1.Clinical Efficacy of Tangning Tongluo Tablets for Nonproliferative Diabetic Retinopathy
Fuwen ZHANG ; Junguo DUAN ; Wen XIA ; Tiantian SUN ; Yuheng SHI ; Shicui MEI ; Xiangxia LUO ; Xing LI ; Yujie PAN ; Yong DENG ; Chuanlian RAN ; Hao CHEN ; Li PEI ; Shuyu YANG
Chinese Journal of Experimental Traditional Medical Formulae 2025;31(3):132-139
ObjectiveTo observe the clinical efficacy and safety of Tangning Tongluo tablets in the treatment of nonproliferative diabetic retinopathy (DR). MethodsFourteen research centers participated in this study, which spanned a time interval from September 2021 to May 2023. A total of 240 patients with nonproliferative DR were included and randomly assigned into an observation group (120 cases) and a control group (120 cases). The observation group was treated with Tangning Tongluo tablets, and the control group with calcium dobesilate capsules. Both groups were treated for 24 consecutive weeks. The vision, DR progression rate, retinal microhemangioma, hemorrhage area, exudation area, glycosylated hemoglobin (HbA1c) level, and TCM syndrome score were assessed before and after treatment, and the safety was observed. ResultsThe vision changed in both groups after treatment (P<0.05), and the observation group showed higher best corrected visual acuity (BCVA) than the control group (P<0.05). The DR progression was slow with similar rates in the two groups. The fundus hemorrhage area and exudation area did not change significantly after treatment in both groups, while the observation group outperformed the control group in reducing the fundus hemorrhage area and exudation area. There was no significant difference in the number of microhemangiomas between the two groups before treatment. After treatment, the number of microhemangiomas decreased in both the observation group (Z=-1.437, P<0.05) and the control group (Z=-2.238, P<0.05), and it showed no significant difference between the two groups. As the treatment time prolonged, the number of microhemangiomas gradually decreased in both groups. There was no significant difference in the HbA1c level between the two groups before treatment. After treatment, the decline in the HbA1c level showed no significant difference between the two groups. The TCM syndrome score did not have a statistically significant difference between the two groups before treatment. After treatment, neither the TCM syndrome score nor the response rate had significant difference between the two groups. With the extension of the treatment time, both groups showed amelioration of TCM syndrome compared with the baseline. ConclusionTangning Tongluo tablets are safe and effective in the treatment of nonproliferative DR, being capable of improving vision and reducing hemorrhage and exudation in the fundus.
2.Effect and mechanism of compatibility of Astragali Radix-Puerariae Lobatae Radix on ferroptosis in T2DM insulin resistance rats
Shuang WEI ; Feng HAO ; Wenchun ZHANG ; Zhangyang ZHAO ; Ji LI ; Dongwei HAN ; Huan XING
China Pharmacy 2025;36(1):57-63
OBJECTIVE To explore the effect and potential mechanism of the compatibility of Astragali Radix-Puerariae Lobatae Radix on ferroptosis of liver cells in type 2 diabetes mellitus (T2DM) insulin resistance (IR) rats. METHODS Sixty male SD rats were randomly divided into control group (12 rats) and modeling group (48 rats). The modeling group was fed with a high- fat diet for 4 consecutive weeks and then given a one-time tail vein injection of 1% streptozotocin to establish T2DM IR model. The model rats were randomly divided into model group, the compatibility of Astragali Radix-Puerariae Lobatae Radix group [QG group, 4.05 g/(kg·d), intragastric administration], ferroptosis inhibitor ferrostatin-1 group [Fer-1 group, 5 mg/kg by intraperitoneal injection, once every other day], the compatibility of Astragali Radix-Puerariae Lobatae Radix+ferroptosis inducer erastin group [QG+erastin group, 4.05 g/(kg·d) by intragastric administration+erastin 10 mg/(kg·d), intraperitoneal injection]. After 4 weeks of intervention, serum fasting blood glucose (FBG) and fasting insulin (FINS) were