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 scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

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 scientific research and innovation capabilities of medical students are intrinsically linked to the sustained and high-quality development of national healthcare initiatives.Cultivating outstanding medi-cal students with independent scientific capabilities and innovative consciousness is a critical component in the education and training of high-level medical professionals.Our investigation revealed that within the imperfections of the cultivating model,some faculty and students at medical schools have an insufficient understanding of scientific research and innovation and lack motivation for engaging in such activities,which hinder the progression of scientific research activities.Consequently,we initiated a teaching practice and exploratory study on the"tutorial system"aimed at fostering medical students'scientific research and innovation abilities.Based on the principle of"research informing teaching,teaching and research advan-cing together,"this study implements a"tutorial system"coordinated by tutors,supplemented by graduate and undergraduate student mentors,to cultivate innovative thinking,stimulate interest in scientific re-search,and enhance practical and research skills among medical students.Through collaborative efforts within"scientific research innovation teams,"various educational methods—including preliminary re-search,in-class and extracurricular activities,intra-group and inter-group interactions,and theoretical and practical applications—are employed to improve and strengthen the cultivation of medical students'scientif-ic research and innovation abilities.This study aims to provide valuable references for optimizing medical education management systems and enhancing the quality of medical student training.

Aim To investigate the relation between the effect of geniposide (GE) in improving angiogenesis in arthralgia spasm syndrome collagen induced arthritis (CIA) rats and the modulation of heat shock proteins 70 (HSP70) release. Methods A CIA model was constructed by multiple intradermal injections of complete Freund's adjuvant (CFA) and an equal volume mixture of chicken type II collagen (CCII) into the dorsal and caudal root regions of rats, on the basis of which a rheumatic fever stimulus was given to build up a moist heat arthralgia spasm syndrome in CIA rats. After successful modeling, the groups were randomly grouped, and the administered groups were gavaged with GE (60, 120 mg · kg

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To explore and analyze the clinical features and prognostic factors of secondary intestinal diffuse large B-cell lymphoma(SI-DLBCL),in order to provide reference for the basic research and clinical diagnosis and treatment of secondary lymphoma of rare sites in the field of hematology.Methods:The clinical data of 138 patients with SI-DLBCL admitted to Fujian Medical University Union Hospital from June 2011 to June 2022 were collected and sorted,the clinical and pathological features,diagnosis,treatment and prognosis were analyzed.Cox regression risk model was used to conduct univariate and multivariate analysis on the prognostic risk factors.Results:Among the 138 patients with SI-DLBCL included in this study,85(61.59％)were male,53(38.41％)were female,the median age of onset was 59.5(16-84)years,the clinical manifestations lacked specificity,the first-line treatment regimen was mainly chemotherapy(67.39％),94 cases(68.12％)received chemotherapy alone,40 cases(28.98％)were treated with chemotherapy combined with surgery,and 4 cases(2.90％)were treated with surgery alone.The median follow-up time was 72(1-148)months.Among the 138 patients with SI-DLBCL,79(57.25％)survived,34(24.64％)died,25 cases(18.12％)lost to follow-up,the PFS rates of 1-year,3-year and 5-year were 57.97％,49.28％and 32.61％,and the OS rates of 1-year,3-year and 5-year were 60.14％,54.35％and 34.06％,respectively.The results of univariate Cox regression analysis showed that age,Lugano stage and IPI score were the influencing factors of OS in SI-DLBCL patients,and age,Lugano stage and IPI score were the influencing factors of PFS in SI-DLBCL patients.The results of multivariate Cox analysis showed that Lugano stage was an independent prognostic factor affecting OS and PFS in SI-DLBCL patients.Conclusion:Patients with SI-DLBCL are more common in middle-aged and elderly men,and the early clinical manifestations lack specificity,and the first-line treatment regimen is mainly R-CHOP chemotherapy,and Lugano stage is an independent prognostic factor affecting OS and PFS in SI-DLBCL patients.

