ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

ObjectiveTo establish the fingerprint of seven commercially available Yangyin Qingfei preparations， to quantitatively analyze the index components， to evaluate their quality uniformity with multivariate statistical analysis， and to explore the quality differences among different dosage forms. MethodA total of 33 batches of commercially available 7 kinds of Yangyin Qingfei preparations were analyzed by high performance liquid chromatography（HPLC）， the fingerprints were established and the common peaks were identified. Paeoniflorin， verbascoside， harpagoside， glycyrrhizic acid and paeonol were selected as the indicators of quality attributes to quantitatively analyze 33 batches of preparations. Based on the administration methods of Yangyin Qingfei preparations， the daily intake was calculated and the radar charts were poltted， and cluster analysis and principal component analysis were used to explore the quality differences of 7 kinds of Yangyin Qingfei preparations and the quality uniformity among different batches of the same dosage form. ResultThe similarity of fingerprints of 7 dosage forms was 0.248-0.956， suggesting that there were significant differences among different dosage forms of Yangyin Qingfei preparations， and a total of 15 common peaks were calibrated， of which peak 7， peak 8， peak 11， peak 13 and peak 15 were paeoniflorin， verbascoside， harpagoside， glycyrrhizic acid and paeonol， respectively. The radar plots showed that the average total daily intake of large honeyed pills and water honeyed pills was the highest， and the uniformity of pill components was better. The quality of 33 batches of samples was divided into poor quality and high quality by cluster analysis. Principal component analysis showed that the uniformity and dosage form of different dosage forms were significantly different， the oral liquid had the best quality homogeneity with the minimum dispersion. And the content of paeonol in different dosage forms was significantly different， which was the key component of quality control of Yangyin Qingfei preparations. ConclusionYangyin Qingfei large honeyed pills and water honeyed pills show high content and good uniformity， which are relatively preferred dosage forms. Different preparation processes have a great influence on the content of paeonol， and its quality control should be emphasized during production. This study provides a scientific method for the comparison of product quality of different dosage forms of traditional Chinese medicine prescriptions， which is helpful for the development of preferred dosage forms of different prescriptions， and provides a reference for efficient use of medication in the clinical practice.

Yigongsan is derived from Xiaoer Yaozheng Zhijue written by QIAN Yi in the Northern Song dynasty， which is the No. 3 formula in the Catalogue of Ancient Famous Classical Formulas（The Second Batch of Pediatrics） released by the National Administration of Traditional Chinese Medicine（TCM） in September 2022， and it can be developed as a class 3.1 new TCM drug. By referring to ancient medical books and modern literature， this study conducted herbal textual research on Yigongsan from five aspects， including historical evolution， origin and processing， dosage conversion， usage and preparation methods， and functional application， then formed the key information table of this formula， in order to provide reference for the development of reference samples and preparations of Yigongsan. Based on the results of the study， it is recommended that Panax ginseng should be removed the basal part of stem（rhizoma）， Poria cocos should be removed the peel， Citrus reticulata should be cut into shreds and Glycyrrhiza uralensis should be used. According to 4.13 g/Qian（钱）， 1 g/slice for ginger， 3 g for each jujube and 300 mL/Zhan（盏）， the doses of Ginseng Radix， Poria， Atractylodis Macrocephalae Rhizoma， Glycyrrhizae Radix et Rhizoma， Citri Reticulatae Pericarpium， Zingiberis Rhizoma Recens， Jujubae Fructus were 1.652， 1.652， 1.652， 1.652， 1.652， 5， 6 g， and the total amount was 19.26 g. The decocting method was to crush the medicinal materials into fine powder with 50-80 mesh， add 300 mL of water and decoct to 210 mL for each dose， then remove the dregs and take it warmly. This formula was recorded in ancient books as the main treatment for the cold-deficiency of spleen and stomach， and Qi stagnation in children with vomiting and diarrhea and lack of appetite. It has been flexibly applied by later generations of physicians， and is often used to treat anorexia， inflammation of the digestive tract， diarrhea and other diseases in children.

The development of famous classical formula compound preparations has been a research hotspot in recent years， carrying great expectations from the government， academia and industry. During the process of its research and development， it reflects many problems related to traditional Chinese medicine （TCM） industry and deserves deep consideration by the academic community. Based on the current status of development of famous classical formulas， this paper summarizes the key problems and made an in-depth analysis. The results show that three key issues should be paid attention. The first is the weakness of basic research in TCM， which affect the healthy development of TCM. The second is the understanding of development of famous classical formula compound preparations is not in-depth， and the formulation of relevant policies needs to be more reasonable. Thirdly， the expectation for the development of famous classical formula compound preparations is too high， which affects the research and development process of the compound preparations. It is hoped that the TCM industry can take this opportunity to face the problems， propose solutions， and form new breakthroughs to promote the development and progress of TCM.

ObjectiveTo explore the pretreatment methods to promote the enzymatic digestion and extraction of active ingredients from Magnoliae Officinalis Cortex dregs（MOCD）， and to provide a reference basis for the utilization of resource components in MOCD. MethodLiquid chromatography-mass spectrometry（LC-MS） was used for qualitative analysis of resource components in MOCD with an Agilent C₁₈ reversed-phase column（3.0 mm×100 mm， 2.7 µm） at the flow rate of 0.4 mL·min^-1， the mobile phase was water（A）-acetonitrile（B） for gradient elution（0-3 min， 25%-48%B； 3-6 min， 48%-59%B； 6-10 min， 59%-80%B； 10-20 min， 80%-90%B； 20-25 min， 90%B）， electrospray ionization（ESI） was employed with negative ion mode scanning and scanning range of m/z 50-1 200. A high performance liquid chromatography（HPLC）， which refered to the determination in the 2020 edition of Chinese Pharmacopoeia， was used for quantitative analysis of resource components in MOCD. Four kinds of pretreatment agents were used to separate the resource components from MOCD， and the mechanism of different pretreatment agents was investigated by field emission scanning electron microscopy（FESEM）， X-ray powder diffraction（XRD） and Fourier transform infrared spectroscopy（FT-IR）. ResultMagnolol， honokiol and lignocellulose were identified as the main resource components of MOCD by qualitative and quantitative analysis. Under the conditions of 1% NaOH， reaction temperature at 80 ℃ and reaction time of 60 min， the concentration of reducing sugar produced by the enzymatic hydrolysis was 32.18 g·L^-1， which was 79.8% higher than that of the untreated MOCD. After adding tween-80， the enzymatic hydrolysis time was reduced to 1/3 of the original time， the concentration of reducing sugar was increased by 102.0%. And the total recovery of magnolol and honokiol in the pretreatment solution was 69.23%. ConclusionMagnolol， honokiol and lignocellulosic components in MOCD are valuable for development and utilization， the combination of alkaline pretreatment and tween-80 can realize the recovery and utilization of these three resource components， which can provide a new idea for comprehensive utilization of resource components in MOCD.

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.

Objective:To investigate the independent risk factors of community-acquired pneumonia (CAP) complicated with acute respiratory distress syndrome (ARDS), and the accuracy and prevention value of ARDS prediction based on artificial neural network model in CAP patients.Methods:A case-control study was conducted. Clinical data of 414 patients with CAP who met the inclusion criteria and were admitted to the comprehensive intensive care unit and respiratory department of Changzhou Second People's Hospital Affiliated to Nanjing Medical University from February 2020 to February 2021 were analyzed. They were divided into two groups according to whether they had complicated with ARDS. The clinical data of the two groups were collected within 24 hours after admission, the influencing factors of ARDS were screened out by univariate analysis, and the artificial neural network model was constructed. Through the artificial neural network model, the importance of input layer independent variables (that was, the influence factors obtained from univariate analysis) on the output layer dependent variables (whether ARDS occurred) was drawn. The artificial neural network modeling data pairs were randomly divided into training group ( n = 290) and verification group ( n = 124) in a ratio of 7∶3. The overall prediction accuracy of the training group and the verification group was calculated respectively. At the same time, the receiver operator characteristic curve (ROC curve) was drawn, and the area under the ROC curve (AUC) was calculated. Results:All 414 patients were enrolled in the analysis, including 82 patients with ARDS and 332 patients without ARDS. Univariate analysis showed that gender, age, heart rate (HR), maximum systolic blood pressure (MSBP), maximum respiratory rate (MRR), source of admission, C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), neutrophil count (NEUT), eosinophil count (EOS), fibrinogen equivalent unit (FEU), activated partial thromboplastin time (APTT), total bilirubin (TBil), albumin (ALB), lactate dehydrogenase (LDH), serum creatinine (SCr), hemoglobin (Hb) and blood glucose (GLU) were significantly different between the two groups, which might be the risk factors of CAP patients complicated with ARDS. Taking the above 19 risk factors as the input layer and whether ARDS occurred as the output layer, the artificial neural network model was constructed. Among the input layer independent variables, the top five indicators with the largest influence weight on the neural network model were LDH (100.0%), PCT (74.4%), FEU (61.5%), MRR (56.9%), and APTT (51.6%), indicating that that these five indicators had a greater impact on the occurrence of ARDS in patients with CAP. The overall prediction accuracy of the artificial neural network model in the training group was 94.1% (273/290), and that of the verification group was 89.5% (111/124). The AUC predicted by the aforementioned artificial neural network model for ARDS in CAP patients was 0.977 (95% confidence interval was 0.956-1.000).Conclusion:The prediction model of ARDS in CAP patients based on artificial neural network model has good prediction ability, which can be used to calculate the accuracy of ARDS in CAP patients, and specific preventive measures can be given.

