ObjectiveTo investigate the effects of Tianma Goutengyin on the tumor necrosis factor-α （TNF-α）/signal transducer and activator of transcription 3 （STAT3）/α1-antichymotrypsin C-terminal tail fragment （α1ACT） signaling pathway and A1-type astrocytes in a rat model of vascular dementia. MethodsSeventy-two male Sprague-Dawley rats were randomly divided into six groups （n=12 per group）： Sham-operated group， model group， Tianma Goutengyin high-， medium-， and low-dose groups （5.13， 10.26， and 20.52 g·kg^-1）， and a nimodipine group （8.1 mg·kg^-1）. The vascular dementia model was established by permanent bilateral common carotid artery occlusion， followed by 4 weeks of intervention. Learning and memory ability were evaluated using the novel object recognition test， and behavioral performance was assessed using the forced swimming test. Levels of interleukin-6 （IL-6） and C-C motif chemokine ligand 2 （CCL2） in hippocampal tissue were measured by enzyme-linked immunosorbent assay （ELISA）. Hippocampal neuronal morphology was observed by Nissl staining， and apoptosis was detected by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL）. Immunohistochemistry was used to detect positive expression of brain-derived neurotrophic factor （BDNF）， glial fibrillary acidic protein （GFAP）， and myelin basic protein （MBP）. Western blot analysis was performed to measure the protein expression levels of TNF-α， TNF receptor 1 （TNFR1）， phosphorylated STAT3 （p-STAT3）， α1ACT， IL-6， complement component 3 （C3）， BDNF， S100 calcium-binding protein A10 （S100A10）， and GFAP in hippocampal tissue. ResultsCompared with the sham-operated group， the model group showed a significantly reduced relative recognition index in the novel object recognition test （P<0.01）， prolonged immobility time and increased immobility frequency in the forced swimming test （P<0.01）. Hippocampal IL-6 and CCL2 levels were significantly increased （P<0.01）. Nissl staining revealed a marked reduction in neuronal number and loss of Nissl bodies （P<0.01）. MBP-positive expression was significantly decreased （P<0.01）， apoptosis was significantly increased （P<0.01）， BDNF-positive expression was significantly reduced （P<0.05）， and GFAP-positive expression was significantly increased （P<0.01）. In addition， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly elevated （P<0.01）， while BDNF and S100A10 expression levels were significantly decreased （P<0.01）. Compared with the model group， all Tianma Gouteng yin dose groups exhibited a significant increase in the relative recognition index （P<0.05）， shortened immobility time and reduced immobility frequency （P<0.05， P<0.01）. IL-6 and CCL2 levels were significantly decreased （P<0.01）， neuronal number was significantly increased （P<0.05， P<0.01）， and MBP-positive expression was significantly enhanced （P<0.01）. Apoptosis was significantly reduced （P<0.01）， BDNF-positive expression was significantly increased （P<0.05）， and GFAP-positive expression was significantly decreased （P<0.01）. Moreover， the protein expression levels of TNF-α， TNFR1， p-STAT3， α1ACT， IL-6， and C3 were significantly decreased （P<0.01）， while BDNF and S100A10 protein expression levels were significantly increased （P<0.01）. ConclusionTianma Goutengyin may inhibit A1-type astrocyte activation in rats with vascular dementia through the TNF-α/STAT3/α1ACT signaling pathway， thereby reducing neuronal apoptosis and improving learning and memory function.

