ObjectiveThis paper aims to investigate the mechanism of Jinyang Dingtong plaster in improving the peripheral pain sensitization and synovial fibrosis in rats with knee osteoarthritis （KOA） by blocking the ion channels of transient receptor potentials （TRPs）. MethodsThe active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by using ultra-high performance liquid chromatography-electrospray ionization-quadrupole ion trap tandem mass spectrometry （UPLC-MS/MS） technology. A KOA rat model was established through intra-articular injection of monoiodoacetic acid. The rats were randomly divided into blank control group， KOA group， compound Nanxing Zhitong plaster Group， and Jinyang Dingtong plaster group， with eight rats per group. Among them， the rats in the compound Nanxing Zhitong plaster group and the Jinyang Dingtong plaster group were intervened with external application treatment. After the intervention period， the cold and mechanical stimulus pain thresholds of rats in each group were detected， and the transverse diameter of the knee joint was measured. The levels of inflammatory factors in the serum such as interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， nerve growth factor （NGF）， and calcitonin gene-related peptide （CGRP） were determined by enzyme-linked immunosorbent assay （ELISA）. Protein expression levels of transient receptor potential ankyrin 1 （TRPA1）， transient receptor potential melastatin 8 （TRPM8）， transient receptor potential vanilloid 1 （TRPV1）， transient receptor potential vanilloid 4 （TRPV4）， transforming growth factor-β （TGF-β）， and vascular endothelial growth factor （VEGF） in synovial tissue were detected by Western blot. Histopathological changes in synovial tissue were observed by using hematoxylin and eosin （HE）， Masson， and Sirius red staining， while the expression of type Ⅰ collagen and alpha-smooth muscle actin （α-SMA） was detected by multiplex immunofluorescence. ResultsA total of 35 active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by UPLC-MS/MS， including phenolic acids， flavonoids， quinones， alkaloids， terpenes， lignans， and coumarins. Among them， the constituents such as berberine， paeoniflorin， ferulic acid， and caffeic acid exhibit clear anti-inflammatory， analgesic， and anti-fibrotic pharmacological effects. Compared to the blank control group， rats in the KOA group showed a significant decrease in cold and mechanical stimuli pain thresholds （P<0.01）. After 14 and 28 days of Jinyang Dingtong plaster intervention， the pain threshold in this group was significantly increased compared to that in KOA group （P<0.01）， showing no significant difference from that in compound Nanxing Analgesic plaster group. Additionally， Jinyang Dingtong plaster reduced the levels of IL-1β， TNF-α， NGF， and CGRP in the serum of KOA rats （P<0.01）， lowered the expression of TRPA1， TRPM8， TRPV1， TRPV4， TGF-β， and VEGF proteins in synovial tissue （P<0.01）， improved synovial pathological damage in KOA rats， and significantly decreased fluorescence intensity of type Ⅰ collagen and α-SMA （P<0.01）. ConclusionJinyang Dingtong plaster can improve the peripheral pain sensitization and synovial fibrosis in KOA rats by downregulating the expression of ion channels of TRPs and related inflammatory and fibrotic factors.

