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ObjectiveTo investigate the identification of kidney Yang deficiency syndrome of patients with osteoporosis（OP）， and to form the clinical syndrome identification rules of traditional Chinese medicine（TCM）. MethodBasic information， etiology， clinical symptoms and other characteristics of 982 OP patients were included， and statistical tests were used to screen the variables associated with kidney Yang deficiency syndrome. Taking the decision tree as the base model， bootstrap aggregation algorithm（Bagging algorithm） was utilized to establish the classification model of kidney Yang deficiency syndrome in OP， generating numerous rules and removing redundancy. Combining least absolute shrinkage and selection operator（LASSO） regression to screen key rules and integrate them to construct an identification model， achieving the identification of kidney Yang deficiency syndrome in OP patients. ResultEighteen key identification rules were screened out， and of these， where 11 rules with regression coefficients>0 correlated positively with the kidney Yang deficiency syndrome， the rule with the highest coefficient was chilliness（present）&feverish sensation over the palm and sole（absent）. The other 7 rules with regression coefficients<0 correlated negatively with the syndrome， the rule with the lowest coefficient was reddish tongue（present）&diarrhea（absent）&deficiency of endowment（absent）. According to the regression coefficients of each key rule， variables with importance>0.2 were ranked as chilliness， reddish tongue， feverish sensation over the palm and sole， cold limbs， clear urine， diarrhea， deficiency of endowment， prolonged illness. The results of the partial dependence analysis of the identification model showed that compared to OP patients without chilliness， those with chilliness（present） had a 0.266 8 higher probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identification of the syndrome. Similarly， compared to OP patients without reddish tongue， those with reddish tongue had a 0.141 9 lower probability of being identified as having kidney Yang deficiency syndrome， indicating that this variable had the highest impact on identifying non-kidney Yang deficiency syndrome. The accuracy， sensitivity， specificity and area under receiver operating characteristic curve（AUC） of the established kidney Yang deficiency syndrome identification model in the test set were 0.865 9， 0.853 7， 0.872 0 and 0.931 5， respectively. ConclusionA precise identification model of OP kidney Yang deficiency syndrome is conducted basing on the rule ensemble method of Bagging combining LASSO regression， and the screened key rules can explain the identification process of kidney Yang deficiency syndrome. In this research， according to the regression coefficients of rules， the importance and partial dependence of variables， combined with the thinking of TCM， the influence of patient characteristics on the identification of syndromes is described， so as to reveal the primary and secondary syndromes of identification and assist the clinical identification of kidney Yang deficiency syndrome.
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Objective: To determine the content of chlorogenic acid in Lonicerae Japonicae Flos by the combined near-infrared and variable selection methods. Methods: Synergy interval partial least squares (SIPLS), competitive adaptive reweighted sampling method (CARS), variable importance in projection (VIP), and successive projections algorithm (SPA) were used to build a chlorogenic acid quantitative model in Lonicerae Japonicae Flos and compare. High performance liquid chromatography (HPLC) was used as a reference to select the optimum variable screening method. Results: Study results showed that SIPLS was the most desirable method for chlorogenic acid in regression performance with Rpre2 at 0.990 3 and RMSEP at 2.316%. Conclusion: The quantitative model of chlorogenic acid established by NIR combined with SIPLS has good performance and meets the requirement of real-time analysis of traditional Chinese medicine production process.
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Quality by design (QbD) highlights the concept of "begin with the end", which means to thoroughly understand the target product quality first, and then guide pharmaceutical process development and quality control throughout the whole manufacturing process. In this paper, the Ginkgo biloba granules intermediates were taken as the research object, and the requirements of the tensile strength of tablets were treated as the goals to establish the methods for identification of granules' critical quality attributes (CQAs) and establishment of CQAs' limits. Firstly, the orthogonal partial least square (OPLS) model was adopted to build the relationship between the micromeritic properties of 29 batches of granules and the tensile strength of ginkgo leaf tablets, and thereby the potential critical quality attributes (pCQAs) were screened by variable importance in the projection (VIP) indexes. Then, a series of OPLS models were rebuilt by reducing pCQAs variables one by one in view of the rule of VIP values from low to high in sequence. The model performance results demonstrated that calibration and predictive performance of the model had no decreasing trend after variables reduction. In consideration of the results from variables selection as well as the collinearity test and testability of the pCQAs, the median particle size (D₅₀) and the bulk density (Da) were identified as critical quality attributes (CQAs). The design space of CQAs was developed based on a multiple linear regression model established between the CQAs (D₅₀ and Da) and the tensile strength. The control constraints of the CQAs were determined as 170 μm< D₅₀<500 μm and 0.30 g•cm⁻³<Da<0.44 g•cm⁻³ according to the design space, which provided a basis for controlling and optimizing the wet granulation process of the ginkgo leaf tablet..