BACKGROUND:Rheumatoid arthritis is influenced by complex genetic and environmental factors.Although observational studies have found some correlation between plasma proteins and rheumatoid arthritis,the susceptibility to confounding and reverse causation makes it difficult to clarify whether these proteins are pathogenic factors of rheumatoid arthritis.OBJECTIVE:To explore the potential of plasma proteins as biomarkers and therapeutic targets in rheumatoid arthritis through Mendelian randomization analysis of plasma proteins in the onset and progression of rheumatoid arthritis.METHODS:A large-scale two-sample Mendelian randomization analysis was conducted to comprehensively assess the causal relationships between 1 553 circulating proteins and rheumatoid arthritis based on the Decode database(developed by Decode Genetics in Iceland,which contains genomic data from the Icelandic population),the MR-Base platform(developed by a team of researchers at the University of Oxford in the United Kingdom,specifically designed to provide genetic and phenotypic data for Mendelian randomization analyses),and the GWAS Catalog platform(developed by the European Institute of Bioinformatics,which provides data for genome wide association studies worldwide).The causal effects were estimated using the Wald ratio and inverse variance weighting methods,with Bonferroni correction applied to control for false positives caused by multiple testing.To ensure the robustness of the results,sensitivity analyses were performed to validate the positive causal relationship between circulating proteins and rheumatoid arthritis,and Bayesian colocalization and phenome scanning were used to exclude confounding effects and horizontal pleiotropy.Additionally,external validation was carried out using new plasma protein datasets to reduce the likelihood of false discoveries.Finally,small-molecule compounds associated with candidate proteins were identified using the Drug Signatures Database(DsigDB),and molecular docking was performed to predict the binding patterns and energies between proteins and compounds,identifying the most stable and likely binding molecules and mechanisms.RESULTS AND CONCLUSION:(1)Sensitivity analyses,including Bayesian colocalization and phenome scanning,identified four plasma proteins with reliable causal relationships with rheumatoid arthritis:FCRL3,IL6R,ICOSLG,and TNFAIP3.Their genetic effects were estimated as follows:FCRL3[odds ratio(OR)=1.12,95％confidence interval(CI):1.07-1.17],IL6R(OR=0.94,95％CI:0.91-0.96),ICOSLG(OR=2.42,95％CI:1.67-3.52),and TNFAIP3(OR=2.19,95％CI:1.88-2.56).Furthermore,molecular docking analysis revealed that the small-molecule compound benzo[a]pyrene exhibited favorable binding with these candidate proteins,suggesting its potential as a therapeutic agent for rheumatoid arthritis.(2)This study provides a comprehensive analysis of the genetic causal relationships of FCRL3,IL6R,ICOSLG,and TNFAIP3 in rheumatoid arthritis.These proteins not only serve as potential molecular biomarkers for rheumatoid arthritis risk screening and disease prevention,but also offer key candidate targets for further understanding the pathogenic mechanisms of rheumatoid arthritis and developing targeted therapies.Although the study is based on European populations,its findings offer important insights for biomedical research in China.By incorporating Mendelian randomization methods to analyze genetic causality,future research on rheumatoid arthritis in the Chinese population could provide more accurate causal inferences,offering theoretical support for localized risk assessment and treatment strategies.

BACKGROUND:Rheumatoid arthritis is influenced by complex genetic and environmental factors.Although observational studies have found some correlation between plasma proteins and rheumatoid arthritis,the susceptibility to confounding and reverse causation makes it difficult to clarify whether these proteins are pathogenic factors of rheumatoid arthritis.OBJECTIVE:To explore the potential of plasma proteins as biomarkers and therapeutic targets in rheumatoid arthritis through Mendelian randomization analysis of plasma proteins in the onset and progression of rheumatoid arthritis.METHODS:A large-scale two-sample Mendelian randomization analysis was conducted to comprehensively assess the causal relationships between 1 553 circulating proteins and rheumatoid arthritis based on the Decode database(developed by Decode Genetics in Iceland,which contains genomic data from the Icelandic population),the MR-Base platform(developed by a team of researchers at the University of Oxford in the United Kingdom,specifically designed to provide genetic and phenotypic data for Mendelian randomization analyses),and the GWAS Catalog platform(developed by the European Institute of Bioinformatics,which provides data for genome wide association studies worldwide).The causal effects were estimated using the Wald ratio and inverse variance weighting methods,with Bonferroni correction applied to control for false positives caused by multiple testing.To ensure the robustness of the results,sensitivity analyses were performed to validate the positive causal relationship between circulating proteins and rheumatoid arthritis,and Bayesian colocalization and phenome scanning were used to exclude confounding effects and horizontal pleiotropy.Additionally,external validation was carried out using new plasma protein datasets to reduce the likelihood of false discoveries.Finally,small-molecule compounds associated with candidate proteins were identified using the Drug Signatures Database(DsigDB),and molecular docking was performed to predict the binding patterns and energies between proteins and compounds,identifying the most stable and likely binding molecules and mechanisms.RESULTS AND CONCLUSION:(1)Sensitivity analyses,including Bayesian colocalization and phenome scanning,identified four plasma proteins with reliable causal relationships with rheumatoid arthritis:FCRL3,IL6R,ICOSLG,and TNFAIP3.Their genetic effects were estimated as follows:FCRL3[odds ratio(OR)=1.12,95％confidence interval(CI):1.07-1.17],IL6R(OR=0.94,95％CI:0.91-0.96),ICOSLG(OR=2.42,95％CI:1.67-3.52),and TNFAIP3(OR=2.19,95％CI:1.88-2.56).Furthermore,molecular docking analysis revealed that the small-molecule compound benzo[a]pyrene exhibited favorable binding with these candidate proteins,suggesting its potential as a therapeutic agent for rheumatoid arthritis.(2)This study provides a comprehensive analysis of the genetic causal relationships of FCRL3,IL6R,ICOSLG,and TNFAIP3 in rheumatoid arthritis.These proteins not only serve as potential molecular biomarkers for rheumatoid arthritis risk screening and disease prevention,but also offer key candidate targets for further understanding the pathogenic mechanisms of rheumatoid arthritis and developing targeted therapies.Although the study is based on European populations,its findings offer important insights for biomedical research in China.By incorporating Mendelian randomization methods to analyze genetic causality,future research on rheumatoid arthritis in the Chinese population could provide more accurate causal inferences,offering theoretical support for localized risk assessment and treatment strategies.

