Objective To investigate the correlation between glycemic variability metrics and the risk of diabetic foot(DF)in patients with type 2 diabetes mellitus(T2DM)utilizing flash glucose monitoring(FGM)tech-nology.Methods A retrospective analysis was conducted on 233 hospitalized patients with T2DM,with or without DF,who were treated in the Department of Endocrinology at Lianyungang First People's Hospital from January 2021 to May 2022 and monitored using FGM.Patients were categorized into a non-DF group(n=147)and a DF group(n=86)based on the presence of DF.The study compared general clinical characteristics,biochemical parameters,and glycemic variability metrics between the two groups and performed subgroup analyses.Binary logistic regression was employed to identify factors associated with the risk of DF,while receiver operating characteristic(ROC)curves were utilized to assess the predictive value of glycemic variability metrics for DF.Results Compared with the non-DF group,patients in the DF group exhibited significantly longer disease duration,higher body mass index(BMI),glycated hemoglobin(HbA1c),urinary albumin-to-creatinine ratio(UACR),alanine aminotransferase(ALT),serum uric acid(SUA),mean amplitude of glycemic excursions(MAGE),coefficient of variation(CV),mean of daily differences(MODD),and mean blood glucose(MBG),but lower fasting C-peptide(FCP),fasting insulin(FINS),high-density lipoprotein cholesterol(HDL-C),and time in range(TIR),with statistically signifi-cant differences(P<0.05).Subgroup analysis revealed that TIR was associated with the incidence of DF and diabetic retinopathy(DR).Binary logistic regression analysis identified HbA1c,MAGE,MODD,and MBG as risk factors for DF,while TIR was a protective factor(P<0.05).ROC curve analysis demonstrated that the area under the curve(AUC)for predicting DF using HbA1c,TIR,MAGE,MODD,MBG,and their combination were 0.646,0.850,0.868,0.764,0.619,and 0.967,respectively,indicating superior performance of the combined prediction model.Conclusions HbA1c,TIR,MAGE,MODD,and MBG are critical factors associated with the development of DF in patients with T2DM.Targeted early interventions aimed at optimizing these glycemic variability indicators may effectively reduce the incidence of DF.

Objective To investigate the correlation between glycemic variability metrics and the risk of diabetic foot(DF)in patients with type 2 diabetes mellitus(T2DM)utilizing flash glucose monitoring(FGM)tech-nology.Methods A retrospective analysis was conducted on 233 hospitalized patients with T2DM,with or without DF,who were treated in the Department of Endocrinology at Lianyungang First People's Hospital from January 2021 to May 2022 and monitored using FGM.Patients were categorized into a non-DF group(n=147)and a DF group(n=86)based on the presence of DF.The study compared general clinical characteristics,biochemical parameters,and glycemic variability metrics between the two groups and performed subgroup analyses.Binary logistic regression was employed to identify factors associated with the risk of DF,while receiver operating characteristic(ROC)curves were utilized to assess the predictive value of glycemic variability metrics for DF.Results Compared with the non-DF group,patients in the DF group exhibited significantly longer disease duration,higher body mass index(BMI),glycated hemoglobin(HbA1c),urinary albumin-to-creatinine ratio(UACR),alanine aminotransferase(ALT),serum uric acid(SUA),mean amplitude of glycemic excursions(MAGE),coefficient of variation(CV),mean of daily differences(MODD),and mean blood glucose(MBG),but lower fasting C-peptide(FCP),fasting insulin(FINS),high-density lipoprotein cholesterol(HDL-C),and time in range(TIR),with statistically signifi-cant differences(P<0.05).Subgroup analysis revealed that TIR was associated with the incidence of DF and diabetic retinopathy(DR).Binary logistic regression analysis identified HbA1c,MAGE,MODD,and MBG as risk factors for DF,while TIR was a protective factor(P<0.05).ROC curve analysis demonstrated that the area under the curve(AUC)for predicting DF using HbA1c,TIR,MAGE,MODD,MBG,and their combination were 0.646,0.850,0.868,0.764,0.619,and 0.967,respectively,indicating superior performance of the combined prediction model.Conclusions HbA1c,TIR,MAGE,MODD,and MBG are critical factors associated with the development of DF in patients with T2DM.Targeted early interventions aimed at optimizing these glycemic variability indicators may effectively reduce the incidence of DF.

