1.Guideline for Adult Weight Management in China
Weiqing WANG ; Qin WAN ; Jianhua MA ; Guang WANG ; Yufan WANG ; Guixia WANG ; Yongquan SHI ; Tingjun YE ; Xiaoguang SHI ; Jian KUANG ; Bo FENG ; Xiuyan FENG ; Guang NING ; Yiming MU ; Hongyu KUANG ; Xiaoping XING ; Chunli PIAO ; Xingbo CHENG ; Zhifeng CHENG ; Yufang BI ; Yan BI ; Wenshan LYU ; Dalong ZHU ; Cuiyan ZHU ; Wei ZHU ; Fei HUA ; Fei XIANG ; Shuang YAN ; Zilin SUN ; Yadong SUN ; Liqin SUN ; Luying SUN ; Li YAN ; Yanbing LI ; Hong LI ; Shu LI ; Ling LI ; Yiming LI ; Chenzhong LI ; Hua YANG ; Jinkui YANG ; Ling YANG ; Ying YANG ; Tao YANG ; Xiao YANG ; Xinhua XIAO ; Dan WU ; Jinsong KUANG ; Lanjie HE ; Wei GU ; Jie SHEN ; Yongfeng SONG ; Qiao ZHANG ; Hong ZHANG ; Yuwei ZHANG ; Junqing ZHANG ; Xianfeng ZHANG ; Miao ZHANG ; Yifei ZHANG ; Yingli LU ; Hong CHEN ; Li CHEN ; Bing CHEN ; Shihong CHEN ; Guiyan CHEN ; Haibing CHEN ; Lei CHEN ; Yanyan CHEN ; Genben CHEN ; Yikun ZHOU ; Xianghai ZHOU ; Qiang ZHOU ; Jiaqiang ZHOU ; Hongting ZHENG ; Zhongyan SHAN ; Jiajun ZHAO ; Dong ZHAO ; Ji HU ; Jiang HU ; Xinguo HOU ; Bimin SHI ; Tianpei HONG ; Mingxia YUAN ; Weibo XIA ; Xuejiang GU ; Yong XU ; Shuguang PANG ; Tianshu GAO ; Zuhua GAO ; Xiaohui GUO ; Hongyi CAO ; Mingfeng CAO ; Xiaopei CAO ; Jing MA ; Bin LU ; Zhen LIANG ; Jun LIANG ; Min LONG ; Yongde PENG ; Jin LU ; Hongyun LU ; Yan LU ; Chunping ZENG ; Binhong WEN ; Xueyong LOU ; Qingbo GUAN ; Lin LIAO ; Xin LIAO ; Ping XIONG ; Yaoming XUE
Chinese Journal of Endocrinology and Metabolism 2025;41(11):891-907
Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.
2.COVID-19 infection illness condition and prognosis related demographic characteristics,laboratory indicators and radiological factors
Yufan XIONG ; Xinchi ZHANG ; Jianhe GAN ; Wei SUN ; Jing GU ; Li CHEN
Chinese Journal of Nosocomiology 2025;35(15):2384-2389
Coronavirus disease 2019(COVID-19)is a respiratory syndrome caused by severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2).Once SARS-CoV-2 was detected in Wuhan(Hubei Province,China),it rap-idly spread widely across China and the world,posing a serious threat to global health and the economy.With the development of Artificial Intelligence(AI)technology and the openness and application of open-source software,tremendous progress has been achieved in the quantitative study of chest radiology for COVID-19,enabling the quantitative dataization of radiological image data,which adds a powerful indicator to the prognosis study of COV-ID-19.Therefore,we reviewed the current literature on the factors predicting the severity and prognosis of COV-ID-19,and summarized demographic,laboratory and radiological factors to help with risk stratification and prog-nosis assessment for COVID-19 patients and assist in their clinical management and treatment.
