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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.

Recent research has shown that creative self-concept might be not only a path to people's creativity but also a path to their positive affect. This study proposed and examined the underlying mechanisms of how creative self-concept is associated with positive affect through the mediating role of people's daily creative behavior and creative self-assessment. We recruited 101 Chinese undergraduate students to report their creative behavior, creative self-assessment, and experiences of positive emotion states each day for 14 days. A multilevel structural equation model was conducted. Results confirmed the mediating role of creative self-assessment and the chain-mediating role of creative behavior and creative self-assessment between creative self-concept and positive affect. The direct effect of creative behavior on positive affect was not verified. These findings highlight the role that creative self-concept plays in everyday activities and everyday well-being and show the importance of creative self-assessment in creativity education and mental health care.

Research has demonstrated that implicit theories of creativity are crucial in shaping an individual's behavior and real-life decisions toward being creative. The present study proposed and examined the underlying mechanisms of how two kinds of implicit theories--the growth mindset of the creative self and the stereotype of creative others--are associated with creative achievements through the mediating role of creativity motivation. Participants were 606 undergraduate students who were enrolled in an education major in two universities in China. Overall, the study found that Chinese students held a positive image toward a creative student, regarding him or her as highly competent, warm, and popular. Student perceptions of a creative other were positively related to their growth mindset of creativity. Moreover, results verified both the mediating role of creativity motivation on growth mindset, as well as the effect of positive stereotyping of the creative other on students' creative achievement. These findings point to promising creativity motivation strategies including the cultivation of a malleable view of creativity and of creative role models, that may, in turn, promote creative achievement by encouraging students to do, learn, and accomplish new things.

In order to study the effect of metal ion mordanting on the dyeing performance of wool with natural dyes, madder pigment was selected to dye wool fabrics. On the basis of optimizing the dyeing process conditions, the influence of metal ion mordanting on the dyeing performance of madder pigment wool fabrics was explored. The research results indicate that the color fastness of wool fabrics can be effectively improved by mordant treatment. The color fastness to light reaches level 4, and other color fastness levels are higher than or equal to level 3-4. The color yield of dyed fabrics significantly increased after being dyed with different metal ions, and the color characteristic values of mordant dyed wool fabric show significant changes. The analysis of surface elements on fabrics indicates that the degree of improvement in color fastness and color change is related to the content of metal ions. [ABSTRACT FROM AUTHOR]

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]