Objective:To explore the value of nomogram model based on multimodal ultrasound features for predicting the malignant risk of micro lesions in breast areola region.Methods:The case data of Beijing Tiantan Hospital affiliated to Capital Medical University from May 2020 to July 2024 were retrospectively analyzed. A total of 50 patients with benign intraductal papilloma（bIDP group）and 54 patients with malignant risk breast tumor（mrBT group）were found to have micro lesions in breast areola region and confirmed by puncture or surgical pathology. Clinical data，conventional ultrasound and contrast-enhanced ultrasound features were compared between the two groups. Multivariate Logistic regression analysis and Lasso regression analysis were performed on statistically significant factors to screen out influencing factors. ROC curves were plotted to evaluate diagnostic efficacy，nomogram model and clinical decision curves were constructed to evaluate clinical benefits.Results:The differences of age，nipple discharge presentation，conventional ultrasound features（including boundary，morphology，aspect ratio，internal echo，internal microcalcification，far-field echo，peripheral irregular hyperechoic ring，dilate of peripheral ducts），and contrast-enhanced ultrasound features（including wash-in time，enhancement intensity，enhancement mode，enhancement scope，blood perfusion defect，crab foot sign，penetrating vessels）were statistically significant between the bIDP group and mrBTgroup（all P<0.05）. Regression analysis showed that age，uniformity of internal echo within the lesion，dilation of surrounding ducts，and enhanced crab foot sign were the affect factors for the diagnosis of mrBT（all P<0.05）. Based on these factors，a nomogram model was constructed with an area under ROC curve（AUC）of 0.907（95% CI=0.851-0.963），a sensitivity of 0.907，and a specificity of 0.780. The decision curve analysis showed that the collective model had good predictive performance. Conclusions:The nomogram model based on multimodal ultrasound features has good value in predicting malignant risk micro breast tumor of areola region.

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 explore the value of nomogram model based on multimodal ultrasound features for predicting the malignant risk of micro lesions in breast areola region.Methods:The case data of Beijing Tiantan Hospital affiliated to Capital Medical University from May 2020 to July 2024 were retrospectively analyzed. A total of 50 patients with benign intraductal papilloma（bIDP group）and 54 patients with malignant risk breast tumor（mrBT group）were found to have micro lesions in breast areola region and confirmed by puncture or surgical pathology. Clinical data，conventional ultrasound and contrast-enhanced ultrasound features were compared between the two groups. Multivariate Logistic regression analysis and Lasso regression analysis were performed on statistically significant factors to screen out influencing factors. ROC curves were plotted to evaluate diagnostic efficacy，nomogram model and clinical decision curves were constructed to evaluate clinical benefits.Results:The differences of age，nipple discharge presentation，conventional ultrasound features（including boundary，morphology，aspect ratio，internal echo，internal microcalcification，far-field echo，peripheral irregular hyperechoic ring，dilate of peripheral ducts），and contrast-enhanced ultrasound features（including wash-in time，enhancement intensity，enhancement mode，enhancement scope，blood perfusion defect，crab foot sign，penetrating vessels）were statistically significant between the bIDP group and mrBTgroup（all P<0.05）. Regression analysis showed that age，uniformity of internal echo within the lesion，dilation of surrounding ducts，and enhanced crab foot sign were the affect factors for the diagnosis of mrBT（all P<0.05）. Based on these factors，a nomogram model was constructed with an area under ROC curve（AUC）of 0.907（95% CI=0.851-0.963），a sensitivity of 0.907，and a specificity of 0.780. The decision curve analysis showed that the collective model had good predictive performance. Conclusions:The nomogram model based on multimodal ultrasound features has good value in predicting malignant risk micro breast tumor of areola region.

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 explore the research status, hotspots, and directions in traditional Chinese medicine treatment for postoperative gastrointestinal dysfunction both domestically and internationally.Methods:The bibliometric software CiteSpace 5.7.R5 was used to visually analyze literature published on the China National Knowledge Infrastructure and Web of Science core collection from establishment until 2022, and to draw the knowledge graph of authors, research institutions, and keywords.Results:After screening by two researchers, a total of 1 012 Chinese articles and 111 English articles were included. The analysis of annual publication volume showed that this field was showing an upward trend both domestically and internationally. The co-occurrence analysis of authors displayed that Hu Kaiwen and Jingwen Yang had the highest number of publications. Institutional co-occurrence analysis indicated that there were relatively few mature and influential teams formed domestically and internationally, and research institutions had limited cross-regional cooperation. Keyword clustering analysis revealed that the research hotspots mainly focused on integrated traditional Chinese and Western medicine nursing, clinical research, and traditional Chinese medicine external treatment methods.Conclusions:The research on the treatment of postoperative gastrointestinal dysfunction with traditional Chinese medicine is mainly concentrated in China, but there are few closely cooperating and stable research teams. Research in this field is still in its developmental stage both domestically and internationally, close cooperation between multiple institutions and authors is needed to form influential teams to conduct in-depth research on the mechanisms and principles of this field, making significant contributions to its development.

