[摘 要] 目的：探究甘氨胆酸（GCA）对肺腺癌A549细胞移植瘤小鼠放射治疗敏感性的影响及其机制。方法：建立A549人肺腺癌细胞裸鼠移植瘤模型，随机分为移植瘤对照组（对照组）、GCA组、放疗组（RT组）和GCA + 放疗组（GCA + RT组）。RT组和GCA + RT组接受单次10  Gy照射，GCA组及GCA + RT组连续7 d每日灌胃GCA 280  mg/kg。间隔2 d测量1次移植瘤体积，末次给药后处死小鼠并取移植瘤组织，检测移植瘤组织中超氧化物歧化酶（SOD）与谷胱甘肽过氧化物酶（GSH-Px）活性，qPCR法和WB法分别检测放疗关键基因（MCM6、ITGA6、CASP3等）mRNA和蛋白表达水平，H-E染色观察移植瘤组织的形态变化。通过GEO（GSE276500、GSE294906、GSE218171）及TCGA数据库数据验证放疗关键基因。结果：GCA单用对瘤体生长有一定抑制作用，但联合放疗的GCA + RT组相比单纯放疗组表现出放疗抵抗的效应（P < 0.05）。GCA处理显著提高移植瘤组织SOD活性（P < 0.01）、降低GSH-Px活性（P < 0.01），提示GCA可改变移植瘤抗氧化酶平衡，减弱放疗诱导的氧化应激。GCA干预上调移植瘤组织中MCM6与ITGA6 mRNA表达、下调CASP3 mRNA表达（均P < 0.05）。GCA + RT组移植瘤组织中的MCM6蛋白表达显著高于对照组（P < 0.05）。H-E染色显示，GCA组部分瘤组织坏死，而GCA + RT组坏死组织面积较RT组有所缩小。GEO和TCGA数据库验证支持MCM6、ITGA6高表达与放疗抵抗和预后不良相关。结论：GCA通过增强SOD活性、降低GSH-Px活性并上调ITGA6、MCM6的表达改变氧化应激与关键信号网络，从而削弱A549移植瘤对放疗的敏感性。

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

BACKGROUND:Traditional methods of urinary tract reconstruction are limited by donor scarcity,high complication rates,and suboptimal functional recovery.Tissue engineering strategies offer new directions in this field.Since the urinary tract is mainly composed of muscle tissue,the key is to find suitable seed cells and efficiently induce them to differentiate into smooth muscle cells.Comparative studies on the efficacy of different smooth muscle cell induction regimens are still lacking. OBJECTIVE:To isolate,culture,and identify human urine-derived stem cells,and to compare the effects of two different induction protocols. METHODS:Human urine-derived stem cells were isolated from urine samples of 11 healthy adult volunteers by multiple centrifugations.Surface markers were identified by flow cytometry.The multi-directional differentiation potential of human urine-derived stem cells was verified through osteogenic and adipogenic differentiation.Differentiation was induced by transforming growth factor-β1 or transforming growth factor-β1 combined with platelet derived growth factor for 14 days.Immunofluorescence staining and western blot assay were employed to compare the expression differences of smooth muscle-specific proteins(α-SMA and SM22). RESULTS AND CONCLUSION:(1)Urine-derived stem cells were successfully isolated from the eight urine samples of healthy people.These cells exhibit a"rice grain"-like morphology and possess a robust proliferative capacity.(2)Urine-derived stem cells exhibited high expression of mesenchymal stem cell surface markers(CD73,CD90,and CD44)and extremely low expression of hematopoietic stem cell surface markers(CD34 and CD45).These cells did not express CD19,CD105,and HLA-DR.(3)After osteogenic and adipogenic differentiation,the formation of calcium nodules and lipid droplets was observed,with positive staining results from Alizarin Red S and Oil Red O staining.(4)After 14 days of smooth muscle induction culture,immunofluorescence staining revealed that the smooth muscle differentiation rate of urine-derived stem cells treated with a combination of transforming growth factor-β1 and platelet derived growth factor was significantly higher compared to those treated with transforming growth factor-β1 alone(P<0.005).(5)After 14 days of smooth muscle induction culture,western blot assay further demonstrated that the expression levels of α-SMA and SM22 in the transforming growth factor-β1/platelet derived growth factor group were significantly elevated compared to those in the transforming growth factor-β1 only group(P<0.005).These findings confirm that urine-derived stem cells can be non-invasively isolated using multiple rounds of centrifugation.Compared with transforming growth factor-β1 alone,the combination of transforming growth factor-β1 and platelet derived growth factor can improve the efficiency of inducing urine-derived stem cells to differentiate into smooth muscle cells.

