Yang Zhao

Background: Hyperplasia of mammary glands (HMG) is a common benign disease of the female breast. Objective: This study aimed to observe the clinical efficacy and safety of Honghua Xiaoyao pills in the treatment of liver depression and haemostasis‐type HMG, as well as their impact on menstruation. Methods: Sixty patients diagnosed with liver depression and haemostasis‐type HMG in the breast disease department of our hospital were selected as the study participants. The treatment involved oral administration of Honghua Xiaoyao pills: four pills per dose, three times a day, for a duration of 3 months without interruption during menstruation. Hormones, Western painkillers and similar Chinese herbal medicines were prohibited during the trial. Changes in breast pain, breast masses, menstrual volume and safety indicators were observed. Results: Three patients were lost to follow‐up after 2 months of treatment, and four patients voluntarily withdrew from the study. The changes in breast pain scores, breast mass size scores and breast mass texture scores relative to baseline and their respective change rates showed statistically significant differences ( p < 0.0001). Changes in lower abdominal distension, breast distension, menstrual blood clots, chest tightness/discomfort and emotional scores relative to baseline were also statistically significant ( p < 0.0001). Conclusion: Honghua Xiaoyao pills are effective in reducing breast pain, shrinking breast masses and improving lower abdominal distension, breast distension, menstrual blood clots, chest tightness/discomfort and emotional symptoms in patients with liver depression and haemostasis‐type HMG. Trial Registration: International Traditional Medicine Clinical Trial Registry: ITMCTR2024000165

With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a “data disaster.” Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting “dirty data,” which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.

Objective. This nationwide, multicenter prospective observational study with a patient registry was designed to evaluate the efficacy of Fufang E’jiao Jiang (FEJ) in Chinese patients with Qi and blood deficiency syndrome (QBDS). Methods. QBDS patients were consecutively recruited from 81 investigational sites in China from July, 2019, to December, 2020. Patients who met the eligibility criteria were enrolled in a prospective registry database. Baseline characteristics and changes in scores on the traditional Chinese medicine (TCM) symptom evaluation scale for Qi and blood deficiency, the clinical global impression (CGI) scale, the fatigue scale‐14 (FS‐14), and the Pittsburgh sleep quality index (PSQI) were analyzed to determine the clinical efficacy of FEJ. Results. A total of 3,203 patients were recruited. The average remission rate (i.e., the sum of the cure rate and improvement rate) of the 20 symptoms of QBDS was 92.49% after 4 weeks of FEJ treatment, which was higher than at baseline; the rate increased to 94.69% at 8 weeks. The CGI scale revealed that the number of total remissions at 4 and 8 weeks was 3,120 (97.41%) and 415 (100%), respectively. The total FS‐14 scores decreased by 1.67 ± 4.11 (p < 0.001) at 4 weeks and 1.72 ± 3.09 (p < 0.001) at 8 weeks of treatment. The PSQI scores were 6.6 ± 4.7 and 6.52 ± 3.07 at 4 and 8 weeks, respectively, which were significantly lower than the baseline scores (p < 0.001; p = 0.0033). Both the subhealth fatigue (SF) and iron deficiency anemia (IDA) groups showed significantly improved clinical symptoms of QBDS (p < 0.01). Between‐group comparisons revealed significantly greater improvements in FS‐14 and PSQI scores in the SF group than in the IDA group (p < 0.05). A multivariate logistic regression analysis showed that disease course, FS‐14 score at baseline, and four‐week FEJ doses were independent risk factors for the degree of symptom relief in QBDS patients (p < 0.05). Conclusion. In real‐world settings, FEJ has a promising effect in treating QBDS and can significantly improve the severity of its symptoms.

UNSTRUCTURED With the rapid development of science, technology, and engineering, large amounts of data have been generated in many fields in the past 20 years. In the process of medical research, data are constantly generated, and large amounts of real-world data form a “data disaster.” Effective data analysis and mining are based on data availability and high data quality. The premise of high data quality is the need to clean the data. Data cleaning is the process of detecting and correcting “dirty data,” which is the basis of data analysis and management. Moreover, data cleaning is a common technology for improving data quality. However, the current literature on real-world research provides little guidance on how to efficiently and ethically set up and perform data cleaning. To address this issue, we proposed a data cleaning framework for real-world research, focusing on the 3 most common types of dirty data (duplicate, missing, and outlier data), and a normal workflow for data cleaning to serve as a reference for the application of such technologies in future studies. We also provided relevant suggestions for common problems in data cleaning.