Objective: To reveal the molecular biological mechanism of a rare Dc-variant individual using PacBio third-generation sequencing technology. Methods: ABO and Rh blood type identification, DAT, unexpected antibody screening and D antigen enhancement test were conducted by serological testing. The absorption-elution test was used to detect the e antigen. RHCE gene typing was performed by PCR-SSP, and the 1-10 exons of RHCE were sequenced by Sanger sequencing. The full-length sequences of RHCE, RHD and RHAG were detected by PacBio third-generation sequencing technology. Results: Serological findings: Blood type O, Dc-phenotype, DAT negative, unexpected antibody screening negative; enhanced D antigen expression; no detection of e antigen in the absorption-elution test. PCR-SSP genotyping indicated the presence of only the RHCE c allele. Sanger sequencing results: Exons 5-9 of RHCE were deleted, exon 1 had a heterozygous mutation at c. 48G/C, and exon 2 had five heterozygous mutations at c. 150C/T, c. 178C/A, c. 201A/G, c. 203A/G and c. 307C/T. Third-generation sequencing results: RHCE genotype was RHCE 02N. 08/RHCE-D(5-9)-CE; RHD genotype was RHD 01/RHD 01; RHAG genotype was RHAG 01/RHAG 01 (c. 808G>A and c. 861G>A). Conclusion: This Dc-individual carries the allele RHCE 02N. 08 and the novel allele RHCE-D(5-9)-CE. The findings of this study provide data support and a theoretical basis for elucidating the molecular mechanisms underlying RhCE deficiency phenotypes.

ObjectiveTo evaluate the effect of Suanzaoren Tang on the rat model of depression established by solitary culture combined with chronic unpredictable mild stress by reshaping the inflammatory microenvironment and mediating changes in hippocampal synaptic plasticity. MethodsSeventy-two male SD rats were randomized by a random number table into six groups： control group， model group， fluoxetine group （0.003 6 g·kg^-1）， and high-（10 g·kg^-1）， medium-（5 g·kg^-1）， low-dose （2.5 g·kg^-1）Suanzaoren Tang groups， with 12 rats per group. The sucrose preference rate and open field test scores of rats in each group were observed. Western blot was employed to determine the expression levels of the key proteins in the specificity protein 1 （SP1）/sphingosine kinase 1 （SK1）/sphingosine-1-phosphate receptor 1 （S1PR1） signaling pathway， as well as hippocampal proteins synaptophysin Ⅰ （SYNⅠ）， postsynaptic density protein-95 （PSD-95）， and family with sequence similarity 19， member A5 （FAM19A5）. Immunohistochemistry was employed to detect the positive expression of SP1， PSD-95， SYNⅠ， interleukin （IL）-10， and IL-6. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the mRNA levels of SP1 and S1PR1. Finally， transmission electron microscopy was employed to observe the ultrastructural changes of hippocampal synapses. ResultsCompared with the control group， the model group exhibited a decrease in sucrose preference index （P<0.01） and reduced total scores for horizontal and vertical movements in the open field test （P<0.01）， which indicated the successful modeling of depression. Moreover， the model group showed reduced synaptic vesicles in the hippocampus （P<0.01）， up-regulated expression of SP1， SK1， S1PR1， and IL-6 （P<0.01）， and down-regulated expression of SYNⅠ， PSD-95， FAM19A5， and IL-10 （P<0.01）. Compared with the model group， high- and medium-dose Suanzaoren Tang and fluoxetine increased the sucrose preference index and the total scores for horizontal and vertical movements in the open field test （P<0.01）. All Suanzaoren Tang groups and the fluoxetine group demonstrated reductions in SP1， SK1， S1PR1， and IL-6 expression （P<0.05， P<0.01）， alongside restored synaptic vesicles in the hippocampus （P<0.05， P<0.01）. ConclusionSuanzaoren Tang modulates hippocampal expression of FAM19A5， SYNⅠ， PSD-95， IL-10， IL-6， and the SP1/SK1/S1PR1 pathway in the rat model of depression. The antidepressant effects may be related to the ability of reducing neuroinflammation and enhancing synaptic plasticity.