Objective:To investigate the expulsion rate of GyneFix postpartum intrauterine device (PPIUD) placed immediately after cesarean section within one year and its influencing factors.Methods:A prospective cohort study was conducted. Women who volunteered to use a GyneFix PPIUD placed immediately after cesarean section (within 10 min after placenta delivery) for postpartum contraception were recruited from September 2017 to November 2020. The relevant information was collected through questionnaires before, during and 24 h after cesarean section. Outpatient follow-up was conducted at 42 d, 3 months, 6 months and 12 months after delivery to obtain information on expulsion of GyneFix PPIUD and unwanted pregnancy. Life table and Cox regression model were used to analyze the cumulative expulsion rate and related influencing factors.Results:A total of 470 subjects were recruited and 461 (98%) subjects were eligible for this study. The cumulative expulsion rate of GyneFix PPIUD within one year after cesarean section was 8.4% (95% CI: 7.0%-9.8%). Multivariate Cox regression analysis showed that women aged >35 years had significantly lower risk of PPIUD expulsion than those aged <25 years ( HR=0.16, 95% CI: 0.04-0.64). The risk of GyneFix PPIUD was not statistically significantly associated with cesarean section history and breastfeeding mode (all P>0.05). Nevertheless, this risk was statistically significant between hospitals. The Pearl index of contraceptive failure of the device was 2.37 (95% CI: 1.09-4.50) per 100 person-years. The rate of contraceptive failure was not associated with maternal age, breastfeeding mode, and history of cesarean delivery (all P>0.05). Conclusion:The one-year cumulative expulsion rate of GyneFix PPIUD placed immediately after cesarean section is 8.4%. Young mothers were at a higher risk of expulsion than their older counterparts. The device users should be counseled regarding the signs of expulsion. In case of expulsion, women should be offered reinsertion or other contraceptive methods. The training of service skills of GyneFix PPIUD should be strengthened in order to mitigate the risk of the device expulsion.

Objective:To investigate the expulsion rate of GyneFix postpartum intrauterine device (PPIUD) placed immediately after cesarean section within one year and its influencing factors.Methods:A prospective cohort study was conducted. Women who volunteered to use a GyneFix PPIUD placed immediately after cesarean section (within 10 min after placenta delivery) for postpartum contraception were recruited from September 2017 to November 2020. The relevant information was collected through questionnaires before, during and 24 h after cesarean section. Outpatient follow-up was conducted at 42 d, 3 months, 6 months and 12 months after delivery to obtain information on expulsion of GyneFix PPIUD and unwanted pregnancy. Life table and Cox regression model were used to analyze the cumulative expulsion rate and related influencing factors.Results:A total of 470 subjects were recruited and 461 (98%) subjects were eligible for this study. The cumulative expulsion rate of GyneFix PPIUD within one year after cesarean section was 8.4% (95% CI: 7.0%-9.8%). Multivariate Cox regression analysis showed that women aged >35 years had significantly lower risk of PPIUD expulsion than those aged <25 years ( HR=0.16, 95% CI: 0.04-0.64). The risk of GyneFix PPIUD was not statistically significantly associated with cesarean section history and breastfeeding mode (all P>0.05). Nevertheless, this risk was statistically significant between hospitals. The Pearl index of contraceptive failure of the device was 2.37 (95% CI: 1.09-4.50) per 100 person-years. The rate of contraceptive failure was not associated with maternal age, breastfeeding mode, and history of cesarean delivery (all P>0.05). Conclusion:The one-year cumulative expulsion rate of GyneFix PPIUD placed immediately after cesarean section is 8.4%. Young mothers were at a higher risk of expulsion than their older counterparts. The device users should be counseled regarding the signs of expulsion. In case of expulsion, women should be offered reinsertion or other contraceptive methods. The training of service skills of GyneFix PPIUD should be strengthened in order to mitigate the risk of the device expulsion.

Hip fractures are among the most common fractures in the elderly, presenting to be a leading cause of disability and mortality. Surgical treatment is currently the main treatment method for hip fractures. The incidence of perioperative malnutrition is increased after hip fractures in the elderly due to the comorbidities, decreased basal metabolic rate, accelerated protein breakdown, weakened anabolism and surgical stress. However, malnutrition not only increases the incidence of postoperative complications, but also leads to increased mortality, indicating an important role of perioperative nursing management of nutrition for the elderly patients with hip fractures. At present, there still lacks scientific guidance and application standards on perioperative nursing management of nutrition for the elderly patients with hip fractures. Therefore, the Orthopedic Nursing Committee of Chinese Nursing Association and the Editorial Board of Chinese Journal of Trauma organized relevant experts to formulate the Expert consensus on perioperative nursing management of nutrition for elderly patients with hip fractures ( version 2023) according to evidence-based medical evidences and their clinical experiences. Fourteen recommendations were made from aspects of nutrition screening, nutrition assessment, nutrition diagnosis, nutrition intervention and nutrition monitoring to provide guidance for perioperative nursing management of nutrition in elderly patients with hip fractures.