ObjectiveTo elucidate the efficacy connotation of Shudi Qiangjin pills （SQP） against liver and kidney deficiency in steroid-induced osteonecrosis of femoral head （SONFH） from the perspective of the "disease-syndrome-formula" association and to clarify the underlying mechanisms based on in vivo and in vitro experiment validation. MethodsThe chemical components and the corresponding putative targets of SQP were collected from the Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine （TCMIP） v2.0， the Encyclopedia of Traditional Chinese Medicine （ETCM） v2.0， and HERB databases. The SONFH-related genes were identified based on the differential expression profiles of peripheral blood of patients with SONFH compared to the healthy volunteers， and the disease phenotype-related targets were collected from the TCMIP v2.0 database. Then， the interaction network of "SONFH-related genes and candidate targets of SQP" was constructed based on "gene-gene interaction information"， and the major network targets were screened by calculating the topological characteristic values of the network followed by the functional mining according to the Kyoto Encyclopedia of Genes and Genomes （KEGG） database and the SoFDA database. After that， the SONFH rat model was prepared by lipopolysaccharide combined with methylprednisolone injection， and 2.5， 5， 7.5 g·kg^-1 SQP （once per day， equivalent to 1， 2， and 3 times the clinical equivalent dose， respectively） or 7.3×10^-3 g·kg^-1 of alendronate sodium （ALS， once per week， equivalent to the clinical equivalent dose） was given for 8 weeks. The effect characteristics of SQP and ALS in the treatment of SONFH were evaluated by micro-computed tomography scanning， hematoxylin and eosin staining， alkaline phosphatase （ALP） staining， immunohistochemical staining， enzyme-linked immunosorbent assay， and terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling（TUNEL）staining， and a comparative efficacy analysis was conducted with ALS. In addition， SONFH cell models were prepared by dexamethasone stimulation of osteoblasts， and the intervention was carried out with the medicated serum of SQP at the aforementioned three doses. Cell counting kit-8， ALP staining， ALP activity assay， alizarin red staining， and flow cytometry were employed to investigate the regulatory effect of SQP on osteoblasts. The expression levels of osteogenesis-related proteins and key factors of the target signaling axis were detected by quantitative real-time polymerase chain reaction and Western blot. ResultsThe network analysis results demonstrated that the candidate targets of SQP primarily exerted their therapeutic effects through key signaling pathways， including phosphoinositide 3-kinase（PI3K）/protein kinase B（Akt）， lipid metabolism and atherosclerosis， prolactin， chemokines， and neurotrophic factors pathways. These pathways were significantly involved in critical biological processes such as muscle and bone metabolism and the regulation of the "neuro-endocrine-immune" network， thereby addressing both modern medical symptoms （e.g.， delayed skeletal maturation and recurrent fractures） and traditional Chinese medicine （TCM） symptoms （e.g.， fatigue， aversion to cold， cold limbs， and pain in the limbs and joints in patients with SONFH characterized by liver and kidney deficiency syndrome. Among these pathways， the PI3K/Akt signaling pathway exhibited the highest degree of enrichment. The in vivo experimental results demonstrated that starting from the 4^th week after modeling， the modeling group exhibited a significant reduction in body weight compared to the control group （P<0.05）. After six weeks of treatment， all dosage groups of SQP showed significantly higher body weights compared to the model group （P<0.01）. Compared with the normal group， the model group exhibited significant decreases in bone mineral density （BMD）， bone volume fraction （BV/TV）， trabecular number （Tb.N）， osteocalcin （OCN）， alkaline phosphatase （ALP） levels in femoral head tissue， and serum bone-specific alkaline phosphatase （BALP） （P<0.01）， along with significant increases in trabecular separation （Tb.Sp）， empty lacunae rate in tissue， and apoptosis rate （P<0.01）. In comparison to the model group， the SQP intervention groups showed significant improvements in BMD， BV/TV and Tb.N （P<0.01）， significant reductions in Tb.Sp， empty lacunae rate and apoptosis rate （P<0.05）， and significant increases in protein levels of OCN and ALP as well as BALP content （P<0.05）. The in vitro experimental results revealed that all dosage groups of SQP medicated serum showed no toxic effects on osteoblast. Compared with the normal group， the model group displayed significant suppression of osteoblast proliferation activity， ALP activity， and calcified nodule formation rate （P<0.01）， significant decreases in mRNA transcription levels of OCN and Runt-related transcription factor 2 （RUNX2） （P<0.01）， significant reductions in protein content of osteopontin （OPN）， typeⅠ collagen （ColⅠ）A1， B-cell lymphoma-2 （Bcl-2）， PI3K， and phosphorylated （p）-Akt （P<0.01）， and a significant increase in apoptosis rate （P<0.01）. Compared with the model group， the SQP medicated serum intervention groups exhibited significant increases in proliferation activity， ALP activity， calcified nodule formation rate， mRNA transcription levels of OCN and RUNX2， and protein content of OPN， ColⅠA1， Bcl-2， PI3K， and p-Akt （P<0.05）， along with a significant decrease in apoptosis rate （P<0.01）. ConclusionSQP can effectively reduce the disease severity of SONFH with liver and kidney deficiency syndrome and improve bone microstructure， with the therapeutic effects exhibiting a dose-dependent manner. The mechanism may be related to its regulation of key processes such as muscle and bone metabolism and the correction of imbalances in the "neuro-endocrine-immune" network， thereby promoting osteoblast differentiation and inhibiting osteoblast apoptosis. The PI3K/Akt signaling axis is likely one of the key pathways through which this formula exerts its effects.

