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.

Objective To understand and grasp the status quo of resistance of Culex pipiens pallens to four commonly used insecticides in Hefei City, and to provide a scientific basis for the chemical control of mosquito larvae. Methods From June to July 2023, Cx. pipiens pallens larvae were collected from 9 counties (cities and districts) in Hefei City. The LC50 of late third-instar to early fourth-instar larvae of Cx. pipiens pallens to commonly used insecticides was determined by larval immersion method (sensitive baseline method). Results Cx.pipiens pallens larvae in Hefei City exhibited different degrees of resistance to four insecticides: permethrin, beta-cypermethrin, temephos, and propoxur. The relative resistance coefficients to permethrin and beta-cypermethrin were 26.96 and 21.17, respectively, indicating the moderate resistance level. The relative resistance coefficients to propoxur were 6.70, indicating a low resistance level. The relative resistance coefficient to temephos was 2.43, indicating a sensitivity level. Culex pipiens pallens against pyrethroids such as 0.25% permethrin, 0.025% deltamethrin and 0.025% cypermethrin in 1 h knockout rate and 24 h mortality rates were 3.25% (4/123) and 46.34% (57/123), 3.60% (5/139) and 35.97% (50/139), 3.85% (6/156) and 40.38% (63/156), respectively. For 5% malathion and 0.1% propoxur, the 1 h knockdown rate and 24 h mortality rate were 97.69% (127/130) and 99.23% (129/130), 94.48% (137/145) and 100.00% (145/145), respectively. It showed resistance to 0.25% permethrin, 0.025% deltamethrin and 0.025% cypermethrin, and sensitivity to 5% malathion and 0.1% propoxur. Conclusions Culex pipiens pallens in Hefei City have developed varying degrees of resistance to parathyroid and carbamate insecticides. In the control of mosquito vectors, it is essential to strengthen the scientific and rational use of chemical control in combination with environmental and physical control measures to form an integrated control strategy. This approach will improve the control efficiency while delaying the occurrence and development of insecticide resistance.

Microglial functions are linked to Ca²⁺ signaling, with endoplasmic reticulum (ER) calcium stores playing a crucial role. Microglial abnormality is a hallmark of Alzheimer's disease (AD), but how ER Ca²⁺ receptors regulate microglial functions under physiological and AD conditions remains unclear. We found reduced ryanodine receptor 2 (Ryr2) expression in microglia from an AD mouse model. Modulation of RyR2 using S107, a RyR-Calstabin stabilizer, blunted spontaneous Ca²⁺ transients in controls and normalized Ca²⁺ transients in AD mice. S107 enhanced ATP-induced migration and phagocytosis while reducing ramification in control microglia; however, these effects were absent in AD microglia. Our findings indicate that RyR2 stabilization promotes an activation state shift in control microglia, a mechanism impaired in AD. These results highlight the role of ER Ca²⁺ receptors in both homeostatic and AD microglia, providing insights into microglial Ca²⁺ malfunctions in AD.
Animals ; Microglia/pathology* ; Alzheimer Disease/pathology* ; Phagocytosis/drug effects* ; Ryanodine Receptor Calcium Release Channel/metabolism* ; Disease Models, Animal ; Mice ; Cell Movement/drug effects* ; Mice, Transgenic ; Calcium Signaling/physiology* ; Calcium/metabolism* ; Mice, Inbred C57BL ; Male ; Endoplasmic Reticulum/metabolism*

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.

Mixed reality is a new digital hologram technology after virtual reality and augmented reality, which combines the real world with the virtual world to form a new visualization environment. At present, mixed reality has been applied in various fields, but its application in medical field is still in the exploratory stage. With the rapid development of the digital age, the prospect of the combination of mixed reality and medicine is boundless. It is believed that mixed reality will bring subversive changes in medical training, disease diagnosis, doctor-patient communication, clinical diagnosis, treatment and so on in the near future. In this paper, the application of mixed reality in medicine was summarized.

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.