Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

Animal experiments are widely used in biomedical research for safety assessment, toxicological analysis, efficacy evaluation, and mechanism exploration. In recent years, the ethical review system has become more stringent, and awareness of animal welfare has continuously increased. To promote more efficient and cost-effective drug research and development, the United States passed the Food and Drug Administration (FDA) Modernization Act 2.0 in September 2022, which removed the federal mandate requiring animal testing in preclinical drug research. In April 2025, the FDA further proposed to adopt a series of "new alternative methods" in the research and development of drugs such as monoclonal antibodies, which included artificial intelligence computing models, organoid toxicity tests, and 3D micro-physiological systems, thereby gradually phasing out traditional animal experiment models. Among these cutting-edge technologies, 3D bioprinting models are a significant alternative and complement to animal models, owing to their high biomimetic properties, reproducibility, and scalability. This review provides a comprehensive overview of advancements and applications of 3D bioprinting technology in the fields of biomedical and pharmaceutical research. It starts by detailing the essential elements of 3D bioprinting, including the selection and functional design of biomaterials, along with an explanation of the principles and characteristics of various printing strategies, highlighting the advantages in constructing complex multicellular spatial structures, regulating microenvironments, and guiding cell fate. It then discusses the typical applications of 3D bioprinting in drug research and development,including high-throughput screening of drug efficacy by constructing disease models such as tumors, infectious diseases, and rare diseases, as well as conducting drug toxicology research by building organ-specific models such as those of liver and heart. Additionally,the review examines the role of 3D bioprinting in tissue engineering, discussing its contributions to the construction of functional tissues such as bone, cartilage, skin, and blood vessels, as well as the latest progress in regeneration and replacement. Furthermore, this review analyzes the complementary advantages of 3D bioprinting models and animal models in the research of disease progression, drug mechanisms, precision medicine, drug development, and tissue regeneration, and discusses the potential and challenges of their integration in improving model accuracy and physiological relevance. In conclusion, as a cutting-edge in vitro modeling and manufacturing technology, 3D bioprinting is gradually establishing a comprehensive application system covering disease modeling, drug screening, toxicity prediction, and tissue regeneration.

The rapid development of artificial intelligence （AI） technology provides new opportunities for the modernisation of traditional Chinese medicine （TCM） diagnosis. By analysing the foundation， research progress and difficulties of the combination of AI and TCM diagnosis， it is concluded that AI has made remarkable development in intelligence-driven modernization of TCM tongue diagnosis， pulse diagnosis， listening and smelling diagnosis and text processing， and there are useful explorations in the field of constructing data-driven TCM diagnostic model and multidisciplinary integration of TCM diagnostic models. However， the current integration of AI technology in TCM diagnosis still faces many challenges， such as the scarcity and uneven quality of clinical data， the limited ability of AI algorithms to express TCM thinking model of syndrome differentiation and empirical knowledge， and the possible existence of ethical and privacy issues. By systematically sorting out the current research status and development direction of AI-empowered TCM diagnostics， it is proposed to promote the application of AI technology in TCM diagnostics in four aspects， namely， strengthening the construction of TCM big data and talent cultivation， encouraging cross-disciplinary cooperation， improving the legal and ethical framework， and promoting the popularity of the technology in primary care， so as to enhance the modernisation of TCM diagnostics.

Cerebral ischemia-reperfusion injury is a secondary and inevitable pathophysiological change in the process of vascular recanalization in acute ischemic stroke,which can lead to neuronal damage,affect cognitive and behavioral functions,and even cause death.Inflammatory response is one of the important mechanisms leading to cerebral ischemia-reperfusion injury.Annexin A1(ANXA1)is a powerful physiological anti-inflammatory protein.The authors reviews the relationship between ANXA1 and cerebral ischemia-reperfusion injury in recent years,in order to provide reference for subsequent research.

Objective To investigate the infection of human parvovirus B19 in Suzhou voluntary blood donors under the current blood screening model. Methods A total of 893 blood donor samples from September to December 2022 were randomly collected. Samples were tested to determine the seroprevalence (anti-B19 IgG and IgM) of B19 antibodies by enzyme-linked immunosorbent assay(ELISA), and B19 DNA of positive samples was further detected by real-time polymerase chain reaction(PCR) assay. Results Among 893 samples, the total seroprevalence of B19 antibody was 20.7% (185/893), with anti-B19 IgG and IgM positive rate at 19.4% (173/893) and 1.9% (17/893), respectively, showing significant difference (P<0.05). No difference in the positive rates of B19 IgG and IgM (20.1%, 1.5% vs 18.0%,2.6%) was noticed by gender(P>0.05). The prevalence of anti-B19 IgG statistically increased with age (P<0.05), while there was no difference in the prevalence of anti-B19 IgM (P>0.05). No statistical difference was not found in anti-B19 IgG and IgM samples among different blood groups. The anti-B19 IgG in repeated blood donors was higher than that in first-time donors(21.5% vs 15.9%)(P<0.05) while there was no difference in the positive rate of IgM antibodies (P>0.05). Three cases were found to be positive for B19 DNA in the B19 antibody positive samples, with the positive rate at 1.6%(3/185). Conclusion Although the prevalence of B19 infection in Suzhou was lower than that in other areas and was mostly past infection, there was still a certain proportion of persistent infection and acute infection, which posed the potential risk of blood transfusion transmission. Therefore, attention should be paid to blood transfusions, especially for the high-risk and susceptible groups.

Objective:To investigate the application value of CT and MRI imageomics based on machine learning method in the diagnosis of pancreatic cancer.Methods:The clinical data of 62 patients with surgically resected and pathologically confirmed pancreatic cancer, who underwent enhanced CT scan, MRI plain or enhanced scan in Shanghai General Hospital between January 2014 and December 2021 were collected. According to the chronological order of surgery, 49 patients from January 2014 to December 2020 were enrolled in the training set and 13 patients from January 2021 to December 2021 were enrolled in the validation set. 3D-slicer 4.8.1 software was used to draw the region of interest in each layer of CT and MRI images for cancerous and paracancerous tissue segment. Image features were extracted by Python and the optimal feature set from the training set data was obtained by using Lasso regression model. The machine learning decision tree model was constructed. The receiver operating characteristic curve(ROC) curve was drawn, and the area under the curve (AUC) was calculated to evaluate the value of these three kinds of imageomics models in the diagnosis of pancreatic cancer.Results:The 1 767 CT features and 1 674 MRI features were obtained from enhanced CT scan, MRI plain scan and enhanced MRI scan, respectively. For the differential diagnosis model of cancerous tissue and paracancerous tissue, the enhanced CT scan data model obtained the optimal feature set involving 6 features, the MRI plain scan model obtained the optimal feature set involving 16 features, and the enhanced MRI scan model obtained the optimal feature set involving 15 features. The diagnostic model based on enhanced CT scan had an AUC of 0.98 in the training set and 1 in the verification group. The AUC of the MRI plain scan and enhanced MRI scan models in both the training set and the validation set was 1. The specificity and sensitivity of machine learning decision tree model based on the three kinds of imageomics models in the diagnosis of cancerous tissue and paracancerous tissue were 100%. For the differential diagnosis model of splenic artery wrapping, the enhanced CT scan model didn′t obtain the optimal features and had no diagnostic efficacy. The MRI plain scan model and enhanced MRI scan model obtained the optimal feature set involving 5 and 4 features, respectively. The AUC of the MRI plain scan model in the training set and the validation set were 0.862 and 0.750, respectively, with diagnostic sensitivity of 93.8% and 50.0%, and specificity of 78.6% and 100%, respectively. The AUC of the enhanced MRI scan model in the training set and the validation set were 0.950 and 0.861, respectively, with diagnostic sensitivity of 90.0% and 93.6%, and specificity of 100% and 78.6%, respectively.Conclusions:Based on the radiomics of CT enhanced, MRI plain scan and enhanced MRI scan, the machine learning diagnostic model has an accuracy of more than 90% in differentiating pancreatic cancer from paracancerous tissue. For the differentiation of splenic artery wrapping in pancreatic cancer, the diagnostic model based on enhanced MRI scan haS the best diagnostic efficiency.

Objective    To investigate the safety and efficacy of 3D single-portal inflatable mediastinoscopic and laparoscopic esophagectomy for esophageal cancer. Methods    Clinical data of 28 patients, including 25 males and 3 females, aged 51-76 years, with esophageal squamous cell carcinoma undergoing single-portal inflatable mediastinoscopic and laparoscopic esophagectomy from June 2018 to June 2019 were retrospectively analyzed. Patients were divided into two groups according to different surgical methods including a 3D mediastinoscopic group (3D group, 10 patients) and a 2D mediastinoscopic group (2D group, 18 patients). The perioperative outcome of the two groups were compared. Results    Compared with the 2D group, the 3D group had shorter operation time (P=0.017), more lymph nodes resected (P=0.005) and less estimated blood loss (P=0.015). There was no significant difference between the two groups in the main surgeon's vertigo and visual ghosting (P>0.05). The other aspects including the indwelling time, postoperative hospital stay, pulmonary infection, arrhythmia, anastomotic fistula, recurrent laryngeal nerve injury were not statistically significant between the two groups (P>0.05). Conclusion    The 3D inflatable mediastinoscopic and laparoscopic esophagectomy for esophageal cancer, which optimizes the surgical procedures of 2D, is safe and feasible, and is worthy of clinical promotion in the future.