When partial nephrectomy is performed by posterior abdominal approach, the surgical field is poorly exposed, resulting in increased surgical difficulty and risk of injury.In this study, 28 patients with T 1a stage kidney tumors underwent retroperitoneal laparoscopic partial nephrectomy. Intraoperatively, exposure of the surgical field was achieved using the percutaneous puncture of the renal fascia suspension technique. There were no dissatisfactory exposures due to peritoneal damage during the surgery, no additional tubes were inserted, and no conversions to open surgery were needed. The operation time was (76.5±20.3) minutes, blood loss was (92.1±18.7) ml, renal artery clamping time was (19.5±4.3) minutes. Postoperatively, there were no complications such as bleeding, infection, or hematuria.

Objective:To identify a novel reassortant H3N2 avian influenza virus using nanopore sequencing technology and analyze its genetic characteristics.Methods:The positive samples of the H3N2 avian influenza virus, collected from the external environment in the farmers' market of Guangzhou, were cultured in chicken embryos. The whole genome was sequenced by targeted amplification and nanopore sequencing technology. The genetic characteristics were analyzed using bioinformatics software.Results:The phylogenetic trees showed that each gene fragment of the strain belonged to the Eurasian evolutionary branch, and the host source was of avian origin. The HA gene was closely related to the origin of the H3N6 virus. The NA gene was closely related to the H3N2 avian influenza virus from 2017 to 2020. The PB1 gene was closely related to the H5N6 avian influenza virus in Guangxi Zhuang Autonomous Region and Fujian Province from 2016 to 2022 and was not related to the PB1 gene of the H5N6 avian influenza epidemic strain in Guangzhou. The other internal gene fragments had complex sources with significant genetic diversity. Molecular characteristics indicated that the strain exhibited the molecular characteristics of a typical low pathogenic avian influenza virus and tended to bind to the receptors of avian origin. On important protein sites related to biological characteristics, this strain had mutations of PB2-L89V, PB1-L473V, NP-A184K, M1-N30D/T215A, and NS1-P42S/N205S.Conclusions:This study identified a novel reassortant H3N2 avian influenza virus by nanopore sequencing, with the PB1 gene derived from the H5N6 avian influenza virus. The virus had a low ability to spread across species, but further exploration was needed to determine whether its pathogenicity to the host was affected.

Humans ; Stomach Neoplasms ; Tumor Microenvironment ; Prognosis ; Adenosine

Zn²⁺ is required for the activity of many mitochondrial proteins, which regulate mitochondrial dynamics, apoptosis and mitophagy. However, it is not understood how the proper mitochondrial Zn²⁺ level is achieved to maintain mitochondrial homeostasis. Using Caenorhabditis elegans, we reveal here that a pair of mitochondrion-localized transporters controls the mitochondrial level of Zn²⁺. We demonstrate that SLC-30A9/ZnT9 is a mitochondrial Zn²⁺ exporter. Loss of SLC-30A9 leads to mitochondrial Zn²⁺ accumulation, which damages mitochondria, impairs animal development and shortens the life span. We further identify SLC-25A25/SCaMC-2 as an important regulator of mitochondrial Zn²⁺ import. Loss of SLC-25A25 suppresses the abnormal mitochondrial Zn²⁺ accumulation and defective mitochondrial structure and functions caused by loss of SLC-30A9. Moreover, we reveal that the endoplasmic reticulum contains the Zn²⁺ pool from which mitochondrial Zn²⁺ is imported. These findings establish the molecular basis for controlling the correct mitochondrial Zn²⁺ levels for normal mitochondrial structure and functions.
Animals ; Caenorhabditis elegans/metabolism* ; Cation Transport Proteins/genetics* ; Homeostasis ; Mitochondria/metabolism* ; Zinc/metabolism*

Listeria monocytogenes (Lm) is zoonotic pathogen that can cause listeriosis, and vaccine is one of the effective methods to prevent this pathogen infection. In this study, we developed a novel vaccine that is a mixture of inactivated bacteria and Montanide™ ISA 61 VG, a mineral oil adjuvant, and evaluated the safety and immune response characteristics of this vaccine. The mice immunized with the ISA 61 VG adjuvant had high safety, and it could induce significantly higher titer of anti-listeriolysin O (LLO) antibody and higher value of IgG2a/IgG1 ratio compared with the group without the adjuvant. In particular, it could provide 100% immune protection against lethal doses of Lm challenge in mice. In summary, ISA 61VG adjuvant significantly enhanced the ability of inactivated listeria vaccine to induce humoral and cellular immune responses, thereby enhanced the protective immune response in the host, and it is a potential vaccine candidate for the prevention of Lm infection in humans and animals.
Adjuvants, Immunologic ; pharmacology ; Animals ; Hemolysin Proteins ; immunology ; pharmacology ; Immunity, Cellular ; drug effects ; Listeria monocytogenes ; immunology ; Listeriosis ; prevention & control ; Mice ; Mice, Inbred BALB C ; Vaccines, Inactivated ; immunology