Osteoporosis is a common bone disease， whose incidence is still on the rise， posing great challenges to patients and society. This review mainly studies the pathogenesis of osteoporosis from the aspects of oxidative stress， inflammatory response， and glucolipotoxicity-induced injury and clarifies the efficacy and mechanism of some active ingredients of traditional Chinese medicine against osteoporosis through the integration of in vitro and in vivo experiments. The experimental results suggest that some active ingredients can improve bone resorption markers and maintain bone homeostasis by modulating inflammation， oxidative stress， etc. These active ingredients regulate osteoporosis through the receptor activator of nuclear transcription factor-κB （NF-κB） ligand （RANKL） pathway， osteoprotegerin （OPG） pathway， Wnt/β-catenin pathway， NF-κB pathway， mitogen-activated protein kinase （MAPK） pathway， adenosine monophosphate （AMP）-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） pathway， and oxidative stress pathway. This review provides ideas for the progress of the prevention and treatment of osteoporosis with the active ingredients of traditional Chinese medicine， aiming to provide new potential lead compounds and reference for the development of innovative drugs and clinical therapy for the treatment of osteoporosis.

OBJECTIVE To explore the effect and mechanism of Zhiqiao gancao decoction （ZQGCD） on pathological angiogenesis of degenerative intervertebral disc. METHODS The rats were randomly divided into sham operation group （normal saline）， model group （normal saline）， hypoxia inducible factor-1α （HIF-1α） inhibitor （YC-1） group [2 mg/（kg·d）， tail vein injection]， and ZQGCD low-dose， medium-dose and high-dose groups [3.06， 6.12， 12.24 g/（kg·d）]， with 8 rats in each group. Except for sham operation group， lumbar disc degeneration model of rat was constructed in all other groups. After modeling， they were given relevant medicine once a day， for consecutive 3 weeks. After the last medication， pathological changes and angiogenesis of the intervertebral disc tissue in rats were observed； the levels of inflammatory factors [interleukin-1β （IL-1β）， IL-6， tumor necrosis factor-α （TNF-α）] and the expressions of angiogenesis-related proteins [HIF-1α， vascular endothelial growth factor （VEGF）， VEGF receptor 2 （VEGFR2）， angiotensin 1（Ang 1）， Ang 2] in the com intervertebral disc tissue in rats were all determined. In cell experiment， the primary nucleus pulposus cells were isolated and cultured from rats， and cellular degeneration was induced using 50 ng/mL TNF-α. The cells were divided into blank control group （10% blank control serum）， TNF-α group （10% blank control serum）， YC-1 group （10% blank control serum+0.2 mmol/L YC-1）， and 5%， 10%， 15% drug-containing serum group （5%， 10%， 15% drug-containing serum）. After 24 hours of intervention， the nucleus pulposus cells were co-cultured with HUVEC. The expressions of Collagen Ⅱ， matrix metalloproteinase-3 （MMP-3） in nucleus pulposus cells were detected. HUVEC proliferation， migration and tube forming ability were detected， and the expression levels of the HIF-1α/VEGF/Ang signal axis and angiogenesis- related proteins （add MMP-2， MMP-9） in HUVEC were detected. RESULTS Animal experiments had shown that compared with model group， the positive expression of CD31 in the intervertebral disc tissues of rats in each drug group was down-regulated （P＜ 0.05）， the levels of inflammatory factors and angiogenesis-related proteins were decreased significantly （P＜0.05）， and the pathological changes in the intervertebral disc were alleviated. Cell experiments had shown that compared with TNF-α group， the expression of Collagen Ⅱ in nucleus pulposus cells of all drug groups was significantly up-regulated （P＜0.05）， and the expression of MMP-3 was significantly down-regulated （P＜0.05）； the proliferation， migration and tubulogenesis of HUVEC were significantly weakened （P＜0.05）. The mRNA and protein expressions of HIF-1α， VEGF， Ang 2 as well as the expression of angiogenesis-related proteins （except for the expression of Ang 2 mRNA and HIF-1α， VEGFR2， Ang 2 protein in 5% drug- containing serum group） were significantly down-regulated （P＜0.05）. CONCLUSIONS ZQGCD may inhibit the HIF-1α/VEGF/ Ang signal axis to weaken the angiogenic ability of vascular endothelial cells， improve pathological angiogenesis in the intervertebral disc， and delay the degeneration of the intervertebral disc.

OBJECTIVES: To explore the molecular mechanism of Jiangzhi Huaban Decoction (JZHBD) for improving atherosclerosis through the "qi meridian-blood channels" pathway. METHODS: ApoE^-/- mouse models of atherosclerosis were established by high-fat diet feeding for 8 weeks, with C57BL/6 mice on a normal diet as the controls. Forty ApoE^-/- mouse models were randomized into model group, low-, medium-, and high-dose JZHBD treatment groups, and atorvastatin treatment group (n=8) for their respective treatments for 8 weeks. The changes in body weight and overall condition of the mice were monitored weekly. After the treatments, serum levels of TC, TG, HDL-C, LDL-C, TBA, ALT, and AST of the mice were measured, pathological changes in the liver and aortic root plaques were examined with HE staining, and lipid accumulation in the liver and aortic wall was assessed using Oil Red O staining. The core molecular mechanism was studied through transcriptomics, and the expressions of the key pathway proteins were confirmed using Western blotting and immunohistochemistry. RESULTS: Treatment with JZHBD significantly reduced blood lipid and total bile acid levels, improved liver function and hepatic steatosis, and decreased aortic lipid deposition and plaque area in the mouse models of atherosclerosis. Transcriptomic analysis suggested that the therapeutic mechanism of JZHBD involved reverse cholesterol transport, PPAR signaling, and the inflammatory pathways. In atherosclerotic mice, JZHBD treatment obviously up-regulated hepatic expressions of PPARγ, LXRα, ABCA1, ABCG1, and CYP7A1, down-regulated hepatic expressions of p-p65/p65, IL-6, IL1β in the liver, increased ABCG5 and ABCG8 expressions in the intestines, and decreased ICAM-1 and VCAM-1 expressions in the aortic plaques. CONCLUSIONS JZHBD improves atherosclerotic vascular damage and plaque formation possibly by regulating hepatic reverse cholesterol transport and inflammation via modulating the hepatic PPARγ/LXRα/NF-κB signaling pathway.
Animals ; Drugs, Chinese Herbal/therapeutic use* ; Mice, Inbred C57BL ; Plaque, Atherosclerotic/metabolism* ; Liver/metabolism* ; Mice ; Atherosclerosis/metabolism* ; Cholesterol/metabolism* ; PPAR gamma/metabolism* ; Male ; Diet, High-Fat ; Biological Transport

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

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.

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.