ObjectiveTo evaluate the effectiveness of Gandou decoction （GDD） by analyzing theclinical efficacy of GDD combined with speech training on the treatment of dysarthria with endoretention of damp-heat in Wilson's disease （WD）， so as to provide more clinical data and theoretical support for the selection of appropriate treatment schemes for WD patients with dysarthria with endoretention of damp-heat. MethodA total of 60 eligible WD patients with dysarthria with endoretention of damp-heat were selected and divided into a control group and a treatment group according to the random grouping method， with 30 cases in each group. The control group was treated with speech training + sodium dimercaptopropanesulfonate （DMPS）， and the treatment group was combined with GDD on the basis of the control group， with eight days as a course of treatment for 32 days. The total clinical effectiveness rate （Goldstein clinical classification）， dysarthria grading assessment from China Rehabilitation Research Center， TCM syndrome scores， 24-hour urine copper content， and modified Frenchay dysarthria rating scale scores of the two groups were compared before and after treatment. ResultAfter treatment， the total effective rate of the observation group was 90.0% （27/30）， and that of the control group was 70.0% （21/30）. The total effective rate of the observation group was significantly higher than that of the control group （Z=-1.986，P<0.05）. After treatment， the modified Frenchay dysarthria score， dysarthria grading assessment from China Rehabilitation Research Center， and 24-h urine copper in the two groups were significantly increased （P<0.05， P<0.01）， and the TCM syndrome score was significantly decreased （P<0.01）. Compared with the control group after treatment， except for the respiratory and jaw score， the modified Frenchay dysarthria score of the observation group was significantly increased （P<0.05， P<0.01）. The dysarthria grading from China Rehabilitation Research Center and 24-h urine copper content were significantly increased （P<0.01）， and the observation group had better efficacy. During the study period， there were no serious adverse reactions such as fever， rash， oral and eyelid mucosal swelling， exfoliative dermatitis， vomiting， diarrhea， or allergic shock during copper excretion treatment of DPMS and oral administration of GDD. ConclusionGDD combined with speech training can improve the symptoms and efficacy of WD patients with dysarthria with endoretention of damp-heat and enhance the patients' living standard to a certain extent， which can be widely used in clinics.

Objective:To investigate the value of an MRI and digital pathology images based omics nomogram for the prediction of recurrence risk in soft tissue sarcoma (STS).Methods:This was a retrospective cohort study. From January 2016 to March 2021, 192 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled, among which 112 patients in the Laoshan campus were enrolled as training set, and 80 patients in the Shinan campus were enrolled as validation set. The patients were divided into recurrence group ( n=87) and no recurrence group ( n=105) during follow-up. The clinical and MRI features of patients were collected. The radiomics features based on fat saturated T 2WI images and pathomics features based on digital pathology images of the lesions were extracted respectively. The clinical model, radiomics model, pathomics model, radiomics-pathomics combined model, and omics nomogram which combined the optimal prediction model and the clinical model were established by multivariate Cox regression analysis. The concordance index (C index) and time-dependent area under the receiver operating characteristic curve (t-AUC) were used to evaluate the