ObjectiveThis paper aims to investigate the mechanism of Jinyang Dingtong plaster in improving the peripheral pain sensitization and synovial fibrosis in rats with knee osteoarthritis （KOA） by blocking the ion channels of transient receptor potentials （TRPs）. MethodsThe active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by using ultra-high performance liquid chromatography-electrospray ionization-quadrupole ion trap tandem mass spectrometry （UPLC-MS/MS） technology. A KOA rat model was established through intra-articular injection of monoiodoacetic acid. The rats were randomly divided into blank control group， KOA group， compound Nanxing Zhitong plaster Group， and Jinyang Dingtong plaster group， with eight rats per group. Among them， the rats in the compound Nanxing Zhitong plaster group and the Jinyang Dingtong plaster group were intervened with external application treatment. After the intervention period， the cold and mechanical stimulus pain thresholds of rats in each group were detected， and the transverse diameter of the knee joint was measured. The levels of inflammatory factors in the serum such as interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， nerve growth factor （NGF）， and calcitonin gene-related peptide （CGRP） were determined by enzyme-linked immunosorbent assay （ELISA）. Protein expression levels of transient receptor potential ankyrin 1 （TRPA1）， transient receptor potential melastatin 8 （TRPM8）， transient receptor potential vanilloid 1 （TRPV1）， transient receptor potential vanilloid 4 （TRPV4）， transforming growth factor-β （TGF-β）， and vascular endothelial growth factor （VEGF） in synovial tissue were detected by Western blot. Histopathological changes in synovial tissue were observed by using hematoxylin and eosin （HE）， Masson， and Sirius red staining， while the expression of type Ⅰ collagen and alpha-smooth muscle actin （α-SMA） was detected by multiplex immunofluorescence. ResultsA total of 35 active components in the transdermal absorption solution of Jinyang Dingtong plaster were identified by UPLC-MS/MS， including phenolic acids， flavonoids， quinones， alkaloids， terpenes， lignans， and coumarins. Among them， the constituents such as berberine， paeoniflorin， ferulic acid， and caffeic acid exhibit clear anti-inflammatory， analgesic， and anti-fibrotic pharmacological effects. Compared to the blank control group， rats in the KOA group showed a significant decrease in cold and mechanical stimuli pain thresholds （P<0.01）. After 14 and 28 days of Jinyang Dingtong plaster intervention， the pain threshold in this group was significantly increased compared to that in KOA group （P<0.01）， showing no significant difference from that in compound Nanxing Analgesic plaster group. Additionally， Jinyang Dingtong plaster reduced the levels of IL-1β， TNF-α， NGF， and CGRP in the serum of KOA rats （P<0.01）， lowered the expression of TRPA1， TRPM8， TRPV1， TRPV4， TGF-β， and VEGF proteins in synovial tissue （P<0.01）， improved synovial pathological damage in KOA rats， and significantly decreased fluorescence intensity of type Ⅰ collagen and α-SMA （P<0.01）. ConclusionJinyang Dingtong plaster can improve the peripheral pain sensitization and synovial fibrosis in KOA rats by downregulating the expression of ion channels of TRPs and related inflammatory and fibrotic factors.

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.

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.

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.

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.

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.

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.

Objective: To analyze the unregistered treatment situation and its influencing factors among pulmonary tuberculosis patients in Quzhou City, Zhejiang Province from 2017 to 2023, so as to provide a basis for promoting the management of tuberculosis patients and optimizing disease prevention and control strategies. Methods: Data of pulmonary tuberculosis patients including demographic information, etiological results, and mortality status were collected through the China Disease Prevention and Control Information System Infectious Disease Reporting and Surveillance System and the Tuberculosis Management Information System. Pulmonary tuberculosis patients not matched in the Tuberculosis Management Information System were defined as unregistered treatment patients, and the unregistered treatment rate was analyzed. Factors affecting unregistered treatment among pulmonary tuberculosis patients were analyzed using a multivariable logistic regression model. Results: A total of 10 779 pulmonary tuberculosis patients were reported in Quzhou City from 2017 to 2023, including 7 700 males (71.44%) and 3 079 females (28.56%). There were 5 484 cases aged <65 years, accounting for 50.88%. Among them, 630 cases were unregistered treatment, with an unregistered treatment rate of 5.84% (95%CI: 5.42%-6.38%). Multivariable logistic regression analysis showed pulmonary tuberculosis patients aged ≥65 years (OR=1.829, 95%CI: 1.512-2.212) had a higher risk of being unregistered treatment than those aged <65 years; patients with non-local household registration (OR=5.710, 95%CI: 4.724-6.901) had a higher risk than local patients; and patients engaged in housework/unemployed (OR=2.001, 95%CI: 1.421-2.818) or other occupations (OR=2.396, 95%CI: 1.789-3.137) had a higher risk than farmers. The mortality of unregistered treatment pulmonary tuberculosis patients was higher than the registered treatment patients (26.67% vs. 5.02%),with a significantly elevated mortality risk (OR=7.147, 95%CI: 5.738-8.902). Conclusions The unregistered treatment rate among pulmonary tuberculosis patients was well controlled in Quzhou City from 2017 to 2023, but the elderly, patients with non-local household registration, and those engaged in housework/unemployed had a higher risk of unregistered treatment. It is recommended to improve medical and social security policies, strengthen health education on tuberculosis prevention, enhance treatment adherence, and reduce mortality risk.

BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort