Molecular mechanism of Ruxian Shuhou prescription in the treatment of triple-negative breast cancer based on network pharmacology.

We aimed to explore the molecular mechanism of Ruxian Shuhou prescription in the treatment of triple-negative breast cancer (TNBC) by using network pharmacology. The active components and targets of the prescription were obtained by Traditional Chinese medicine systems pharmacology database. Gencards database, online mendelian inheritance in man database, therapeutic target database, and DRUGBANK database were used to search for the TNBC-related targets. The potential targets of Ruxian Shuhou prescription for TNBC were screened out by the intersection of effective ingredient action targets and disease targets. A herb-active ingredient-target network was constructed and analyzed for key ingredients. A protein-protein interaction network was constructed for studying key targets. Furthermore, gene ontology analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis were carried out. Finally, the relationship between key ingredients and key genes was evaluated by molecular docking. The key ingredients of Ruxian Shuhou prescription for the treatment of TNBC may be Quercetin, Luteolin and Kaempferol, while the key therapeutic targets may be protein kinase B, interleukin-6, cellular tumor antigen p53, and vascular endothelial growth factor A. The related signaling pathways were mainly involved in tumor, apoptosis and virus infection, among which the PI3K-Akt signaling pathway was the most closely related to TNBC. Molecular docking showed that the key ingredients had high binding activity with the key targets. The molecular mechanisms of Ruxian Shuhou prescription for TNBC are likely to involve multi-ingredient, multi-target and multi-pathway.

Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss.

Purpose: To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer. Methods: The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset. Results: A total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691). Conclusion: The XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.

Association between endocrine therapy and cognitive decline in breast cancer based on propensity score matching.

Purpose: To study the status quo of the cognitive function of the breast cancer patients with (who went through) the endocrine therapy by the epidemiological investigation, analyze the key factor of the cognition impairment and explore the impact of the endocrine therapy time on the cognition decline after using Propensity Score Matching to balance the covariates. Methods: In this study, the epidemiological questionnaire information was collected from 226 female breast cancer endocrine treatment patients who visited the Breast Clinic of Longhua Hospital Affiliated to Shanghai University of Chinese Medicine from November 2020 to February 2022, and the results of the overall cognitive function, the function test of each cognitive domain, the patient's self-cognition, quality of life, and emotional status evaluation of the patients. In this study, according to the principle of random matching, the nearest matching method with a matching tolerance of 0.2 and a matching ratio of 1:2 was used for orientation score matching. After the covariant such as age, BMI, and duration of education were balanced, the effects of the duration of endocrine therapy on the overall cognitive function and the functions of each cognitive domain were analyzed. Results: In 226 cases of female breast cancer patients (who went through) the endocrine therapy, the propensity score matching was performed, ultimately, 99 were ruled out, successful matched ones were 49 of the cognition-decline group and 78 of the standard group. With age, education time, BMI and other covariates balanced, the endocrine therapy duration was the risk factor of the cognition impairment (P < 0.05, OR = 1.296, 95% CI = 1.008-1.665), with the extension of endocrine treatment time, there was a rising risk of the cognition impairment (LLA statistic = 5.872, P < 0.05). The cognitive domain scores in the cognition-decline group were lower than the standard group (P < 0.05), but there was a difference in self-report cognition. Conclusion: The endocrine therapy duration was the risk factor for the cognition impairment of the breast cancer patients, and with prolonged endocrine treatment, there was a rising (an increasing) risk for the cognition impairment.

Analysis of methicillin-resistant Staphylococcus aureus major clonal lineages by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS).

Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen associated with nosocomial infections in many countries. Multilocus sequence typing (MLST) is one of the genetic typing methods used to type MRSA with a high discriminatory power, however, it is labor-intensive, timely, and costly. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) coupled with ClinProTools is a potential tool to discover biomarker peaks and to generate a classification model based on highly sophisticated mathematical algorithms to discriminate clonal lineages. We investigated the performance of MALDI-TOF MS for discriminating 154 MRSA-ST239, 72 MRSA-ST5, 30 MRSA-ST59, 14 MRSA-ST45, and 20 MRSA-OST (other clonal lineages). Our results indicate that the model construction and validation have good potency to discriminate ST45 from other lineages with a sensitivity and a specificity of both 100%, and a sensitivity of 95.80% and a specificity of 94.62% to identify ST239. For Biotyper classification, the sensitivity and specificity were more than of 90% for ST239, ST59 and ST45, whereas only 81.94% sensitivity for ST5. By single-peak analysis, the peaks m/z 4808 and 9614 can correctly discriminate ST45 a sensitivity and a specificity of both 100%; the peak m/z 6554 can also discriminate ST239 with a sensitivity of 91.9% and a specificity of 85.4%. In conclusion, MALDI-TOF MS coupled with ClinProTools has a high detection performance for MRSA typing with obvious advantages of being rapid, highly accurate, and being a low cost in comparison with MLST.

