Objective: To analyze the factors affecting direct-acting antiviral (DAA) therapy among cases with hepatitis C virus (HCV) infection in Zhejiang Province, so as to provide evidence for optimizing local HCV prevention and treatment strategies and improving DAA treatment coverage. Methods: Eight counties (cities, districts) in Zhejiang Province were selected as survey sites. Consecutive sampling was used to enroll HCV cases reported from November 2022 to June 2024. Demographic information, diagnosis and treatment status, DAA therapy, and awareness of relevant medical insurance policies were collected through questionnaire surveys. Factors affecting DAA therapy among cases with HCV infection were analyzed using a multivariable logistic regression model. Results: A total of 412 questionnaires were distributed, and 404 valid questionnaires were collected, with an effective rate of 98.06%. There were 291 males (72.03%) and 113 females (27.97%). The median age was 49.00 (interquartile range, 11.00) years. A total of 303 cases received DAA therapy, with a proportion of 75.00%. Multivariable logistic regression analysis showed that HCV cases aged 40-<50 years (OR=3.469, 95%CI: 1.228-9.345) and with per capita monthly household income >3 000 yuan (>3 000-5 000 yuan, OR=3.198, 95%CI: 1.597-6.403; >5 000 yuan, OR=5.391, 95%CI: 2.230-13.033) had higher likelihood of receiving DAA therapy. HCV cases diagnosed in non-infectious disease departments (OR=0.334, 95%CI: 0.173-0.643), concerned about economic burden of DAA therapy (OR=0.309, 95%CI: 0.160-0.599), and unaware of HCV payment policy by disease type (OR=0.262, 95%CI: 0.111-0.622) had lower likelihood of receiving DAA therapy. Conclusions The proportion of DAA therapy among cases with HCV infection in Zhejiang Province is relatively high. Age, per capita monthly household income, department of diagnosis, concerns about DAA therapy, and medical insurance/payment policies are factors influencing DAA therapy among cases with HCV infection.

Purpose: Postoperative adhesions following thyroid surgery can lead to multiple complications that significantly impact quality of life. The use of an acellular dermal matrix (ADM) adhesion barrier device has been proposed as a potential solution to reduce the risk of such adhesions. This study aimed to evaluate the safety and anti-adhesive effect of an ADM in patients undergoing thyroid surgery. Methods: In this multicenter randomized controlled trial, patients undergoing thyroid surgery were randomly assigned to receive either ADM (n = 42) or no ADM (n = 39) during surgery. The primary outcome was the Swallowing Impairment Score (SIS-6), measured 6 weeks after surgery and compared between groups. Secondary outcomes included intergroup comparisons of the SIS-6, the Voice Handicap Index (VHI)-10, and the Glasgow-Edinburgh Throat Scale (GETS) at baseline, and 2, 6, and 18 weeks after surgery. Results: At week 6, the mean SIS-6 scores were 4.0 ± 4.1 and 3.3 ± 4.2 in the ADM and control groups, respectively, which was not significantly different. Both groups showed similar postoperative improvements in SIS-6, VHI-10, and GETS scores over time, without significant differences between groups at any time point, indicating that the ADM did not reduce the incidence of postoperative adhesions or alter the course of recovery compared to the control group. Conclusion Although application of the ADM is safe for use in patients undergoing thyroid surgery, it did not produce a clinically significant advantage in preventing postoperative adhesions. Future research should focus on identifying specific patient populations or surgical scenarios where the use of the ADM may be beneficial.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: Postoperative adhesions following thyroid surgery can lead to multiple complications that significantly impact quality of life. The use of an acellular dermal matrix (ADM) adhesion barrier device has been proposed as a potential solution to reduce the risk of such adhesions. This study aimed to evaluate the safety and anti-adhesive effect of an ADM in patients undergoing thyroid surgery. Methods: In this multicenter randomized controlled trial, patients undergoing thyroid surgery were randomly assigned to receive either ADM (n = 42) or no ADM (n = 39) during surgery. The primary outcome was the Swallowing Impairment Score (SIS-6), measured 6 weeks after surgery and compared between groups. Secondary outcomes included intergroup comparisons of the SIS-6, the Voice Handicap Index (VHI)-10, and the Glasgow-Edinburgh Throat Scale (GETS) at baseline, and 2, 6, and 18 weeks after surgery. Results: At week 6, the mean SIS-6 scores were 4.0 ± 4.1 and 3.3 ± 4.2 in the ADM and control groups, respectively, which was not significantly different. Both groups showed similar postoperative improvements in SIS-6, VHI-10, and GETS scores over time, without significant differences between groups at any time point, indicating that the ADM did not reduce the incidence of postoperative adhesions or alter the course of recovery compared to the control group. Conclusion Although application of the ADM is safe for use in patients undergoing thyroid surgery, it did not produce a clinically significant advantage in preventing postoperative adhesions. Future research should focus on identifying specific patient populations or surgical scenarios where the use of the ADM may be beneficial.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: Postoperative adhesions following thyroid surgery can lead to multiple complications that significantly impact quality of life. The use of an acellular dermal matrix (ADM) adhesion barrier device has been proposed as a potential solution to reduce the risk of such adhesions. This study aimed to evaluate the safety and anti-adhesive effect of an ADM in patients undergoing thyroid surgery. Methods: In this multicenter randomized controlled trial, patients undergoing thyroid surgery were randomly assigned to receive either ADM (n = 42) or no ADM (n = 39) during surgery. The primary outcome was the Swallowing Impairment Score (SIS-6), measured 6 weeks after surgery and compared between groups. Secondary outcomes included intergroup comparisons of the SIS-6, the Voice Handicap Index (VHI)-10, and the Glasgow-Edinburgh Throat Scale (GETS) at baseline, and 2, 6, and 18 weeks after surgery. Results: At week 6, the mean SIS-6 scores were 4.0 ± 4.1 and 3.3 ± 4.2 in the ADM and control groups, respectively, which was not significantly different. Both groups showed similar postoperative improvements in SIS-6, VHI-10, and GETS scores over time, without significant differences between groups at any time point, indicating that the ADM did not reduce the incidence of postoperative adhesions or alter the course of recovery compared to the control group. Conclusion Although application of the ADM is safe for use in patients undergoing thyroid surgery, it did not produce a clinically significant advantage in preventing postoperative adhesions. Future research should focus on identifying specific patient populations or surgical scenarios where the use of the ADM may be beneficial.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Syringa pinnatifolia, belonging to the family Oleaceae, is a species endemic to China. It is predominantly distributed in the Helan Mountains region of Inner Mongolia and Ningxia of China. The peeled roots, stems, and thick branches have been used as a distinctive Mongolian medicinal material known as "Shan-chen-xiang", which has effects such as suppressing "khii", clearing heat, and relieving pain and is employed for the treatment of cardiovascular and pulmonary diseases and joint pain. Over the past five years, significant increase was achieved in research on chemical constituents and pharmacological effects. There were a total of 130 new constituents reported, covering sesquiterpenoids, lignans, and alkaloids. Its effects of anti-myocardial ischemia, anti-cerebral ischemia/reperfusion, sedation, and analgesia were revealed, and the mechanisms of agarwood formation were also investigated. To better understand its medical value and potential of clinical application, this review updates the research progress in recent five years focusing on the chemical constituents and pharmacological effects of S. pinnatifolia, providing reference for subsequent research on active ingredient and support for its innovative application in modern medicine system.
Medicine, Mongolian Traditional ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Syringa/chemistry*