3.COVID-19 infection illness condition and prognosis related demographic characteristics,laboratory indicators and radiological factors
Yufan XIONG ; Xinchi ZHANG ; Jianhe GAN ; Wei SUN ; Jing GU ; Li CHEN
Chinese Journal of Nosocomiology 2025;35(15):2384-2389
Coronavirus disease 2019(COVID-19)is a respiratory syndrome caused by severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2).Once SARS-CoV-2 was detected in Wuhan(Hubei Province,China),it rap-idly spread widely across China and the world,posing a serious threat to global health and the economy.With the development of Artificial Intelligence(AI)technology and the openness and application of open-source software,tremendous progress has been achieved in the quantitative study of chest radiology for COVID-19,enabling the quantitative dataization of radiological image data,which adds a powerful indicator to the prognosis study of COV-ID-19.Therefore,we reviewed the current literature on the factors predicting the severity and prognosis of COV-ID-19,and summarized demographic,laboratory and radiological factors to help with risk stratification and prog-nosis assessment for COVID-19 patients and assist in their clinical management and treatment.
4.Guideline for Adult Weight Management in China
Weiqing WANG ; Qin WAN ; Jianhua MA ; Guang WANG ; Yufan WANG ; Guixia WANG ; Yongquan SHI ; Tingjun YE ; Xiaoguang SHI ; Jian KUANG ; Bo FENG ; Xiuyan FENG ; Guang NING ; Yiming MU ; Hongyu KUANG ; Xiaoping XING ; Chunli PIAO ; Xingbo CHENG ; Zhifeng CHENG ; Yufang BI ; Yan BI ; Wenshan LYU ; Dalong ZHU ; Cuiyan ZHU ; Wei ZHU ; Fei HUA ; Fei XIANG ; Shuang YAN ; Zilin SUN ; Yadong SUN ; Liqin SUN ; Luying SUN ; Li YAN ; Yanbing LI ; Hong LI ; Shu LI ; Ling LI ; Yiming LI ; Chenzhong LI ; Hua YANG ; Jinkui YANG ; Ling YANG ; Ying YANG ; Tao YANG ; Xiao YANG ; Xinhua XIAO ; Dan WU ; Jinsong KUANG ; Lanjie HE ; Wei GU ; Jie SHEN ; Yongfeng SONG ; Qiao ZHANG ; Hong ZHANG ; Yuwei ZHANG ; Junqing ZHANG ; Xianfeng ZHANG ; Miao ZHANG ; Yifei ZHANG ; Yingli LU ; Hong CHEN ; Li CHEN ; Bing CHEN ; Shihong CHEN ; Guiyan CHEN ; Haibing CHEN ; Lei CHEN ; Yanyan CHEN ; Genben CHEN ; Yikun ZHOU ; Xianghai ZHOU ; Qiang ZHOU ; Jiaqiang ZHOU ; Hongting ZHENG ; Zhongyan SHAN ; Jiajun ZHAO ; Dong ZHAO ; Ji HU ; Jiang HU ; Xinguo HOU ; Bimin SHI ; Tianpei HONG ; Mingxia YUAN ; Weibo XIA ; Xuejiang GU ; Yong XU ; Shuguang PANG ; Tianshu GAO ; Zuhua GAO ; Xiaohui GUO ; Hongyi CAO ; Mingfeng CAO ; Xiaopei CAO ; Jing MA ; Bin LU ; Zhen LIANG ; Jun LIANG ; Min LONG ; Yongde PENG ; Jin LU ; Hongyun LU ; Yan LU ; Chunping ZENG ; Binhong WEN ; Xueyong LOU ; Qingbo GUAN ; Lin LIAO ; Xin LIAO ; Ping XIONG ; Yaoming XUE
Chinese Journal of Endocrinology and Metabolism 2025;41(11):891-907
Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.