Specific domains of plant defensins differentially disrupt colony initiation, cell fusion and calcium homeostasis in Neurospora crassa.

MsDef1 and MtDef4 from Medicago spp. are small cysteine-rich defensins with potent antifungal activity against a broad range of filamentous fungi. Each defensin has a hallmark γ-core motif (GXCX(3-9) C), which contains major determinants of its antifungal activity. In this study, the antifungal activities of MsDef1, MtDef4, and peptides derived from their γ-core motifs, were characterized during colony initiation in the fungal model, Neurospora crassa. These defensins and their cognate peptides inhibited conidial germination and accompanying cell fusion with different potencies. The inhibitory effects of MsDef1 were strongly mediated by the plasma membrane localized sphingolipid glucosylceramide. Cell fusion was selectively inhibited by the hexapeptide RGFRRR derived from the γ-core motif of MtDef4. Fluorescent labelling of this hexapeptide showed that it strongly bound to the germ tube plasma membrane/cell wall. Using N. crassa expressing the Ca(2+) reporter aequorin, MsDef1, MtDef4 and their cognate peptides were each shown to perturb Ca(2+) homeostasis in specific and distinct ways, and the disruptive effects of MsDef1 on Ca(2+) were mediated by glucosylceramide. Together, our results demonstrate that MsDef1 and MtDef4 differ markedly in their antifungal properties and specific domains within their γ-core motifs play important roles in their different modes of antifungal action.

The bacterial secondary metabolite 2,4-diacetylphloroglucinol impairs mitochondrial function and affects calcium homeostasis in Neurospora crassa.

The bacterial secondary metabolite 2,4-diacetylphloroglucinol (DAPG) is of interest as an active ingredient of biological control strains of Pseudomonas fluorescens and as a potential lead pharmaceutical molecule because of its capacity to inhibit growth of diverse microbial and non-microbial cells. The mechanism by which this occurs is unknown and in this study the filamentous fungus Neurospora crassa was used as a model to investigate the effects of DAPG on a eukaryotic cell. Colony growth, conidial germination and cell fusion assays confirmed the inhibitory nature of DAPG towards N. crassa. A number of different fluorescent dyes and fluorescent protein reporters were used to assess the effects of DAPG treatment on mitochondrial and other cellular functions. DAPG treatment led to changes in mitochondrial morphology, and rapid loss of mitochondrial membrane potential. These effects are likely to be responsible for the toxicity of DAPG. It was also found that DAPG treatment caused extracellular calcium to be taken up by conidial germlings leading to a transient increase in cytosolic free Ca(2+) with a distinct concentration dependent Ca(2+) signature.

The antifungal activity of the Penicillium chrysogenum protein PAF disrupts calcium homeostasis in Neurospora crassa.

The antifungal protein PAF from Penicillium chrysogenum exhibits growth-inhibitory activity against a broad range of filamentous fungi. Evidence from this study suggests that disruption of Ca(2+) signaling/homeostasis plays an important role in the mechanistic basis of PAF as a growth inhibitor. Supplementation of the growth medium with high Ca(2+) concentrations counteracted PAF toxicity toward PAF-sensitive molds. By using a transgenic Neurospora crassa strain expressing codon-optimized aequorin, PAF was found to cause a significant increase in the resting level of cytosolic free Ca(2+) ([Ca(2+)](c)). The Ca(2+) signatures in response to stimulation by mechanical perturbation or hypo-osmotic shock were significantly changed in the presence of PAF. BAPTA [bis-(aminophenoxy)-ethane-N,N,N',N'-tetraacetic acid], a Ca(2+) selective chelator, ameliorated the PAF toxicity in growth inhibition assays and counteracted PAF induced perturbation of Ca(2+) homeostasis. These results indicate that extracellular Ca(2+) was the major source of these PAF-induced effects. The L-type Ca(2+) channel blocker diltiazem disrupted Ca(2+) homeostasis in a similar manner to PAF. Diltiazem in combination with PAF acted additively in enhancing growth inhibition and accentuating the change in Ca(2+) signatures in response to external stimuli. Notably, both PAF and diltiazem increased the [Ca(2+)](c) resting level. However, experiments with an aequorin-expressing Deltacch-1 deletion strain of N. crassa indicated that the L-type Ca(2+) channel CCH-1 was not responsible for the observed PAF-induced elevation of the [Ca(2+)](c) resting level. This study is the first demonstration of the perturbation of fungal Ca(2+) homeostasis by an antifungal protein from a filamentous ascomycete and provides important new insights into the mode of action of PAF.

