Clinical effect of astragaloside IV on breast carcinoma cells based on MDR1: A randomised trial

Purpose: To study the clinical effect of astragaloside IV on breast carcinoma cells (BCCs), and its potential mechanisms with respect to multiple drug resistance-1 (MDR1)Methods: The cytotoxicity of astragaloside IV to BCCs was determined using CCK-8 test, and values of its half inhibitory concentration (IC50) were determined. Transwell assay and flow cytometry were performed to determine the effect of astragaloside (13 μg/mL) on cell invasion and apoptosis. The contents of MDR1 mRNA in BC tissues and cells were determined using real-time quantitative polymerase chain reaction (qRT-PCR), while the protein expression levels of MDR1 in BC cells were determined using western blot assay.Results: The IC50 of astragaloside IV for MCF-7 and MDA-MB-231 BCCs were 12.57 μg/mL and 13.91 μg/mL, respectively. Transwell experiment showed significantly inhibited invasive capacity and enhanced apoptotic potential of the BCCs after astragaloside IV intervention. However, invasive capacities of the BCCs were markedly enhanced, while their apoptotic capacities were inhibited after transfection with si-MDR1, when compared with controls (p < 0.05). Results of qRT-PCR revealed that the mRNA content of MDR1 in BC tissues and cells (0.42±0.11) was significantly lower than that in normal tissues (0.95±0.18; p < 0.05). Results from western blot assay revealed that the relative expression levels of MDR1 protein were decreased, with values of 0.21±0.05, 0.32±0.07 and 0.74±0.15 for MCF-10A, MCF-7, MAD-MB-231 and MCF-10A, respectively (p < 0.05).Conclusion: Astragaloside IV regulates the metastasis and apoptosis of BCCs through regulation of MDR1. It also inhibits cell invasion but enhances the apoptosis of BC cells transfected with si-MDR1. These results highlight the prospects of the compound for the treatment of BC

Chronic Infection Depletes Hematopoietic Stem Cells through Stress-Induced Terminal Differentiation

Chronic infections affect a third of the world’s population and can cause bone marrow suppression, a severe condition that increases mortality from infection. To uncover the basis for infection-associated bone marrow suppression, we conducted repeated infection of WT mice with Mycobacterium avium. After 4–6 months, mice became pancytopenic. Their hematopoietic stem and progenitor cells (HSPCs) were severely depleted and displayed interferon gamma (IFN-γ) signaling-dependent defects in self-renewal. There was no evidence of increased HSPC mobilization or apoptosis. However, consistent with known effects of IFN-γ, transcriptome analysis pointed toward increased myeloid differentiation of HSPCs and revealed the transcription factor Batf2 as a potential mediator of IFN-γ-induced HSPC differentiation. Gain- and loss-of-function studies uncovered a role for Batf2 in myeloid differentiation in both murine and human systems. We thus demonstrate that chronic infection can deplete HSPCs and identify BATF2 as a mediator of infection-induced HSPC terminal differentiation

BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard

A Novel Pricing Algorithm Based on Reward-Punishment Mechanism for Supply and Demand Balancing

Demand response (DR) is a powerful tool to maintain the stability of the power system and maximize the profit of the electricity market, where the customers engage in the pricing scheme and adjust their electricity demand proactively based on the price. In DR programs, most existing works are based on the assumption that the prediction of the electricity demand from customers is always accurate and trustworthy, which will lead to high cost and fluctuation of the electricity market once the prediction is obeyed. In this paper, we design a reward and punishment mechanism to constrain customers’ dishonest behaviors and propose a novel pricing algorithm based on the reward and punishment mechanism to relax the assumption, which guarantees the total electricity demands of all customers are within a secure range and obtain the maximum profit of the supplier. Meanwhile, we obtain the optimal demand and provide a upper and lower bound of the proposed price for the electricity market. In addition to a single type of customer, we also consider multiple types of customers, each of whom has different characteristics to prices. Extensive simulation results are constructed to demonstrate the effectiveness of the proposed algorithm compared with other pricing algorithms. It also shows that the average electricity consumption of a whole community is mostly affected by the residents’ electricity consumption and the balance of the supply and all types of customers is achieved under the proposed pricing algorithm

Allelic Interactions among Pto-MIR475b and Its Four Target Genes Potentially Affect Growth and Wood Properties in Populus

MicroRNAs (miRNAs) play crucial roles in plant growth and development, but few studies have illuminated the allelic interactions among miRNAs and their targets in perennial plants. Here, we combined analysis of expression patterns and single-nucleotide polymorphism (SNP)-based association studies to explore the interactions between Pto-MIR475b and its four target genes (Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4) in 435 unrelated individuals of Populus tomentosa. Expression patterns showed a significant negative correlation (r = -0.447 to -0.411, P < 0.01) between Pto-MIR475b and its four targets in eight tissues of P. tomentosa, suggesting that Pto-miR475b may negatively regulate the four targets. Single SNP-based association studies identified 93 significant associations (P < 0.01, Q < 0.1) representing associations of 80 unique SNPs in Pto-MIR475b and its four targets with nine traits, revealing their potential roles in tree growth and wood formation. Moreover, one common SNP in the precursor region significantly altered the secondary structure of the pre-Pto-miR475b and changed the expression level of Pto-MIR475b. Analysis of epistatic interactions identified 115 significant SNP–SNP associations (P < 0.01) representing 45 unique SNPs from Pto-MIR475b and its four targets for 10 traits, revealing that genetic interactions between Pto-MIR475b and its targets influence quantitative traits of perennial plants. Our study provided a feasible strategy to study population genetics in forest trees and enhanced our understanding of miRNAs by dissecting the allelic interactions between this miRNA and its targets in P. tomentosa

Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration

We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation. Unlike state-of-the-art approaches that rely on multi-stage technical schemes and are computationally expensive, SeRum converts document image understanding and recognition tasks into a local decoding process of the visual tokens of interest, using a content-aware token merge module. This mechanism enables the model to pay more attention to regions of interest generated by the query decoder, improving the model's effectiveness and speeding up the decoding speed of the generative scheme. We also designed several pre-training tasks to enhance the understanding and local awareness of the model. Experimental results demonstrate that SeRum achieves state-of-the-art performance on document understanding tasks and competitive results on text spotting tasks. SeRum represents a substantial advancement towards enabling efficient and effective end-to-end document understanding.Comment: Accepted to ICCV 2023 main conferenc

Prognostic factors of the short-term outcomes of patients with hepatitis B virus-associated acute-on-chronic liver failure

OBJECTIVE: To investigate the impact of the baseline status of patients with hepatitis B virus-associated acute-on-chronic liver failure on short-term outcomes. METHODS: A retrospective study was conducted that included a total of 138 patients with hepatitis B virus-associated acute-on-chronic liver failure admitted to the Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, from November 2013 to October 2016. The patients were divided into a poor prognosis group (74 patients) and a good prognosis group (64 patients) based on the disease outcome. General information, clinical indicators and prognostic scores of the patients’ baseline status were analyzed, and a prediction model was established accordingly. RESULTS: Elder age, treatment with artificial liver support systems and the frequency of such treatments, high levels of white blood cells, neutrophils, neutrophil count/lymphocyte count ratio, alanine aminotransferase, gamma-glutamyl transferase, total bilirubin, urea, and prognostic scores as well as low levels of albumin and sodium were all significantly associated with the short-term outcomes of hepatitis B virus-associated acute-on-chronic liver failure. The predictive model showed that logit (p) = 3.068 + 1.003 × neutrophil count/lymphocyte count ratio - 0.892 × gamma-glutamyl transferase - 1.138 × albumin - 1.364 × sodium + 1.651 × artificial liver support therapy. CONCLUSION: The neutrophil count/lymphocyte count ratio and serum levels of gamma-glutamyl transferase, albumin and sodium were independent risk factors predicting short-term outcomes of hepatitis B virus-associated acute-on-chronic liver failure, and the administration of multiple treatments with artificial liver support therapy during the early stage is conducive to improved short-term outcomes