Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical...Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.展开更多
基金National Key Research and Development Program of China(2022YFC3502302)National Natural Science Foundation of China(82074580)Graduate Research Innovation Program of Jiangsu Province(KYCX23_2078).
文摘Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
文摘目的 观察芪苈强心胶囊对慢性心(chronic heart failure,CHF)衰后认知功能障碍的干预作用并探讨其相关机制。方法 SD大鼠建立心梗后心衰大鼠模型,分为假手术组、模型组、芪苈组和缬沙坦组。Morris水迷宫检测大鼠认知功能,超声心动图和血流动力学检测心功能,HE染色观察心脏结构,尼氏染色观察海马神经元,ELISA法检测血浆和海马血管紧张素(angiotensin,Ang)Ⅱ、海马β淀粉样蛋白(amyloid-β,Aβ)42浓度。结果 与假手术组比较,模型组大鼠的左心室舒张末压(left ventricular end diastolic pressure,LVEDP)、海马Aβ42、血浆和海马AngⅡ浓度显著升高(P<0.05),穿台次数、心室射血分数(ejection fraction,EF)、心室缩短分数(fractional shortening,FS)、左心室收缩压(left ventricular systolic pressure,LVSP)、左心室内压最大上升速率(the maximal rate of increase of left ventricular pressure,+dp/dt max)和最大下降速率(the maximal rate of decrease of left ventricular pressure,-dp/dt max)显著降低(P<0.05),心肌细胞结构模糊,有明显炎性细胞浸润,海马神经元数量减少且染色较浅。与模型组比较,芪苈组大鼠的海马Aβ42、血浆和海马AngⅡ浓度明显降低(P<0.05),穿台次数、EF、FS、LVSP显著增加(P<0.05),心肌和海马染色结果均有改善。结论芪苈强心胶囊可能通过抑制外周和海马AngⅡ及脑内Aβ42水平,改善慢性心衰大鼠心功能和认知功能障碍。