Objective    To investigate the short-term follow-up results of inflatable mediastinoscopy combined with laparoscopy in the treatment of esophageal cancer. Methods    Clinical data of 102 patients with esophageal cancer who underwent minimally invasive esophagectomy were enrolled in our hospital from January 2017 to January 2019. Patients were divided into two groups according to different surgical methods, including a single-port inflatable mediastinoscopy combined with laparoscopy group (group A, n=59, 53 males and 6 females, aged 63.3±7.6 years, ranging from 45 to 75 years) and a video-assisted thoracoscopy combined with laparoscopy group (group B, n=43, 35 males and 8 females, aged 66.7±6.7 years, ranging from 50-82 years). The short-term follow-up results of the two groups were compared. Results    Compared with the group A, the rate of postoperative pulmonary complication of the group B was significantly lower (18.64% vs. 4.65%, P<0.05). There was no significant difference between the two groups in other postoperative complications (P>0.05). The 6-month, 1-year, and 2-year survival rates were 96.61%, 89.83%, and 73.33%, respectively in the group A, and were 95.35%, 93.02%, and 79.17%, respectively in the group B. There was no significant difference in short-term survival rate after operation (P>0.05). Conclusion    In the treatment of esophageal cancer, the incidence of pulmonary complications of inflatable mediastinoscopy combined with laparoscopy is lower than that of traditional video-assisted thoracoscopy combined with laparoscopy, and there is no significant difference in other postoperative complications or short-term survival rate between the two methods. Inflatable mediastinoscopy combined with laparoscopy for radical esophageal cancer is a relatively safe surgical method with good short-term curative effects, and long-term curative effects need to be further tested.

Objective: To investigate eating behavior and associated family factors of preschool children during the prevalence of Corona Virus Disease(COVID-19), and to provide scientific basis for healthy eating behavior for preschool children. Methods: A total of 1 829 preschool children were investigated by electronic questionnaire from 5 kindergartens in Shenzhen from March 18-20, 2020. SPSS 19.0 was used to perform logistic regression analysis on the factors for eating behavior. Results: The rate of unhealthy eating behavior during the prevalence of COVID-19 was 47.2%. The rate of unhealthy eating behaviors in boys and girls were 46.1% and 48.4%, respectively. And there was no significant difference between different genders(χ 2=1.02，P=0.31). The rate of unhealthy eating behaviors of preschool children aged 3-, 4-, 5- and 6- were 56.6%, 56.8%, 42.3% and 29.9%, respectively, and the differences between different ages were significant(χ 2=72.17，P<0.01). Logistic regression analysis showed that fathers high education level, mothers education level(OR=0.78，0.77，0.21), and parents satisfaction with children s eating were associated with low nutritional problems of child, while parental permission to play while eating, criticize while eating, force child to eat more and force child to eat foods they do not like, and parental concerns about children s appetite, were associated with more unhealthy eating behavior(OR=6.29，1.80，2.20，1.70，2.16)(P<0.05). Conclusion The prevalence of unhealthy eating behaviors of preschool children during the prevalence of COVID-19 is lower than usual, but the health education of preschool children s families still needs to be strengthened.

Objective:To investigate the efficacy and prognosis of hypofractionated intensity-modulated radiation therapy combined with hormonal therapy in the treatment of pelvic lymph node metastatic prostate cancer.Methods:Clinical data of 42 IV A prostate cancer patients who received hypofractionated intensity-modulated radiation therapy combined with hormonal therapy in Cancer Hospital of Chinese Academy of Medical Sciences between 2006 and 2018 were retrospectively analyzed. The total irradiation doses to the prostate and seminal vesicles were 67.5 Gy/25f, 2.7 Gy/f. The prophylactic irradiation doses to the pelvic lymph nodes were 45-50 Gy with a daily fraction dose of 1.8-2.0 Gy. Thirty-three patients with residual lymph nodes were boosted to 60.0-67.5 Gy for the residual area, 2.4-2.7 Gy/f. Androgen deprivation therapy included surgical castration or luteinizing hormone-releasing hormone agonists combined with antiandrogens. Survival rate was calculated using Kaplan- Meier method. The differences between two groups were analyzed by log-rank test. Prognostic factors were identified by univariate and multivariate analyses. Results:The median follow-up was 65.5 months (range, 5 to 150 months). The 5-year and 10-year failure-free survival (FFS) rates in the whole group were 67% and 45%, respectively. No clinical recurrence was observed in the irradiation field. The 5-year and 10-year prostate cancer-specific survival/overall survival (PCSS/OS) rates were 85% and 60%, respectively. Gleason score (≥8 and<8) and duration of hormonal therapy impacted the FFS (both P<0.05). The duration of hormonal therapy was an independent prognostic factor for PCSS/OS ( P=0.003). Conclusions:Hypofractionated intensity-modulated radiotherapy combined with hormonal therapy yields optimistic clinical efficacy in the treatment of pelvic lymph node metastatic prostate cancer. Gleason score (≥8 and <8) and duration of hormonal therapy are critical prognostic factors.