Objective To evaluate the effect of a multidisciplinary collaborative quality control management (hereinafter referred to as "QC management") on improving the performance of biological safety cabinets in hospital. Methods A total of 63 ClassⅡbiological safety cabinets in active use at Peking University Third Hospital were selected as the study subjects using the before-after study mode. Conventional management was implemented on the biological safety cabinets from 2018 to 2021. QC management was used in 2022. The compliance of biological safety cabinets management norm and performance differences under the two models were compared. Results The median and the 25th and 75th percentiles [M(P₂₅, P₇₅)] of the service life among these 63 biological safety cabinets were 3 (1,6) years. The overall performance pass rate and inflow velocity pass rate of biological safety cabinets were higher in the QC management than that in the conventional management (90.5% vs 65.1%, 96.8% vs 84.1%, both P<0.05). However, there was no significant difference in downflow velocity, high-efficiency particulate air filter integrity, cleanliness, airflow smoke pattern, noise, and illumination pass rates of biosafety cabinets before and after the implementation of QC management (79.4% vs 88.9%, 90.5% vs 100.0%, 96.8% vs 100.0%, 85.7% vs 100.0%, 100.0% vs 100.0%, and 85.7% vs 96.8%, respectively; all P>0.05). Conclusion sQC management improves the standardization of biological safety cabinet management and key performance indicators in hospital.

ObjectiveTo evaluate the effect of Suanzaoren Tang on the rat model of depression established by solitary culture combined with chronic unpredictable mild stress by reshaping the inflammatory microenvironment and mediating changes in hippocampal synaptic plasticity. MethodsSeventy-two male SD rats were randomized by a random number table into six groups： control group， model group， fluoxetine group （0.003 6 g·kg^-1）， and high-（10 g·kg^-1）， medium-（5 g·kg^-1）， low-dose （2.5 g·kg^-1）Suanzaoren Tang groups， with 12 rats per group. The sucrose preference rate and open field test scores of rats in each group were observed. Western blot was employed to determine the expression levels of the key proteins in the specificity protein 1 （SP1）/sphingosine kinase 1 （SK1）/sphingosine-1-phosphate receptor 1 （S1PR1） signaling pathway， as well as hippocampal proteins synaptophysin Ⅰ （SYNⅠ）， postsynaptic density protein-95 （PSD-95）， and family with sequence similarity 19， member A5 （FAM19A5）. Immunohistochemistry was employed to detect the positive expression of SP1， PSD-95， SYNⅠ， interleukin （IL）-10， and IL-6. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was employed to determine the mRNA levels of SP1 and S1PR1. Finally， transmission electron microscopy was employed to observe the ultrastructural changes of hippocampal synapses. ResultsCompared with the control group， the model group exhibited a decrease in sucrose preference index （P<0.01） and reduced total scores for horizontal and vertical movements in the open field test （P<0.01）， which indicated the successful modeling of depression. Moreover， the model group showed reduced synaptic vesicles in the hippocampus （P<0.01）， up-regulated expression of SP1， SK1， S1PR1， and IL-6 （P<0.01）， and down-regulated expression of SYNⅠ， PSD-95， FAM19A5， and IL-10 （P<0.01）. Compared with the model group， high- and medium-dose Suanzaoren Tang and fluoxetine increased the sucrose preference index and the total scores for horizontal and vertical movements in the open field test （P<0.01）. All Suanzaoren Tang groups and the fluoxetine group demonstrated reductions in SP1， SK1， S1PR1， and IL-6 expression （P<0.05， P<0.01）， alongside restored synaptic vesicles in the hippocampus （P<0.05， P<0.01）. ConclusionSuanzaoren Tang modulates hippocampal expression of FAM19A5， SYNⅠ， PSD-95， IL-10， IL-6， and the SP1/SK1/S1PR1 pathway in the rat model of depression. The antidepressant effects may be related to the ability of reducing neuroinflammation and enhancing synaptic plasticity.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*