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.

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.

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*

Bawei Chenxiang Powder(BCP), first documented in the Tibetan medical work Four Medical Classics, has been widely applied in clinical practices in Tibetan and Mongolian medicines since its development. It has the effect of clearing the heart heat, calming the mind, and inducing resuscitation. On the basis of BCP, multiple types of formulae have been developed, such as Bawei Yiheyi Chenxiang Powder, Bawei Rang Chenxiang Powder, and Bawei Pingchuan Chenxiang Powder, which are widely used for treating cardiovascular and respiratory diseases. Current pharmacological research has revealed the pharmacological effects of BCP and its related formulae against myocardial ischemia, cerebral ischemia, renal ischemia, and anti-hypoxia. BCP and its related formulae introduced more treatment options for related clinical diseases and provided insights for fully comprehending the essence and pharmacological components of the formulae. This paper systematically reviewed the clinical and pharmacological research on BCP and its related formulae, analyzing the formulation principles and potential key flavors and active ingredients. This lays a fundamental scientific basis for the clinical use, quality evaluation, and subsequent development and application of BCP and its related formulae, providing references for studying traditional Chinese medicine formulae in a thorough and systematic manner.
Drugs, Chinese Herbal/chemistry* ; Humans ; Powders/chemistry* ; Animals ; Medicine, Chinese Traditional