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The many problems faced by the theory of general relativity (GR) have always motivated us to explore the modified theory of GR. Considering the importance of studying the black hole (BH) entropy and its correction in gravity physics, we study the correction of thermodynamic entropy for a kind of spherically symmetric black holes under the generalized Brans-Dicke (GBD) theory of modified gravity. We derive and calculate the entropy and heat capacity. It is found that when the value of event horizon radius~$r_+$~is small, the effect of the entropy correction term on the entropy is very obvious, while for larger values~$r_+$~, the contribution of the correction term on entropy can be almost ignored. In addition, we can observe that as the radius of the event horizon increases, the heat capacity of BH in GBD theory will change from a negative value to a positive value, indicating that there is a phase transition in black holes. Given that studying the structure of geodesic lines is important for exploring the physical characteristics of a strong gravitational field, we also investigate the stability of the geodesic motion of particles in static spherically symmetric BHs within the framework of GBD theory. Concretely, we analyzes the dependence of the innermost stable circular orbit on model parameters. Also, the geodesic deviation equation is also applied to investigate the stable circular orbit of particles in GBD theory. The conditions for the stability of BH solution and the limited range of radial coordinates required to achieve stable circular orbit motion are given. Finally, we show the locations of stable circular orbits, and obtain the angular velocity, the specific energy and angular momentum of the particles which move in circular orbits.

Background. As a classic prescription in Chinese medicine treatment, Xiaochaihutang (XCHT) can improve the clinical effect and reduce serum tumor markers in patients with breast cancer (BC). However, there has not been any study to confirm the mechanism. We used bioinformatics analysis and network pharmacology to find the potential targets. Methods. The differentially expressed genes (DEGs) of BC were identified from the Cancer Genome Atlas (TCGA) dataset. Then, we utilized weighted coexpression network analysis (WGCNA) with the same dataset. The target genes of BC were obtained by comparing genes of DEGs and in significant modules of WGCNA. Drug targets of XCHT from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database were intersected with the targets of BC. The protein-protein interaction (PPI) of the drug targets was analysed by using the STRING database. We utilized the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) enrichment analysis to identify the specific pathways and key target proteins. Receiver operator characteristic (ROC) curve was used as the verification of drug targets. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein. Results. We obtained a set of 21 target genes, which mainly encode neurotransmitter receptors or related transporters, such as OPRD1, 5-HT2A, and so on. In addition, enrichment analyses of 21 target genes showed that they were mainly concentrated in pathways related to the nervous system. Molecular docking was performed on the target gene of BC. Six active compounds can enter the active pocket of target gene, namely, naringenin, beta-sitosterol, coumestrol, nuciferine, beta-sitosterol, and protopine, thereby exerting potential therapeutic effects in BC. Conclusions. Our analysis shows that the mechanism of XCHT in the treatment of BC is mainly acting on the neurogenesis in the microenvironment of breast tumor tissue.

Objective To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers.Methods We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the breast lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves.Results In total, 57 of 121 lesions were benign, and the other 64 were malignant. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC=0.716) was not as good as that of ADC values (AUC=0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-63 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively.Conclusions SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values.

The present study aimed to investigate the underlying regulatory mechanism of MYCL proto‑oncogene (MYCL) in triple‑negative breast cancer (TNBC) progression.

Objective. Necroptosis was recently identified as a form of programmed cell death that plays an essential role in breast cancer metastasis. MicroRNAs (miRNAs) have long been recognized to affect cell death and tumor growth. In this study, we aimed to screen for necroptosis-associated miRNAs that predict breast cancer metastasis. Method. This study used The Cancer Genome Atlas (TCGA) public database to obtain miRNA expression data and associated clinical data from breast cancer patients and then retrieved miRNA data related to necrosis and apoptosis. Next, using Cox regression model analysis (univariate or multivariate) as well as a comparison analysis (differential analysis), a prognostic multi-miRNA molecular marker was established. Finally, prognosis-related miRNAs were utilized to identify target genes, and the functions of the target genes were analyzed for enrichment to investigate the probable mechanisms of the miRNAs. Results. Ten miRNAs were screened through differential analysis to build models: hsa-miR-148a-3p, hsa-miR-223-3p, hsa-miR-331-3p, has-miR-181a-5p, hsa-miR-181b-5p, hsa-miR-181c-5p, hsa-miR-181d-5p, hsa-miR-200a-5p, hsa-miR-141-3p, and hsa-miR-425-5p. The multivariate Cox regression model was an independent prognostic factor (univariate Cox regression results: HR = 3.2642 , 95 % CI = 1.5773 − 6.7554 , P = 0.0014 ; multivariate Cox regression results: HR = 3.1578 , 95 % CI = 1.5083 − 6 , P = 0.0023 ). The survival curve of the risk score also revealed that patients with a high risk score had a poor prognosis ( P = 2 e − 04 ). The receiver operating characteristic (ROC) curve showed that the model has a certain prediction ability. Batch survival analysis of the miRNAs in the model was conducted and showed that hsa-miR-331-3p ( P = 0.0182 ) was strongly associated with prognosis. Twenty-three predicted target genes were obtained, and Gene Ontology (GO) enrichment analysis showed that these target genes were strongly enriched in transcriptional initiation and cell membrane trafficking. Conclusion. Our research identified a novel miRNA marker for predicting breast cancer patient prognosis and lays the groundwork for future research on necroptosis-related genes.