performance of each model in predicting STS postoperative recurrence. The DeLong test was used for comparison of t-AUC between every two models. The X-tile software was used to determine the cut-off value of the omics nomogram, then the patients were divided into low risk ( n=106), medium risk ( n=64), and high risk ( n=22) groups. Three groups′ cumulative recurrence-free survival (RFS) rates were calculated and compared by the Kaplan-Meier survival curve and log-rank test. Results:The performance of the radiomics-pathomics combined model was superior to the radiomics model and pathomics model, with C index of 0.727 (95% CI 0.632-0.823) and medium t-AUC value of 0.737 (95% CI0.584-0.891) in the validation set. The omics nomogram was established by combining the clinical model and the radiomics-pathomics combined model, with C index of 0.763 (95% CI 0.685-0.842) and medium t-AUC value of 0.783 (95% CI0.639-0.927) in the validation set. The t-AUC value of omics nomogram was significantly higher than that of clinical model, TNM model, radiomics model, and pathomics model in the validation set ( Z=3.33, 2.18, 2.08, 2.72, P=0.001, 0.029, 0.037, 0.007). There was no statistical difference in t-AUC between the omics nomogram and radiomics-pathomics combined model ( Z=0.70, P=0.487). In the validation set, the 1-year RFS rates of STS patients in the low, medium, and high recurrence risk groups were 92.0% (95% CI 81.5%-100%), 55.9% (95% CI 40.8%-76.6%), and 37.5% (95% CI 15.3%-91.7%). In the training and validation sets, there were statistically significant in cumulative RFS rates among the low, medium, and high groups of STS patients (training set χ2=73.90, P<0.001; validation set χ2=18.70, P<0.001). Conclusion:The omics nomogram based on MRI and digital pathology images has favorable performance for the prediction of STS recurrence risk.

Objective Exploring the clinical application value of long chain non-coding RNA(lncRNA)H19 in peripheral blood mononuclear cells(PBMCs)in type 2 diabetes mellitus(T2DM)patients with osteoporosis(OP).Methods A total of 176 patients with T2DM admitted to the Department of Endocrinology of the First People's Hospital of Lianyungang City from December 2022 to June 2023 were selected.They were divided into OP group(n=100)and simple T2DM group(n=76)according to the results of bone mineral density(BMD)determination by dual-energy X-ray.General data and biochemical indicators were compared between the two groups.The correlation between lncRNA H19 and other clinical indicators was analyzed by Spearman correlation analysis and the influencing factors for T2DM with OP were analyzed by logistic regression.Receiver operating characteristic(ROC)curve was used to evaluate the diagnostic value of lncRNA H19 in T2DM with OP.Results Compared with T2DM group,the proportion of females,age,HbA1c,TC and TGF-β1 were higher(P<0.05),while BMI,SUA,BMD and lncRNA H19 were lower in OP group(P<0.05).Spearman correlation analysis showed that lncRNA H19 expression level was positively correlated with BMI,SUA and BMD(P<0.05),and negatively correlated with age,HbA1c and TGF-β1(P<0.05).Logistic regression analysis showed that age,BMI,TC,TGF-β1 and lncRNA H19 were the influencing factors for T2DM combined with OP(P<0.05).ROC curve analysis showed that the AUC of lncRNA H19 in the diagnosis of T2DM with OP was 0.839,the sensitivity was 76.3%,and the specificity was 86.0%.Multiple linear regression analysis showed that age,TGF-β1 and OP were the influencing factors for lncRNA H19(P<0.05).Conclusion LncRNA H19 expression decreased in PBMCs in patients with T2DM with OP,which may participate in the occurrence and development of T2DM with OP through glucose metabolism and lncRNA H19/TGF-β1 pathway.

