ObjectiveTo characterize the quality differences among different germplasm and introduced varieties of Bupleurum scorzonerifolium roots（BSR）， and explore the underlying molecular mechanisms， providing a basis for high-quality production and quality control. MethodsWild BSR from Yulin（YLW） served as the quality reference， we conducted comparative analysis among YLW， locally domesticated wild germplasm in Yulin（YLC3）， Daqing germplasm introduced and cultivated in Yulin（YLDQC3）， and locally cultivated germplasm in Daqing（DQC3）. A combination of traditional pharmacognostic methods and modern multi-omics analyses was employed， including macroscopic traits（appearance， odor）， microscopic features（proportions of cork， phloem， xylem）， cell wall component contents（hemicellulose， cellulose， lignin）， carbohydrate contents（starch， water-soluble polysaccharides）， marker compound contents（ethanol-soluble extracts， total saponins， liposoluble extracts， and saikosaponins A， B₂， C， D）， metabolomics， and transcriptomics， in order to systematically characterize quality differences and investigate molecular mechanisms among these samples. ResultsMacroscopically， Yulin-produced BSR（YLW， YLC3， YLDQC3） exhibited significantly greater weight， length， and upper and middle diameters than Daqing-produced BSR（DQC3）. Odor-wise， YLW and YLC3 had a a fragrance taste， YLDQC3 had a rancid oil odor， and DQC3 had a sweet and fragrant taste. Microscopically， Yulin germplasm（YLW， YLC3） and Daqing germplasm（YLDQC3， DQC3） shared similar structural features， respectively. However， Yulin germplasm showed significantly higher proportions of cork and phloem， as well as stronger xylem vessel staining intensity compared to Daqing germplasm. Regarding various component contents， Yulin germplasm contained significantly higher levels of ethanol-soluble extracts， total saponins， and saikosaponins A， B₂， C， D， while Daqing germplasm had significantly higher levels of hemicellulose， starch， and liposoluble extracts. After introduction to Yulin， the Daqing germplasm（YLDQC3） showed increased starch， water-soluble polysaccharides and liposoluble extracts contents， decreased cell wall component content， but no significant difference in other component contents. Metabolomics revealed that saponins and terpenes accumulated significantly in Yulin germplasm， while alcohols and aldehydes accumulated predominantly in Daqing germplasm. Transcriptomics indicated similar gene expression patterns within the same germplasm but specificity between different germplasms. Integrative metabolomic-transcriptomic analysis identified 145 potential key genes associated with the saikosaponin biosynthesis pathway， including one acetyl-coenzyme A（CoA） acetyltransferase gene（ACAT）， one 3-hydroxy-3-methylglutaryl-coenzyme A synthase gene（HMGS）， two hydroxymethylglutaryl-CoA（HMG-CoA） reductase genes（HMG）， one phosphomevalonate kinase gene（PMK）， one 1-deoxy-D-xylose-5-phosphate synthase gene（CLA）， one hydroxymethylbuten-1-aldol synthase gene（HDR）， two farnesyl pyrophosphate synthase genes（FPPS）， one squalene synthase gene（SQS）， one β-amyrin synthase gene（BAS）， 102 cytochrome P450（CYP450） gene family members， and 32 uridine diphosphate-glucuronosyltransferase（UGT） gene family members. ConclusionAmong the three cultivated types， YLC3 most closely resembles YLW in appearance， microscopic features， contents of major bioactive constituents， metabolomic and transcriptomic profiles. Yulin germplasm exhibits superior saponin synthesis capability compared to Daqing germplasm， and Yulin region is more suitable for the growth of B. scorzonerifolium. Based on these findings， it is recommended that artificial cultivation in northern Shaanxi and similar regions utilize the local Yulin germplasm source cultivated for at least three years.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective: Para-aortic lymph node dissection (PALND) is a widely used treatment that causes many complications. This study is to evaluate the efficacy and safety of nerve-sparing para-aortic lymph node dissection (NSPALND) by comparing it with conventional PALND in gynecological malignancies and to prove whether locating the superior hypogastric plexus (SHP) can help reveal the para-aortic nerves. Methods: This is a retrospective study of the patients who underwent para-aortic lymphadenectomy from January 2020 to December 2022 at Zhejiang Cancer Hospital. All of them were divided into NSPALND and PALND groups according to whether or not nervesparing was performed. The surgical, functional and oncological outcomes were evaluated. Results: There were 43 patients enrolled, of which, 20 patients underwent NSPALND and 23 patients underwent PALND. The para-aortic nerves were successfully revealed by locating the SHP in all 20 cases of NSPALND. The post-operative anal exhaust time in the NSPALND group was significantly shorter than that in the PALND group (2.5 vs. 4 days, p=0.006), and the incidence of acute intestinal obstruction in the NSPALND group was significantly lower than that in the PALND group (10% vs. 39%, p=0.029). There was no difference between the two groups in terms of catheterization duration, urinary retention, dysuria, as well as the number of lymph nodes removed and the para-aortic recurrence rate. Conclusion NSPALND can significantly reduce the rate of acute intestinal obstruction and improve post-operative intestinal function. Locating the SHP and using it as an anatomical landmark to reveal the para-aortic nerves is feasible. Its exact clinical value needs to be further studied.

