ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

[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。

ObjectiveTo compare the differences between the ultrafiltration method and the dextran gel filtration method during the purification of Tfn-modified PCL micelles by using purification efficiency and micelle purity as indicators. MethodsCoumarin-6 （C6） was used as a fluorescent probe and was loaded into HOOC-PEG-PCL to form PCL micelles by the film-dispersion method. Tfn was then conjugated to the surface of PCL micelles via an amidation reaction， resulting in two types of micelles： Tfn/PCL_H and Tfn/PCL_L. The pharmaceutical properties of the two types of micelles were characterized. The micelles were then purified through ultrafiltration method and dextran gel method respectively， and the efficiency of the two methods， along with the purity of the final micelles， was compared. The density of Tfn on the surface of PCL micelles was also calculated. ResultsThe hydrated diameter of PCL micelles was approximately 73 nm， and the C6 loading efficiency was around 0.046%. The size increased to 134 nm and 158 nm for Tfn/PCL_L and Tfn/PCL_H， respectively. The micelle population was monodisperse. The purification results showed that， for the ultrafiltration method， after two and one rounds of purification， the Tfn/C6 ratio stabilized at 23.6 and 3.4 for Tfn/PCL_H and Tfn/PCL_L， respectively. For the dextran gel filtration method， the Tfn/C6 ratio reached 23.7 for the Tfn/PCL_H group after two rounds of purification. However， for the Tfn/PCL_L group， the Tfn/C6 ratio increased during four rounds of dextran gel purification， and a significant difference （P = 0.042 4） was observed between the first and last filtrations. The density of Tfn in the final micelles were calculated. For the ultrafiltration method， the Tfn density of Tfn/PCL_H and Tfn/PCL_L were 94.9% and 13.8%， respectively. For the dextran gel filtration method， the density of the two micelles were 95.6% and 14.4%， respectively. For Tfn/PCL_L group， the density results revealing a statistically significant difference （P=0.000 2）. ConclusionThe purification efficiency of the two methods is comparable. However， the purity of the final micelles shows a significant difference， with the dextran gel filtration method resulting in higher purity， particularly for the Tfn/PCL_L micelles.

Drawing on the concepts of behavioural change taxonomies and theoretical coding schemes， this paper designs a theoretical model of "manipulative effect units" for manual medicine， establishing a logical framework that links "treatment objectives （theory-driven）-treatment techniques （precise implementation）-expected effects （immediate verification）". First， it analyses the methodological challenges faced by manual medicine as a complex intervention， reviewing the insights and limitations of existing research frameworks. It then focuses on the model's core concepts， theoretical architecture and multi-level coding system of "manipulative effect units"， illustrating its application logic using tuina for knee osteoarthritis as an example. Finally， it proposes a development pathway of "manipulative effect units" model in complex interventions based on consensus， technological integration and artificial intelligence. This model aims to provide a unified methodological foundation for the standardised description， reproducible research and identification of active components in manual interventions， thereby propelling manual medicine from empirical transmission towards a new phase guided by theory and evidence optimization.

ObjectiveTo develop a core outcome set for musculoskeletal pain （MKP-COS） and a specific outcome set for traditional Chinese medicine external treatment （MKP-SOS-ETCM）， providing a standardized basis for clinical research in musculoskeletal pain and serving as a reference for core outcome set （COS） development in related fields. MethodsExisting COS studies on musculoskeletal pain （MKP） were systematically searched， and core outcome indicators were extracted to form an initial item pool. The Delphi method was used， in which experts were invited to reach consensus on MKP-COS and MKP-SOS-ETCM. The inclusion criteria for consensus are set as an importance score of 7-9 points with ≥75% of experts reaching agreement. ResultsA total of 47 COS studies were included， and after discussion and consolidation， a preliminary item pool containing 34 outcome indicators was established. After three rounds of Delphi expert consensus， MKP-COS was identified， including five outcomes including pain， motor function， joint range of motion， activities of daily living， and adverse events/ adverse reactions； MKP-SOS-ETCM included six outcomes involving stiffness （including morning stiffness）， joint tenderness count， work status/ productivity， overall physician assessment， structural damage， and muscle strength. ConclusionThis study has established MKP-COS and MKP-SOS-ETCM， forming a dual-dimensional outcome assessment system integrating both disease commonality and intervention specificity.