Objective To investigate the role of human umbilical cord mesenchymal stem cell-derived extracellular vesicle (hUC-MSC-EV) in the regeneration of fibrotic liver. Methods C57BL/6 mice were randomly divided into the 70% normal liver resection group (Oil+PHx group), 70% liver fibrosis resection group (CCl₄+PHx group) and 70% liver fibrosis resection+mesenchymal stem cell-derived extracellular vesicle (MSC-EV) treatment group (CCl₄+PHx+MSC-EV group), with 8 mice in each group. LX-2 cell lines were assigned into the phosphate buffer solution (PBS) group, transforming growth factor (TGF)-β group and TGF-β+MSC-EV group. The serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and lactate dehydrogenase (LDH) in mice after partial liver resection were detected in each group. The expression levels of liver fibrosis and proliferation-related parameters were analyzed in each group. The messenger RNA (mRNA) expression levels of epidermal growth factor (EGF), fibroblast growth factor (FGF), vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF) in LX-2 cells were detected in each group, and their effects on HGF expression in mouse liver were observed. Results Compared with the Oil+PHx group, the serum levels of AST, ALT and LDH were up-regulated, and the degree of fibrosis was more severe, the positive area of Sirius red and α-smooth muscle actin (α-SMA) staining was larger, and the expression level of α-SMA protein was up-regulated in the CCl₄+PHx group. Compared with the CCl₄+PHx group, the serum levels of AST, ALT and LDH were decreased, the degree of fibrosis was slighter, the positive area of Sirius red and α-SMA staining was decreased, and the expression level of α-SMA protein was down-regulated in the CCl₄+PHx+MSC-EV group, and the differences were statistically significant (all P < 0.05). Compared with the Oil+PHx group, the protein expression levels of Ki67 and proliferating cell nuclear antigen (PCNA) were lower in the CCl₄+PHx group. Compared with the CCl₄+PHx group, the protein expression levels of Ki67 and PCNA were increased in the CCl₄+PHx+MSC-EV group, and the differences were statistically significant (all P < 0.05). Compared with the PBS group, the expression level of CollagenⅠ mRNA in LX-2 cells was increased, the expression level of α-SMA protein was up-regulated and the expression level of HGF protein was decreased in the TGF-β group. Compared with the TGF-β group, the expression level of CollagenⅠ mRNA in LX-2 cells was decreased, the expression levels of HGF mRNA and protein were increased, and the expression level of α-SMA protein was decreased in the TGF-β+MSC-EV group, the differences were statistically significant (all P < 0.05). The expression level of HGF protein in the CCl₄+PHx group was lower than that in the Oil+PHx group, whereas the difference was not statistically significant (P > 0.05). The expression level of HGF protein in the CCl₄+PHx+MSC-EV group was higher than that in the CCl₄+PHx group, and the difference was statistically significant (P < 0.05). Conclusions The regenerative capacity of fibrotic liver is weaker than that of normal liver. hUC-MSC-EV may alleviate liver fibrosis and improve liver regeneration by promoting HGF secretion from actived hepatic stellate cells and effectively enhancing the regenerative capacity of fibrotic liver.

Objective:To observe the clinical efficacy of autologous platelet rich gel (APG) in the treatment of type 2 diabetic foot (DF) patients and the effect of APG on the expression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in peripheral blood mononuclear cells (PBMCs).Methods:A total of 62 patients with DF admitted to the Affiliated Hospital of Kangda College of Nanjing Medical University from February 2021 to May 2022 were randomly divided into a control group (30 cases) and an observation group (32 cases) using a random number table method. The control group received ultrasound debridement and dressing change treatment, while the observation group received ultrasound debridement combined with APG treatment. After 6 weeks of treatment, the effective rate, transcutaneous oxygen partial pressure (TcPO 2), and serum tumor necrosis factor- α (TNF-α), interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), hypoxia inducible factor α (HIF-1 α)and the level of MALAT1 expression in PBMCs of the two groups of patients were observed. The Pearson correlation analysis was used to investigate the relationship between the expression change of MALAT (△ MALAT1) and the total effective rate of treatment. Results:The total effective rate of the observation group was higher than that of the control group [93.75%(30/32) vs 73.33%(22/30), P<0.05]. After treatment, the systolic blood pressure (SBP), diastolic blood pressure (DBP), cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FPG), glycosylated hemoglobin (HbA 1c), urinary microalbumin/creatinine (UACR), uric acid (UA), white blood cells (WBC), TNF- α and IL-6 of both groups had decreased compared to before; HIF-1 α, VEGF and MALAT1 increased compared to before treatment (all P<0.05); After treatment, there was a statistically significant difference in UA, HIF-1α, VEGF, and MALAT1 between the observation group and the control group (all P<0.05). Pearson correlation analysis showed that Δ MALAT1 in DF patients was negatively correlated with TNF -α ( r=-0.61, P=0.02), IL-6 ( r=-0.52, P=0.04), WBC ( r=-0.53, P=0.03), and positively correlated with VEGF ( r=0.58, P=0.03) and HIF-1α ( r=0.54, P=0.03). The total effective rate of DF treatment was higher in the high change group of△ MALAT [88.37%(38/43) vs 73.68%(14/19), P<0.05]. There was no statistically significant difference in the incidence of adverse reactions between the two groups ( P>0.05). Conclusions:APG can significantly upregulate the expression of MALAT, improve wound tissue blood perfusion, wound angiogenesis, and inflammatory response, promote ulcer healing, and changes in MALAT expression can help determine the prognosis of DF.

