Holographic Dark Energy Characterized by the Total Comoving Horizon and Insights to Cosmological Constant and Coincidence Problem

The observed acceleration of the present universe is shown to be well explained by the holographic dark energy characterized by the total comoving horizon of the universe ($\eta$HDE). It is of interest to notice that the very large primordial part of the comoving horizon generated by the inflation of early universe makes the $\eta$HDE behave like a cosmological constant. As a consequence, both the fine-tuning problem and the coincidence problem can reasonably be understood with the inflationary universe and holographical principle. We present a systematic analysis and obtain a consistent cosmological constraint on the $\eta$HDE model based on the recent cosmological observations. It is found that the $\eta$HDE model gives the best-fit result $\Omega_{m0}=0.270$ ($\Omega_{de0}=0.730$) and the minimal $\chi^2_{min}=542.915$ which is compatible with $\chi^2_{\Lambda {\rm CDM}}=542.919$ for the $\Lambda$CDM model.Comment: 17 pages, 4 figures, two eqs. (26)(27) added for the consistent approximate solution of dark energy in early universe, references added, published version in PR

Multiple machine learning methods aided virtual screening of Na(V)1.5 inhibitors

Na(v)1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na(v)1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na(v)1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na(v)1.5 ion channel and key privileged substructures with high affinity were also extracted.Peer reviewe

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).Peer reviewe

Receptor-Targeting Phthalocyanine Photosensitizer for Improving Antitumor Photocytotoxicity

Photodynamic therapy (PDT) is a promising therapeutic modality which uses a photosensitizer to capture visible light resulting in phototoxicity in the irradiated region. PDT has been used in a number of pathological indications, including tumor. A key desirable feature of the photosensitizer is the high phototoxicity on tumor cells but not on normal cells. In this study, we conjugate a gonadotropin-releasing hormone (GnRH) to a photosensitizer, Zinc phthalocyanine (ZnPc), in order to enhance its specificity to breast cancer, which over-expresses GnRH receptor. ZnPc has unique advantages over other photosensitizers, but is difficult to derivatize and purify as a single isomer. We previously developed a straight-forward way to synthesize mono-substituted β-carboxy-phthalocyanine zinc (ZnPc-COOH). Photophysical and photochemical parameters of this ZnPc-GnRH conjugate including fluorescence quantum yield (Фf), fluorescence decay time (τs) and singlet oxygen quantum yield (ФΔ) were evaluated and found comparable with that of ZnPc, indicating that addition of a GnRH peptide does not significantly alter the generation of singlet oxygen from ZnPc. Cellular uptakes and phototoxicities of this conjugate were tested and found significantly enhanced on human breast cancer cell lines overexpressing GnRH receptors (MDA-MB-231 and MCF-7 cells) compared to cells with low levels of GnRH receptors, such as human embryonic lung fibroblast (HELF) and human liver carcinoma (HepG2) cells. In addition, the cellular uptake of this conjugate toward MCF-7 cells were found clearly alleviated by a GnRH receptor blocker Cetrorelix, suggesting that the cellular uptake of this conjugate was GnRH receptor-mediated. Put together, these findings revealed that coupling ZnPc with GnRH analogue was an effective way to improve the selectivity of ZnPc towards tumors with over-expressed GnRH receptors

Cosmological Constraint and Analysis on Holographic Dark Energy Model Characterized by the Conformal-age-like Length

We present a best-fit analysis on the single-parameter holographic dark energy model characterized by the conformal-age-like length, $L=\frac{1}{a^4(t)}\int_0^tdt' a^3(t')$. Based on the Union2 compilation of 557 supernova Ia data, the baryon acoustic oscillation results from the SDSS DR7 and the cosmic microwave background radiation data from the WMAP7, we show that the model gives the minimal $\chi^2_{min}=546.273$, which is comparable to $\chi^2_{\Lambda{\rm CDM}}=544.616$ for the $\Lambda$CDM model. The single parameter $d$ concerned in the model is found to be $d=0.232\pm 0.006\pm 0.009$. Since the fractional density of dark energy $\Omega_{de}\sim d^2a^2$ at $a \ll 1$, the fraction of dark energy is naturally negligible in the early universe, $\Omega_{de} \ll 1$ at $a \ll 1$. The resulting constraints on the present fractional energy density of matter and the equation of state are \Omega_{m0}=0.286^{+0.019}_{-0.018}^{+0.032}_{-0.028} and w_{de0}=-1.240^{+0.027}_{-0.027}^{+0.045}_{-0.044} respectively. The model leads to a slightly larger fraction of matter comparing to the $\Lambda$CDM model. We also provide a systematic analysis on the cosmic evolutions of the fractional energy density of dark energy, the equation of state of dark energy, the deceleration parameter and the statefinder. It is noticed that the equation of state crosses from $w_{de}>-1$ to $w_{de}<-1$, the universe transits from decelerated expansion ($q>0$) to accelerated expansion ($q<0$) recently, and the statefinder may serve as a sensitive diagnostic to distinguish the CHDE model with the $\Lambda$CDM model.Comment: 17 pages, 5 figures, minor changes for the fitting data, references adde

