Objective:To construct and validate a predictive model for pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy.Methods:This retrospective observational study included 595 patients with stage T2-4 and (or) N+M0 LARC diagnosed in the Cancer Hospital of Chinese Academy of Medical Sciences and the Fourth Hospital of Hebei Medical University who had no metastases, tolerated neoadjuvant therapy, completed neoadjuvant therapy, and had undergone radical surgery after neoadjuvant therapy. The training set comprised 299 patients admitted to the Cancer Hospital of Chinese Academy of Medical Sciences from 2013 to 2018, the internal validation set 155 patients admitted from 2019 to 2023, and the external validation set 141 patients admitted to the Fourth Hospital of Hebei Medical University from 2013 to 2021. They were divided into pCR group and non-pCR groups according to postoperative pathology. Among the 299 patients in the training set, 247 were in the non-PCR and 52 in the pCR group; among the 155 patients verified internally, 113 were in the non-PCR and 42 in the pCR group; and among the 141 patients validated externally, 132 were in the non-pCR and nine in the pCR group. Logistic regression was used for univariate and multifactorial analysis to explore the factors associated with pCR and construct a nomogram prediction model. Receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to validate the performance of the predictive model.Results:Univariate and multivariate logistic regression analysis showed that carbohydrate antigen 19-9 ( P=0.040, OR=0.97, 95%CI: 0.93-0.99), neutrophil count ( P<0.001, OR=0.66, 95%CI: 0.52-0.84), tumor T stage: Stage IV ( P=0.011, OR=0.22, 95%CI: 0.07-0.70), tumor N stage: Stage I ( P=0.003, OR=0.22,95%CI:0.08-0.60), Stage II ( P<0.001, OR=0.03, 95%CI: 0.01-0.09) and involvement of mesorectal fascia ( P=0.004, OR=0.09, 95%CI: 0.02-0.47) were independent predictors of pCR. In the training set, the area under the receiver operating characteristic curve of the model was 0.92 (95%CI: 0.87-0.96), whereas in the internal and external validation sets, the AUCs were 0.78 and 0.81, respectively. The calibration curve showed that the prediction model had good prediction efficiency in both the training and verification sets. Decision curve analysis showed that the net benefit of the model was largest when the threshold probability was in the range of 5.2% to 89.7% (in the internal and external validation sets, the threshold probabilities were in the range of 15.7% to 92.3% and 2.2% to 84.1%, respectively). Conclusion:The nomogram model constructed in this study showed efficacy in predicting whether patients with LARC will achieve pCR after receiving neoadjuvant chemoradiotherapy.

Objective:To construct and validate a predictive model for pathological complete response (pCR) in patients with locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy.Methods:This retrospective observational study included 595 patients with stage T2-4 and (or) N+M0 LARC diagnosed in the Cancer Hospital of Chinese Academy of Medical Sciences and the Fourth Hospital of Hebei Medical University who had no metastases, tolerated neoadjuvant therapy, completed neoadjuvant therapy, and had undergone radical surgery after neoadjuvant therapy. The training set comprised 299 patients admitted to the Cancer Hospital of Chinese Academy of Medical Sciences from 2013 to 2018, the internal validation set 155 patients admitted from 2019 to 2023, and the external validation set 141 patients admitted to the Fourth Hospital of Hebei Medical University from 2013 to 2021. They were divided into pCR group and non-pCR groups according to postoperative pathology. Among the 299 patients in the training set, 247 were in the non-PCR and 52 in the pCR group; among the 155 patients verified internally, 113 were in the non-PCR and 42 in the pCR group; and among the 141 patients validated externally, 132 were in the non-pCR and nine in the pCR group. Logistic regression was used for univariate and multifactorial analysis to explore the factors associated with pCR and construct a nomogram prediction model. Receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to validate the performance of the predictive model.Results:Univariate and multivariate logistic regression analysis showed that carbohydrate antigen 19-9 ( P=0.040, OR=0.97, 95%CI: 0.93-0.99), neutrophil count ( P<0.001, OR=0.66, 95%CI: 0.52-0.84), tumor T stage: Stage IV ( P=0.011, OR=0.22, 95%CI: 0.07-0.70), tumor N stage: Stage I ( P=0.003, OR=0.22,95%CI:0.08-0.60), Stage II ( P<0.001, OR=0.03, 95%CI: 0.01-0.09) and involvement of mesorectal fascia ( P=0.004, OR=0.09, 95%CI: 0.02-0.47) were independent predictors of pCR. In the training set, the area under the receiver operating characteristic curve of the model was 0.92 (95%CI: 0.87-0.96), whereas in the internal and external validation sets, the AUCs were 0.78 and 0.81, respectively. The calibration curve showed that the prediction model had good prediction efficiency in both the training and verification sets. Decision curve analysis showed that the net benefit of the model was largest when the threshold probability was in the range of 5.2% to 89.7% (in the internal and external validation sets, the threshold probabilities were in the range of 15.7% to 92.3% and 2.2% to 84.1%, respectively). Conclusion:The nomogram model constructed in this study showed efficacy in predicting whether patients with LARC will achieve pCR after receiving neoadjuvant chemoradiotherapy.