measured in each group of rats, and homeostasis model assessment of insulin resistance (HOMA-IR) and the natural logarithm of insulin action index(IAI) were calculated; the serum levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), aspartate transaminase (AST) and alanine transaminase (ALT), Fe2+ and Fe content, glutathione (GSH), malondialdehyde (MDA) and superoxide dismutase (SOD) levels, NADP+/NADPH ratio and reactive oxygen species (ROS) were determined. The pathological morphology of its liver tissue was observed; the protein expressions of glutathione peroxidase 4 (GPX4), ferritin heavy chain 1 (FTH1), long-chain acyl-CoA synthetase 3 (ACSL3), ACSL4, ferritin mitochondrial (FTMT), and cystine/glutamate anti-porter (xCT) in the liver tissue of rats were detected. RESULTS Compared with control group, the liver cells in the model group of rats showed disordered arrangement, swelling, deepened nuclear staining, and more infiltration of inflammatory cells, as well as a large number of hepatocyte vacuoles and steatosis; FBG (after medication), the levels of TC, TG, LDL-C, AST, ALT, FINS, MDA and ROS, HOMA-IR, Fe2+ and Fe content, NADP+/NADPH ratio and protein expression of ACSL4 were significantly increased or up-regulated, while the levels of HDL-C, GSH and SOD, IAI, protein expressions of GPX4, FTH1, ACSL3, FTMT and xCT were significantly reduced or down-regulated (P<0.01). Compared with the model group, both QG group and Fer-1 group showed varying degrees of improvement in pathological damage of liver tissue and the levels of the above indicators, the differences in the changes of most indicators were statistically significant (P<0.01 or P<0.05). Compared with QG group, the improvement of the above indexes of QG+erastin group had been reversed significantly (P<0.01). CONCLUSIONS The compatibility decoction of Astragali Radix-Puerariae Lobatae Radix can reduce the level of FBG in T2DM IR rats, and alleviate IR degree, ion overload and pathological damage of liver tissue. The above effects are related to the inhibition of ferroptosis.
3.In situ Analytical Techniques for Membrane Protein Interactions
Zi-Yuan KANG ; Tong YU ; Chao LI ; Xue-Hua ZHANG ; Jun-Hui GUO ; Qi-Chang LI ; Jing-Xing GUO ; Hao XIE
Progress in Biochemistry and Biophysics 2025;52(5):1206-1218
Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.
4.Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas
Yaolin SONG ; Guangqi LI ; Zhenqi ZHANG ; Yinbo LIU ; Huiqing JIA ; Chao ZHANG ; Jigang WANG ; Yanjiao HU ; Fengyun HAO ; Xianglan LIU ; Yunxia XIE ; Ding MA ; Ganghua LI ; Zaixian TAI ; Xiaoming XING
Cancer Research and Treatment 2025;57(1):250-266
Purpose:
The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Materials and Methods:
Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients.
Results:
A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804.
Conclusion
USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.
5.Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas
Yaolin SONG ; Guangqi LI ; Zhenqi ZHANG ; Yinbo LIU ; Huiqing JIA ; Chao ZHANG ; Jigang WANG ; Yanjiao HU ; Fengyun HAO ; Xianglan LIU ; Yunxia XIE ; Ding MA ; Ganghua LI ; Zaixian TAI ; Xiaoming XING
Cancer Research and Treatment 2025;57(1):250-266
Purpose:
The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Materials and Methods:
Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients.
Results:
A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804.
Conclusion
USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.
6.Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas
Yaolin SONG ; Guangqi LI ; Zhenqi ZHANG ; Yinbo LIU ; Huiqing JIA ; Chao ZHANG ; Jigang WANG ; Yanjiao HU ; Fengyun HAO ; Xianglan LIU ; Yunxia XIE ; Ding MA ; Ganghua LI ; Zaixian TAI ; Xiaoming XING
Cancer Research and Treatment 2025;57(1):250-266
Purpose:
The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Materials and Methods:
Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients.
Results:
A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804.
Conclusion
USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.
