Machine learning model to estimate probability of remission in patients with idiopathic membranous nephropathy.

BACKGROUND: Idiopathic membranous nephropathy (IMN) is a type of nephrotic syndrome and the leading cause of chronic kidney disease. As far as we know, no predictive model for assessing the prognosis of IMN is currently available. This study aims to establish a nomogram to predict remission probability in patients with IMN and assists clinicians to make treatment decisions. METHODS: A total of 266 patients with histopathology-proven IMN were included in this study. Least absolute shrinkage and selection operator regression was utilized to identify the most important variables. Subsequently, multivariate Cox regression analysis was conducted to construct a nomogram, and bootstrap resampling was employed for internal validation. Receiver operating characteristic and calibration curves and decision curve analysis (DCA) were utilized to assess the performance and clinical utility of the developed model. RESULTS: A prognostic nomogram was established, which incorporated creatinine, glomerular_basement_membrane_thickening, gender, IgG_deposition, low-density lipoprotein cholesterol, and fibrinogen. The areas under the curves of the 3-, 12-, 24-month were 0.751, 0.725, and 0.830 in the training set, and 0.729, 0.730, and 0.948 in the validation set respectively. These results and calibration curves demonstrated the good discrimination and calibration of the nomogram in the training and validation sets. Additionally, DCA indicated that the nomogram was useful for remission prediction in clinical settings. CONCLUSION: The nomogram was useful for clinicians to evaluate the prognosis of patients with IMN in early stage.

Clinical predictive model to estimate probability of remission in patients with lupus nephritis.

BACKGROUND: Lupus nephritis (LN) is a major organ complication and cause of morbidity and mortality in patients with systemic lupus erythematosus. This study aims to provide the clinician with a quantitative tool for the prediction of the individual remission probability of LN and obtain new insights for improved clinical management in LN treatment. METHODS: A total of 301 patients with renal biopsy-proven LN were recruited and randomly divided into model construction and validation group. The least absolute shrinkage and selection operator regression analysis was conducted to select significant variables, and a multivariate Cox regression predictive model was established. The performance of the model was verified and tested with 1000-bootstrap validation in the validation group. Finally, the nomogram was constructed, and the performance was evaluated. The predictive accuracy and efficiency were verified through receiver operation characteristic and calibration curves. RESULTS: A total of 210 and 91 patients who all received renal biopsy were included in the training and validation group, respectively. A final prognostic model was established, which included the course of LN, gender, 24h-proteinuria, creatinine, triglycerides, FIB, Complement C3, anti-dsDNA antibody, tubular atrophy and classification of kidney biopsy. Moreover, an easy-to-use nomogram was built based on the predictive model. The areas under the curve (AUC) of the 1, 2, 5-year prediction were 77.12, 77.98 and 87.01 in the training group, respectively. In the validation group, the AUC of the 1, 2, 5-year prediction were 81.42, 87.20 and 92.81 respectively, which indicated good performance in predicting the remission probability of LN. CONCLUSION: This novel model was constructed to predict the remission probability of patients with LN for the first time. This model displayed good predictive performance and was easy to use for clinical practice.

SERS determination of dopamine using metal-organic frameworks decorated with Ag/Au noble metal nanoparticle composite after azo derivatization with p-aminothiophenol.

A specific surface-enhanced Raman scattering (SERS) assay for dopamine (DA) based on an azo derivatization reaction is proposed for the first time by preparation of p-aminothiophenol (PATP)-modified composite SERS substrate, composed of metal-organic framework (MIL-101) decorated with Au and Ag nanoparticles. As the result, the SERS method for detection of the azo reaction between PATP and DA exhibits superior sensitivity, selectivity, and stability. A reasonable linearity in the range 10-6 to 10-10 mol∙L-1 is achieved, and the limit of detection is 1.2 × 10-12 mol∙L-1. The reactive SERS assay is free from interference in complex physiological fluid. The feasibility of the proposed SERS method for the detection of DA levels in fetal bovine serum (FBS) samples and human serum samples is validated by HPLC-MS methods, displaying promising application potential in early disease diagnosis.

Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy.

AIMS/INTRODUCTION: Diabetic nephropathy (DN) is among the leading causes of end-stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co-expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases. MATERIALS AND METHODS: The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre-processing, weighted gene co-expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis-based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments. RESULTS: A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN. CONCLUSION: Weighted gene co-expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN.

ZnO Tips Dotted with Au Nanoparticles-Advanced SERS Determination of Trace Nicotine.

Long-term exposure to nicotine causes a variety of human diseases, such as lung damage/adenocarcinoma, nausea and vomiting, headache, incontinence and heart failure. In this work, as a surface-enhanced Raman scattering (SERS) substrate, zinc oxide (ZnO) tips decorated with gold nanoparticles (AuNPs) are fabricated and designated as ZnO/Au. Taking advantage of the synergistic effect of a ZnO semiconductor with morphology of tips and AuNPs, the ZnO/Au-based SERS assay for nicotine demonstrates high sensitivity and the limit of detection 8.9 × 10-12 mol/L is reached, as well as the corresponding linear dynamic detection range of 10-10-10-6 mol/L. Additionally, the signal reproducibility offered by the SERS substrate could realize the reliable determination of trace nicotine in saliva.

Clinical predictive model for the 1-year remission probability of IgA vasculitis nephritis.

PURPOSE: Early remission of Immunoglobulin A vasculitis nephritis (IgAVN) substantially affects its prognosis. In this work, a multivariate model to predict the 1-year remission probability of patients with IgAVN was developed on the basis of clinical laboratory data. METHODS: Data of 187 patients with IgAVN confirmed by renal biopsy were retrospectively assessed. Least absolute shrinkage and selection operator regression analysis were conducted to establish a multivariate logistic regression model. A nomogram based on the multivariate logistic regression model was constructed for easy application in clinical practice. Concordance index, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram. RESULTS: The predictive factors contained in the multivariate model included duration, gender, respiratory infection, arthritis, edema, estimated glomerular filtration rate, 24 h urine protein, uric acid, and renal ultrasound intensity. The area under the curves (AUC) of the nomogram in the training set and testing set were 0.814 and 0.822, respectively, indicating its good predictive ability. Moreover, the DCA curve and CIC revealed its clinical utility. CONCLUSION: The developed multivariate predictive model combines the clinical and laboratory factors of patients with IgAVN and is useful in the individualized prediction of the 1-year remission probability aid for clinical decision-making during treatment and management of IgAVN.

Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease.

BACKGROUND: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST. METHODS: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. RESULTS: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively. CONCLUSIONS: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Touch-Based Stressless Cortisol Sensing.

Tracking fluctuations of the cortisol level is important in understanding the body's endocrine response to stress stimuli. Traditional cortisol sensing relies on centralized laboratory settings, while wearable cortisol sensors are limited to slow and complex assays. Here, a touch-based non-invasive molecularly imprinted polymer (MIP) electrochemical sensor for rapid, simple, and reliable stress-free detection of sweat cortisol is described. The sensor readily measures fingertip sweat cortisol via highly selective binding to the cortisol-imprinted electropolymerized polypyrrole coating. The MIP network is embedded with Prussian blue redox probes that offer direct electrical signaling of the binding event to realize sensitive label-free amperometric detection. Using a highly permeable sweat-wicking porous hydrogel, instantaneously secreted fingertip sweat can be conveniently and rapidly collected without any assistance. By eliminating time lags, such rapid (3.5 min) fingertip assay enables the capture of sharp variations in cortisol levels, compared to previous methods. Such advantages are demonstrated by tracking cortisol response in short cold-pressor tests and throughout day-long circadian rhythm, along with gold-standard immunoassay validation. A stretchable epidermal MIP sensor is also described for directly tracking cortisol in exercise-induced sweat. The rapid touch-based cortisol sensor offers an attractive, accessible, stressless avenue for quantitative stress management.

Bioinformatic analysis reveals that the OAS family may play an important role in lupus nephritis.

BACKGROUND: Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. However, the molecular mechanisms of LN remain unclear. The lack of understanding hinders the development of specific targeted therapy for this progressive disease. OBJECTIVES: In the present study, we used bioinformatics analysis of gene expression profiles from the Gene Expression Omnibus to identify novel targets and potential biomarkers for LN. MATERIAL AND METHODS: A GSE32591 dataset, which included 31 LN glomerular biopsy tissues and 14 living donors' glomerular tissues, was downloaded for further analysis. Differentially expressed genes in LN were analyzed by the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the differentially expressed genes by using the Disease Ontology Semantic and Enrichment and the clusterProfiler software. The protein-protein interaction (PPI) network was then formed using STRING online tool. RESULTS: 440 genes, including 310 upregulated genes and 130 downregulated genes, were found as differentially expressed genes. GO and KEGG analyses revealed that immune response is significantly enriched in such genes. The PPI network showed that ISG15, MX1, OAS1, OAS2, and OAS3 were the hub genes enriched in LN. Along with literature review, the OAS family genes were revealed to be closely associated with LN progression. CONCLUSIONS: our studies provided new insight into the molecular pathogenesis of LN. The OAS family may play an important role in LN and act as a novel molecular candidate for the further study of LN.

Transcriptome sequencing of circular RNA reveals a novel circular RNA-has_circ_0114427 in the regulation of inflammation in acute kidney injury.

Acute kidney injury (AKI) is a common serious syndrome characterized by rapid decrease of glomerular filtration rate and the progressive increase of serum creatinine. Circular RNAs (circRNAs) are regulatory RNAs that recently became popular among various diseases. However, the expression profile and function of circRNAs in AKI remain largely unknown. The main function of circRNAs is acting as competing endogenous RNAs (ceRNAs) by binding with microRNAs (miRNAs), as indicated by recent research. In the present study, we established cisplatin-induced AKI model in mice and isolated renal tubular tissues to extract circRNAs for next-generation sequencing (NGS) and bioinformatics analysis. We analyzed the composition, distribution and Gene Ontology terms of circRNAs in cisplatin-induced AKI and revealed differentially expressed circRNAs related to AKI. By finding homologous genes between mouse and human, we identified circRNA- circ-0114427 in humans. We further investigated its function in AKI cell model. Circ-0114427 expression was significantly up-regulated in different AKI cell models. Knockdown of circ-0114427 indicated that circ-0114427 bound to miR-494 as a miRNA sponge to regulate ATF3 expression and further affected the expression of downstream cytokine IL-6. Circ-0114427 regulates inflammatory progression in AKI's early stage via circ-0114427/miR-494/ATF3 pathway. Our findings reveal the expression profile of circRNAs in cisplatin-induced AKI and provide a novel insight into the regulatory mechanism of circRNAs, which may become a new molecular target resource for early diagnosis and treatment strategies.

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis.

BACKGROUND: Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN. RESULTS: Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8, were closely related to clinical features. CONCLUSIONS: This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

Identification of prognosis-related alternative splicing events in kidney renal clear cell carcinoma.

