Why we need to pursue both universal and targeted prevention to reduce the incidence of affective and psychotic disorders: Systematic review and meta-analysis.

The effectiveness of universal preventive approaches in reducing the incidence of affective/psychotic disorders is unclear. We therefore aimed to synthesise the available evidence from randomised controlled trials. For studies reporting change in prevalence, we simulated all possible scenarios for the proportion of individuals with the disorder at baseline and at follow-up to exclude them. We then combined these data with studies directly measuring incidence and conducted random effects meta-analysis with relative risk (RR) to estimate the incidence in the intervention group compared to the control group. Eighteen studies (k=21 samples) were included investigating the universal prevention of depression in 66,625 individuals. No studies were available investigating universal prevention on the incidence of bipolar/psychotic disorders. 63 % of simulated scenarios showed a significant preventive effect on reducing the incidence of depression (k=9 - 19, RR=0.75-0.94, 95 %CIs=0.55-0.87,0.93-1.15, p=0.007-0.246) but did not survive sensitivity analyses. There is some limited evidence for the effectiveness of universal interventions for reducing the incidence of depression but not for bipolar/psychotic disorders.

Exploring the complex trade-offs and synergies of global ecosystem services.

The trade-off and synergy relationship of ecosystem services is an important topic in the current assessment. The value of each service provided by the ecosystem is substantially affected by human activities, and conversely, its changes will also affect the relevant human decisions. Due to varying trade-offs among ecosystem services and synergies between them that can either increase or decrease, it is difficult to optimize multiple ecosystem services simultaneously, making it a huge challenge for ecosystem management. This study firstly develops a global Gross Ecosystem Product (GEP) accounting framework. It uses remote sensing data with a spatial resolution of 1 km to estimate the ecosystem services of forests, wetlands, grasslands, deserts, and farmlands in 179 major countries in 2018. The results show that the range of global GEP values is USD 112-197 trillion, with an average value of USD 155 trillion (the constant price), and the ratio of GEP to gross domestic product (GDP) is 1.85. The trade-offs and the synergies among different ecosystem services in each continent and income group have been further explored. We found a correspondence between the income levels and the synergy among ecosystem services within each nation. Among specific ecosystem services, there are strong synergies between oxygen release, climate regulation, and carbon sequestration services. A trade-off relationship has been observed between flood regulation and other services, such as water conservation and soil retention services in low-income countries. The results will help clarify the roles and the feedback mechanisms between different stakeholders and provide a scientific basis for optimizing ecosystem management and implementing ecological compensation schemes to enhance human well-being.

A Time-varying Equivalent Circuit Modeling and Measuring Approach for Intracardiac Communication in Leadless Pacemakers.

Intracardiac wireless communication is crucial for the development of multi-chamber leadless cardiac pacemakers (LCP). However, the time-varying characteristics of intracardiac channel pose major challenges. As such, mastering the dynamic conduction properties of the intracardiac channel and modeling the equivalent time-varying channel are imperative for realizing LCP multi-chamber pacing. In this paper, we present a limiting volume variational approach based on the electrical properties of cardiac tissues and trends in chamber volume variation. This approach was used to establish a quasi-static and a continuous time-varying equivalent circuit model of an intracardiac channel. An equivalence analysis was conducted on the model, and a discrete time-varying equivalent circuit phantom grounded on the cardiac cycle was subsequently established. Moreover, an ex vivo cardiac experimental platform was developed for verification. Results indicate that in the frequency domain, the congruence between phantom and ex vivo experimental outcomes is as high as 94.3%, affirming the reliability of the equivalent circuit model. In the time domain, the correlation is up to 75.3%, corroborating its effectiveness. The proposed time-varying equivalent circuit model exhibits stable and standardized dynamic attributes, serving as a powerful tool for addressing time-varying challenges and simplifying in vivo or ex vivo experiments.

Intelligent wound dressing for simultaneous in situ detection and elimination of pathogenic bacteria.

