This case report presents a 16-year-old male patient with deficiency of adenosine deaminase 2(DADA2). The patient had a history of Raynaud′s phenomenon with digital ulcers since childhood. As the disease progressed, the patient developed retinal vasculitis, intracranial hemorrhage, skin necrosis, severe malnutrition, refractory hypertension, and gastrointestinal bleeding. Genetic testing revealed compound heterozygous mutations in the ADA2 gene (paternal c.1072G > A pathogenic mutation + maternal c.1065C > A variant of unknown clinical significance). ADA2 enzyme activity was significantly reduced (0.1 U/L). A multidisciplinary treatment team developed an individualized plan to address key issues, including nutritional support, blood pressure control, rehabilitation, pain management, and balancing anticoagulation/bleeding risks. After systematic intervention, the patient′s condition showed partial improvement. This case reveals clinical challenges such as anticoagulation decisions and nutritional support of DADA2. It also emphasizes the importance of early molecular diagnosis, tumor necrosis factor-α inhibitor targeted therapy, and multidisciplinary collaboration in management, underlining the core value of precision medicine in rare disease management.

Objective:To investigate the relationship between intramuscular adipose tissue index (IATI) calculated from computed tomography images at transverse process of the first lumbar and all-cause mortality in maintenance dialysis patients, and to provide a reference for improving the prognosis in these patients.Methods:It was a multicenter retrospective cohort study. The clinical data of patients who received maintenance hemodialysis or peritoneal dialysis treatment from January 1, 2017 to December 31, 2019 in 4 grade Ⅲ hospitals including Zhongda Hospital Affiliated to Southeast University, Taizhou People's Hospital Affiliated to Nanjing Medical University, Affiliated Hospital of Yangzhou University, and the Third Affiliated Hospital of Soochow University were retrospectively collected. IATI was calculated by low attenuation muscle (LAM) density/skeletal muscle density. The receiver-operating characteristic curve was used to determine the optimal cut-off value of IATI, and the patients were divided into high IATI group and low IATI group according to the optimal cut-off value. The differences of baseline clinical data and measurement parameters of the first lumbar level between the two groups were compared. The follow-up ended on December 23, 2022. The endpoint event was defined as all-cause mortality within 3 years. Kaplan-Meier survival curve and log-rank test were used to analyze the survival rates and the differences between the two groups. Multivariate Cox regression analysis models were used to analyze the association between IATI and the risk of all-cause mortality in maintenance dialysis patients. Multivariate logistic regression analysis model was used to analyze the influencing factors of high IATI.Results:A total of 478 patients were eligibly recruited in this study, with age of (53.55±13.19) years old and 319 (66.7%) males, including 365 (76.4%) hemodialysis patients and 113 (23.6%) peritoneal dialysis patients. There were 376 (78.7%) patients in low IATI (<0.42) group and 102 (21.3%) patients in high IATI (≥0.42) group. The proportion of age ≥ 60 years old ( χ2=24.746, P<0.001), proportion of diabetes mellitus ( χ2=5.570, P=0.018), fasting blood glucose ( t=-2.145, P=0.032), LAM density ( t=-3.735, P<0.001), LAM index ( t=-7.072, P<0.001), and LAM area/skeletal muscle area ratio ( Z=-9.630, P<0.001) in high IATI group were all higher than those in low IATI group, while proportion of males ( χ2=11.116, P<0.001), serum albumin ( Z=2.708, P=0.007) and skeletal muscle density ( t=12.380, P<0.001) were lower than those in low IATI group. Kaplan-Meier survival analysis showed that the 3-years overall survival rate of low IATI group was significantly higher than that in high IATI group (Log-rank χ2=19.188, P<0.001). Multivariate Cox regression analysis showed that IATI<0.42 [<0.42/≥0.42, HR(95% CI): 0.50 (0.31-0.83), P=0.007] was an independent protective factor of all-cause mortality, and age ≥60 years old [ HR (95% CI): 2.61 (1.60-4.23), P<0.001], diabetes mellitus [ HR (95% CI): 1.71 (1.06-2.78), P=0.029] and high blood neutrophil/lymphocyte ratio [ HR (95% CI): 1.04 (1.00-1.07), P=0.049] were the independent risk factors of all-cause mortality in maintenance dialysis patients. Stepwise Cox regression analysis showed that IATI<0.42 was still an independent protective factor of all-cause mortality in maintenance dialysis patients [<0.42/≥0.42, HR (95% CI): 0.45 (0.27-0.76), P=0.003]. Multivariate logistic regression analysis showed that low skeletal muscle density [ OR (95% CI): 0.84 (0.81-0.88), P<0.001] and high serum triglyceride [ OR (95% CI): 1.39 (1.07-1.82), P=0.015] were the independent influencing factors of IATI≥0.42. Conclusion:IATI<0.42 of the first lumbar level is an independent protective factor of all-cause mortality in maintenance dialysis patients. Localized myosteatosis within high-quality skeletal muscle may reduce the risk of all-cause mortality in these patients.

