BACKGROUND:Lijin manipulation can promote skeletal muscle repair and treat skeletal muscle injury.However,the formation of fibrosis and scar tissue hyperplasia are closely related to the quality of skeletal muscle repair.To study the regulatory effect of Lijin manipulation on the formation of fibrosis and scar tissue hyperplasia is helpful to explain the related mechanism of Lijin manipulation to improve the repair quality of skeletal muscle injury. OBJECTIVE:To explore the mechanism of Lijin manipulation to improve the repair quality of skeletal muscle injury in rabbits,thereby providing a scientific basis for clinical treatment. METHODS:Forty-five healthy adult Japanese large-ear white rabbits were randomly divided into blank group,model group and Lijin group,with 15 rats in each group.Gastrocnemius strike modeling was performed in both model group and Lijin group.The Lijin group began to intervene with tendon manipulation on the 3rd day after modeling,once a day,and 15 minutes at a time.Five animals in each group were killed on the 7th,14th and 21st days after modeling.The morphology and inflammatory cell count of gastrocnemius were observed by hematoxylin-eosin staining,the collagen fiber amount was observed by Masson staining,the expression of interleukin-6 and interleukin-10 in gastrocnemius was detected by ELISA.The protein and mRNA expressions of paired cassette gene 7,myogenic differentiation factor,myoblastogenin,alpha-actin,transforming growth factor beta 1,and type Ⅰ collagen were detected by western blot and RT-PCR,respectively,and the expression of type Ⅰ collagen protein was detected by immunohistochemistry. RESULTS AND CONCLUSION:Hematoxylin-eosin staining and Masson staining showed that compared with the model group,inflammatory cell infiltration and collagen fiber content decreased in the Lijin group(P<0.01),and the muscle fibers gradually healed.ELISA results showed that compared with the model group,the expression of interleukin-6 in the Lijin group continued to decrease(P<0.05),and the expression of interleukin-10 increased on the 7th day after modeling(P<0.05)and then showed a decreasing trend(P<0.05).Western blot and RT-PCR results showed that compared with the model group,the protein and mRNA expressions of paired cassette gene 7,myogenic differentiation factor,myoblastogenin in the Lijin group were significantly increased on the 14th day after modeling(P<0.05),but decreased on the 21st day(P<0.05);the protein and mRNA expressions of alpha-actin,transforming growth factor beta 1,and type Ⅰ collagen in the Lijin group were significantly decreased compared with those in the model group(P<0.05).Immunohistochemical results showed that the expression of type Ⅰ collagen in the Lijin group was significantly lower than that in the model group(P<0.05).To conclude,Lijin manipulation could improve the repair quality of skeletal muscle injury by inhibiting inflammation,promoting the proliferation and differentiation of muscle satellite cells,and reducing fibrosis.

Fuxiong has a long history of cultivation. Since its first record in the Beneficial Formulas from the Taiping Imperial Pharmacy of the Song Dynasty, Fuxiong had always been used by ancient physicians and became a preponderant variety for some reasons during the periods of the Ming Dynasty, Qing Dynasty, and Republic of China. However, as for modern use, only Chuanxiong Rhizoma is valued, and the medicinal value of Fuxiong is gradually being overlooked. This article systematically researches the nomenclature, producing area, origin, and efficacy of Fuxiong, proving that the planting technology of Fuxiong matured in the Song Dynasty at the latest, slightly later than the emergence of Chuanxiong Rhizoma in the Sui and Tang Dynasties. Over the years, the producing area of Fuxiong has not undergone significant changes, and it is mainly cultivated within Jiangxi province. According to the analysis of the origin of Xiongqiong, combined with modern genetic research, it can be basically clarified that the early source of Xiongqiong may not be single. With the popularization of cultivation, Chuanxiong Rhizoma became a Dao-di herb earliest, gradually replacing Xiongqiong and being recognized clinically. After cultivation, the polyploidy of Chuanxiong Rhizoma varieties formed stable inheritance, forming the later Fuxiong. Medical experts have gradually deepened their understanding of the efficacy of Fuxiong. Initially, they believed that it was a substitute for Chuanxiong Rhizoma and had weaker efficacy than Chuanxiong Rhizoma. Medical experts in Jin and Yuan Dynasties such as Zhu Danxi and Dai Sigong believed that Fuxiong was good at relieving stagnation. Books and records of materia medica in the Ming and Qing Dynasties explicitly proposed the great ability of Fuxiong to relieve stagnation. Fuxiong should be distinguished from Chuanxiong Rhizoma when applied, and the application differences should be clearly reflected in medical records. Based on the comprehensive research in this article, it can be concluded that although most of ancient physicians have attached great importance to genuineness of Chuanxiong Rhizoma, Fuxiong, as a dominant variety of traditional application, has a clear historical context and significant efficacy characteristics, worthy of further in-depth study.
Drugs, Chinese Herbal/history* ; China ; Medicine, Chinese Traditional/history* ; History, Ancient ; Humans ; History, Medieval ; Plants, Medicinal/chemistry* ; Rhizome/growth & development*

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

Obesity usually exacerbates the immunosuppressive tumor microenvironment (ITME), hindering CD8⁺ T cell infiltration and function, which further represents a significant barrier to the efficacy of immunotherapy. Herein, a multifunctional liposomal system (CR-Lip) for encapsulating celastrol (CEL) was utilized to remodel obesity-related ITME and improve cancer immunotherapy, wherein Ginsenoside Rg3 (Rg3) was detected interspersed in the phospholipid bilayer and its glycosyl exposed on the surface of the liposome. CR-Lip had a relatively uniform size (116.5 nm), facilitating favorable tumor tissue accumulation through the interaction between Rg3 and glucose transporter 1 overexpressed in obese tumor cells. Upon reaching the tumor region, CR-Lip was found to induce the immunogenic cell death (ICD) of HFD tumor cells. Notably, the level of PHD3 in HFD tumor cells was effectively boosted by CR-Lip to effectively block metabolic reprogramming and increase the availability of major free fatty acids fuel sources. In vivo, experiments studies revealed that the easy-obtained nano platform stimulated enhanced the production of various cytokines in tumor tissues, DC maturation, CD8⁺ T-cell infiltration, and synergistic anticancer therapeutic potency with aPD-1 (tumor inhibition rate = 82.1%) towards obesity-related melanoma. Consequently, this study presented an efficacious approach to tumor immunotherapy in obese mice by encompassing tumor eradication, inducing ICD, and reprogramming metabolism. Furthermore, it offered a unique insight into a valuable attempt at the immunotherapy of obesity-associated related tumors.

