibacterial activity and the ability to disrupt preestablished microbial biofilms. The lead peptide (Peptide K6) showed bactericidal activity against Pseudomonas aeruginosa and Staphylococcus aureus in culture and in monocultures and mixed biofilms in vitro. Biophysical analysis revealed that Peptide K6 self-assembled into nanostructured micelles that correlated with its strong antibiofilm activity. When surface displayed on the outer membrane protein LamB, two copies of the Peptide K6 were highly bactericidal to Escherichia coli. Peptide K6 rapidly increased the permeability of bacterial cells, and resistance to this toxic peptide occurred less quickly than that to the potent antibiotic gentamicin. Furthermore, we found that Peptide K6 was safe and effective in clearing mixed P. aeruginosa–S. aureus biofilms in a mouse model of persistent infection. Taken together, the properties of Peptide K6 suggest that it is a promising antibiotic candidate and that design of additional short peptides that form micelles represents a worthwhile approach for the development of antimicrobial agents.

To assess the variation in left atrial (LA) and biventricular strain and its prognostic value in the course of suspected myocarditis, this retrospective study included 55 patients with clinically suspected myocarditis who underwent cardiac magnetic resonance (CMR) examinations at baseline and follow-up periods. Cine images were used for feature tracking analysis. Paired Student’s t test, McNemar’s test, and Cox proportional hazard regression were used for statistical analysis. The LA total emptying fraction was the only functional index that showed a statistically significant improvement. The initial LA peak’s late negative strain rate (SRa) was the only parameter with a significant predictive power of major adverse cardiac events under univariable (hazard ratio [HR] 2.396, 95% confidence interval [CI] 1.044–5.498, p = 0.039) and multivariable Cox survival analysis when adjusted by LA strain parameters (HR 5.072, 95% CI 1.478–17.404, p = 0.010), LA strain and functional parameters (HR 7.197, 95% CI 1.679–30.846, p = 0.008), and LA and biventricular strain and functional parameters (HR 10.389, 95% CI 2.250–47.977, p = 0.003). Thus, our findings indicate that CMR strain is useful for monitoring LA and ventricular function in suspected myocarditis, that LA function may recover preceding ventricular function changes, and that LA strain may serve as an incremental tool to predict adverse outcomes.

[Image: see text] Antifungal peptides are effective, biocompatible, and biodegradable, and thus, they are promising to be the next generation of drugs for treating infections caused by fungi. The identification processes of highly active peptides, however, are still time-consuming and labor-intensive. Quantitative structure–activity relationships (QSARs) have dramatically facilitated the discovery of many bioactive drug molecules without a priori knowledge. In this study, we have established an effective QSAR protocol for screening antifungal peptides. The screening protocol integrates an accurate antifungal peptide classification model and four activity prediction models against specified target fungi. A demonstrative application was performed on more than three million candidate peptides, and three outstanding peptides were identified. The whole screening took only a few days, which was much faster than our previous experimental screening works. In conclusion, the protocol is useful and effective for reducing repetitive laboratory efforts in antifungal peptide discovery. The prediction server (antifungal Web server) is freely available at www.chemoinfolab.com/antifungal.

Background Glomerular deposition of C4d is a widely used biomarker for activation of the lectin pathway in the complement system and is reported to be associated with kidney progression in immunoglobulin A nephropathy (IgAN). The aim of this study was to evaluate whether glomerular C4d deposition, as a new biomarker, improves the prediction of kidney prognosis in IgAN. Study Design Systematic review and meta-analysis. Setting & Population Patients with biopsy-proven primary IgAN without age limitations. Selection Criteria for Studies: Cross-sectional or cohort studies reporting the prevalence of glomerular C4d deposition or evaluating its association with IgAN progression. Predictor Glomerular C4d deposition. Outcome Composite progression event of a >30% decline in estimated glomerular filtration rate or end-stage kidney disease. Results 12 studies with 1,251 patients were included. The prevalence of glomerular C4d deposition was 34% (95% CI, 27%-41%), with large heterogeneity (I2 = 86%; P