Objective To investiagte the adipogenic differentiative ability of adipose tissue-derived stromal cells(ADSCs) between the patients with type 2 diabetes mellitus(T2DM) and healthy persons.Methods The adipose tissues were taken from the adipose tissue in T2DM patients and healthy persons for separating and culturing ADSCs.The cells of third generation were taken for inoculation.The difference in cellular phenotype and growth speed were compared between the two groups.Adding adipogenesis inducing fluid,the adipogenic differentiative situation was observed in the two groups.The oil red O was added on 14 d for conducting the cell staining and observation.The oil red O was extracted by isopropanol,and the cellular absorbances were compared between two groups.Meanwhile,the expression of PPAR-γ,C/EBP-α and C/EBP-β on 14 d of adipogenic differentiation were compared between two groups by using qPCR method.Results The cellular phenotype and growth speed of ADSCs had no statisticat difference between T2DM patients and healthy persons.On 14 d of adipogenic differentiation,the oil red O absorbance value of AD-SCs in T2DM patients was significantly higher than that in the healthy persons,and the expression of PPAR-γ,C/EBP-α and C/EBP-β were significantly higher than those in the healthy persons.Conclusion The adipogenic differentiative ability of ADSCs in T2DM patients is obviously higher than that in healthy persons,which may be one of causes easy to be obese in T2DM patients.

Objective:To establish a scoring system for preoperative prediction of the malignant potential of gastric stromal tumors based on gastroscopic and endoscopic ultrasound features, along with validation.Methods:A total of 286 patients who were treated in Jiangsu Province Hospital of Chinese Medicine from January 1, 2017 to December 31, 2023 and diagnosed as having gastric stromal tumors by postoperative pathology were enrolled in the modeling group. According to National Institutes of Health classification system, 227 very-low/low-risk patients were classified into the low malignant potential (LMP) group, and the 59 intermediate/high-risk patients into the high malignant potential (HMP) group. LASSO regression analysis was performed to identify predictive factors for HMP gastric stromal tumors, and a nomogram prediction model was developed. Internal validation using the Bootstrap method was performed on the modeling group, and external validation was performed on data from 85 patients who were treated and diagnosed as having gastric stromal tumors by postoperative pathology in Taizhou People's Hospital from January 1, 2021 to December 31, 2023. The receiver operator characteristic (ROC) curves, calibration curves, and decision curve analyses were employed in both the modeling and external validation groups.Results:Tumor size (coef=0.755), tumor shape (coef=0.015), tumor location (coef=0.008), growth pattern (coef=-0.026), cystic change (coef=0.685), and surface unceration change (coef=-0.545) were the independent predictive factors for HMP gastric stromal tumors. The nomogram-based prediction model constructed using these factors achieved an area under the ROC curve of 0.959 (95% CI: 0.898-0.903) in the modeling group and 0.959 (95% CI: 0.857-1.000) in the external validation group. The model demonstrated good accuracy (0.917) and a Kappa value of 0.737 in internal validation. Calibration curve and decision curve analyses indicated strong calibration and high net benefit in both the modeling and the external validation groups. Conclusion:Tumor size, tumor shape, tumor location, growth pattern, cystic change, and surface ulceration change are independent predictive factors for HMP gastric stromal tumors. The nomogram model developed based on these factors offers effective and convenient visualization for clinicians to predict the malignant potential of gastric stromal tumors preoperatively.

AIM: To analyze the effect of influential factors on the estimation of pharmacokinetic parameters of teicoplanin, this study was proposed to develop the population pharmacokinetic (PPK) model of teicoplanin in patients with renal insufficiency. METHODS: A total of 66 routine blood teicoplanin concentration monitoring data were collected from 46 cases with renal insufficiency, and a nonlinear mixed effect modeling program was used to establish one-compartment model with Monolix 2021R1 software. Furthermore, 20 routine blood teicoplanin concentration monitoring data were also collected from the other 20 cases with renal insufficiency, and the external validation of the model was performed by goodness-of-fit parameter method. RESULTS: The one compartment model was an appropriate model for simulating the pharmacokinetics of teicoplanin in patients with renal insufficiency. The typical values of apparent volume of distribution and clearance rate were 148.9 L and 0.13 L/h, respectively. Glomerular filtration rate and body weight, instead of other factors, were the primary variables that affected the elimination of teicoplanin in vivo. CONCLUSION: The population pharmacokinetic model of teicoplanin established in the present study was effective and stable, which could also predict the dynamic change of teicoplanin concentration. As a result, the population pharmacokinetic model could provide references for the rational use of teicoplanin in special populations.