De novo assembly of a 40 Mb eukaryotic genome from short sequence reads: Sordaria macrospora, a model organism for fungal morphogenesis.

Filamentous fungi are of great importance in ecology, agriculture, medicine, and biotechnology. Thus, it is not surprising that genomes for more than 100 filamentous fungi have been sequenced, most of them by Sanger sequencing. While next-generation sequencing techniques have revolutionized genome resequencing, e.g. for strain comparisons, genetic mapping, or transcriptome and ChIP analyses, de novo assembly of eukaryotic genomes still presents significant hurdles, because of their large size and stretches of repetitive sequences. Filamentous fungi contain few repetitive regions in their 30-90 Mb genomes and thus are suitable candidates to test de novo genome assembly from short sequence reads. Here, we present a high-quality draft sequence of the Sordaria macrospora genome that was obtained by a combination of Illumina/Solexa and Roche/454 sequencing. Paired-end Solexa sequencing of genomic DNA to 85-fold coverage and an additional 10-fold coverage by single-end 454 sequencing resulted in approximately 4 Gb of DNA sequence. Reads were assembled to a 40 Mb draft version (N50 of 117 kb) with the Velvet assembler. Comparative analysis with Neurospora genomes increased the N50 to 498 kb. The S. macrospora genome contains even fewer repeat regions than its closest sequenced relative, Neurospora crassa. Comparison with genomes of other fungi showed that S. macrospora, a model organism for morphogenesis and meiosis, harbors duplications of several genes involved in self/nonself-recognition. Furthermore, S. macrospora contains more polyketide biosynthesis genes than N. crassa. Phylogenetic analyses suggest that some of these genes may have been acquired by horizontal gene transfer from a distantly related ascomycete group. Our study shows that, for typical filamentous fungi, de novo assembly of genomes from short sequence reads alone is feasible, that a mixture of Solexa and 454 sequencing substantially improves the assembly, and that the resulting data can be used for comparative studies to address basic questions of fungal biology.

Simultaneously monitoring the particle size distribution, morphology and suspended solids concentration in wastewater applying digital image analysis (DIA).

The efficiency of particle sedimentation in wastewater treatment is seriously affected by particle size distribution and morphology. A laser particle size analyzer is typically to analyze particle size distribution in wastewater. However, using this analyzer for on-line monitoring is difficult. This analyzer cannot measure particle morphology. An on-line digital image analysis (DIA) system was setup in this study to simultaneously measure particle size distribution and morphology in wastewater. The DIA measurement results show that the predominant particle size with an equivalent diameter (ED) was 10-40 microm. The particle size distribution measured by the DIA method similar to that measured with the laser particle size analyzer, indicating that the DIA method precisely measures particle size distribution. In addition, the mean fractal dimension (D (f)) of a set of particles was measured simultaneously, resulting in a good linear relationship between suspended solids (SS) precipitation efficiencies in samples. Finally, by combining the DIA measurement results with an artificial neural network (ANN), the SS concentrations of sample can be precisely predicted and the SS precipitation efficiencies were also evaluated.

Measurements of wastewater true color by 4/6 wavelength methods and artificial neural network.

The ADMI (American Dye Manufactures' Institute) 3 and 31 wavelength (WL) methods are the most popular measurements of the wastewater true color. However, significantly different measured results were found between the ADMI 3 and 31 WL methods for the same sample. This finding indicates that the ADMI 3 and 31 WL values should not be directly substituted for each other, resulting in the incomparability of these two color values. An innovative calculation using the Back-Propagation Neural Network (BPN) was therefore proposed to replace the original calculation method proposed in Standard Method. The BPN-calculated ADMI 3 WL values were found to be very close to the corresponding measured ADMI 31 WL values. Additionally, for more accurate measurement of the true color, new measurement methods containing 4 and 6 wavelengths were proposed and cooperated with the BPN calculation model. The new approaches performed very accuracy measured results of the wastewater true color. Finally, the trained BPN calculation models were built to be applied to measure the true color of real textile samples and also performed very good measured results.