Wound infections hinder the healing process and potentially result in life-threatening complications, which urgently require rapid and timely detection and treatment pathogens during the early stages of infection. Here, an intelligent wound dressing was developed to enable in situ detection and elimination of pathogenic bacteria through a combination of point-of-care testing and antibacterial photodynamic therapy technology. The dressing is an injectable hydrogel composed of carboxymethyl chitosan and oxidized sodium alginate, with addition of 4-methylumphulone beta-D-glucoside (MUG) and up-converted nanoparticles coated with titanium dioxide (UCNPs@TiO2). The presence of bacteria can be visually detected by monitoring the blue fluorescence of 4-methylumbellione, generated through the reaction between MUG and the pathogen-associated enzyme. The UCNPs@TiO2 photosensitizers were synthesized and demonstrated high antibacterial activity through the generation of reactive oxygen species when exposed to near-infrared irradiation. Meanwhile, a smartphone-based portable detection system equipped with a self-developed Android app was constructed for in situ detection of pathogens in mere seconds, detecting as few as 103 colony-forming unit. Additionally, the dressing was tested in a rat infected wound model and showed good antibacterial activity and pro-healing ability. These results suggest that the proposed intelligent wound dressing has potential for use in the diagnosis and management of wound infections. STATEMENT OF SIGNIFICANCE: An intelligent wound dressing has been prepared for simultaneous in situ detection and elimination of pathogenic bacteria. The presence of bacteria can be visually detected by tracking the blue fluorescence of the dressing. Moreover, a smartphone-based detection system was constructed to detect and diagnose pathogenic bacteria before reaching the infection limit. Meanwhile, the dressing was able to effectively eliminate key pathogenic bacteria on demand through antibacterial photodynamic therapy under NIR irradiation. The proposed intelligent wound dressing enables timely detection and treatment of infectious pathogens at an early stage, which is beneficial for wound management.

Long-term exposure to ambient air pollutants and increased risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease: a retrospective cohort study in Beijing, China.

Limited data have examined the association between air pollution and the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). We aimed to investigate whether long-term exposure to air pollutants is related to the development of ESRD among patients with T2DM and CKD. A total of 1,738 patients with T2DM and CKD hospitalized in Peking University Third Hospital from January 1, 2013, to December 31, 2021 were enrolled in this study. The outcome was defined as the occurrence of ESRD. Data on six air pollutants (PM2.5, PM10, CO, NO2, SO2, and O3) from 35 monitoring stations were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center. Long-term exposure to air pollutants during the follow-up period was measured using the ordinary Kriging method. During a mean follow-up of 41 months, 98 patients developed ESRD. Multivariate logistic regression analysis showed that an increase of 10 µg/m3 in PM2.5 (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.03-1.36) and PM10 (OR 1.15, 95% CI 1.02-1.30) concentration were positively associated with ESRD. An increase of 1 mg/m3 in CO (2.80, 1.05-7.48) and an increase of 1 µg/m3 in SO2 (1.06, 1.00-1.13) concentration were also positively associated with ESRD. Apart from O3 and NO2, all the above air pollutants have additional predictive value for ESRD in patients with T2DM and CKD. The results of Bayesian kernel machine regression and the weighted quantile sum regression all showed that PM2.5 was the most important air pollutant. Backward stepwise logistic regression showed that PM2.5 was the only pollutant remaining in the prediction model. In patients with T2DM and CKD, long-term exposure to ambient PM2.5, PM10, CO, and SO2 was positively associated with the development of ESRD.

Agricultural Methane Emissions in China: Inventories, Driving Forces and Mitigation Strategies.

Identification of the spatial distribution, driving forces, and future trends of agricultural methane (AGM) emissions is necessary to develop differentiated emission control pathways and achieve carbon neutrality by 2060 in China, which is the largest emitter of AGM. However, such research is currently lacking. Here, we estimated China's AGM emissions from 2010 to 2020 and then decomposed six factors that affect AGM emissions via the LMDI model. The results indicated that the AGM emissions in China in 2020 were 23.39 Tg, with enteric fermentation being the largest source, accounting for 43.9% of the total emissions. A total of 39.3% of the AGM emissions were from western China. The main driver of AGM emission reduction was emission intensity, accounting for 59% and 33.7% of methane emission reduction in the livestock sector and rice cultivation, respectively. Additionally, higher levels of urbanization contributed to AGM emission reductions, accounting for 31.3% and 43.0% of the livestock sector and rice cultivation emission reductions, respectively. Based on the SSP-RCP scenarios, we found that China's AGM emissions in 2060 were reduced by approximately 90% through a combination of technology measures, behavioral changes, and innovation policies. Our study provides a scientific basis for optimizing existing AGM emission reduction policies not only in China but also potentially in other high AGM-emitting countries, such as India and Brazil.

Association between triglyceride-glucose index and risk of end-stage renal disease in patients with type 2 diabetes mellitus and chronic kidney disease.

Aims: It has been suggested that the triglyceride-glucose (TyG) index is a novel and reliable surrogate marker of insulin resistance (IR). However, its relationship with the risk of end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) remains uncertain. Accordingly, we sought to examine the relationship between the TyG index and ESRD risk in patients with T2DM and CKD. Methods: From January 2013 to December 2021, 1,936 patients with T2DM and CKD hospitalized at Peking University Third Hospital (Beijing, China) were enrolled into the study. The formula for calculating the TyG index was ln[fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. ESRD was defined as an estimated glomerular filtration rate of less than 15 mL/min/1.73 m2 or the commencement of dialysis or renal transplantation. The relationship between the TyG index and ESRD risk was analyzed using Cox proportional hazard regression. Results: 105 (5.42%) participants developed ESRD over a mean follow-up of 41 months. The unadjusted analysis revealed a 1.50-fold (95% confidence interval [CI] 1.17-1.93; P = 0.001) increased risk for ESRD per one unit rise in the TyG index, and the positive association remained stable in the fully adjusted model (hazard ratio, 1.49; 95% CI, 1.12-1.99; P = 0.006). Analysis using restricted cubic spline revealed a significant positive association between the TyG index and ESRD risk. In addition, Kaplan-Meier analysis revealed significant risk stratification with a TyG index cutoff value of 9.5 (P = 0.003). Conclusion: In individuals with T2DM and CKD, a significant and positive association was shown between an elevated TyG index and the risk of ESRD. This conclusion provides evidence for the clinical importance of the TyG index for evaluating renal function decline in individuals with T2DM and CKD.

Dynamics Combined With Hill Model for Functional Electrical Stimulation Ankle Angle Prediction.

Musculoskeletal models play an essential role in ankle rehabilitation research. The majority of the existing models have established the relationship between EMG and joint torque. However, EMG signal acquisition requires higher clinical conditions, such as sensitivity to external circumstances, motion artifacts and electrode position. To solve the nonlinear and time-varying nature of joint movement, a Functional Electrical Stimulation (FES) model was proposed in this study to simulate the whole process of ankle dorsiflexion. The model is combined with muscle contraction dynamics based on Hill model and ankle inverse dynamics to connect FES parameters, torques, and ankle angles. In addition, the extended Kalman filter (EKF) algorithm was applied to identify the unknown parameters of the model. Model validation experiment was performed by acquiring the actual data of healthy volunteers. Results showed that the root mean square error (RMSE) and normalized root mean square error (NRMSE) of this model were 11.93%±0.53% and 1.39°±0.26°, respectively, which means it can effectively predict the output variation of ankle joint angle while changing electrical stimulation parameters. Therefore, the proposed mode is essential for developing closed-loop feedback control of electrical stimulation and has the potential to help patients to conduct rehabilitation training.

Feasibility Analysis and Implementation of Head-Mounted Electrical Impedance Respiratory Monitoring.

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

Cardiorenal protection of SGLT2 inhibitors-Perspectives from metabolic reprogramming.

Sodium-glucose co-transporter 2 (SGLT2) inhibitors, initially developed as a novel class of anti-hyperglycaemic drugs, have been shown to significantly improve metabolic indicators and protect the kidneys and heart of patients with or without type 2 diabetes mellitus. The possible mechanisms mediating these unexpected cardiorenal benefits are being extensively investigated because they cannot solely be attributed to improvements in glycaemic control. Notably, emerging data indicate that metabolic reprogramming is involved in the progression of cardiorenal metabolic diseases. SGLT2 inhibitors reprogram systemic metabolism to a fasting-like metabolic paradigm, involving the metabolic switch from carbohydrates to other energetic substrates and regulation of the related nutrient-sensing pathways, which might explain some of their cardiorenal protective effects. In this review, we will focus on the current understanding of cardiorenal protection by SGLT2 inhibitors, specifically its relevance to metabolic reprogramming.

A Variable-Volume Heart Model for Galvanic Coupling-Based Conductive Intracardiac Communication.

Conductive intracardiac communication (CIC) has become one of the most promising technologies in multisite leadless pacemakers for cardiac resynchronization therapy. Existing studies have shown that cardiac pulsation has a significant impact on the attenuation of intracardiac communication channels. In this study, a novel variable-volume circuit-coupled electrical field heart model, which contains blood and myocardium, is proposed to verify the phenomenon. The influence of measurements was combined with the model as the equivalent circuit. Dynamic intracardiac channel characteristics were obtained by simulating models with varying volumes of the four chambers according to the actual cardiac cycle. Subsequently, in vitro experiments were carried out to verify the model's correctness. Among the dependences of intracardiac communication channels, the distance between pacemakers exerted the most substantial influence on attenuation. In the simulation and measurement, the relationship between channel attenuation and pulsation was found through the variable-volume heart model and a porcine heart. The CIC channel attenuation had a variation of less than 3 dB.

Hypoxia and chronic kidney disease.

Hypoxia is an inherent pathophysiological characteristic of chronic kidney disease (CKD), which is closely associated with the development of renal inflammation and fibrosis, as well as CKD-related complications such as anaemia, cardiovascular events, and sarcopenia. This review outlined the characteristics of oxygen supply in the kidney, changes in oxygen metabolism and factors leading to hypoxia in CKD. Mechanistically, we discussed how hypoxia contributes to renal injury as well as complications associated with CKD. Furthermore, we also discussed the potential therapeutic approaches that target chronic hypoxia, as well as the challenges in the study of oxygen homeostasis imbalance in CKD.

Development and External Validation of a Nomogram and a Risk Table for Prediction of Type 2 Diabetic Kidney Disease Progression Based on a Retrospective Cohort Study in China.

Purpose: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. Risk assessment provides information about patient prognosis, contributing to the risk stratification of patients and the rational allocation of medical resources. We aimed to develop a model for individualized prediction of renal function decline in patients with type 2 DKD (T2DKD). Patients and Methods: In a retrospective observational study, we followed 307 T2DKD patients and evaluated the determinants of 1) risk of doubling in serum creatinine (Scr), 2) risk of eGFR<15 mL/min/1.73m2 using potential risk factors at baseline. A prediction model represented by a nomogram and a risk table was developed using Cox regression and externally validated in another cohort with 206 T2DKD patients. The discrimination and calibration of the prediction model were evaluated by the concordance index (C-index) and calibration curve, respectively. Results: Four predictors were selected to establish the final model: Scr, urinary albumin/creatinine ratio, plasma albumin, and insulin treatment. The nomogram achieved satisfactory prediction performance, with a C-index of 0.791 [95% confidence interval (CI) 0.762-0.820] in the derivation cohort and 0.793 (95% CI 0.746-0.840) in the external validation cohort. Then, all predictors were scored according to their weightings. A risk table with the highest score of 11.5 was developed. The C-index of the risk table was 0.764 (95% CI: 0.731-0.797), which was similar to the external validation cohort (0.763; 95% CI: 0.714-0.812). Additionally, the patients were divided into two groups based on the risk table, and significant differences in the probability of outcome events were observed between the high-risk (score >2) and low-risk (score ≤2) groups in the derivation and external validation cohorts (P < 0.001). Conclusion: The nomogram and the risk table using readily available clinical parameters could be new tools for bedside prediction of renal function decline in T2DKD patients.