Background: Although intrinsic molecular subtype has been extensively used, the risk stratification have not been fully elucidated in estrogen receptor (ER) or progesterone receptor (PR) positive and human epidermal growth factor receptor 2 (HER2) negative breast cancer. Methods: RNA transcriptional data from The Cancer Genome Atlas (TCGA), METABRIC and GEO were used. Immune-related genes were obtained from the datasets and literature search. Univariate, lasso regression and multivariate cox regression were employed to identify prognostic immune-related genes and establish the risk signature. Relationships between the risk signature and clinical parameters, tumor-infiltrating immune cell abundances and cancer phenotypes were further evaluated.Results: Noted, 102 immune-related prognostic genes were identified in METABRIC dataset by univariate cox analysis. Consecutively, 7 immune genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1 and CCL19) were selected as risk signature by lasso regression and multivariate cox analysis. Its performance was further verified in TCGA,GSE20685 and GSE9195 datasets. Multivariate Cox regression indicated that the risk signature was an independent predictor. The prognostic signature showed significant correlation with intrinsic molecular subtypes, 70-gene signature and tamoxifen resistance signature. The CIBERSORT algorithm revealed that CD4+ memory T cells were significant higher in low-risk group. Conversely, M0-type macrophages were significant higher in high-risk group in both TCGA and METABRIC cohorts, which may have effect on the prognosis. Furthermore, we found that low-risk group may be associated with immune-related pathway and high-risk group was with cell cycle-related pathway, which also showed impact on the prognosis.Conclusion: The present study constructed a robust seven immune-related gene signature and established an effective method in risk stratification and prediction of clinical outcome in ER or PR positive and HER2 negative breast cancer.

Sufficient evidence had shown that the secretion and functional properties of saliva affect retronasal smell, taste and texture perception during food oral processing. A less noticed but important functional indicator of saliva is its buffering capacity and its influence on sensory perception. This study hypothesized that salivary buffering capacity differs among individuals due to possibly varied salivary compositions, and salivary buffering capacity could be the main cause of individual’s perception variation of acid-related sensory properties. To test this hypothesis, salivary buffering capacity was assessed both ex vivo and with oral samples, then correlated with sour taste perception. Ex vivo acid titration experiment of saliva samples found that the titration curve could be divided into three distinct sections and sectional buffer values had very good indication of corresponding function of different saliva buffer systems: at lower pH (pH

This study discusses measures for improving the precision of optical remote-sensing detection of soil salinity and the possibility of soil salinity detection at different depths of 0–10 cm, 10–30 cm and 30–50 cm using optical remote-sensing data, and analyzes the mechanism by which deep-layer soil salinity influences the soil spectrum. The findings show that there is a high and significant correlation between soil-spectral reflectance and soil salinity, and that the correlation between soil-spectral reflectance and soil salinity decreases gradually from the blue band to the shortwave infrared band of ETM + images. The partial least squares regression model is used to estimate soil salinity in the 0–10-cm surface-layer, confirming that the selected soil-salinity-detecting bands of Band 1 and Band 4, the established difference soil salinity index, the derivative of the normalized differential vegetation index, and the deep-layer soil moisture can improve the precision of remote-sensing detection of surface-layer soil salinity. The precise estimation of the 0–10-cm surface-layer soil salinity with variables features an R2 = 0.752, an RMSE = 26.84 g/Kg, and a p = 0.000. There is a strong mediating effect between deep-layer soil salinity, 0–10-cm surface-layer soil salinity, and soil spectral reflectance in the study area; namely, deep-layer soil salinity influences soil spectral reflectance by influencing surface-layer soil salinity. There is a significant and very strong power function relation between 0 and 10-cm surface-layer soil salinity and deep-layer soil salinity. Based on this relationship, this study estimates deep-layer soil salinity using optical remote-sensing images.

Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Currently, no targeted treatment is available for TNBC, and the most common clinical therapy is tumor resection, which often promotes metastasis risks. Strong evidence suggests that the lymphatic metastasis is mediated by the C-C chemokine receptor type 7 (CCR7)/C-C motif chemokine ligand 21 crosstalk between tumor cells and the lymphatic system. It is hypothesized that CCR7 is a key immune modulator in the tumor microenvironment and the local blockade of CCR7 could effectively inhibit TNBC lymphatic metastasis. Accordingly, a plasmid encoding an antagonistic CCR7 affinity protein-CCR7 trap is delivered by tumor targeting nanoparticles in a highly metastatic 4T1 TNBC mouse model. Results show that CCR7 traps are transiently expressed, locally disrupt the signaling pathways in the tumor site, and efficiently inhibit TNBC lymphatic metastasis, without inducing immunosuppression as observed in systemic therapies using CCR7 monoclonal antibody. Significantly, upon applying CCR7 trap therapy prior to tumor resection, a 4T1 TNBC mouse model shows good prognosis without any further metastasis and relapse. In addition, CCR7 trap therapy efficiently inhibits the lymphatic metastasis in a B16F10 melanoma mouse model, indicating its great potential for various metastatic diseases treatment.

The water and energy transfer of land surface is complex due to its large spatial and temporal variability. The modeling and simulation is an important means to study land water and energy transfer, but most selection and analysis of model parameters are empirical and qualitative. This paper has proposed a method of quantitatively identifying the most influential parameters of Common Land Model through Sobol’ sensitivity analysis. Considering sensible heat flux as the model output, the first order and total sensitivity indices of 25 model input parameters are estimated using an improved Sobol’ method. The simulated results are resampled using a bootstrapping method and the corresponding sensitivity indices are calculated. Confidence intervals for the bootstrapping sensitivity indices are estimated by using a percentile method. The results show that the parameters phi0 and porsl are the most important parameters, followed by ref(2,1), tran(2,1) and bsw. Five out of 25 parameters need to have an accurate evaluation, while the other parameters are fixed to a certain value. The sensitivity indices of parameters phi0 and porsl are decreasing after precipitation, while the sensitivity indices of parameters tran(2, 1) and ref(2, 1) are increasing after precipitation.

HJ-1A hyperspectral data were used to distinguish topsoil salt components and estimate soil salinity, and the relationship between soil salt chemical components and sensitive bands of soil reflectance spectra was analyzed. The correlation between the soil salt content and the soil spectra obtained from the hyperspectral data was analyzed, proving that topsoil salinity has a very significant correlation with soil reflectance spectra. The relationship between soil reflectance spectra and salt chemical ions was investigated. The soil spectral reflectance at wavelength 510.975 nm and a difference vegetation index were selected to estimate soil salinity and the dominant salt chemical ion concentrations at a depth of 0 to 10 cm using a partial least squares regression model. It was found that the bands sensitive to various levels of chemical components of soil salt were shown to differ, controlled by the dominant component of the soil salt. The sensitive bands in the soil salinity estimation will change with differences in salt components. Estimating the dominant salt in the soil using soil reflectance spectra will lead to greater prediction accuracy. This study provided a possible method for the estimation of salinity and chemical component levels in topsoil, using the hyperspectral data to estimate topsoil salt components.

Noninvasive diffusion-weighted magnetic resonance imaging (DWI) is a well-studied MR imaging technique for quantifying water diffusion especially in tumor area. The correlation between apparent diffusion coefficient (ADC) value and apoptosis or proliferation is not clear by now. This study aimed to investigate whether DWI-ADC value could be used as an imaging marker related with pathologic indexes of tumors.A total of 30 Balb/c mice with HT29 colorectal carcinoma were subjected to DWI and histologic analysis. The percentage of ADC changes and the apoptotic and proliferating indexes were calculated at predefined time points. Kolmogorov-Smirnov distances were considered to determine whether the percentage of ADC changes, and the apoptotic and proliferating indexes were normally distributed. An independent-samples t-test was used to analyze the difference between apoptotic and proliferating indexes in the two groups.THERE WAS A STATISTICALLY SIGNIFICANT DIFFERENCE IN PROLIFERATING INDEX BETWEEN THE RADIOTHERAPY AND CONTROL GROUPS (MEAN PROLIFERATING INDEX: 49.27% vs. 83.09%), and there was a statistically significant difference in apoptotic index between the two groups (mean apoptotic index: 37.7% vs. 2.71%). A significant positive correlation was found between the percentage of ADC changes of the viable tissue and apoptotic index. Pearson's correlation coefficient was 0.655 (P=0.015). A significant negative correlation was found between the percentage of ADC changes of the viable tissue and ki-67 proliferation index. Pearson's correlation coefficient was 0.734 (P0.001).Our results suggest that ADC value may be used in measurement of cell apoptotic and proliferating indexes in colorectal carcinoma.