A wireless smartphone-based "tap-and-detect" formaldehyde sensor with disposable nano-palladium grafted laser-induced graphene (nanoPd@LIG) electrodes.

We developed a fully integrated smart sensing device for on-site testing of food to detect trace formaldehyde (FA). A nano-palladium grafted laser-induced graphene (nanoPd@LIG) composite was synthesized by one-step laser irradiation of a Pd2+-chitosan-polyimide precursor. The composite was synthesized in the form of a three-electrode sensor on a polymer substrate. The electrochemical properties and morphology of the fabricated composite were characterized and the electrochemical kinetics of FA oxidation at the nanoPd@LIG electrode were investigated. The nanoPd@LIG electrode was combined with a smart electrochemical sensing (SES) device to determine FA electrochemically. The proposed SES device uses near field communication (NFC) to receive power and transfer data between a smartphone interface and a battery-free sensor. The proposed FA sensor exhibited a linear detection range from 0.01 to 4.0 mM, a limit of detection of 6.4 µM, good reproducibility (RSDs between 2.0 and 10.1%) and good anti-interference properties for FA detection. The proposed system was used to detect FA in real food samples and the results correlated well with the results from a commercial potentiostat and a spectrophotometric analysis.

Elevated expression of the MYB proto-oncogene like 2 (MYBL2)-encoding gene as a prognostic and predictive biomarker in human cancers.

Recently, MYBL2 is frequently found to be overexpressed and associated with poor patient outcome in breast cancer, colorectal cancer, bladder carcinoma, hepatocellular carcinoma, neuroblastoma and acute myeloid leukemia. In view of the fact that there is an association between MYBL2 expression and the clinicopathological features of human cancers, most studies reported so far are limited in their sample size, tissue type and discrete outcomes. Furthermore, we need to verify which additional cancer entities are also affected by MYBL2 deregulation and which patients could specifically benefit from using MYBL2 as a biomarker or therapeutic target. We characterized the up-regulated expression level of MYBL2 in a large variety of human cancer via TCGA and oncomine database. Subsequently, we verified the elevated MYBL2 expression effect on clinical outcome using various databases. Then, we investigate the potential pathway in which MYBL2 may participate in and find 4 TFs (transcript factors) that may regulate MYBL2 expression using bioinformatic methods. At last, we confirmed elevated MYBL2 expression can be useful as a biomarker and potential therapeutic target of poor patient prognosis in a large variety of human cancers. Additionally, we find E2F1, E2F2, E2F7 and ZNF659 could interact with MYBL2 promotor directly or indirectly, indicating the four TFs may be the upstream regulator of MYBL2. TP53 mutation or TP53 signaling altered may lead to elevated MYBL2 expression. Our findings indicate that elevated MYBL2 expression represents a prognostic biomarker for a large number of cancers. What's more, patients with both P53 mutation and elevated MTBL2 expression showed a worse survival in PRAD and BRCA.

Diagnosis and prognosis potential of four gene promoter hypermethylation in prostate cancer.

The current prostate special antigen (PSA) test causes the overtreatment of indolent prostate cancer (PCa). It also increases the risk of delayed treatment of aggressive PCa. DNA methylation aberrations are important events for gene expression dysregulation during tumorigenesis and have been suggested as novel candidate biomarkers for PCa. This may improve the diagnosis and prognosis of PCa. This study assessed the differential methylation and messenger RNA (mRNA) expression between normal and PCa samples. Correlation between promoter methylation and mRNA expression was estimated using Pearson's correlation coefficients. Moreover, the diagnostic potential of candidate methylation markers was estimated by the receiver operating characteristic (ROC) curve using continuous beta values. Survival and Cox analysis was performed to evaluate the prognostic potential of the candidate methylation markers. A total of 359 hypermethylated sites 3435 hypomethylation sites, 483 upregulated genes, and 1341 downregulated genes were identified from The Cancer Genome Atlas database. Furthermore, 17 hypermethylated sites (covering 13 genes), including known genes associated with hypermethylation in PCa (e.g., AOX1 and C1orf114), showed high discrimination between adjacent normal tissues and PCa samples with the area under the ROC curve from 0.88 to 0.94. Notably, ANXA2, FGFR2, HAAO, and KCNE3 were identified as valuable prognostic markers of PCa through the Kaplan-Meier analysis. Using gene methylation as a continuous variable, four promoter hypermethylation was significantly associated with disease-free survival in univariate Cox regression and multivariate Cox regression. This study identified four novel diagnostic and prognostic markers for PCa. The markers provide important strategies for improving the timely diagnosis and prognosis of PCa.

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction.

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package "cluster profile" and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

Soft and flexible material-based affinity sensors.

Recent advances in biosensors and point-of-care (PoC) devices are poised to change and expand the delivery of diagnostics from conventional lateral-flow assays and test strips that dominate the market currently, to newly emerging wearable and implantable devices that can provide continuous monitoring. Soft and flexible materials are playing a key role in propelling these trends towards real-time and remote health monitoring. Affinity biosensors have the capability to provide for diagnosis and monitoring of cancerous, cardiovascular, infectious and genetic diseases by the detection of biomarkers using affinity interactions. This review tracks the evolution of affinity sensors from conventional lateral-flow test strips to wearable/implantable devices enabled by soft and flexible materials. Initially, we highlight conventional affinity sensors exploiting membrane and paper materials which have been so successfully applied in point-of-care tests, such as lateral-flow immunoassay strips and emerging microfluidic paper-based devices. We then turn our attention to the multifarious polymer designs that provide both the base materials for sensor designs, such as PDMS, and more advanced functionalised materials that are capable of both recognition and transduction, such as conducting and molecularly imprinted polymers. The subsequent content discusses wearable soft and flexible material-based affinity sensors, classified as flexible and skin-mountable, textile materials-based and contact lens-based affinity sensors. In the final sections, we explore the possibilities for implantable/injectable soft and flexible material-based affinity sensors, including hydrogels, microencapsulated sensors and optical fibers. This area is truly a work in progress and we trust that this review will help pull together the many technological streams that are contributing to the field.

Early Stage Biomarkers Screening of Prostate Cancer Based on Weighted Gene Coexpression Network Analysis.

Although the morbidity and mortality rates of prostate cancer (PCa) are considerably high, many PCas are characterized as indolent and slow growing, which do not require overtreatment. Overdiagnosis and overtreatment of early detected PCa are an emerging problem, owing to a lack of biomarkers that detect advanced disease at an earlier stage. In this study, RNA-Seq data of 57,045 genes for 495 PCa samples and 52 normal samples in the The Cancer Genome Atlas (TCGA) database were downloaded. Subsequently, we performed weighted gene coexpression network analysis to identify the Gleason score-related coexpression gene module, and further screened out oncogenes and tumor suppressors that were upregulated or downregulated in the early stage of PCa as well as those related to the clinical prognosis of PCa patients. Based on this study, some novel biomarkers were identified for the disease-free survival, which are helpful for fast diagnosis and prognosis.