Objective:To evaluate the left ventricular myocardial strain in patients with light chain cardiac amyloidosis (AL-CA) with normal left ventricular ejection fraction (LVEF) by three-dimensional speckle tracking imaging(3D-STI), and to explore the clinical value of 3D-STI in predicting the prognosis of AL-CA patients with normal LVEF.Methods:A total of 80 patients with AL-CA and LVEF≥50% were retrospectively analyzed in the Xijing Hospital of Air Force Military Medical University from October 2014 to May 2020.According to whether the patients had endpoint events, they were divided into endpoint event group and non-endpoint event group. The clinical data, conventional echocardiographic parameters, 3D-STI related parameters and follow-up results were collected. Cox regression proportional hazards model was used to analyze the survival status of AL-CA patients with univariate and multivariate regression analyses, in order to find the relevant indicators of conventional echocardiography and 3D-STI to predict adverse events.Results:All patients were followed up for 20(7.3, 40.8) months. At the end of follow-up, 25 patients had all-cause deaths. Compared with the non-endpoint group, the endpoint event group had significantly increased left ventricular end diastolic maximum wall thickness (MLVWT), peak early diastolic flow velocity/peak early diastolic velocity at mitral annulus(E/e′) (all P<0.05), and decreased LVEF, left ventricular global longitudinal strain (GLS) and basal segment longitudinal strain (LS) (all P<0.05). Multivariate cox regression analysis after adjusting for age and gender showed that basal segment LS ( HR=0.812, 95% CI=0.675-0.976, P=0.026) was an independent predictor of end-point events in patients with AL-CA. Kaplan-Meier survival curve showed that AL-CA patients with basal segment LS≤13.07% were more likely to have endpoint events. Conclusions:Basal segment LS can be used as a predictor of endpoint events in patients with AL-CA.

Objective    To evaluate the clinical outcomes of larynx-preserving limited resection with total thoracic esophagectomy and gastric pull-up reconstruction for the treatment of cervical esophageal squamous cell carcinoma (ESCC) without tumor involvement of the larynx and hypopharynx compared with the upper thoracic ESCC. Methods    Retrospective and comparative analysis of consecutive patients with cervical and upper thoracic ESCC who underwent R0 surgical resection from 2006 to 2011 in our center was performed. Kaplan-Meier method was used to calculate the patients’ survival. Results    In total, 44 pairs of patients, including 71 males and 17 females with an average age of 60.66±8.49 years were enrolled in the study after propensity score matching. The baseline characteristics of the two groups of patients were well balanced. There was no statistical difference in the operation time (P=0.100), blood loss (P=0.685), mortality rate in 30 days (P=1.000), total complication rate (P=0.829), cervical anastomosis leakage (P=0.816), mechanical ventilation (P=1.000), normal oral diet within 15 days (P=0.822) and anastomosis recurrence rate (P=0.676) between the two groups. Survival analysis showed that there was no statistical difference in survival time between the cervical group [31.83 (95%CI 8.65-55.02) months] and upper thoracic group [37.73 (95%CI 25.29-50.18) months, P=0.533]. The 5-year survival rates were 32.6% and 42.1%, respectively. Conclusion    Larynx-preserving limited resection  with total thoracic esophagectomy and gastric pull-up reconstruction for the treatment of cervical ESCC without involvement of the larynx and hypopharynx may result in a similar clinical outcome to upper thoracic ESCC.