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 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.

The purpose of this study was to clone and characterize the ZFP36L1 (zinc finger protein 36-like 1) gene, clarify its expression characteristics, and elucidate its expression patterns in different tissues of goats. Samples of 15 tissues from Jianzhou big-eared goats, including heart, liver, spleen, lung and kidney were collected. Goat ZFP36L1 gene was amplified by reverse transcription-polymerase chain reaction (RT-PCR), then the gene and protein sequence were analyzed by online tools. Quantitative real-time polymerase chain reaction (qPCR) was used to detect the expression level of ZFP36L1 in intramuscular preadipocytes in different tissues and adipocytes of goat at different differentiation stages. The results showed that the length of ZFR36L1 gene was 1 224 bp, and the coding sequence (CDS) region was 1 017 bp, encoding 338 amino acids, which was a non-secretory unstable protein mainly located in nucleus and cytoplasm. Tissue expression profile showed that ZFP36L1 gene was expressed in all selected tissues. In visceral tissues, the small intestine showed the highest expression level (P < 0.01). In muscle tissue, the highest expression level was presented in longissimus dorsi muscle (P < 0.01), whereas the expression level in subcutaneous adipose tissue was significantly higher than that in other tissues (P < 0.01). The results of induced differentiation showed that the expression of this gene was up-regulated during adipogenic differentiation of intramuscular precursor adipocytes (P < 0.01). These data may help to clarify the biological function of the ZFP36L1 gene in goat.
Animals ; Goats/genetics* ; Amino Acid Sequence ; Liver ; Cloning, Molecular

In this study, we cloned the complete sequence coding for aminoacids in protein (CDS) of goat ST13 gene, analyzed the bioinformation of it, and explored the expression pattern in different goat tissues and goat subcutaneous preadipocytes at different differentiation stages. To be specific, ST13 gene was cloned by reverse transcription PCR (RT-PCR), and the bioinformation was analyzed by online tools or software. The expression in various goat tissues and subcutaneous preadipocytes at different differentiation stages was detected by quantitative reverse transcription PCR (qRT-PCR). The results showed that the cloned goat ST13 gene was 1 380 bp, with CDS of 1 101 bp, encoding 366 amino acids. Protein prediction results showed that ST13 had 26 phosphorylation sites and that some sequences were highly hydrophilic and unstable. Moreover, ST13 was a non-transmembrane and non-secretory protein. Subcellular localization demonstrated that ST13 was mostly distributed in the nucleus (69.6%). Phylogeny analysis suggested that goat ST13 had the highest identity to sheep ST13. Tissue expression pattern showed that ST13 gene expressed in all of the collected 13 tissues of goat, including heart, liver, spleen, lung and kidney, especially in triceps brachii and subcutaneous fat (P < 0.01) and that the expression among heart, liver, spleen, lung, kidney, large intestine, small intestine and pancreas was insignificantly different (P > 0.05). In addition, according to the temporal expression pattern in adipocytes, the expression of ST13 was up-regulated in differentiated adipocytes, and the expression was the highest at the 108th hour of induction, significantly higher than that at other time points (P < 0.01). In conclusion, this gene expresses in various tissues of goat and regulates the differentiation of goat subcutaneous adipocytes.
Adipocytes ; Animals ; Cloning, Molecular ; Goats/genetics* ; Liver ; Phylogeny ; Real-Time Polymerase Chain Reaction ; Sheep

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.