A Surface-Enhanced Raman Scattering Sensor Integrated with Battery-Controlled Fluidic Device for Capture and Detection of Trace Small Molecules

For surface-enhanced Raman scattering (SERS) sensors, one of the important issues is the development of substrates not only with high SERS-activity but also with strong ability to capture analytes. However, it is difficult to achieve the two goals simultaneously especially when detecting small molecules. Herein a compact battery-controlled nanostructure-assembled SERS system has been demonstrated for capture and detection of trace small molecule pollutants in water. In this SERS fluidic system, an electrical heating constantan wire covered with the vertically aligned ZnO nanotapers decorated with Ag-nanoparticles is inserted into a glass capillary. A mixture of thermo-responsive microgels, Au-nanorods colloids and analyte solution is then filled into the remnant space of the capillary. When the system is heated by switching on the battery, the thermo-responsive microgels shrink, which immobilizes the analyte and drives the Au-nanorod close to each other and close to the Ag-ZnO nanotapers. This process has also created high-density “hot spots” due to multi-type plasmonic couplings in three-dimensional space, amplifying the SERS signal. This integrated device has been successfully used to measure methyl parathion in lake water, showing a great potential in detection of aquatic pollutants

miR-122-5p Inhibits the Proliferation, Invasion and Growth of Bile Duct Carcinoma Cells by Targeting ALDOA

Background/Aims: Bile duct cancer, although not among the most common tumors, still accounts for more and more worldwide deaths each year. By attempting to verify an overexpression of ALDOA in cholangiocarcinoma tissues and cells and explore the underlying molecular mechanism regulated by miR-122-5p, this study was designed to provide a potential molecular target in bile duct cancer treatment. Methods: Western blot and immunohistochemistry were performed to detect the ALDOA protein level in duct carcinoma tissues. The transfection efficiency was confirmed by western blot and/or RT-qPCR assay. The proliferation of bile duct carcinoma cells was determined by MTT and colony formation assay. The invasion ability of bile duct carcinoma cells was evaluated with Transwell invasion assay. Flow cytometry detected cell apoptosis of bile duct carcinoma cells. The miRNAs which modulate ALDOA were filtrated from bioinformatics software and clinical specimens. The target relationship was confirmed by dual luciferase reporter assay. Furthermore, a xenograft model was completed to verify the impact of miRNA on inhibition growth of bile duct carcinoma cells. Results: ALDOA was found up-regulated in bile duct carcinoma tissues and cells. Knockdown of ALDOA promoted the apoptosis of cells and inhibited the proliferation and invasion of bile duct carcinoma cells. Bioinformatics and clinical specimens indicated the negative correlation and targeted regulation between miR-122-5p and ALDOA. By down-regulating ALDOA, overexpression of miR-122-5p appeared to promote cell apoptosis and significantly inhibit cell proliferation, invasion in vitro and suppress the tumor growth in vivo. Conclusion: miR-122-5p inhibited proliferation and invasion of bile duct carcinoma cells and promoted cell apoptosis by targeting ALDOA expression

Synthesis of spirocyclic enones by rhodium-catalyzed dearomatizing oxidative annulation of 2-alkenylphenols with alkynes and enynes

The dearomatizing oxidative annulation of 2-alkenylphenols with alkynes and enynes proceeds with high yields and regioselectivities under Rh(III) catalysis. These reactions are successful using Cu(OAc)2 or air as the stoichiometric oxidant, and provide spirocyclic enones, the basic ring system of which appears in several natural products. Application of this process to the preparation of a highly functionalized tetracycle is also demonstrated