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Electronic nematicity, in which rotational symmetry is spontaneously broken by electronic degrees of freedom, has been demonstrated as a ubiquitous phenomenon in correlated quantum fluids including high-temperature superconductors and quantum Hall systems

Impact of GFRA1 gene reactivation by DNA demethylation on prognosis of patients with metastatic colon cancer

The biological functional network of tumor tissues is relatively stable for a period of time and under different conditions, so the impact of tumor heterogeneity is effectively avoided. Based on edge perturbation, functional gene interaction networks were used to reveal the pathological environment of patients with non-small cell carcinoma at the individual level, and to identify cancer subtypes with the same or similar status, and then a multi-dimensional and multi-omics comprehensive analysis was put into practice. Two edge perturbation subtypes were identified through the construction of the background interaction network and the edge-perturbation matrix (EPM). Further analyses revealed clear differences between those two clusters in terms of prognostic survival, stemness indices, immune cell infiltration, immune checkpoint molecular expression, copy number alterations, mutation load, homologous recombination defects (HRD), neoantigen load, and chromosomal instability. Additionally, a risk prediction model based on TCGA for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) was successfully constructed and validated using the independent data set (GSE50081).

Scanning tunneling microscopy of a kagome superconductor---an unusual metal with a triangular lattice of atoms---confirms a number of previously hinted at electronic states.

The goal of this study was to predict the probability of transfusion of red blood cells and the volume of blood consumption based on the clinical characteristics of patients before surgery.The medical records of 565 patients over 65 years old who underwent posterior lumbar surgery and 586 patients over 65 years old receiving femoral fracture surgery were reviewed. The clinical characteristics of the patients were subjected to multivariate regression analysis. The scores of these factors' influences on intraoperative red blood cells infusion were based on the odds ratio of each multivariate risk factor. Non-linear regression was performed to predict the probability of intraoperative blood transfusion and the volume of blood used for patients with different scores.The factors that significantly influenced blood use during lumbar spinal stenosis and femoral fracture surgery in aged patients(P0.05) included age, body mass index, abnormal coagulation function, preoperative hemoglobin, administration of antithrombotic drugs, multisegmental lesions of the lumbar spine, femoral shaft fracture, secondary lumbar surgery and the time from fracture to surgery exceeding 48 h. According to our risk scoring system, patients of posterior lumbar surgery scored 0-10 and patients of femoral fracture had a score of 0-12. More than 50 % of patients receiving an intraoperative red blood cells transfusion during surgery scored1.The scoring system can be used as a predictive model for the probability of red blood cells transfusion and the blood volume in aged patients undergoing lumbar spinal stenosis and femoral fracture surgeries.

Background: Lumbar spinal stenosis and femoral fracture are common orthopedic problems in aged patients, and the probability of transfusion of red blood cell products during surgery is relatively high. The goal of this study was to predict the probability of transfusion of allogeneic red blood cells and the volume of blood consumption based on the clinical characteristics of patients before surgery.Methods: The medical records of 565 patients over 65 years old who underwent posterior lumbar surgery for lumbar spinal stenosis and those of 586 patients over 65 years old receiving surgery for femoral fracture were reviewed. The clinical characteristics of the patients were subjected to multivariate regression analysis. The influence of these characteristics based on the odds ratio of each multivariate risk factor was assessed to predict the probability of intraoperative red blood cell transfusion. Non-linear regression was performed to predict the probability of intraoperative blood transfusion and the volume of blood used for patients with different scores.Results: The factors that significantly influenced blood use during lumbar spinal stenosis surgery in aged patients(P75 years old, body mass index (BMI)>24, abnormal coagulation function, preoperative hemoglobin (Hb)≤110 g/L, multisegmental lesions of the lumbar spine and secondary lumbar surgery. The factors that significantly influenced blood use during femoral fracture surgery in aged patients (P75 years old, BMIConclusion: The probability of transfusion of allogeneic red blood cells and the blood volume needed can be predicted preoperatively in aged patients undergoing lumbar spinal stenosis and femoral fracture surgeries. This can reduce the need for blood matching during surgery, allow adjustment of the blood inventory, and ensure safety.

In this study, the preparation of molecularly imprinted polymers for bilobalide (BBMIPs) was successfully achieved by bulk polymerization with methacrylamide (MAM), trimethylolpropane triacrylate (TMPTA), and acetonitrile (ACN) as functional monomer, cross-linker, and solvent, respectively. After Gaussian software simulation and single factor experiments, the prepared MIPs with a molar ratio of 1:4:15 for BB-MAM-TMPTA were systematically characterized. The hydrogen bonding interaction between BB and MAM was confirmed by a combination of FTIR and NMR analysis. Thermal gravimetric analysis results displayed that MIPs have excellent thermal stability under high temperature. Additionally, the average pore size and surface area of MIPs were found to be higher than those of NIPs through nitrogen adsorption results. The results of static adsorption and kinetic adsorption suggested that the adsorption equilibrium concentration was 0.6 mg/mL and the equilibrium time was 5 h, and the Langmuir and pseudo-second-order kinetic models were proven to fit with static and kinetic adsorption behaviors, respectively. Meanwhile, the selective adsorption study revealed that MIPs show high adsorption and great selectivity towards BB in comparison with other substances having similarly structure. MIPs also possessed a good performance on reusability, maintaining a high recovery rate after being reused 5 times. The application experiment further indicated that MIPs can effectively separate BB from low purity samples. Therefore, the prepared MIPs had a great potential for BB separation.

Background The CDKN2A/B locus contains crucial tumor suppressors and a lncRNA gene ANRIL. However, the mechanisms that coordinately regulate their expression levels are not clear. Methods Novel RNAs transcribed from the CDKN2A gene were screened by CDKN2A-specific RNA capture deep-sequencing and confirmed by Northern blotting and clone-sequencing. Long non-coding RNA (lncRNA) binding proteins were characterized by RNA pull-down combined with mass spectrometry and RNA immunoprecipitation. LncRNA functions in human cells were studied using a set of biological assays in vitro and in vivo. Results We characterized a novel lncRNA, P14AS with its promoter in the antisense strand of the fragment near CDKN2A exon 1b in human cells. The mature P14AS is a three-exon linear cytoplasmic lncRNA (1043-nt), including an AU-rich element (ARE) in exon 1. P14AS decreases AUF1-ANRIL/P16 RNA interaction and then increases ANRIL/P16 expression by competitively binding to AUF1 P37 and P40 isoforms. Interestingly, P14AS significantly promoted the proliferation of cancer cells and tumor formation in NOD-SCID mice in a P16-independent pattern. Moreover, in human colon cancer tissues, the expression levels of P14AS and ANRIL lncRNAs were significantly upregulated compared with the paired normal tissues. Conclusion A novel lncRNA, P14AS, transcribed from the antisense strand of the CDKN2A/P14 gene, promotes colon cancer development by cis upregulating the expression of oncogenic ANRIL.

Additional file 5 Figure S3. P14AS expression decreased P16 mRNA-AUF1 binding. (a) AUF1 directly bound to ANRIL and P16 mRNA in HCT116 cells in the AUF1-RIP-PCR. (b) P14AS overexpression decreased ANRIL and P16 mRNA-AUF1 interaction in by the AUF1-RIP-qPCR. (c) An illustration of how the competitive AUF1-P14AS binding protects ANRIL and P16 mRNA from the decay. AUF1 complexes were drawn as dimers based on the reports that AUF1 isoforms (p37, p40, p42, and p45) could form functional dimers [25, 26].

Additional file 8 Table S3. Function annotations for P14AS-downregulated genes (n = 299) with fold change > 2 in HCT116 cells with the David 6.8: Functional Annotation Tools at the website http://david.ncifcrf.gov/tools.jsp [13]

Additional file 4 Figure S2.P14AS affects the expression level of the neighboring genes at 9p21.3. qRT-PCR data are normalized to GAPDH mRNA levels and shown as the means ± SD. (a) The expression levels of P14, P16, and P15 genes in the P14AS vector stably transfected colon cancer cells (HCT116 and SW480), and gastric cancer cells (MGC803) were analyzed by qRT-PCR. (b) Detection of P16/P15/P14 proteins in MGC803 cells in Western blot analyses. (c) A fragment deletion in P14AS exon 1 was detected by PCR (top chart) in HEK293T or HCT116 P14AS-KO clones (KO). The expression changes of P14, P16, and P15 genes in HCT116 and HEK293T cells whose ARE-containing elements in P14AS exon-1 were homogenously deleted in the qRT-PCR analysis (bottom chart). Pooled P14AS ARE-KO-negative subclones were used as a wild-type (WT) control. (d) Detection of the P16, P15, and P14 proteins in HEK293T cells in Western blot analyses.

Additional file 10 Figure S6. Comparison of the levels of P14AS, P16, and P15 expression in colon tissues from cancer and noncancer patients. (a) The expression status of P14AS, P16, and P15 in colon cancer (CC), paired surgical margin (SM), and normal colon biopsy (Normal) tissues from noncancer patients by qRT-PCR. (b) The level of P14AS expression in ANRIL-positive and -negative (by RT-PCR) colon CC and SM tissues. (c and d) Comparison of the expression levels of P16 and P15 mRNA (by qRT-PCR) in P14AS-positive and -negative (by RT-PCR; 1043 bp) colon CC and SM tissues. Error bars, S.E.M. *, P

Additional file 3 Table S1. List of proteins interacted with P14AS characterized in the HEK293T cells in RNA pull-down mass spectrum analysis

Additional file 6 Figure S4. Genome-wide analyses of transcriptome by RNA sequencing for HCT116 cells with and without P14AS overexpression and/or AUF1 downregulation. The HCT116 cells with stable P14AS overexpression were transfected with AUF1 siRNAs (siAUF1) for 72 h, and then harvested for RNA sequencing. The number of genes with > 2 fold changes (UP, upregulated; Down, downregulated) for different types of RNAs were labeled. Western blot analysis for the determination of AUF1 downregulation by siRNAs was inserted into the top chart. Two samples were sequenced for each group.

Additional file 7 Table S2. Function annotations for P14AS-upregulated genes (n = 241) with fold change > 2 in HCT116 cells with the David 6.8: Functional Annotation Tools at the website http://david.ncifcrf.gov/tools.jsp [13]

Additional file 2 Figure S1. Characterization of P14AS in the CDKN2A/B locus. (A) Sashimi view for transcripts detected by CDKN2A-specific probe-captured RNA (RNACap)-Seq in HEK293T cells containing two wild-type CDKN2A/B alleles and in MCF7 cells with the homogenous P16 deletion. (B) Graphic view of the protein-coding potential for P14AS, CDKN2A/P14, and ANRIL genes (adapted from the UCSC website).

Additional file 9 Figure S5. Association analyses between the expression levels of AUF1 and ANRIL, P16, or P15 using the publicly available transcriptome databases for human cancer cell lines in the CCLE project. (A) All 1037 cell lines; (B) 224 cell lines without the CDKN2A allele deletion (relative copy number > 0).

Objective To investigate the relationship between the transcription of ANRIL, P15, P14 and P16 at the same locus and the regulation mechanism of ANRIL. Methods Publicly available database of Cancer Cell Line Encyclopedia (CCLE) was used in bioinformatic analyses. Methylation of CpG islands was detected by denaturing high performance liquid chromatography (DHPLC). Gene transcript levels were determined using quantitative real-time polymerase chain reaction (qRT-PCR) assays. An engineered P16-specific transcription factor and DNA methyltransferase were used to induce P16-specific DNA demethylation and methylation. Results The expression level of ANRIL was positively and significantly correlated with that of P16 but not with that of P15 in the CCLE database. This was confirmed in human cell lines and patient colon tissue samples. In addition, ANRIL was significantly upregulated in colon cancer tissues. Transcription of ANRIL and P16 was observed only in cell lines in which the P16 alleles were unmethylated and not in cell lines with fully methylated P16 alleles. Notably, P16-specific methylation significantly decreased transcription of P16 and ANRIL in BGC823 and GES1 cells. In contrast, P16-specific demethylation re-activated transcription of ANRIL and P16 in H1299 cells (P

Background and Objective The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors. Methods Using a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output. Results The correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed. Conclusions ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

To investigate the relationship between the transcription ofPublicly available database of Cancer Cell Line Encyclopedia (CCLE) was used in bioinformatic analyses. Methylation of CpG islands was detected by denaturing high performance liquid chromatography (DHPLC). Gene transcript levels were determined using quantitative real-time polymerase chain reaction (qRT-PCR) assays. An engineeredThe expression level of