Chinese Herbal Medicine as an Adjunctive Therapy for Breast Cancer: A Systematic Review and Meta-Analysis

Chinese herbal medicine (CHM) has been widely used as an adjunctive therapy for breast cancer, while its efficacy remains unexplored. The purpose of this study is to evaluate the efficacy of CHM combined with chemotherapy for breast cancer. The study results showed that CHM combined with chemotherapy significantly increased tumor response and KPS as compared to using chemotherapy alone (RR = 1.36; 95% CI = 1.24–1.48; P<0.00001; RR = 1.38; 95% CI = 1.26–1.52; P<0.00001, resp.). Besides, CHM as an adjunctive therapy significantly reduced the nausea and vomiting at toxicity grade of III–IV (RR = 0.37; 95% CI = 0.27–0.52; P<0.00001). Moreover, the combined therapy significantly prevented the decline of WBC in patients under chemotherapy at toxicity grade of III–IV (RR = 0.49; 95% CI = 0.34–0.69; P<0.00001) and prevented the decline of platelet at toxicity grade of III–IV or I–IV (RR = 0.29; 95% CI = 0.12–0.73; P=0.008; RR = 0.77; 95% CI = 0.63–0.94; P=0.009, resp.). This study suggests that CHM combined with chemotherapy in comparison with chemotherapy alone can significantly enhance tumor response, improve KPS, and alleviate toxicity induced by chemotherapy in breast cancer patients. However, a firm conclusion could not be reached due to the lack of high quality trials and large-scale RCTs, so further trials with higher quality and larger scale are needed

Anonymous and Efficient Message Authentication Scheme for Smart Grid

Smart grid has emerged as the next-generation electricity grid with power flow optimization and high power quality. Smart grid technologies have attracted the attention of industry and academia in the last few years. However, the tradeoff between security and efficiency remains a challenge in the practical deployment of the smart grid. Most recently, Li et al. proposed a lightweight message authentication scheme with user anonymity and claimed that their scheme is provably secure. But we found that their scheme fails to achieve mutual authentication and mitigate some typical attacks (e.g., impersonation attack, denial of service attack) in the smart grid environment. To address these drawbacks, we present a new message authentication scheme with reasonable efficiency. Security and performance analysis results show that the proposed scheme can satisfy the security and lightweight requirements of practical implementations and deployments of the smart grid

Soil pH, total phosphorus, climate and distance are the major factors influencing microbial activity at a regional spatial scale

Considering the extensive functional redundancy in microbial communities and great difficulty in elucidating it based on taxonomic structure, studies on the biogeography of soil microbial activity at large spatial scale are as important as microbial community structure. Eighty-four soil samples were collected across a region from south to north China (about 1,000 km) to address the questions if microbial activity displays biogeographic patterns and what are driving forces. These samples represented different soil types, land use and climate. Redundancy analysis and nonmetric multidimensional scaling clearly revealed that soil microbial activities showed distinct differentiation at different sites over a regional spatial scale, which were strongly affected by soil pH, total P, rainfall, temperature, soil type and location. In addition, microbial community structure was greatly influenced by rainfall, location, temperature, soil pH and soil type and was correlated with microbial activity to some extent. Our results suggest that microbial activities display a clear geographic pattern that is greatly altered by geographic distance and reflected by climate, soil pH and total P over large spatial scales. There are common (distance, climate, pH and soil type) but differentiated aspects (TP, SOC and N) in the biogeography of soil microbial community structure and activity

DFN modelling constrained by multiple seismic attributes using the steering pyramid technology

Fracture modelling is essential for understanding fluid flow in fractured hydrocarbon reservoirs, particularly in the phase of production; however, traditional discrete fracture network (DFN) modelling methods lack constraints that reflect characteristics of fracture development. Fractures or fracture networks exhibit a high degree of randomness; as such, it is difficult to model fracture characteristics. This paper proposes a new approach for DFN modelling constrained by seismic attributes. Firstly, the steerable pyramid method is adopted to improve seismic data resolution; secondly, multiple seismic attributes are extracted and combined into a composite attribute to characterize fracture spatial distribution; finally, a DFN modelling method is established by using the composite attribute as a location constraint. To verify the effectiveness of the approach, a case study is conducted in the Bonan Depression, in East China. The results show that, compared with the traditional DFN modelling methods, the DFN modelling with the location constraint create a more realistic fracture model which accurately reflects fracture distribution characteristics. The application demonstrates the potential of wide application prospects in fractured reservoirs

Enhanced Degradation of Rh 6G by Zero Valent Iron Loaded on Two Typical Clay Minerals With Different Structures Under Microwave Irradiation

Nanoscale zero valent iron has been a widespread concern in various fields due to its large specific surface area and high reactivity. However, nanoscale zero valent iron (nZVI) is very likely to aggregate and be oxidized, which limit its wide application in industry. Most clay minerals have a large adsorption capacity of cations due to their negative charges and high specific surface areas. In the present work, nZVI was loaded onto two typical clay minerals: kaolinite and sepiolite, to inhibit its oxidation and aggregation. The composites were applied to degrade Rhodamine 6G (Rh 6G) under microwave irradiation. The effects of pH value and microwave power on degradation were studied. The results showed that the removal amount of Rh 6G by nZVI/kaolinite was 110 mg/g in 15 min, while it reached 300 mg/g by nZVI/sepiolite. The difference between these two composites was mostly determined by the structures of these two clay minerals

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo), a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.Comment: Accepted by CVPR2023. Code is available at https://github.com/CGCL-codes/TeC