Objective: Para-aortic lymph node dissection (PALND) is a widely used treatment that causes many complications. This study is to evaluate the efficacy and safety of nerve-sparing para-aortic lymph node dissection (NSPALND) by comparing it with conventional PALND in gynecological malignancies and to prove whether locating the superior hypogastric plexus (SHP) can help reveal the para-aortic nerves. Methods: This is a retrospective study of the patients who underwent para-aortic lymphadenectomy from January 2020 to December 2022 at Zhejiang Cancer Hospital. All of them were divided into NSPALND and PALND groups according to whether or not nervesparing was performed. The surgical, functional and oncological outcomes were evaluated. Results: There were 43 patients enrolled, of which, 20 patients underwent NSPALND and 23 patients underwent PALND. The para-aortic nerves were successfully revealed by locating the SHP in all 20 cases of NSPALND. The post-operative anal exhaust time in the NSPALND group was significantly shorter than that in the PALND group (2.5 vs. 4 days, p=0.006), and the incidence of acute intestinal obstruction in the NSPALND group was significantly lower than that in the PALND group (10% vs. 39%, p=0.029). There was no difference between the two groups in terms of catheterization duration, urinary retention, dysuria, as well as the number of lymph nodes removed and the para-aortic recurrence rate. Conclusion NSPALND can significantly reduce the rate of acute intestinal obstruction and improve post-operative intestinal function. Locating the SHP and using it as an anatomical landmark to reveal the para-aortic nerves is feasible. Its exact clinical value needs to be further studied.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Objective: Para-aortic lymph node dissection (PALND) is a widely used treatment that causes many complications. This study is to evaluate the efficacy and safety of nerve-sparing para-aortic lymph node dissection (NSPALND) by comparing it with conventional PALND in gynecological malignancies and to prove whether locating the superior hypogastric plexus (SHP) can help reveal the para-aortic nerves. Methods: This is a retrospective study of the patients who underwent para-aortic lymphadenectomy from January 2020 to December 2022 at Zhejiang Cancer Hospital. All of them were divided into NSPALND and PALND groups according to whether or not nervesparing was performed. The surgical, functional and oncological outcomes were evaluated. Results: There were 43 patients enrolled, of which, 20 patients underwent NSPALND and 23 patients underwent PALND. The para-aortic nerves were successfully revealed by locating the SHP in all 20 cases of NSPALND. The post-operative anal exhaust time in the NSPALND group was significantly shorter than that in the PALND group (2.5 vs. 4 days, p=0.006), and the incidence of acute intestinal obstruction in the NSPALND group was significantly lower than that in the PALND group (10% vs. 39%, p=0.029). There was no difference between the two groups in terms of catheterization duration, urinary retention, dysuria, as well as the number of lymph nodes removed and the para-aortic recurrence rate. Conclusion NSPALND can significantly reduce the rate of acute intestinal obstruction and improve post-operative intestinal function. Locating the SHP and using it as an anatomical landmark to reveal the para-aortic nerves is feasible. Its exact clinical value needs to be further studied.