Traditional development of small protein scaffolds has relied on display technologies and mutation-based engineering, which limit sequence and functional diversity, thereby constraining their therapeutic and application potential. Protein design tools have significantly advanced the creation of novel protein sequences, structures, and functions. However, further improvements in design strategies are still needed to more efficiently optimize the functional performance of protein-based drugs and enhance their druggability. Here, we extended an evolution-based design protocol to create a novel minibinder, BindHer, against the human epidermal growth factor receptor 2 (HER2). It not only exhibits super stability and binding selectivity but also demonstrates remarkable properties in tissue specificity. Radiolabeling experiments with ^99mTc, ⁶⁸Ga, and ¹⁸F revealed that BindHer efficiently targets tumors in HER2-positive breast cancer mouse models, with minimal nonspecific liver absorption, outperforming scaffolds designed through traditional engineering. These findings highlight a new rational approach to automated protein design, offering significant potential for large-scale applications in therapeutic mini-protein development.

ObjectiveTo investigate the differences in clinical efficacy of different medication regimens of Wangbi Tablets （尪痹片） for knee osteoarthritis （KOA） in a real-world setting， providing a basis for rational clinical use of Wangbi Tablets. MethodsA prospective registry study was conducted， involving 2，999 KOA patients registered in 30 hospitals nationwide from January 26th， 2019， to December 17th， 2021. Based on the use of Wangbi Tablets during the observation period， patients were divided into a monotherapy group （1，507 cases） and a combination therapy group （1，492 cases）， and the combination group can be further divided into Wangbi Tablets plus Chinese medicine （CM）， Wangbi Tablets plus western medicine （WM）， and Wangbi Tablets plus Chinese and western medicine （CM+WM） subgroups. The baseline data of patients in the monotherapy group and the combination group were compared， including age， gender， body weight， medication time， clinical stage， K-L grade， and others. Efficacy indicators included the Visual Analog Scale （VAS） score， Western Ontario and McMaster Universities Osteoarthritis Index （WOMAC） score， and EuroQol five-dimensional （EQ-5D） health index， which were evaluated before and after 4-， 8- and 12-week treatment， and the difference before and after treatment was calculated after 4， 8 and 12 weeks of treatment. The difference between the baseline and 12 weeks of treatment of all the above indicators was used as the dependent variables， and gender， age， body mass index （BMI）， course of disease， K-L grade， and clinical stage were used as independent variables， when multiple linear regression was taken to explore the influencing factors of the efficacy. At the same time， the occurrence of major symptoms （including morning stiffness， joint swelling， soreness of waist and knees， fear of wind， and fear of cold） was counted， and the disappearance of symptoms at each time point was counted after 4， 8， and 12 weeks of treatment. ResultsAt baseline， there were no statistically significant differences in gender and age distribution between the monotherapy and combination therapy groups （P＞0.05）； the proportion of patients in the acute stage and recovery stage was higher in the monotherapy group than in the combination therapy group， while the proportion in the remission stage was lower （P＜0.05）； the VAS score was higher in the monotherapy group， and the EQ-5D index was lower （P＜0.01）， with no statistically significant difference in total WOMAC score between the two groups （P＞0.05）. Compared to those measured before treatment and at previous timepoint， the VAS score and WOMAC total score significantly decreased in both groups， while EQ-5D value increased （P＜0.05）. The difference in VAS score between baseline and after 12-week treatment was higher in the monotherapy group than the combination group， while the differences in WOMAC total score and EQ-5D value between baseline and after 4-， 8- and 12-week treatment were higher in the combination group （P＜0.05）. Multiple linear regression showed that VAS score before treatment had greatest impact on pain improvement （P＜0.01）， and compared to Wangbi Tablets monotherapy， the combination of Wangbi tablets with WM or CM had larger associations with pain improvement （P＜0.05）； and Wangbi Tablets had better efficacy when the course of treatment was ＞28 days （P＜0.01）. Wangbi Tablets plus WM had a better effect on improving the overall function of the knee joint than Wangbi Tablets alone （P＜0.01）； and the efficacy of Wangbi Tablets with a course of treatment ＞28 days was better （P＜0.05）. The improvement of quality of life of patients in the attack and remission stages was more obvious than that in the recovery stage （P＜0.01）； Wangbi Tablets plus WM or CM had a better effect on improving quality of life than Wangbi Tablets alone （P＜0.05）. Before treatment， the proportion of patients with morning stiffness， soreness of waist and knees， fear of wind and chills in the monotherapy group was higher than that in the combination group （P＜0.01）. The proportion of main symptoms in both groups decreased after 4， 8 and 12 weeks of treatment （P＜0.05）. After 4 weeks of treatment， the disappearance rate of each main symptom in the combination group was higher than that in the monotherapy group， and after 12 weeks of treatment， the disappearance rate of fear of wind in the monotherapy group was higher than that in the combination group， while the disappearance rate of joint swelling and soreness of waist and knees was lower （P＜0.05）. ConclusionWangbi Tablets， whether used alone or in combination with other medications， is effective throughout the course of KOA， with greater benefits in improving joint function and quality of life during the acute and remission stages compared to the recovery stage. Combination therapy had a faster onset of effect， but began to converge with monotherapy after 8 weeks. The best efficacy was observed with the combination of Wangbi Tablets with WM， followed by combination with CM.

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.

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.