Urinary sediment CCL5 messenger RNA as a potential prognostic biomarker of diabetic nephropathy.

BACKGROUND: Urinary sediment messenger RNAs (mRNAs) have been shown as novel biomarkers of kidney disease. We aimed to identify targeted urinary mRNAs in diabetic nephropathy (DN) based on bioinformatics analysis and clinical validation. METHODS: Microarray studies of DN were searched in the GEO database and Nephroseq platform. Gene modules negatively correlated with estimated glomerular filtration rate (eGFR) were identified by informatics methods. Hub genes were screened within the selected modules. In validation cohorts, a quantitative polymerase chain reaction assay was used to compare the expression levels of candidate mRNAs. Patients with renal biopsy-confirmed DN were then followed up for a median time of 21 months. End-stage renal disease (ESRD) was defined as the primary endpoint. Multivariate Cox proportional hazards regression was developed to evaluate the prognostic values of candidate mRNAs. RESULTS: Bioinformatics analysis revealed four chemokines (CCL5, CXCL1, CXLC6 and CXCL12) as candidate mRNAs negatively correlated with eGFR, of which CCL5 and CXCL1 mRNA levels were upregulated in the urinary sediment of patients with DN. In addition, urinary sediment mRNA of CXCL1 was negatively correlated with eGFR (r = -0.2275, P = 0.0301) and CCL5 level was negatively correlated with eGFR (r = -0.4388, P < 0.0001) and positively correlated with urinary albumin:creatinine ratio (r = 0.2693, P = 0.0098); also, CCL5 and CXCL1 were upregulated in patients with severe renal interstitial fibrosis. Urinary sediment CCL5 mRNA was an independent predictor of ESRD [hazard ratio 1.350 (95% confidence interval 1.045-1.745)]. CONCLUSIONS: Urinary sediment CCL5 and CXCL1 mRNAs were upregulated in DN patients and associated with a decline in renal function and degree of renal interstitial fibrosis. Urinary sediment CCL5 mRNA could be used as a potential prognostic biomarker of DN.

Event-Driven Intrinsic Plasticity for Spiking Convolutional Neural Networks.

The biologically discovered intrinsic plasticity (IP) learning rule, which changes the intrinsic excitability of an individual neuron by adaptively turning the firing threshold, has been shown to be crucial for efficient information processing. However, this learning rule needs extra time for updating operations at each step, causing extra energy consumption and reducing the computational efficiency. The event-driven or spike-based coding strategy of spiking neural networks (SNNs), i.e., neurons will only be active if driven by continuous spiking trains, employs all-or-none pulses (spikes) to transmit information, contributing to sparseness in neuron activations. In this article, we propose two event-driven IP learning rules, namely, input-driven and self-driven IP, based on basic IP learning. Input-driven means that IP updating occurs only when the neuron receives spiking inputs from its presynaptic neurons, whereas self-driven means that IP updating only occurs when the neuron generates a spike. A spiking convolutional neural network (SCNN) is developed based on the ANN2SNN conversion method, i.e., converting a well-trained rate-based artificial neural network to an SNN via directly mapping the connection weights. By comparing the computational performance of SCNNs with different IP rules on the recognition of MNIST, FashionMNIST, Cifar10, and SVHN datasets, we demonstrate that the two event-based IP rules can remarkably reduce IP updating operations, contributing to sparse computations and accelerating the recognition process. This work may give insights into the modeling of brain-inspired SNNs for low-power applications.

The Role of Vasoactive Intestinal Peptide and Mast Cells in the Regulatory Effect of Lactobacillus casei ATCC 393 on Intestinal Mucosal Immune Barrier.

Vasoactive intestinal peptide (VIP) plays an important role in the neuro-endocrine-immune system. Mast cells (MCs) are important immune effector cells. This study was conducted to investigate the protective effect of L. casei ATCC 393 on Enterotoxigenic Escherichia coli (ETEC) K88-induced intestinal mucosal immune barrier injury and its association with VIP/MC signaling by in vitro experiments in cultures of porcine mucosal mast cells (PMMCs) and in vivo experiments using VIP receptor antagonist (aVIP) drug. The results showed that compared with the ETEC K88 and lipopolysaccharides (LPS)-induced model groups, VIP pretreatment significantly inhibited the activation of MCs and the release of ß-hexosaminidase (ß-hex), histamine and tryptase. Pretreatment with aVIP abolished the protective effect of L. casei ATCC 393 on ETEC K88-induced intestinal mucosal immune barrier dysfunction in C57BL/6 mice. Also, with the blocking of VIP signal transduction, the ETEC K88 infection increased serum inflammatory cytokines, and the numbers of degranulated MCs in ileum, which were decreased by administration of L. casei ATCC 393. In addition, VIP mediated the regulatory effect of L. casei ATCC 393 on intestinal microbiota in mice. These findings suggested that VIP may mediate the protective effect of L.casei ATCC 393 on intestinal mucosal immune barrier dysfunction via MCs.

Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia.

Objective: This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia. Methods: First, the epidemic data of Beijing, China, were utilized to make a definite susceptible-infected-removed (SIR) model fitting to determine the estimated value of the COVID-19 removal intensity ß, which was then used to do a determined SIR model and a stochastic SIR model fitting for the hospital. In addition, the reasonable ß and γ estimates of the hospital were determined, and the spread of the epidemic in hospital was simulated, to discuss the impact of basal reproductive number changes, isolation, vaccination, and so forth on COVID-19. Results: There was a certain gap between the fitting of SIR to the remover and the actual data. The fitting of the number of infections was accurate. The growth rate of the number of infections decreased after measures, such as isolation, were taken. The effect of herd immunity was achieved after the overall immunity reached 70.9%. Conclusion: The SIR model based on deep learning and the stochastic SIR fitting model were accurate in judging the development trend of the epidemic, which can provide basis and reference for hospital epidemic infection control.

Kim-1 Targeted Extracellular Vesicles: A New Therapeutic Platform for RNAi to Treat AKI.

BACKGROUND: AKI is a significant public health problem with high morbidity and mortality. Unfortunately, no definitive treatment is available for AKI. RNA interference (RNAi) provides a new and potent method for gene therapy to tackle this issue. METHODS: We engineered red blood cell-derived extracellular vesicles (REVs) with targeting peptides and therapeutic siRNAs to treat experimental AKI in a mouse model after renal ischemia/reperfusion (I/R) injury and unilateral ureteral obstruction (UUO). Phage display identified peptides that bind to the kidney injury molecule-1 (Kim-1). RNA-sequencing (RNA-seq) characterized the transcriptome of ischemic kidney to explore potential therapeutic targets. RESULTS: REVs targeted with Kim-1-binding LTH peptide (REVLTH) efficiently homed to and accumulated at the injured tubules in kidney after I/R injury. We identified transcription factors P65 and Snai1 that drive inflammation and fibrosis as potential therapeutic targets. Taking advantage of the established REVLTH, siRNAs targeting P65 and Snai1 were efficiently delivered to ischemic kidney and consequently blocked the expression of P-p65 and Snai1 in tubules. Moreover, dual suppression of P65 and Snai1 significantly improved I/R- and UUO-induced kidney injury by alleviating tubulointerstitial inflammation and fibrosis, and potently abrogated the transition to CKD. CONCLUSIONS: A red blood cell-derived extracellular vesicle platform targeted Kim-1 in acutely injured mouse kidney and delivered siRNAs for transcription factors P65 and Snai1, alleviating inflammation and fibrosis in the tubules.

Identification of Lumican and Fibromodulin as Hub Genes Associated with Accumulation of Extracellular Matrix in Diabetic Nephropathy.

INTRODUCTION: Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). METHODS: Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. RESULTS: Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e-15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. CONCLUSION: By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN.