High miR-34a and miR-26b expressions inhibit prostate cancer cell OPCN-1 proliferation and enhances apoptosis

Purpose: To investigate the effects of miR-34a and miR-26b on the targeted genes, LEF1 and EphA2, and proliferation and apoptosis of OPCN-1.Methods: Sixty specimens of cancer tissue (CT) and equivalent tissue adjacent to tumors (TAT) were collected from prostate cancer patients. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to determine the mRNA expression levels of miR-34a, miR-26b, LEF1, and EphA2 in the above tissues, while protein expression levels of LEF1 and EphA2 were evaluated by Western blot.Results: Compared with TAT, the expression levels of miR-26b and miR-34a in CT decreased significantly (p < 0.05), whereas the mRNA and protein expression levels of EphA2 and LEF1 in CT significantly increased (p < 0.05). TargetScanHuman7.2 assay data revealed that miR-26b targeted EphA2, while miR-34a targeted LEF1. MiR-26b MG showed decreased EphA2 mRNA and protein levels when compared with miR-26b-NC group after overexpression. The miR-34a MG exhibited decreased expression levels of LEF1 mRNA and protein compared with the miR-34a-NC group. Between 48 and 72 h, miR-26b MG grew more slowly than miR-26b-NC group; miR-34a MG also showed significantly slower growth than miR-34a-NC group. The miR-26b MG and miR-34a MG groups displayed higher apoptosis rate than miR-26b-NC and miR-34a-NC groups, respectively.Conclusion: High expressions of miR-34a and miR-26b targeted the inhibition of LEF1 and EphA2, respectively, indicating that they inhibit the proliferation, and also control the increased apoptosis rate of OPCN-1 cells. Hence, miR-34a and miR-26b are probable molecular targets for the development of new prostate cancer drugs

The relationship between frequent premature ventricular complexes and epicardial adipose tissue volume

BackgroundEpicardial adipose tissue (EAT) is related to atrial fibrillation. The association between EAT volume and premature ventricular complexes (PVCs) remains unclear. Our study aimed to investigate the effect of EAT volume on the risk of frequent PVCs and burden levels of PVCs.MethodsThis observational study retrospectively recruited consecutive patients who had consultation between 2019 and 2021 at the First Affiliated Hospital of Zhengzhou University. Frequent PVC patients (n = 402) and control patients (n = 402) undergoing non-contrast computed tomography (CT) were enrolled. We selected evaluation criteria for the conduct of a 1:1 propensity score matching (PSM) analysis. Multivariable logistic analysis was used to investigate factors related to frequent PVCs. Furthermore, the determinants of EAT volume and the burden levels of PVCs were evaluated.ResultsPatients with PVCs had a significantly larger EAT volume than control patients. EAT volume was significantly larger in male PVC patients with BMI ≥24 kg/m2, diabetes mellitus, and E/A ratio <1. EAT volume was independently associated with PVCs. Moreover, the larger EAT volume was an independent predictor for the high burden level of PVCs. We revealed that the risk of high PVC burden level was increased with the rising of EAT volume by restricted cubic splines.ConclusionsEAT volume was larger in frequent PVC patients than in control patients, regardless of other confounding factors. A large EAT volume was independently associated with high burden levels of PVCs. EAT volume may be a new mechanism to explain the pathogenesis of PVCs

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain

The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL https://github.com/zhaoyw456/DIPRAS

An oxidative stress biomarkers predict prognosis in gastric cancer patients receiving immune checkpoint inhibitor

ObjectiveThe development and advance of gastric cancer are inextricably linked to oxidative and antioxidant imbalance. Although immunotherapy has been shown to be clinically effective, the link between oxidative stress and gastric cancer patients treated with immune checkpoint inhibitor (ICIs) remains unknown. This study aims at looking into the prognostic value of oxidative stress scores in gastric cancer patients treated with ICIs.MethodsBy taking the propagation to receiver operating characteristic (ROC) we got the best cut-off values, and divided 265 patients receiving ICIs and chemotherapy into high and low GC-Integrated Oxidative Stress Score (GIOSS) groups. We also used Kaplan-Meier and COX regression models to investigate the relationship between oxidative stress biomarkers and prognosis.ResultsThrough both univariate and multivariate analyses, it’s shown that GIOSS severs as an independent prognostic factor for progression-free survival (PFS) and Overall survival (OS). Based on GIOSS cutoff values, patients with high GIOSS levels, compared to those with low levels exhibited shorter PFS and OS, both in the high GIOSS group, which performed poorly in the ICIs subgroup and other subgroup analyses.ConclusionGIOSS is a biomarker that responds to systemic oxidative stress in the body and can predict prognosis in patients with gastric cancer who are taking ICIs. Additionally, it might come to medical professionals’ aid in making more effective or more suitable treatment plans for gastric cancer

Conjugation polymer nanobelts: a novel fluorescent sensing platform for nucleic acid detection†

In this article, we report on the facile and rapid synthesis of conjugation polymer poly(p-phenylenediamine) nanobelts (PNs) via room temperature chemical oxidation polymerization of p-phenylenediamine monomers by ammonium persulfate in aqueous medium. We further demonstrate the proof-of-concept that PNs can be used as an effective fluorescent sensing platform for nucleic acid detection for the first time. The general concept used in this approach lies in the facts that the adsorption of the fluorescently labeled single-stranded DNA probe by PN leads to substantial fluorescence quenching, followed by specific hybridization with the complementary region of the target DNA sequence. This results in desorption of the hybridized complex from PN surface and subsequent recovery of fluorescence. We also show that the sensing platform described herein can be used for multiplexing detection of nucleic acid sequences

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties

Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China

<p>Abstract</p> <p>Background</p> <p>Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantavirus, with characteristics of fever, hemorrhage, kidney damage, and hypotension. HFRS is recognized as a notifiable public health problem in China, and Liaoning Province is one of the most seriously affected areas with the most cases in China. It is necessary to investigate the spatial, temporal, and space-time distribution of confirmed cases of HFRS in Liaoning Province, China for future research into risk factors.</p> <p>Methods</p> <p>A cartogram map was constructed; spatial autocorrelation analysis and spatial, temporal, and space-time cluster analysis were conducted in Liaoning Province, China over the period 1988-2001.</p> <p>Results</p> <p>When the number of permutation test was set to 999, Moran's I was 0.3854, and was significant at significance level of 0.001. Spatial cluster analysis identified one most likely cluster and four secondary likely clusters. Temporal cluster analysis identified 1998-2001 as the most likely cluster. Space-time cluster analysis identified one most likely cluster and two secondary likely clusters.</p> <p>Conclusions</p> <p>Spatial, temporal, and space-time scan statistics may be useful in supervising the occurrence of HFRS in Liaoning Province, China. The result of this study can not only assist health departments to develop a better prevention strategy but also potentially increase the public health intervention's effectiveness.</p

Research on an online self-organizing radial basis function neural network

A new growing and pruning algorithm is proposed for radial basis function (RBF) neural network structure design in this paper, which is named as self-organizing RBF (SORBF). The structure of the RBF neural network is introduced in this paper first, and then the growing and pruning algorithm is used to design the structure of the RBF neural network automatically. The growing and pruning approach is based on the radius of the receptive field of the RBF nodes. Meanwhile, the parameters adjusting algorithms are proposed for the whole RBF neural network. The performance of the proposed method is evaluated through functions approximation and dynamic system identification. Then, the method is used to capture the biochemical oxygen demand (BOD) concentration in a wastewater treatment system. Experimental results show that the proposed method is efficient for network structure optimization, and it achieves better performance than some of the existing algorithms