5.Establishment of a nomogram model for predicting liver cirrhosis with esophagogastric variceal bleeding based on aspartate aminotransferase-to-platelet ratio index and platelet-albumin-bilirubin score
Xinyi LI ; Jiaojiao LI ; Yingying LI ; Honghe WEI ; Yufan XIONG ; Xinchi ZHANG ; Wei SUN ; Li CHEN
Journal of Clinical Hepatology 2024;40(3):521-526
ObjectiveTo investigate the value of aspartate aminotransferase-to-platelet ratio index (APRI) and platelet-albumin-bilirubin (PALBI) score in predicting the risk of esophagogastric variceal bleeding in patients with liver cirrhosis. MethodsA total of 119 patients with liver cirrhosis who were admitted to The First Affiliated Hospital of Soochow University from May 2021 and June 2022 were enrolled, and clinical data, routine blood test results, serum biochemistry, and coagulation test results were collected from all patients. According to the presence or absence of esophagogastric variceal bleeding, the patients were divided into non-bleeding group with 59 patients and bleeding group with 60 patients, and a comparative analysis was performed for the two groups. The independent samples t-test was used for comparison of normally distributed continuous data between two groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups; the chi-squared test or the Fisher’s exact test was used for comparison of categorical data between groups. The multivariate Logistic regression analysis was used to identify the independent risk factors for esophagogastric variceal bleeding in patients with liver cirrhosis and establish a nomogram predictive model. ResultsThe male patients accounted for 75.00% in the bleeding group and 40.68% in the non-bleeding group, and there was a significant difference in sex composition between the two groups (χ2=14.384, P<0.001). Chronic hepatitis B was the main etiology in both the bleeding group and the non-bleeding group (53.33% vs 38.98%), and there was no significant difference in composition ratio between the two groups (χ2=2.464, P=0.116). Compared with the non-bleeding group, the bleeding group had a significantly higher activity of AT-IIIA (t=3.329, P=0.001) and significantly lower levels of PLT, TBil, Ca, TC, and TT (all P<0.05). There were significant differences in APRI and PALBI between the two groups (χ2=6.175 and 19.532, both P<0.05). The binary logistic regression analysis showed that APRI (odds ratio [OR]=0.309, 95% confidence interval [CI]: 0.109 — 0.881, P=0.028), PALBI (OR=7.667, 95%CI: 2.005 — 29.327, P=0.003), Ca (OR=0.001, 95%CI: 0.000 — 0.141, P=0.007), TC (OR=0.469, 95%CI: 0.226 — 0.973, P=0.042), and TT (OR=0.599, 95%CI: 0.433 — 0.830, P=0.002) were independent influencing factors for esophagogastric variceal bleeding in liver cirrhosis. A nomogram model was established based on the above factors and had an index of concordance of 0.899 and a well-fitted calibration curve. ConclusionAPRI and PALBI have a good value in predicting esophagogastric variceal bleeding in patients with liver cirrhosis, and the nomogram model established based on this study can predict the incidence rate of esophagogastric variceal bleeding in patients with liver cirrhosis.
6.Association between the severity of hypodontia and the characteristics of craniofacial morphology in a Chinese population: A cross-sectional study
Xin XIONG ; Jiaqi LIU ; Yange WU ; Chengxinyue YE ; Qinlanhui ZHANG ; Yufan ZHU ; Wenke YANG ; Jun WANG
The Korean Journal of Orthodontics 2023;53(3):150-162
Objective:
To investigate craniofacial differences in individuals with hypodontia and explore the relationship between craniofacial features and the number of congenitally missing teeth.
Methods:
A cross-sectional study was conducted among 261 Chinese patients (males, 124; females, 137; age, 7–24 years), divided into four groups (without hypodontia: no teeth missing, mild: one or two missing teeth, moderate: three to five missing teeth, severe: six or more missing teeth) according to the number of congenitally missing teeth. Differences in cephalometric measurements among the groups were analyzed. Further, multivariate linear regression and smooth curve fitting were performed to evaluate the relationship between the number of congenitally missing teeth and the cephalometric measurements.
Results:
In patients with hypodontia, SNA, NA-AP, FH-NA, ANB, Wits, ANS-Me/N-Me, GoGn-SN, UL-EP, and LL-EP significantly decreased, while Pog-NB, AB-NP, N-ANS, and S-Go/N-Me significantly increased. In multivariate linear regression analysis, SNB, Pog-NB, and S-Go/N-Me were positively related to the number of congenitally missing teeth. In contrast, NA-AP, FH-NA, ANB, Wits, N-Me, ANS-Me, ANS-Me/N-Me, GoGn-SN, SGn-FH (Y-axis), UL-EP, and LL-EP were negatively related, with absolute values of regression coefficients ranging from 0.147 to 0.357. Further, NA-AP, Pog-NB, S-Go/N-Me, and GoGn-SN showed the same tendency in both sexes, whereas UL-EP and LL-EP were different.
Conclusions
Compared with controls, patients with hypodontia tend toward a Class III skeletal relationship, reduced lower anterior face height, flatter mandibular plane, and more retrusive lips. The number of congenitally missing teeth had a greater effect on certain characteristics of craniofacial morphology in males than in females.

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