OBJECTIVE: To explore the clinical features of patients with necrotizing soft tissue infection (NSTI) and the related factors for sepsis, so as to provide a basis for early intervention and improvement of patients' prognosis. METHODS: A retrospective case series study was conducted to analyze the clinical data of NSTI patients admitted to the department of burns and wound repair surgery of Guangdong Provincial People's Hospital from October 2021 to December 2024. Demographic information, underlying diseases, infection characteristics, laboratory test results and etiological findings at admission, treatment status, occurrence of complications (including sepsis) and prognosis were collected. Univariate and multivariate Logistic regression analyses were used to identify the associated factors for sepsis in NSTI patients. Receiver operator characteristic curves (ROC curves) were plotted to evaluate the predictive value of individual and combined factors for sepsis. RESULTS: A total of 159 NSTI patients were enrolled, mainly middle-aged and elderly males. Most patients had comorbidities, including diabetes mellitus (110 cases, 69.2%) and hypertension (67 cases, 42.1%). The main infection site was the lower extremities (104 cases, 65.4%). Common symptoms included redness (96 cases, 60.4%), swelling (129 cases, 81.1%), local heat (60 cases, 37.7%), pain (100 cases, 62.9%), and skin ulceration or necrosis (9 cases, 5.7%). Imaging findings included soft tissue swelling (66 cases, 57.9%), gas accumulation (41 cases, 36.0%), and abnormal signal/density shadows (50 cases, 43.9%). Staphylococcus aureus was the main pathogenic bacterium [12.0% (31/259)], and drug-resistant Escherichia coli had the highest detection rate among drug-resistant bacteria [35.1% (13/37)]. Regarding debridement and repair, most patients (80 cases, 50.3%) underwent debridement ≥ 72 hours after admission, while only 10.1% (16 cases) received debridement within 6 hours. Most patients underwent multiple debridements, with 2 times of debridements being the most common (68 cases, 42.8%), and the maximum times of debridements reached 6. The largest number of patients received secondary suture (44 cases, 27.7%). In terms of complications, sepsis was the most common (66 cases, 41.51%), followed by acute kidney injury, respiratory failure requiring mechanical ventilation, and multiple organ dysfunction syndrome (MODS), while disseminated intravascular coagulation (DIC) was the least common. During the follow-up period, 9 patients (5.66%) were readmitted within 90 days, and 11 patients died, with a mortality rate of 6.92%. Univariate analysis showed that diabetes, coronary heart disease, gout, body temperature, heart rate, C-reactive protein, platelet count, total bilirubin, albumin, creatinine, out-of-hospital treatment, and out-of-hospital use of antimicrobial agents were significantly associated with sepsis in NSTI patients (all P < 0.05). Multivariate Logistic regression analysis showed that coronary heart disease [odds ratio (OR) = 30.085, 95% confidence interval (95%CI) was 2.105-956.935], C-reactive protein (OR = 1.026, 95%CI was 1.009-1.054), and total bilirubin (OR = 1.436, 95%CI was 1.188-1.948) were independent associated factors for sepsis in NSTI patients (all P < 0.05). ROC curve analysis revealed that the combination of the three predictors yielded the highest AUC for predicting sepsis in NSTI patients compared to any individual predictor [area under the curve (AUC) = 0.799 (95%CI was 0.721-0.878)]. CONCLUSIONS The clinical features of NSTI patients show certain regularity. Coronary heart disease, C-reactive protein, and total bilirubin are independent associated factors for sepsis in NSTI patients.
Humans ; Retrospective Studies ; Male ; Sepsis ; Soft Tissue Infections/microbiology* ; Female ; Middle Aged ; Aged ; Adult ; Prognosis ; Risk Factors ; Necrosis ; Logistic Models ; Fasciitis, Necrotizing

Objective:To investigate the correlation between the expression of GLI1 and im-mune invasion and clinical prognosis in gastric cancer.To study the effect of GLI1 expression on drug resistance in gastric cancer.Methods:The expression difference of GLI1 in gastric cancer and normal tissues was analyzed by using TCGA database,and the effect of clinical features and GLI1 gene ex-pression level on prognosis of patients with gastric cancer was analyzed.The correlation between GLI1 gene expression and tumor immune cell infiltration in gastric cancer tissues was analyzed to explore its influence on drug resistance of chemotherapy drugs and targeted drugs.Clinical samples were collect-ed to analyze the difference of GLI1 expression in gastric cancer and paracancer tissues.Results:The expression of GLI1 in gastric cancer tissues was 1.7 times that in normal tissues,and the overall sur-vival and disease-free survival of patients with high expression are shorter than those with low ex-pression(P＜0.05).The interstitial score,immune score and abundance of immunoinfiltrating cells were higher in the high expression of GLI1 in gastric cancer tissues.High expression of GLI1 reduces drug sensitivity and is positively correlated with the expression of immune checkpoint markers PDCD1(P＜0.05).GLI1 expression was significantly increased in patients with subdifferentiated gastric cancer.Conclusions:GLI1 expression is associated with the prognosis and immune infiltration of patients with gastric cancer,and it may lead to poor prognosis of patients by regulating chemotherapy resis-tance,which may be a potential therapeutic target and molecular marker for gastric cancer.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.