ObjectiveTo develop a core outcome set for musculoskeletal pain （MKP-COS） and a specific outcome set for traditional Chinese medicine external treatment （MKP-SOS-ETCM）， providing a standardized basis for clinical research in musculoskeletal pain and serving as a reference for core outcome set （COS） development in related fields. MethodsExisting COS studies on musculoskeletal pain （MKP） were systematically searched， and core outcome indicators were extracted to form an initial item pool. The Delphi method was used， in which experts were invited to reach consensus on MKP-COS and MKP-SOS-ETCM. The inclusion criteria for consensus are set as an importance score of 7-9 points with ≥75% of experts reaching agreement. ResultsA total of 47 COS studies were included， and after discussion and consolidation， a preliminary item pool containing 34 outcome indicators was established. After three rounds of Delphi expert consensus， MKP-COS was identified， including five outcomes including pain， motor function， joint range of motion， activities of daily living， and adverse events/ adverse reactions； MKP-SOS-ETCM included six outcomes involving stiffness （including morning stiffness）， joint tenderness count， work status/ productivity， overall physician assessment， structural damage， and muscle strength. ConclusionThis study has established MKP-COS and MKP-SOS-ETCM， forming a dual-dimensional outcome assessment system integrating both disease commonality and intervention specificity.

Background Lung tissue fibrosis caused by exposure to free silica (SiO₂) dust is the core pathological feature of silicosis. Lipocalin-2 (LCN2), as a secreted glycoprotein, plays an important role in various inflammatory diseases, but its specific role in silicosis-induced inflammatory injury remains unclear. Objective To identify differentially expressed genes (DEGs) and enriched pathways in the lung tissues of rats with silicosis, and to explore the regulatory mechanism of LCN2 in silicosis inflammation. Methods Twenty specific pathogen-free male SD rats were randomly divided into a control group and a silicosis model group (n=10 per group). The silicosis model was established via a single intratracheal instillation of silicon dioxide (SiO₂) suspension (50 mg), while the control group received an equal volume of normal saline. In vitro, NR8383 cells were divided into a control group and a SiO₂ treatment group. Hematoxylin-eosin (HE) staining and Van Gieson (VG) staining were used to evaluate alveolar structure damage, inflammatory infiltration, and collagen deposition in the lung tissues. Transcriptomic sequencing was applied to screen for DEGs and enriched signaling pathways. Western blot was used to detect the protein expression of interleukin-6 (IL-6), E-cadherin (ECA), cluster of differentiation 36 (CD36), liver X receptor (LXR), ATP-binding cassette transporter A1 (ABCA1), and LCN2 in both rat lung tissues and NR8383 cells. Immunohistochemistry was used to further assess LCN2 expression and localization. Additionally, NR8383 cells were treated with the LCN2 inhibitor ZINC00640089 and assigned to four groups: control, ZINC00640089-only, SiO₂, and SiO₂+ZINC00640089. Western blot was used to evaluate the expression of LCN2, IL-6, and interleukin-1β (IL-1β). Results HE and VG staining revealed disorganized lung tissue structure, damaged alveolar walls, nodule formation, and increased collagen deposition in the silicosis model group compared to the controls. Transcriptomic analysis identified 2723 DEGs in the lung tissues of silicosis rats (312 down-regulated and 2411 up-regulated) with LCN2 expression being significantly up-regulated. These DEGs were primarily enriched in pathways related to the innate immune response, extracellular matrix (ECM)-receptor interaction, and inflammatory response. Immunohistochemical staining confirmed elevated LCN2 levels in the lung tissues of the model rats. Western blot demonstrated that the protein levels of IL-6, CD36, and LCN2 were significantly increased (P<0.05), whereas ABCA1 and ECA were significantly decreased in the silicosis group (P<0.05). Similarly, in SiO₂-treated NR8383 cells, IL-6, CD36, and LCN2 expressions significantly increased (P<0.05), while ABCA1 and LXR significantly decreased (P<0.05). Compared with the SiO₂ group, treatment with ZINC00640089 significantly reduced the expression levels of LCN2, IL-6, and IL-1β (P<0.05). Conclusion LCN2 is significantly upregulated in both the lung tissues of silicosis rats and SiO₂-treated NR8383 cells. Inhibiting LCN2 expression effectively reduces the SiO₂-induced inflammatory response, indicating that LCN2 serves as a potential therapeutic target for silicosis.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective To explore the correlation of MET status in patients with advanced prostatic acinar adenocarcinoma with the clinical pathological parameters and prognosis. Methods The specimen from 135 patients with advanced prostatic acinar adenocarcinoma was included. The expression of c-MET protein was detected via immunohistochemistry, and MET gene amplification was assessed by fluorescence in situ hybridization. The relationships of c-MET expression and gene amplification with clinicopathological features and prognosis were analyzed. Results The positive expression rate of c-MET was 52.60% (71/135). Compared with the c-MET expression in adjacent tissues, that in tumor tissues showed lower heterogeneous expression. Among the cases, 1.71% (2/117) exhibited MET gene polyploidy, but no gene amplification was detected. Positive c-MET expression was significantly correlated with high Gleason scores and grade groups (P=0.0073). However, no significant relationship was found between c-MET expression and progression-free survival or post-treatment t-PSA levels. Conclusion c-MET protein is expressed at a relatively low level in advanced prostatic acinar adenocarcinoma, suggesting that c-MET may not be a viable target for ADC drug development in prostatic acinar adenocarcinoma.

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult