Search

In the past decade, with the introduction of precision medicine, the treatment of breast cancer has entered a new era. It now tends towards the de-escalation and individualization of treatment plans, aiming to minimize iatrogenic toxicity while maximizing benefits for patients without compromising survival outcomes. The 21-gene assay evaluates the expression levels of 21 genes associated with breast cancer recurrence and provides a 21-gene recurrence score (RS) to assess the risk of recurrence and potential benefit from chemotherapy in breast cancer patients. Currently, RS is recommended by multiple guidelines such as the American Society of Clinical Oncology (ASCO), National Comprehensive Cancer Network (NCCN), and Chinese Anti-Cancer Association(CACA) for guiding adjuvant therapy in hormone receptor-positive (HR+) and human epidermal growth factor receptor 2 negative (HER2-) early-stage breast cancer patients. However, there are still controversies surrounding its clinical application. Current research mainly focuses on optimizing RS to accurately identify patients who would benefit from adjuvant therapy, thus enabling more personalized treatment plans for breast cancer patients. This review provides an overview of the 21-gene assay, its application in HR+ breast cancer patients' adjuvant therapy, its impact on clinical decision-making, existing controversies regarding RS, and prospects for integrating RS with clinical pathological information. The aim is to guide further expansion of RS's application in clinical practice and achieve more precise adjuvant therapy for breast cancer patients.

In the actual business, parts demand occured randomly and demand fluctuates, so the demand sequence for spare parts showed obvious intermittent distribution. At the same time, due to factors such as manual reporting errors or special events, the actual demand for spare parts was prone to abnormal changes, making it difficult for traditional time series prediction methods to capture the evolution of the demand for accessories, resulting in high uncertainty and insufficient reliability of prediction results. To solve this problem, an adaptive interval prediction method for intermittent series based on tensor representation was proposed. Firstly, hierarchical clustering was used to screen similar sequences based on the average demand interval and square of the coefficient of variation of accessory sequences, forming sequence clusters to increase predictability. Secondly, the original demand sequence was reconstructed by tensor decomposition. The outliers in the sequence were then corrected while retaining the core information of the original sequence to maximum extent. Finally, an adaptive prediction interval algorithm was constructed, which could obtain the predicted value and prediction interval of the parts demand through the dynamic update mechanism to ensure the reliability of the results. The proposed method was validated on the aftersales dataset from a large vehicle manufacturing enterprise. Compared with existing time series prediction methods, the proposed method could effectively extract the evolutionary trend of various types of intermittent series and improve the prediction accuracy on the intermittent time series with small size as well. Experiments showed that the average root mean square scaled error (RMSSE) of this method was 0. 32 lower than that of the mainstream in-depth learning method of demand prediction. More importantly, when the prediction results were distorted, the proposed method could provide a reliable and flexible prediction interval, which could be helpful to provide a feasible solution for intelligent parts management. [ABSTRACT FROM AUTHOR]

In view of gas channeling in the course of fracture construction in Sulige Gas Field, a gas channeling prevention and high strength cementation slurry has been researched to the enterprise by experiment optimized latex and cement additives. Experiment in lab shows that the slurry has low free fluid and good rheological property, the flexural strength increased about ten percents, the maximum compressive strength reach 29.5 MPa in 48 hours, It has passed through 3 wells test, the first interface merit factor of one hundred percent and the second interface merit factor more than 94%.The construction pressure is normal, gas channeling is not appeared, meet the stratification of Changqing gas field fracturing of annular cement stone integrity requirements. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.