1.Comparison of chemical components of Lonicera fragrantissima and Lonicera japonica based on LC-MS
Ying JIN ; Le-Wen XIONG ; Gao-Bin PU ; Fang ZHANG ; Jia LI ; Long-Fei ZHANG ; Yong-Qing ZHANG
Chinese Traditional Patent Medicine 2024;46(3):850-859
AIM To compare the components difference between Lonicera fragrantissima Lindl.et Paxt.(LFL)and Lonicerae japonicae Flos(LJF),and to evaluate the medicinal value of LFL,so as to provide reference for the development and utilization of LFL and LJF.METHODS With 70%methanol as extraction solvent,the components were analyzed by UPLC-TOF-MS,and the contents of 20 components were determined by HPLC-QQQ-MS.The components difference was determined by multivariate statistical analysis.RESULTS A total of 52 components were identified in the buds of LFL and LJF.There were 4 different components in LJF,and the contents of 20 quantitative components were significantly different.The contents of isochlorogenic acid C,ferulic acid,luteolin and rutin in the buds of LFL were more than 2 times that of LJF,and the contents of marchanic acid and marchanin were 11.96 times and 37.23 times that of LJF respectively.Maganin,isochlorogenic acid A,maganic acid,rutin and dicomachanic acid are the key differentiating components of LFL and LJF.CONCLUSION The buds of LFL and LJF have similar species,but the content difference is obvious.The buds of LFL have important medicinal value,which need further development and utilization.
2.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.
3.Structural characteristics and phylogenetic analysis of chloroplast genomes of four species of Lonicera
Yao XIONG ; Ling-fei TONG ; Lan CAO ; Ze-jing MU ; Cheng-ying SHEN ; Xiao-lang DU
Acta Pharmaceutica Sinica 2024;59(11):3164-3171
italic>Lonicera Linn.
4.Determining Disease Activity and Glucocorticoid Response in Thyroid-Associated Ophthalmopathy:Preliminary Study Using Dynamic Contrast-Enhanced MRI
Hao HU ; Xiong-Ying PU ; Jiang ZHOU ; Wen-Hao JIANG ; Qian WU ; Jin-Ling LU ; Fei-Yun WU ; Huan-Huan CHEN ; Xiao-Quan XU
Korean Journal of Radiology 2024;25(12):1070-1082
Objective:
To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs).
Materials and Methods:
We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance.
Results:
Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821).
Conclusion
DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.
5.Determining Disease Activity and Glucocorticoid Response in Thyroid-Associated Ophthalmopathy:Preliminary Study Using Dynamic Contrast-Enhanced MRI
Hao HU ; Xiong-Ying PU ; Jiang ZHOU ; Wen-Hao JIANG ; Qian WU ; Jin-Ling LU ; Fei-Yun WU ; Huan-Huan CHEN ; Xiao-Quan XU
Korean Journal of Radiology 2024;25(12):1070-1082
Objective:
To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs).
Materials and Methods:
We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance.
Results:
Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821).
Conclusion
DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.
6.Determining Disease Activity and Glucocorticoid Response in Thyroid-Associated Ophthalmopathy:Preliminary Study Using Dynamic Contrast-Enhanced MRI
Hao HU ; Xiong-Ying PU ; Jiang ZHOU ; Wen-Hao JIANG ; Qian WU ; Jin-Ling LU ; Fei-Yun WU ; Huan-Huan CHEN ; Xiao-Quan XU
Korean Journal of Radiology 2024;25(12):1070-1082
Objective:
To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs).
Materials and Methods:
We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance.
Results:
Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821).
Conclusion
DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.
7.Determining Disease Activity and Glucocorticoid Response in Thyroid-Associated Ophthalmopathy:Preliminary Study Using Dynamic Contrast-Enhanced MRI
Hao HU ; Xiong-Ying PU ; Jiang ZHOU ; Wen-Hao JIANG ; Qian WU ; Jin-Ling LU ; Fei-Yun WU ; Huan-Huan CHEN ; Xiao-Quan XU
Korean Journal of Radiology 2024;25(12):1070-1082
Objective:
To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs).
Materials and Methods:
We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance.
Results:
Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821).
Conclusion
DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.
8.Determining Disease Activity and Glucocorticoid Response in Thyroid-Associated Ophthalmopathy:Preliminary Study Using Dynamic Contrast-Enhanced MRI
Hao HU ; Xiong-Ying PU ; Jiang ZHOU ; Wen-Hao JIANG ; Qian WU ; Jin-Ling LU ; Fei-Yun WU ; Huan-Huan CHEN ; Xiao-Quan XU
Korean Journal of Radiology 2024;25(12):1070-1082
Objective:
To assess the role of dynamic contrast-enhanced (DCE)-MRI of the extraocular muscles (EOMs) for determining the activity of thyroid-associated ophthalmopathy (TAO) and treatment response to glucocorticoids (GCs).
Materials and Methods:
We prospectively enrolled 65 patients with TAO (41 active, 82 eyes; 24 inactive, 48 eyes). Twenty-two active patients completed the GC treatment and follow-up assessment, including 15 patients (30 eyes) and 7 patients (14 eyes), defined as responsive and unresponsive, respectively. Model-free (time to peak [TTP], area under the curve [AUC], and Slope max) and model-based (Ktrans , Kep, and Ve) parameters of EOMs in embedded simplified histogram analyses were calculated and compared between groups. Multivariable logistic regression analysis was used to identify the independent predictors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic performance.