8. Down-regulation of METTL5 inhibits proliferation, migration and invasion of triple-negative breast cancer cells through Wnt/6-catenin signaling pathway
Kun-Lin WU ; Hui-Hao ZHANG ; Kun-Lin WU ; Xiu-Ying LIAO ; Hui-Hao ZHANG ; Qian-Yi YAN ; De-Xing WANG
Chinese Pharmacological Bulletin 2024;40(2):285-291
Aim To investigate the role and potential mechanism of methyltransferase-like 5 (METTL5) in triple-negative breast cancer (TNBC) . Methods The expression of METTL5 in TNBC tumor tissues and cell lines was detected by immunohistochemistry and Western blot. After shRNA targeting METTL5 (shRNAMETTL5) was transfected into TNBC cells, cell proliferation, migration and invasion were detected by CCK-8, colony formation, wound healing and Transwell assays, respectively. Western blot was used to detect the expression of Wnt/p-catenin signaling-related key proteins. A xenograft tumor model was constructed to verify the effect of METTL5 knockdown on the growth of TNBC cells and Wnt/p-catenin signaling activity in vivo. Results The expression of METTL5 was up-regulated in TNBC tumor tissues and cell lines (P < 0. 01) . Knockdown of METTL5 significantly inhibited the proliferation, migration and invasion of TNBC cells and reduced the expression of Wnt/p-catenin signaling molecules (3-catenin, cyclin Dl, matrix metalloproteinase (MMP) -2 and MMP-7 (all P < 0. 01) . Knockdown of METTL5 reduced tumor growth and Wnt/pcatenin signaling activity in vivo. Conclusions Knockdown of METTL5 can inhibit the proliferation, migration and invasion of TNBC cells, which may be related to the inhibition of Wnt/p-catenin signaling pathway.
9. MW-9, a chalcones derivative bearing heterocyclic moieties, ameliorates ulcerative colitis via regulating MAPK signaling pathway
Zhao WU ; Nan-Ting ZOU ; Chun-Fei ZHANG ; Hao-Hong ZHANG ; Qing-Yan MO ; Ze-Wei MAO ; Chun-Ping WAN ; Ming-Qian JU ; Chun-Ping WAN ; Xing-Cai XU
Chinese Pharmacological Bulletin 2024;40(3):514-520
Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.
10.A unicenter real-world study of the correlation factors for complete clinical response in idiopathic inflammatory myopathies
Zhanhong LAI ; Jiachen LI ; Zelin YUN ; Yonggang ZHANG ; Hao ZHANG ; Xiaoyan XING ; Miao SHAO ; Yue-Bo JIN ; Naidi WANG ; Yimin LI ; Yuhui LI ; Zhanguo LI
Journal of Peking University(Health Sciences) 2024;56(2):284-292
Objective:To investigate the correlation factors of complete clinical response in idiopathic inflammatory myopathies(IIMs)patients receiving conventional treatment.Methods:Patients diagnosed with IIMs hospitalized in Peking University People's Hospital from January 2000 to June 2023 were in-cluded.The correlation factors of complete clinical response to conventional treatment were identified by analyzing the clinical characteristics,laboratory features,peripheral blood lymphocytes,immunological indicators,and therapeutic drugs.Results:Among the 635 patients included,518 patients finished the follow-up,with an average time of 36.8 months.The total complete clinical response rate of IIMs was 50.0%(259/518).The complete clinical response rate of dermatomyositis(DM),anti-synthetase syn-drome(ASS)and immune-mediated necrotizing myopathy(IMNM)were 53.5%,48.9%and 39.0%,respectively.Fever(P=0.002)and rapid progressive interstitial lung disease(RP-ILD)(P=0.014)were observed much more frequently in non-complete clinical response group than in complete clinical re-sponse group.The aspartate transaminase(AST),lactate dehydrogenase(LDH),D-dimer,erythrocyte sedimentation rate(ESR),C-reaction protein(CRP)and serum ferritin were significantly higher in non-complete clinical response group as compared with complete clinical response group.As for the treat-ment,the percentage of glucocorticoid received and intravenous immunoglobin(IVIG)were significantly higher in non-complete clinical response group than in complete clinical response group.Risk factor analysis showed that IMNM subtype(P=0.007),interstitial lung disease(ILD)(P=0.001),eleva-ted AST(P=0.012),elevated serum ferritin(P=0.016)and decreased count of CD4+T cells in peripheral blood(P=0.004)might be the risk factors for IIMs non-complete clinical response.Conclu-sion:The total complete clinical response rate of IIMs is low,especially for IMNM subtype.More effec-tive intervention should be administered to patients with ILD,elevated AST,elevated serum ferritin or decreased count of CD4+T cells at disease onset.

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