Alternative splicing (AS) contributes to protein diversity by modifying most gene transcriptions. Cancer generation and progression are associated with specific splicing events. However, AS signature in kidney renal clear cell carcinoma (KIRC) remains unknown. In this study, genome-wide AS profiles were generated in 537 patients with KIRC in the cancer genome atlas. With a total of 42 522 mRNA AS events in 10 600 genes acquired, 8164 AS events were significantly associated with the survival of patients with KIRC. Logistic regression analysis of the least absolute shrinkage and selection operator was conducted to identify an optimized multivariate prognostic predicting mode containing four predictors. In this model, the receptor-operator characteristic curves of the training set were built, and the areas under the curves (AUCs) at different times were >0.88, thus indicating a stable and powerful ability in distinguishing patients' outcome. Similarly, the AUCs of the test set at different times were >0.73, verifying the results of the training set. Correlation and gene ontology analyses revealed some potential functions of prognostic AS events. This study provided an optimized survival-predicting model and promising data resources for future in-depth studies on AS mechanisms in KIRC.

Integrated bioinformatics analysis reveals novel hub genes closely associated with pathological mechanisms of immunoglobulin A nephropathy.

Immunoglobulin A (IgA) nephropathy (IgAN) is the most common glomerular disease. The major pathological changes associated with it affect cell proliferation, fibrosis, apoptosis, inflammation and extracellular matrix (ECM) organization. However, the molecular events underlying IgAN remain to be fully elucidated. In the present study, an integrated bioinformatics analysis was applied to further explore novel potential gene targets for IgAN. The mRNA expression profile datasets GSE93798 and GSE37460 were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) enrichment analysis of DEGs was performed. Protein-protein interaction (PPI) networks of the DEGs were built with the STRING online search tool and visualized by using Cytoscape, and hub genes were identified through the degree of connectivity in the PPI. The hub genes were subjected to Kyoto Encyclopedia of Genes and Genomes pathway analysis, and co-expression analysis was performed. A total of 298 DEGs between IgAN and control groups were identified, and 148 and 150 of these DEGs were upregulated and downregulated, respectively. The DEGs were enriched in distinct GO terms for Biological Process, including cell growth, epithelial cell proliferation, ERK1 and ERK2 cascades, regulation of apoptotic signaling pathway and ECM organization. The top 10 hub genes were then screened from the PPI network by Cytoscape. As novel hub genes, Fos proto-oncogene, AP-1 transcription factor subunit and early growth response 1 were determined to be closely associated with apoptosis and cell proliferation in IgAN. Tumor protein 53, integrin subunit ß2 and fibronectin 1 may also be involved in the occurrence and development of IgAN. Co-expression analysis suggested that these hub genes were closely linked with each other. In conclusion, the present integrated bioinformatics analysis provided novel insight into the molecular events and novel candidate gene targets of IgAN.

Label-free potentiometric aptasensing platform for the detection of Pb2+ based on guanine quadruplex structure.

Potentiometric aptasensors enhanced by integrating advanced nanomaterials are of particular interest for the detection of multiplex species (e.g., proteins, bacteria, micro-organisms) due to their low cost, ease of operation, and low detection limits. However, potentiometric detection of small ionic species aptasensors is still challenging. This article describes the first example of a label-free G-quadruplex-based potentiometric aptasensing platform for the detection of Pb2+. Polyion oligonucleotide-labeled gold nanoparticles (AuNPs-DNA) as probes are modified on Au electrode, providing high-density negative charge on the electrode surface. These signal-amplifying probes can selectively form G-quadruplexes with the presence of Pb2+ ions and reduce the negative charges on the electrode surface, hence achieving potentiometric detection of Pb2+ ions with high selectivity. The AuNPs-DNA-based aptasensor shows an acceptable sensitivity over a wide range from 10-11 to 10-6 M with a detection limit of 8.5 pM. Furthermore, confirmed by coupled plasma mass spectrometry, the sensing platform is capable of performing effective and accurate detection of Pb2+ level in real water samples. The presented aptasensor offers a fast, convenient, low-maintenance, and highly sensitive alternative for on-site water pollution detections.

Bioinformatic Analysis Reveals Novel Immune-Associated Hub Genes in Human Membranous Nephropathy.

BACKGROUND: Membranous nephropathy (MN) is one of the most common pathologies of the nephrotic syndrome. MN is closely associated with the autoimmune response but its molecular mechanism remains unclear. Bioinformatic network analysis can be used to identify disease-related hub genes. METHODS: The microarray data set GSE47183 of patients with MN containing 21 MN samples and 13 control samples that were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package. Thereafter, gene ontology (GO) enrichment was performed for DEGs using the clusterProfiler package. The protein-protein interaction (PPI) network was established through the Search Tool for the Retrieval of Interacting Genes database and visualized using Cytoscape. Finally, the hub genes were identified through the maximal clique centrality method. RESULT: A total of 642 DEGs were recognized, consisting of 458 upregulated genes and 184 downregulated genes. GO enrichment analysis indicates that DEGs for MN are mainly related to antigen processing and presentation. For the PPI network, we identified approximately nine hub genes. Considering data from the literature, we ultimately identified PSMB8 as a novel hub gene, which could play a significant role in the occurrence and development of MN. CONCLUSION: This study is the first to identify novel hub genes with transcriptome microarray data in MN using bioinformatics. The newly discovered hypothetical hub genes should be functionally tested to determine if they truly play an etiologic role in MN.

Bioinformatic analysis reveals novel hub genes and pathways associated with hypertensive nephropathy.

AIM: Hypertensive nephropathy (HTN) is one of the leading causes of end-stage renal disease and is closely associated with inflammation and tubule-interstitial fibrosis. The molecular mechanism underlying HTN remains unclear. This study used bioinformatic analysis to identify the novel gene targets for HTN. METHODS: We downloaded the microarray data of GSE99325 and GSE32591 from Gene Expression Omnibus. The dataset comprised 20 HTN and 15 normal samples. The differentially expressed genes (DEG) were identified, and then gene ontology (GO) enrichment was performed, and a GO tree was constructed by using clusterProfiler and ClueGO. In addition, a protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape. The novel hub genes were validated in in vitro experiments. RESULTS: A total of 267 genes (117 up-regulated and 150 down-regulated genes) were identified as DEG. GO analysis and the GO tree indicated that the DEG were mainly associated with steroid hormone response and the extracellular matrix. Based on the protein-protein interaction network, we screened out several novel hub genes. Considering the findings and the literature review, we focused on and validated the dual specificity phosphatase 1, tissue inhibitor of matrix metalloproteinases 1, fos proto-oncogene and jun proto-oncogenes, which may play significant roles in the pathogenesis of HTN. These findings were consistent with the bioinformatic results for the in vitro validation. CONCLUSION: This study identified for the first time novel hub genes with microarray data in HTN by using bioinformatic analysis and provided novel evidence and clues for future works.

PYCARD Gene Plays a Key Role in Rapidly Progressive Glomerulonephritis: Results of a Weighted Gene Co-Expression Network Analysis.

BACKGROUND: Rapidly progressive glomerulonephritis (RPGN) is caused by various diseases process, thereby resulting in extensive crescent formation, which could lead to a rapid loss of kidney function. The molecular pathogenesis of RPGN remains largely unknown and requires clarification. The weighted gene co-expression network analysis (WGCNA) is a powerful bioinformatics tool to identify meaningful molecules in diseases. METHODS: The dataset of GSE104948, which contains 22 RPGN and 18 normal samples, was obtained from Gene Expression Omnibus database. After data pre-processing, the WGCNA was performed to successfully cluster several significant modules. The most significant module was selected for further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Visualization of network and screening of hub genes were performed by using Cytoscape software. RESULTS: A total of 11 modules were clustered by WGCNA, and the most significant module-turquoise module was selected. As discovered via GO enrichment and KEGG pathway analysis, the turquoise module was mainly associated with neutrophil activation and degranulation. After visualization and calculation for the network, the PYCARD gene has higher relationship score in 2 clusters, namely, neutrophil activation and degranulation. In accordance with the literature review, the hub gene could be closely related to the inflammation response and could act as the potential therapeutic targets in RPGN. CONCLUSIONS: WGCNA in RPGN expression profiling was used for the first time in this paper. A novel hub gene closely associated with RPGN was screened out, thereby providing the brand-new molecular candidate for RPGN.

Internal Calibration Potentiometric Aptasensors for Simultaneous Detection of Hg2+, Cd2+, and As3+ Based on a Screen-Printed Carbon Electrodes Array.

An all-solid-state potentiometric aptasensor array based on a multichannel disposable screen-printed carbon electrode (SPCE) was demonstrated for the simultaneous detection of Hg2+, Cd2+, and As3+ by open circuit potential (OCP) technology. The potential of the channel with an internal calibration DNA sequence (IC-DNA) was employed as the internal calibration potential (ICP) to subtract the background signal generated by the detection system, providing a built-in correction methodology. As a result, the developed aptasensor array showed high sensitivity and accuracy for detecting Hg2+, Cd2+, and As3+ without mutual interference or interference from other ions. The linear response ranged from 2.5 pM to 2.5 µM, and the detection limits for Hg2+, Cd2+, and As3+ were 2.0, 0.62, and 0.17 pM, respectively. Furthermore, the potentiometric aptasensor array was successfully applied for the simultaneous detection of three ions in real samples. The results obtained from the developed approach agreed well with the results obtained from inductively coupled plasma mass spectrometry.

Molecularly imprinted polymers-based electrochemical DNA biosensor for the determination of BRCA-1 amplified by SiO2@Ag.

A novel electrochemical DNA (E-DNA) biosensing strategy was designed and used for the detection of breast cancer susceptibility gene (BRCA-1). The biosensor was based on gold nanoparticles-reduced graphene oxide (AuNPs-GO) modified glass carbon electrode (GCE) covered with the layer of molecularly imprinted polymers (MIPs) synthesized with rhodamine B (RhB) as template, methacrylic acid (MAA) as the monomer, and Nafion as additive. The signal amplification tracing tag SiO2@Ag NPs were prepared by covering AgNPs on the surface of SiO2 nanoparticles in situ, and then DNA probes were modified on AgNPs by Ag-S bond, forming the composites SiO2@Ag/DNA. In presence of target DNA (T-DNA), homogeneous hybridization was performed with SiO2@Ag/DNA and RhB labeled DNA, and the resulting SiO2@Ag/dsDNA/RhB was specifically recognized by MIPs via the interaction between imprinting cavities and RhB. Under optimal conditions, the proposed biosensor exhibited wide linear range from 10 fM to 100 nM, low detection limit of 2.53 fM (S/N = 3), excellent selectivity, reproducibility, stability, and feasibility in serum analysis. Overall, these findings suggest the promising prospects of the proposed biosensing strategy in clinical diagnostics.

Extracting Stage-Specific and Dynamic Modules Through Analyzing Multiple Networks Associated with Cancer Progression.

Determining the dynamics of pathways associated with cancer progression is critical for understanding the etiology of diseases. Advances in biological technology have facilitated the simultaneous genomic profiling of multiple patients at different clinical stages, thus generating the dynamic genomic data for cancers. Such data provide enable investigation of the dynamics of related pathways. However, methods for integrative analysis of dynamic genomic data are inadequate. In this study, we develop a novel nonnegative matrix factorization algorithm for dynamic modules ( NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules. NMF-DM applies the temporal smoothness framework by balancing the networks at the current stage and the previous stage. Experimental results indicate that the NMF-DM algorithm is more accurate than the state-of-the-art methods in artificial dynamic networks. In breast cancer networks, NMF-DM reveals the dynamic modules that are important for cancer stage transitions. Furthermore, the stage-specific and dynamic modules have distinct topological and biochemical properties. Finally, we demonstrate that the stage-specific modules significantly improve the accuracy of cancer stage prediction. The proposed algorithm provides an effective way to explore the time-dependent cancer genomic data.