[摘 要] 目的：探讨异位表达血红蛋白亚基（HBA/HBB）对缺氧条件下嵌合抗原受体T细胞（CAR-T细胞）功能障碍的改善作用及其对肿瘤细胞的杀伤效应。方法：全基因合成技术合成靶向HER2的CAR序列，构建共表达HBA或HBB的CAR慢病毒载体，包装慢病毒后感染人原代T淋巴细胞，制备异位表达HBA/HBB的CAR-T细胞，命名为HBA CAR-T和HBB CAR-T。采用缺氧探针检测小鼠实体瘤缺氧状态。通过流式细胞术检测瘤内CAR-T细胞占比、异位表达血红蛋白亚基的CAR-T细胞阳性率及CAR-T细胞的活性氧、凋亡水平。WB法检测HBA CAR-T和HBB CAR-T内相关血红蛋白亚基表达情况，采用细胞计数板计数检测细胞增殖水平，通过萤光素酶报告基因法检测CAR-T细胞对肿瘤细胞的杀伤能力，qPCR检测CAR-T细胞中缺氧诱导因子-1α（HIF-1α）表达水平，利用MitoXpress Intra试剂盒检测CAR-T细胞内氧气含量。结果：不同细胞构建的实体瘤模型均存在明显缺氧情况，且CAR-T细胞浸润水平与缺氧程度呈显著负相关（P < 0.000 1）。HBA CAR-T与HBB CAR-T构建成功（阳性率 > 60%），相应血红蛋白亚基可稳定表达。缺氧环境下HBA CAR-T和HBB CAR-T的ROS水平、凋亡水平显著下降，增殖、对肿瘤细胞的体外杀伤能力显著强于传统CAR-T细胞（均P < 0.05）。HBA CAR-T与HBB CAR-T内HIF-1α表达降低（均P < 0.001），且缺氧程度显著降低（均P < 0.001）。结论：异位表达血红蛋白亚基可改善缺氧条件下CAR-T细胞功能障碍并增强其对肿瘤细胞的体外杀伤作用。

[摘 要] 目的：探究重楼皂苷Ⅱ（PPⅡ）抑制甲状腺癌（TC）恶性生物学行为的分子机制。方法：常规培养甲状腺癌细胞TPC1，实验分为sh-NC、sh-可溶性半乳糖凝集素3（sh-LGALS3）、OE-NC、OE-LGALS3和 OE-LGALS3 + PPⅡ组，用转染试剂将应用质粒转染至各组TPC1细胞中。qPCR法检测TPC1细胞中LGALS3 mRNA的表达，WB法检测各组TPC1细胞中LGALS3、PI3K/AKT信号通路相关蛋白的表达，CCK-8法、Transwell实验、划痕愈合实验和流式细胞术分别检测各组TPC1细胞增殖、迁移和侵袭能力，以及细胞凋亡情况。结果：PPⅡ抑制TPC1细胞的增殖、迁移和侵袭，并诱导其凋亡（均P < 0.000 1）。数据库数据分析显示LGALS3在甲状腺癌组织中高表达（P < 0.001）且是PPⅡ的靶基因。LGALS1在TPC1细胞中呈高表达（P < 0.000 1），敲减LGALS3抑制TPC1细胞的恶性生物学行为，并促进其凋亡（均P < 0.000 1），PPⅡ通过抑制LGALS3 mRNA和蛋白的表达（P < 0.01或P < 0.001）从而抑制TPC1细胞的恶性生物学行为（P < 0.01或P < 0.000 1），PPⅡ抑制LGALS3表达抑制PI3K/AKT信号通路的激活水平（P <0.001或P <0.000 1），LGALS3通过PI3K/AKT信号通路促进TPC1细胞的恶性生物行为（P < 0.000 1）。结论：PPⅡ通过抑制TPC1细胞中LGALS3的表达，缓解PI3K/AKT信号通路的过度激活从而发挥抑癌作用。