Objective:To investigate the value of a preoperatively MRI-based deep learning (DL) radiomics machine learning model to distinguish low-grade and high-grade soft tissue sarcomas (STS).Methods:From November 2007 to May 2019, 151 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled as training sets, and 131 patients in the Affiliated Hospital of Shandong First Medical University and the Third Hospital of Hebei Medical University were enrolled as external validation sets. According to the French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) system, 161 patients with FNCLCC grades Ⅰ and Ⅱ were defined as low-grade and 121 patients with grade Ⅲ were defined as high-grade. The hand-crafted radiomic (HCR) and DL radiomic features of the lesions were extracted respectively. Based on HCR features, DL features, and HCR-DL combined features, respectively, three machine-learning models were established by decision tree, logistic regression, and support vector machine (SVM) classifiers. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each machine learning model and choose the best one. The univariate and multivariate logistic regression were used to establish a clinical-imaging factors model based on demographics and MRI findings. The nomogram was established by combining the optimal radiomics model and the clinical-imaging model. The AUC was used to evaluate the performance of each model and the DeLong test was used for comparison of AUC between every two models. The Kaplan-Meier survival curve and log-rank test were used to evaluate the performance of the optimal machine learning model in the risk stratification of progression free survival (PFS) in STS patients.Results:The SVM radiomics model based on HCR-DL combined features had the optimal predicting power with AUC values of 0.931(95%CI 0.889-0.973) in the training set and 0.951 (95%CI 0.904-0.997) in the validation set. The AUC values of the clinical-imaging model were 0.795 (95%CI 0.724-0.867) and 0.615 (95%CI 0.510-0.720), and of the nomogram was 0.875 (95%CI 0.818-0.932) and 0.786 (95%CI 0.701-0.872) in the training and validation sets, respectively. In validation set, the performance of SVM radiomics model was better than those of the nomogram and clinical-imaging models ( Z=3.16, 6.07; P=0.002,<0.001). Using the optimal radiomics model, there was statistically significant in PFS between the high and low risk groups of STS patients (training sets: χ2=43.50, P<0.001; validation sets: χ2=70.50, P<0.001). Conclusion:Preoperative MRI-based DL radiomics machine learning model has accurate prediction performance in differentiating the histopathological grading of STS. The SVM radiomics model based on HCR-DL combined features has the optimal predicting power and was expected to undergo risk stratification of prognosis in STS patients.

To analyze the clinical data of 2 neonates with Langerhans cell histiocytosis (LCH). The rashes appeared in both cases shortly after birth.Case 1 had both rashes and neonatal sepsis, and no other tissues and organs were involved.After anti-infective treatment, the rashes gradually disappeared.Case 2 had secondary pneumonia, abnormal coagulation function and gastrointestinal bleeding.Both cases were positive for CD1a and S-100 by immunohistochemical staining of skin biopsy, and they were diagnosed as multi system-LCH.The early diagnosis of LCH is particularly important.The detection methods of skin or lymph node biopsy like immunohistochemistry, need to be performed as early as possible.Because the course of the disease is not clear, a close monitoring and follow-up are needed.