Results:
Active patients exhibited significantly higher TTP at the 10th percentile (-10th), TTP-mean, and TTP at the 90th percentile (-90th); AUC-10th, AUC-mean, AUC-90th, and AUC-max; Ktrans -10th and Ktrans -mean; and Ve-10th, Ve-mean, Ve-90th, and Ve-max than inactive patients (P < 0.05). Responsive patients exhibited significantly lower TTP-min; higher Ktrans -mean and Ktrans -max; and higher Kep-10th, Kep-mean, and Kep-max than unresponsive patients (P < 0.05). TTP-mean and Ve-mean were independent variables for determining disease activity (P = 0.017 and 0.022, respectively). A combination of the two parameters could determine active TAO with moderate performance (AUROC = 0.687). TTP-min and Ktrans -mean were independent predictors of the response to GCs (P = 0.023 and 0.004, respectively), uniting which could determine the response to GCs with decent performance (AUROC = 0.821).
Conclusion
DCE-MRI-derived model-free and model-based parameters of EOMs can assist in the evaluation of TAO. In particular, TTP-mean and Ve-mean could be useful for determining the activity of TAO, whereas TTP-min and K trans -mean could be promising biomarkers for determining the response to GCs.
9.Mechanism of Mongolian drug Naru-3 in initiation of neuroinflammation of neuropathic pain from MMP9/IL-1β signaling pathway.
Fang-Ting ZHOU ; Ying ZONG ; Yuan-Bin LI ; Ren-Li CAO ; Wu-Qiong HOU ; Li-Ting XU ; Fei YANG ; Yan-Li GU ; Xiao-Hui SU ; Qiu-Yan GUO ; Wei-Jie LI ; Hui XIONG ; Chao WANG ; Na LIN
China Journal of Chinese Materia Medica 2023;48(15):4173-4186
Neuropathic pain(NP) has similar phenotypes but different sequential neuroinflammatory mechanisms in the pathological process. It is of great significance to inhibit the initiation of neuroinflammation, which has become a new direction of NP treatment and drug development in recent years. Mongolian drug Naru-3 is clinically effective in the treatment of trigeminal neuralgia, sciatica, and other NPs in a short time, but its pharmacodynamic characteristics and mechanism of analgesia are still unclear. In this study, a spinal nerve ligation(SNL) model simulating clinical peripheral nerve injury was established and the efficacy and mechanism of Naru-3 in the treatment of NPs was discussed by means of behavioral detection, side effect evaluation, network analysis, and experimental verification. Pharmacodynamic results showed that Naru-3 increased the basic pain sensitivity threshold(mechanical hyperalgesia and thermal radiation hyperalgesia) in the initiation of SNL in animals and relieved spontaneous pain, however, there was no significant effect on the basic pain sensitivity threshold and motor coordination function of normal animals under physiological and pathological conditions. Meanwhile, the results of primary screening of target tissues showed that Naru-3 inhibited the second phase of injury-induced nociceptive response of formalin test in mice and reduced the expression of inflammatory factors in the spinal cord. Network analysis discovered that Naru-3 had synergy in the treatment of NP, and its mechanism was associated with core targets such as matrix metalloproteinase-9(MMP9) and interleukin-1β(IL-1β). The experiment further took the dorsal root ganglion(DRG) and the stage of patho-logical spinal cord as the research objects, focusing on the core targets of inducing microglial neuroinflammation. By means of Western blot, immunofluorescence, agonists, antagonists, behavior, etc., the mechanism of Naru-3 in exerting NP analgesia may be related to the negative regulation of the MMP9/IL-1β signaling pathway-mediated microglia p38/IL-1β inflammatory loop in the activation phase. The relevant research enriches the biological connotation of Naru-3 in the treatment of NP and provides references for clinical rational drug use.
Rats
;
Mice
;
Animals
;
Matrix Metalloproteinase 9/metabolism*
;
Rats, Sprague-Dawley
;
Neuroinflammatory Diseases
;
Interleukin-1beta/metabolism*
;
Spinal Cord/metabolism*
;
Signal Transduction
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Hyperalgesia/metabolism*
;
Neuralgia/metabolism*
10.Association between dietary pattern and cognitive function of elderly in Tianjin
Liping ZHU ; Ying XIONG ; Chong CHEN ; Xiaomin WU ; Fei MA
Shanghai Journal of Preventive Medicine 2022;34(4):348-350
ObjectiveTo determine the association of dietary diversity and dietary pattern with cognitive function in elderly in community. MethodsA cross-sectional study was conducted to randomly select 143 elderly people over 65 years old in Wangdingdi Community of Tianjin. Self-designed questionnaire was used and then dietary diversity index was calculated. Wechsler Adult Intelligence Scale-Chinese Revised (WAIS-RC) was used to measure intelligence quotient (IQ) to assess cognitive function. Factor analysis and multivariate linear regression model were used to extract dietary patterns and determine the association of dietary diversity index and dietary patterns with cognitive function, respectively. ResultsFactor analysis revealed four dietary patterns, which were meat and cereal dietary pattern, fish and poultry milk dietary pattern, bean vegetable dietary pattern, and egg dietary pattern. Multivariate linear regression showed that egg dietary pattern was significantly associated with performance IQ (P<0.05), suggesting that egg dietary pattern may have a protective effect on IQ of the elderly. There was no significant association between dietary diversity and cognitive function in the elderly (P>0.05). ConclusionEgg dietary pattern may protect cognitive function in the elderly. Therefore, the elderly should increase the intake of eggs in daily diet.

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