Objective Through studying the chemical composition changes before and after the compatibility of Hedysari Radix Paeparata Cum Melle(HRPCM)-vinegar processed Curcumae Rhizoma(VPCR);To discuss the significance of the compatibility of HRPCM and VPCR;To establish the fingerprints before and after their compatibility.Methods ZORBAX Eclipse Plus C18 column was used;acetonitrile-0.01%phosphoric acid water was set as mobile phase,with gradient elution;column temperature was 30℃;detection wavelength was 214 nm;sample injection was 10 μL,which was used to investigate the content difference of major chemical components such as vanillic acid,calycosin-O-β-D-glucopyranoside,ononin,calycosin,onocerin,curdione,cincumol and germacrone,and establish the fingerprint of HRPCM,VPCR and HRPCM-VPCR.Results HPLC chromatographic conditions were established for the determination of 8 components in HRPCM-VPCR.Meanwhile,fingerprints were established before and after the compatibility of HRPCM-VPCR.26 common peaks were identified,among which 11 components such as vanillic acid were derived from HRPCM,14 components such as curcuma zedoariae were derived from VPCR,and 1 component was shared by both.Conclusion The material basis of the compatibility of HRPCM-VPCR differs from that of HRPCM and VPCR.The content of most chemical components decreases while the content of some components increases.The established HPLC method for content determination and fingerprint is simple,stable and reproducible,which can be used to evaluate and control the quality of HRPCM and VPCR.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective Through studying the chemical composition changes before and after the compatibility of Hedysari Radix Paeparata Cum Melle(HRPCM)-vinegar processed Curcumae Rhizoma(VPCR);To discuss the significance of the compatibility of HRPCM and VPCR;To establish the fingerprints before and after their compatibility.Methods ZORBAX Eclipse Plus C18 column was used;acetonitrile-0.01%phosphoric acid water was set as mobile phase,with gradient elution;column temperature was 30℃;detection wavelength was 214 nm;sample injection was 10 μL,which was used to investigate the content difference of major chemical components such as vanillic acid,calycosin-O-β-D-glucopyranoside,ononin,calycosin,onocerin,curdione,cincumol and germacrone,and establish the fingerprint of HRPCM,VPCR and HRPCM-VPCR.Results HPLC chromatographic conditions were established for the determination of 8 components in HRPCM-VPCR.Meanwhile,fingerprints were established before and after the compatibility of HRPCM-VPCR.26 common peaks were identified,among which 11 components such as vanillic acid were derived from HRPCM,14 components such as curcuma zedoariae were derived from VPCR,and 1 component was shared by both.Conclusion The material basis of the compatibility of HRPCM-VPCR differs from that of HRPCM and VPCR.The content of most chemical components decreases while the content of some components increases.The established HPLC method for content determination and fingerprint is simple,stable and reproducible,which can be used to evaluate and control the quality of HRPCM and VPCR.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

OBJECTIVE:To prepare Diacerein (DCR)-loaded (poly lactic-co-glycolic acid) PLGA microspheres for intra-articular injection and investigate its related properties. METHODS:PLGA was used as microspheres material,and the microsphere was prepared by emulsification solvent evaporation method. The contents of DCR-PLGA microspheres were determined by HPLC,and drug-loading amount and entrapment efficiency were also calculated. Using entrapment efficiency as evaluation index,the preparation technology was optimized by orthogonal test. The morphology and particle size of microspheres were observed by optical microscope and SEM. Accumulative release rate was investigated by using in vitro release test. RESULTS:The linear range of DCR was 2.1-105.0 μg/mL(r＝0.999 9). RSDs of precision,stability,reproducibility and recovery tests were all lower than 2.0%. The optimal technology was PLGA concentration of 200 mg/mL,volume ratio of oil-water 1∶50,polyvinyl alcohol concentration of 1%. The prepared DCR-PLGA microspheres were spherical,average particle size was(11.2±4.7)μm, drug-loading amount was(4.25 ± 0.26)% and encapsulation rate was(92.30 ± 1.93)%,respectively. The drug release rate of DCR-PLGA microspheres within 360 h was about(73.08 ± 5.33)%. CONCLUSIONS:DCR-PLGA microspheres are prepared successfully with good morphology,suitable particle size and obvious sustained release effect,which are suitable for intra-articular injection.

BACKGROUND: It is stil controversial about whether percutaneous vertebroplasty can be as an option for treatment of non-osteoporotic single-segmental vertebral traumatic compression fractures. OBJECTIVE: To observe the effect of percutaneous vertebroplasty in repair of non-osteoporotic single-segmental vertebral traumatic compression fractures. METHODS: Total y 20 patients who underwent percutaneous vertebroplasty in repair of non-osteoporotic single-segmental vertebral traumatic compression fractures between March 2010 and January 2013 were col ected. The variation of visual analog scale scores and the Oswestry disability index scores of patients was observed before and after the repair. RESULTS AND CONCLUSION: (1) The visual analog scale scores and the Oswestry disability index scores of patients were significantly reduced after repair compared with those before repair, moreover, the visual analog scale scores and the Oswestry disability index scores of patients at the 3, 6, 12 and 18 months after repair were similar. (2) Al patients had no adverse effects and complications. (3) These results suggest that percutaneous vertebroplasty in repair of non-osteoporotic single-segmental vertebral traumatic compression fractures quickly relieves back pain and improves the actives of thoracolumbar segments.