The breakage pattern of unit particles during the production of oral solid dosage forms (OSD) is closely related to the quality of intermediate or final products. To accurately characterize the particles and study the evolution law of particle breakage, the Bonding model of the discrete element method (DEM) was used to investigate the breakage patterns of model parameters, particle shape and process conditions (loading mode and loading rate) on the dynamic breakage, force-time curve, breakage rate, maximum breakage size ratio and fracture strength of particles. The results showed that the particle breakage force was positively correlated with normal strength and bonded disk scale, negatively correlated with normal stiffness per unit area and tangential stiffness per unit area, and weakly correlated with tangential strength. The particle breakage rate was negatively correlated with the aspect ratio of the particles, and the maximum breakage size ratio was positively correlated with the aspect ratio of the particles; among the three loading modes, the breakage rate of compression breakage model was the largest, the breakage rate of shear breakage model was the second largest, and the breakage rate of wear breakage model was the smallest; the maximum breakage size ratio was positively correlated with the loading rate, the loading mode and the loading rate had no mutual influence on particle breakage rate, but had mutual influence on the maximum breakage size ratio. The research results will provide a theoretical basis for the shift of OSD from batch manufacturing to advanced manufacturing.

Objective To observe the clinical efficacy of compound Wufengcao liquid combined with negative pressure wound therapy with instillation(NPWTi)for the treatment of stage Ⅲ-Ⅳ pressure injury(PI),and to preliminarily explore its action mechanism.Methods(1)Clinical research:from January 2019 to October 2022,60 PI patients who were admitted to the Scrofula Department and Wound Care Clinic at Nanjing Municipal Hospital of Traditional Chinese and Western Medicine were randomly divided into normal saline NPWTi group and compound Wufengcao liquid NPWTi group,with 30 cases in each group.Both groups underwent NPWTi under the premise of systemic basic treatment,before treatment,after removing the negative pressure device in the 1st,2nd and 3rd weeks of treatment,the pressure ulcer scale for healing(PUSH)score,the wound bacterial culture detection rate and the wound healing time were counted,and the vascular endothelial growth factor(VEGF)content of wound tissue was detected by ELISA method.(2)Animal experiments:24 SD rats were randomly divided into blank group,model group,normal saline NPWTi group and compound Wufengcao liquid NPWTi group,6 rats in each group.PI rat model was established by local tissue ischemia/reperfusion injury method,and the negative pressure device was removed at the end of each day of treatment.Before treatment and 3,7 and 10 days after treatment,the wound morphology of each group of rats was observed,the wound histopathology was observed by HE staining,the CD34 positive cells rate of wound tissue was detected by immunohistochemistry,and the expressions of p38 mitogen-activated protein kinase(p38 MAPK),nuclear factor-κB p65(NF-κB p65),inducible nitric oxide synthase(iNOS),tumor necrosis factor-α(TNF-α),arginase-1(Arg-1)and transforming growth factor-β(TGF-β)in rat blood and wound tissue were detected by ELISA and RT-qPCR.Results(1)Clinical research:Both groups could effectively reduce the PUSH score and the wound bacterial culture detection rate,shorten the wound healing time,and promote the expression of VEGF in wound tissue,the compound Wufengcao liquid NPWTi group was better than the normal saline NPWTi group(P<0.05).(2)Animal experiments:Compared with blank group,the rats in the model group showed obvious wound inflammatory response and tissue damage,and the CD34 positive cells rate,blood and wound tissue p38 MAPK,NF-κB p65,iNOS and TNF-α levels were significantly increased,Arg-1 and TGF-β level was significantly reduced(P<0.05);Compared with model group,after 7 days of treatment,the normal saline NPWTi group and the compound Wufengcao liquid NPWTi group significantly decreased the wound morphology score,the histopathological morphology was significantly improved,the CD34 positive cells rate was significantly increased(P<0.05),the levels of blood and wound tissue p38 MAPK,NF-κB p65,iNOS,and TNF-α were significantly reduced,and the levels of Arg-1 and TGF-β were significantly increased(P<0.05),and the compound Wufengcao liquid NPWTi group was better than that of the normal saline NPWTi group(P<0.05).Conclusion Compound Wufengcao liquid combined with NPWTi can effectively promote the healing of PI wounds,and its mechanism of action may be by inhibiting the activation and expression of p38 MAPK/NF-κB signaling pathway,thereby regulating the polarization balance of M1/M2 macrophages.

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.