OBJECTIVE To study the efficacy of Tianjiang xueshuantong pills in the treatment of coronary heart disease. METHODS In accordance with the common pathogenesis of coronary heart disease， acute myocardial ischemia model， hyperlipidemia model， blood stasis model， and carotid artery thrombosis model were established using Wistar rats or SD rats as the experimental subjects. The effects of Tianjiang xueshuantong pills administered at high， medium， and low doses （0.6， 1.2 and 2.4 g/kg） on hemodynamic parameters and myocardial enzyme markers [lactate dehydrogenase （LDH）， creatine kinase-MB （CK- MB）]， oxidative stress factors [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione （GSH）]， inflammatory cytokines [tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， IL-1β， monocyte chemotactic protein-1 （MCP-1）， intercellular adhesion molecule-1 （ICAM-1）]， myocardial infarction percentage， serum lipid indexes [total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， high-density lipoprotein （HDL）]， platelet aggregation function 话：022-84845240。E-mail：jiangx@tjipr.com [maximum aggregation rate （MAR）]， and thrombus formation indexes [thrombosis time， thrombus mass， thrombus protein content， plasminogen activator inhibitor-1 （PAI-1）， and tissue-type plasminogen activator （t-PA）] were evaluated in the rat models. RESULTS In myocardial ischemia tests， Tianjiang xueshuantong pills significantly reduced the percentage of myocardial infarction and the levels of CK-MB， LDH， MDA， GSH， IL-6， TNF-α， IL- 1β， and MCP-1 in serum （P＜0.05 or P＜0.01）. In hyperlipidemia tests， high dose of Tianjiang xueshuantong pills significantly reduced the serum levels of TC， LDL and significantly increased the level of HDL in rats after 2 weeks and 4 weeks of administration. In blood stasis tests， different doses of Tianjiang xueshuantong pills significantly reduced MAR of rats （P＜0.01）. In artery thrombosis tests， high dose of Tianjiang xueshuantong pills significantly prolonged the time of thrombosis formation （P＜ 0.01）， significantly reduced the weight and protein content of thrombus and the level of PAI-1 in serum （P＜0.01）. CONCLUSIONS Tianjiang xueshuantong pills exert therapeutic effects on coronary heart disease through multi-dimensional synergistic actions， including anti-myocardial ischemia， lipid-lowering， and anti-thrombotic effects.

OBJECTIVE To summarize the implementation experiences of the China Hospital Association’s Clinical Pharmacist Instructor Training Program Reform， and to evaluate the effectiveness of the reform， thus continuously enhancing the quality and standards of clinical pharmacist instructor training. METHODS The study drew on project evaluation methodologies to summarize the main characteristics of the comprehensive system and new model for clinical pharmacist instructor training established through the reform by literature review. The “learning assessment” and “reaction assessment” were conducted by using Kirkpatrick’s four-level model of evaluation in order to evaluate the effectiveness of the clinical pharmacist instructor training reform through statistically processing and analyzing the performance data and teaching evaluation data of the instructor participants. Based on problem and trend analysis， the future development directions were anticipated for the reform of clinical pharmacist instructor training. RESULTS & CONCLUSIONS The latest round of clinical pharmacist instructor training reform initiated by the Chinese Hospital Association had initially established a four-pronged training system encompassing “recruitment， training， assessment， and management”. It had also forged a training 。 model “oriented towards enhancing clinical teaching competency， with practical learning and skill-based assessment conducted on clinical teaching sites as its core”. Following a period of over three years of gradual reform， the new training system and model became increasingly mature. In both 2023 and 2024， the participants achieved relatively high average total scores in their initial completion assessments [with scores of （84.05± 5.83） and （85.82±4.35） points， respectively]. They also reported a strong sense of gain from the training reform [with self- perceived gain scores of （4.80±0.44） and （4.85±0.39） points， respectively]. The operation and implementation effects of the reform were generally satisfactory. In the future， clinical pharmacist instructor training reforms should continue to address the issues remaining from the current phase， while aligning with global trends in pharmacy education and industry development. Additionally， sustained exploration and practice will be carried out around the core objective of “enhancing clinical teaching competence”.