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Monotropein is a compound classified into iridoid which is found in herbaceous plants Morindae officinalis. It possesses anti-inflammatory, antioxidant, and anti-osteoarthritic activities. Previous study indicates that monotropein may have the potential to combat cardiovascular disease, although the related mechanism remains unclear. In this study, we constructed the model of atherosclerosis by oxidized low density lipoprotein-induced vascular smooth muscle cells and LDLR –/–mice given high-fat diet to investigate the effects of monotropein on atherosclerosis.Our results showed that monotropein treatment significantly reduced the area of atherosclerotic plaques and necrotic cores in mice, inhibited the proliferation and migration of vascular smooth muscle cells, and reduced inflammatory responses and oxidative stress, which in turn alleviated atherosclerosis. In addition, we found that monotropein reduced the expression levels of P-NF-κB and P-AP-1. In conclusion, our data suggest that monotropein inhibited the proliferation and migration of vascular smooth muscle cells by mediating the activity of NF-κB, AP-1, reducing the level of inflammation and oxidative stress, and thus resisting the development of atherosclerosis. These findings demonstrate the efficacious therapeutic impact of monotropein on atherosclerosis and elucidate its specific target.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the association between blood selenium levels and sex hormones in Chinese men aged 18-79 years.Methods:Data were derived from the China National Human Biomonitoring survey conducted in 2017-2018, with a final sample size of 5 414 men. General demographic characteristics, behavioral habits, and dietary frequency were collected through questionnaires and physical examinations. Fasting blood samples were collected to measure blood lead, serum testosterone, and estradiol levels. Complex sampling linear regression models were used to analyze the associations between blood selenium levels and testosterone, estradiol, and the testosterone/estradiol ratio, adjusting for confounding factors including age, education level, marital status, smoking status, alcohol consumption, seafood intake, soy product intake, protein supplement intake, BMI, and diabetes status.Results:The mean age of the 5 414 participants was (46.85±27.91) years; 4 774 (91.65%) were of Han ethnicity and 4 505 (86.68%) were married. The median ( Q1, Q3) blood selenium concentration in men was 97.80 (80.64, 116.99) μg/L. After adjusting for confounding factors, the complex sampling linear regression model revealed negative associations between blood selenium levels and both testosterone levels and the testosterone/estradiol ratio, with a significant linear trend ( Ptrend<0.05). Compared with the Q1 group, the β (95% CI) values for testosterone in the Q2, Q3, and Q4 groups were -0.02 (-0.06 to 0.02), -0.03 (-0.08 to 0.01), and -0.06 (-0.09 to -0.02), respectively. Similarly, the β (95% CI) values for the testosterone/estradiol ratio in the Q2, Q3, and Q4 groups were -0.01 (-0.03 to 0.02), -0.01 (-0.04 to 0.04), and -0.03 (-0.06 to -0.01), respectively. Subgroup analysis indicated stronger associations between blood selenium levels and testosterone/estradiol levels in non-smoking and obese men (BMI≥28 kg/m2). Conclusion:Blood selenium levels are negatively associated with testosterone levels and the testosterone/estradiol ratio in Chinese adult males.

Objective:To analyze the baseline characteristics and prognostic factors of Pneumocystis carinii pneumonia (PCP) in patients with liver diseases (LD), and to develop the nomogram prognosis model. Methods:The patients with liver diseases complicated with PCP (LD-PCP) who admitted to Beijing Youan Hospital, Capital Medical University from January 1st, 2021 to October 31st, 2024 were enrolled in this study. This study was a retrospective case-control study. Data were collected, including baseline characteristics at admission and clinical outcomes. Multivariate logistic regression analysis was employed to identify the independent risk factors of poor prognosis in LD-PCP patients. Subsequently, a nomogram was developed to construct the prognostic model. The performance of this model was evaluated using the receiver operater characteristic (ROC) curve and calibration curve.Results:A total of 61 patients with LD-PCP were enrolled, including 21 in the death group and 40 in the survival group. Multivariate logistic regression analysis revealed that co-infection with Aspergillus and elevated lactate dehydrogenase (LDH) levels were independent risk factors for mortality in patients with LD-PCP (odds ratio ( OR)=12.802, 95% confidence interval ( CI) 1.334 to 122.845, P=0.027; OR=1.009, 95% CI 1.002 to 1.016, P=0.014). The predictive value of prognostic model based on the nomogram was better evidenced by the ROC curve, which yielded an area under the curve (AUC) of 0.835 (95% CI 0.725 to 0.944), with the sensitivity of 71.4% and the specificity of 87.5%. The predictive accuracy of the model was further validated by the calibration curve, and the Brier value was 0.151, the model fitting line had a high degree of agreement with the standard reference line ( P=0.953). Conclusions:Co-infection with Aspergillus and LDH levels are independent risk factors for the poor prognosis of patients with LD-PCP. The prognosis model based on the nomogram has better predictive value and clinical practicality, which could identify high-risk patients in the early stage, and provide theoretical guidance on the patient management.

AIM To establish a UPLC-ESI-MS/MS method for analyzing the toxic material basis of 95％ethanol cold soaked ultrasonic extract(EC),95％ethanol heated reflux extract(EH)and water decoction extract(WD)from Dryopteris crassirhizoma Nakai.METHODS The analysis was performed on a 25 ℃ thermostatic agilent ZORBAX RRHD StableBond C18 column(2.1 mm×150 mm,1.8 μm),with the mobile phase comprising of methanol-0.2％formic acid flowing at 0.30 mL/min,and heated electrospray ion source was adopted in positive and negative ion scanning.Compounds were identified by Compound Discover 3.3 software combined with the database and related literature,and the main differential components were screened by Heatmap cluster analysis and partial least squares discriminant analysis.RESULTS 72 compounds were identified(22 phloroglucinols,19 flavonoids,8 phenylpropanoids,6 terpenoids and 17 other components).The main toxic differential components were phloroglucinols such as flavaspidic acid AB,didemethylpseudoaspidin AA and filixic acid PBP,flavonoids such as(-)-epicatechin,(-)-epigallocatechin,cianidanol,and other compounds such as indole-3-carboxaldehyde.CONCLUSION This method can rapidly,effectively and comprehensively characterize the main chemical composition of D.crassirhizoma,and provide a reference for the study of its pharmacological mechanism.