ObjectiveTo summarize the history and modern clinical application of Renshen Baidusan. MethodThe bibliometric method was used to retrieve the relevant publications of Renshen Baidusan from the ancient book database and China National Knowledge Infrastructure （CNKI）. The publications were screened according to the inclusion and exclusion criteria. The information of dynasty， book title， function， dosage and so on was extracted， on the basis of which the history， composition， dosage， decocting method， original medicinal plants， processing， and modern clinical application of this prescription were analyzed. ResultRenshen Baidusan was first recorded in the Formulary of the Bureau of Taiping People's Welfare Pharmacy， consisting of Bupleuri Radix， Glycyrrhizae Radix et Rhizoma， Platycodonis Radix， Ginseng Radix et Rhizoma， Chuanxiong Rhizoma， Poria， Aurantii Fructus， Peucedani Radix， Notopterygii Rhizoma et Radix， Angelicae Pubescentis Radix， Zingiberis Rhizoma Recens， and Menthae Haplocalycis Herba and with the effect of dispersing cold， removing dampness， reinforcing Qi， and relieving exterior. Later generations of physicians used this prescription on the basis of the record in Formulary of the Bureau of Taiping People's Welfare Pharmacy to treat cold （frequency of 112， 22.63%） and seasonal cold （frequency of 83， 16.77%）. Renshen Baidusan is widely used in modern clinical practice to treat respiratory diseases （frequency of 42， 17.65%）， skin diseases （frequency of 34， 14.29%）， and infectious diseases （frequency of 33， 13.87%）. This prescription is often modified to treat the syndrome of internal deficiency and external contraction， or external contraction of wind， cold and damp pathogens without deficiency of healthy Qi， which fully embodies the concept of treating different diseases with the same method in traditional Chinese medicine. ConclusionThe textual research reveals the key information of the classical prescription Renshen Baidusan， providing a basis for the subsequent development and application of compound preparations.

The present study aimed to explore the correlation between agronomic traits and quality indexes of Dendrobium nobile and its application value in agricultural breeding. The cultivated strains of D. nobile in Hejiang-Chishui producing areas were extensively collected,and the main agronomic traits and quality indexes were measured. The agronomic traits with significant correlation with quality indexes were screened out by the correlation analysis,and then the parental lines and self-bred F_1 generation plants were furtherverified. Among 96 lines of D. nobile,the content of soluble polysaccharides showed a significant negative correlation with dendrobine( P < 0. 01),and no significant correlation with agronomic traits in stems and leaves. The content of dendrobine exhibited a significant positive correlation with the stem width-thickness ratio( at the largest cross section; P < 0. 01),and no significant correlation with other agronomic traits. Regression analysis further verified the positive correlation between dendrobine content and stem width-thickness ratio( R2> 0. 9). Two lines,JC-10 and JC-35,with significant differences in stem width-thickness ratio were screened out( P <0. 05). The corresponding F1 generation plants by self-pollination both showed that the dendrobine content was higher with greater stem width-thickness ratio( P < 0. 01). The experimental results suggested that within a certain range,the dendrobine content was higher in D. nobile with flatter stem. Therefore,in the breeding of D. nobile,this specific trait could be used for screening plants with high content of quality indexes such as dendrobine.
Agriculture ; Dendrobium/genetics* ; Plant Breeding ; Plant Leaves/genetics* ; Polysaccharides

In recent years, with the accuracy of tumor treatment, more and more nomograms have been created to guide clinical decision-making, assist tumor diagnosis and evaluate the prognosis of patients. However, the selected variables in the nomograms and the differences in doctors′ diagnostic levels will affect the accuracy of their predictions. Therefore, it is important to understand the limitations of the nomograms and use them reasonably and effectively to aid clinical decisions. This paper reviews the progress of nomogram in the field of tumor research and expounds the construction of nomogram and its application and limitations in tumor risk prediction, prognosis evaluation.