Systemic lupus erythematosus (SLE) is an autoimmune disease of unknown etiology, primarily characterized by systemic inflammation and hyperactivation of both B and T lymphocytes. Key immunological features include increased consumption of complement components, sustained overproduction of type I interferons (IFN-I), and persistent production of a broad spectrum of autoantibodies, such as anti-dsDNA antibodies. However, the use of autoantibodies as biomarkers for the early detection of SLE is associated with a high false-positive rate, suggesting that antibody characteristics evolve during disease progression.N-glycosylation is a critical post-translational modification of antibodies that significantly influences their structure and receptor-binding properties, thereby modulating biological activities and functions. In particular, glycosylation patterns affect the antibody’s affinity for Fc gamma receptors (FcγRs), subsequently regulating various antibody-mediated immune responses. Numerous studies have investigated the impact of individual monosaccharides—such as sialic acid, fucose, and N-acetylglucosamine, which constitute N-glycans—on the immunological functions of antibodies. This review systematically summarizes the aberrant immunoglobulin G (IgG) N-glycosylation patterns observed in SLE patients, with a focus on correlations between disease progression or complications and quantitative alterations in individual glycan components. We first review how different types of N-glycosylation modifications affect the biological activity and functional properties of IgG, particularly regarding the effects of specific monosaccharides—such as sialic acid, fucose, and galactose—on FcγR binding affinity and the resulting downstream immune functions. We then summarize the differential expression of IgGN-glycans and glycosyltransferase genes between SLE patients and healthy controls, and outline the associations between glycosylation changes and SLE-related pathological responses. In response to the inconsistencies and limitations in current research, we propose potential explanations from the perspectives of study methodologies, participant characteristics, and variations in N-glycan structures, aiming to provide a constructive reference for future studies. Given the close relationship between antibody glycosylation and SLE, this review highlights the potential of IgG N-glycosylation patterns as promising biomarkers for early diagnosis and disease monitoring. In terms of therapy, we discuss how IgG glycosylation can enhance the efficacy of intravenous immunoglobulin (IVIg) treatment and introduce emerging therapeutic strategies that aim to modulate endogenous IgG N-glycans as a novel glycan-based approach for SLE management. In summary, N-glycans are essential structural components of antibodies that regulate immune responses by modulating antibody-receptor interactions. Aberrant glycosylation is closely associated with the pathogenesis of autoimmune diseases, including SLE. However, due to the structural diversity of N-glycans and the complexity of glycosylation processes, the precise roles of IgGN-glycosylation in SLE pathophysiology remain incompletely understood. Moreover, therapeutic strategies targeting IgG glycosylation are still in early development and have not yet reached clinical application. Continued progress in glycan analysis technologies and other biological tools, along with interdisciplinary collaboration, will be essential for advancing this field.

Objective: To investigate the status of drug resistance before anti-retroviral therapy among newly dignosed HIV/AIDS patients aged ≥50 years in Yangzhou City, Jiangsu Province, so as to provide the evidence for improving the anti-retroviral therapy effect of AIDS. Methods: HIV/AIDS patients aged ≥50 years who were newly dignosed in Yangzhou City from 2021 to 2024 and did not receive anti-retroviral therapy were selected. Basic information were collected through the Chinese Disease Prevention and Control Information System. Blood samples were collected to determine CD4+T lymphocyte (CD4 cell) counts and HIV-1 viral load. Following nucleic acid extraction, the pol gene region was amplified using reverse transcription and nested PCR, and subsequently subjected to Sanger sequencing. The resulting sequences were uploaded to the Stanford University HIV Drug Resistance Database to analyze drug resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and nucleoside reverse transcriptase inhibitors (NRTIs). Results: Totally 404 blood samples from HIV/AIDS patients were collected, with successful sequencing of the pol gene region in 341 cases. Among them, 253 (74.19%) were males and 88 (25.81%) were females, with a mean age of (62.48±7.60) years. A total of 152 cases (44.57%) had CD4 cell counts below 200 cells/μL, and 296 cases (86.80%) had HIV-1 viral loads exceeding 5 000 copies/mL. A total of 87 cases exhibited drug resistance-associated mutations, corresponding to a mutation rate of 25.51%. The predominant mutation site was V179, with a mutation rate of 17.01%. A total of 29 cases exhibited resistance to at least one drug, resulting in a resistance rate of 8.50%. The resistance rates to NNRTIs, PIs, and NRTIs were 5.57%, 2.93%, and 1.17%, respectively. The HIV/AIDS patients exhibited varying degrees of resistance to 13 anti-retroviral drugs, with low- or intermediate-level drug resistance being predominant. High-level drug resistance cases were observed against NNRTIs such as nevirapine and efavirenz. Conclusions The drug resistance rate before anti-retroviral therapy among newly dignosed HIV/AIDS patients aged ≥50 years in Yangzhou City was at a moderate level. The predominant resistance mutation was observed at V179 site, with NNRTIs resistance being most prevalent, primarily demonstrating low- or intermediate-level drug resistance.

