The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

This paper summarizes clinical experience in treating acne based on the staged therapeutic principles of "eruption in warm diseases". It is considered that acne results from wind-heat retained in the lungs， invading the ying level and obstructing the blood collaterals， and is primarily a disorder involving both the wei and ying systems. In clinical practice， the treatment emphasizes the use of acrid-cool and sweet-cold methods. The core prescription is namely Yinqiaosan Qu Douchi Jia Xishengdi Danpi Daqingye Bei Xuanshen Fang （from Epidemic Warm Diseases ［《温病条辨》］）， and is adjusted according to the stage of disease. In the non-inflammatory stage， when the pathogen initially attacks the wei level， treatment focuses on acrid-cool herbs to release the exterior， with supplementary bitter-sweet ingredients such as Yejuhua （Chrysanthemum Indicum）. In the inflammatory stage， with pronounced heat toxin in the qi level affecting the ying and blood， and local stagnation of qi and blood， the approach is to clear heat and resolve toxin， using blood-cooling and stasis-resolving herbs early to prevent progression. Herbs such as Pugongying （Taraxacum Mongolicum）， Zihuadiding （Viola Yedoensis）， Tiankuizi （Semiaquilegia Adoxoides）， Chonglou （Paris Polyphylla）， Machixian （Portulaca Oleracea）， Zaojiaoci （Gleditsia Sinensis）， Chuanshanjia （Manis Pentadactyla） may be added. In the post-inflammatory erythema stage， when yin of the ying level is depleted and internal deficiency-heat arises， sweet-cold herbs are recommended to nourish the stomach and generate fluids， with the possible addition of Yiwei Decoction （益胃汤）.

BACKGROUND: Congenital heart disease (CHD) is a leading cause of birth defect-related mortality. However, more recent CHD mortality data for China are lacking. Additionally, limited studies have evaluated sex, rural-urban, and region-specific disparities of CHD mortality in China. METHODS: We designed a population-based study using data from the Dataset of National Mortality Surveillance in China between 2008 and 2021. We calculated age-adjusted CHD mortality using the sixth census data of China in 2010 as the standard population. We assessed the temporal trends in CHD mortality by age, sex, area, and region from 2008 to 2021 using the joinpoint regression model. RESULTS: From 2008 to 2021, 33,534 deaths were attributed to CHD. The period witnessed a two-fold decrease in the age-adjusted CHD mortality from 1.61 to 0.76 per 100,000 persons (average annual percent change [AAPC] = -5.90%). Females tended to have lower age-adjusted CHD mortality than males, but with a similar decline rate from 2008 to 2021 (females: AAPC = -6.15%; males: AAPC = -5.84%). Similar AAPC values were observed among people living in urban (AAPC = -6.64%) and rural (AAPC = -6.12%) areas. Eastern regions experienced a more pronounced decrease in the age-adjusted CHD mortality (AAPC = -7.86%) than central (AAPC = -5.83%) and western regions (AAPC = -3.71%) between 2008 and 2021. Approximately half of the deaths (46.19%) due to CHD occurred during infancy. The CHD mortality rates in 2021 were lower than those in 2008 for people aged 0-39 years, with the largest decrease observed among children aged 1-4 years (AAPC = -8.26%), followed by infants (AAPC = -7.01%). CONCLUSIONS CHD mortality in China has dramatically decreased from 2008 to 2021. The slower decrease in CHD mortality in the central and western regions than in the eastern regions suggested that public health policymakers should pay more attention to health resources and health education for central and western regions.
Humans ; Heart Defects, Congenital/mortality* ; Male ; Female ; China/epidemiology* ; Infant ; Child, Preschool ; Adult ; Child ; Adolescent ; Infant, Newborn ; Middle Aged ; Young Adult ; Aged ; Rural Population