Objective: To investigate the Alzheimer's disease (AD) influencing factors among the elderly, so as to provide a basis for early prevention and intervention. Methods: From March to June 2024, participants aged 60 years and above from a sub-district in Haishu District, Ningbo City, Zhejiang Province were selected using a convenience sampling method. Data on demographics, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were evaluated using the short-form Geriatric Depression Scale. The Chinese Mini-Mental State Examination (MMSE) was used for the initial screening of AD, and individuals who screened positive were further diagnosed by psychiatrists. Factors affecting AD among the elderly were analyzed using a multivariable logistic regression model. Results: A total of 3 644 individuals were surveyed, comprising 1 526 males (41.88%) and 2 118 females (58.12%). The mean age was (71.85±7.44) years. AD was detected in 200 cases, with a detection rate of 5.49%. Multivariable logistic regression analysis showed that individuals aged ≥65 years (65-<70 years, OR=3.012, 95%CI: 1.007-9.012; 70-<75 years, OR=3.131, 95%CI: 1.059-9.260; 75-<80 years, OR=5.779, 95%CI: 1.989-16.784; ≥80 years, OR=16.810, 95%CI: 5.926-47.685), those who were unmarried, divorced, or widowed (OR=1.973, 95%CI: 1.383-2.815), those with hearing loss (OR=1.573, 95%CI: 1.128-2.193), those with diabetes mellitus (OR=1.958, 95%CI: 1.362-2.814), and those with depressive symptoms (OR=4.143, 95%CI: 2.997-5.728) had a higher risk of AD. Conversely, individuals with an educational level of primary school or above (primary school, OR=0.579, 95%CI: 0.401-0.835; junior high school or above, OR=0.438, 95%CI: 0.259-0.741), and those who engaged in regular physical exercise (OR=0.414, 95%CI: 0.264-0.649) had a lower risk of AD. Conclusions The detection rate of AD was relatively high among the elderly in Haishu District. AD among the elderly was related to age, educational level, marital status, physical exercise, hearing loss, diabetes mellitus, and depressive symptoms.

Artificial intelligence (AI), particularly deep learning, has demonstrated remarkable performance in medical imaging across a variety of modalities, including X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), and pathological imaging. However, most existing state-of-the-art AI techniques are task-specific and focus on a limited range of imaging modalities. Compared to these task-specific models, emerging foundation models represent a significant milestone in AI development. These models can learn generalized representations of medical images and apply them to downstream tasks through zero-shot or few-shot fine-tuning. Foundation models have the potential to address the comprehensive and multifactorial challenges encountered in clinical practice. This article reviews the clinical applications of both task-specific and foundation models, highlighting their differences, complementarities, and clinical relevance. We also examine their future research directions and potential challenges. Unlike the replacement relationship seen between deep learning and traditional machine learning, task-specific and foundation models are complementary, despite inherent differences. While foundation models primarily focus on segmentation and classification, task-specific models are integrated into nearly all medical image analyses. However, with further advancements, foundation models could be applied to other clinical scenarios. In conclusion, all indications suggest that task-specific and foundation models, especially the latter, have the potential to drive breakthroughs in medical imaging, from image processing to clinical workflows.
Humans ; Artificial Intelligence ; Deep Learning ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed ; Positron-Emission Tomography