Objective To explore the influence of excessive PTEN expression to fibroblast cycle and collagen secretion induced by LPS.Methods Normal skin fibroblast in the patient with hyperplastic scar were cultured in vitro.When the primary culture was close to 80% fusion,the digestive passage was performed,cultured to the third generation.LPS(0.5 μg/mL) was adopted to stimulate the third generation of normal skin fibroblasts.Defective adenovirus carrying PTEN gene was transfected to the third passage fibroblasts after LPS stimulation.Flow cytometer was adopted to detect the cell cycle.ELISA method was adopted to detect the secreted collagen amont.Results Excessive PTEN expression could inhibit the increase of G2M cell cycle induced by LPS.LPS stimulation could increase the secretion of collagen in skin fibroblasts,yet excessive PTEN expression could inhibit the secretion of collagen induced by LPS.Conclusion LPS could increase the amont of fibroblasts on G2M cell cycle and secretion of collagen,yet excessive PTEN expression can inhibit the effect.

Objective:To establish a predictive model for myocardial contusion (MC) in patients with rib fractures and evaluate its clinical application value.Methods:A retrospective case-control study was conducted to analyze the clinical data of 370 patients with rib fractures admitted to the Affiliated Jiangsu Shengze Hospital of Nanjing Medical University from January 2017 to December 2019, including 257 males and 113 females, aged 18-95 years [(56.5±14.0)years]. All the patients underwent electrocardiogram examination and myocardial biomarker test within 24 hours on admission, of whom 159 were diagnosed with MC, and 211 with non-MC (NMC). The 370 patients were divided into a training set of 264 patients (106 with MC, 158 with NMC) and a validation set of 106 patients (53 with MC, 53 with NMC) at a ratio of 7∶3 through the completely randomized method. In the training set, the MC group and NMC group were compared in terms of their demographic characteristics, vital signs on admission, types of rib fractures, number of rib fractures, locations of rib fractures, associated thoracic injuries, trauma scores, and laboratory indices. Variables of positive correlation with MC in patients with rib fractures were screened by Spearman correlation analysis, followed by univariate binary Logistic regression analysis for these variables to determine the risk factors for MC in patients with rib fractures. LASSO regression analysis and multivariate Logistic regression analysis were applied to identify the independent risk factors for MC in patients with rib fractures, and the regression equation was constructed. A nomogram prediction model was plotted based on the regression equation with R software. The receiver operating characteristic (ROC) curve was plotted to evaluate the model′s discriminability. Hosmer-Lemeshow (H-L) goodness-of-fit test and calibration curves of 1000 repeated samplings by the Bootstrap method were used to evaluate the calibration of the model. The decision curve analysis (DCA) and clinical impact curve analysis (CIC) were plotted to evaluate its clinical efficacy. A risk scoring was performed according to the assigned β coefficient of independent risk factors. Accordingly, the 370 selected patients with rib fractures were divided into low-risk subgroup of 202 patients, moderate-risk subgroup of 108 patients, high-risk subgroup of 50 patients, and extremely high-risk subgroup of 10 patients. The incidence of MC and in-hospital mortality were compared among different subgroups so as to further verify the clinical application value of the predictive model.Results:In the training set, there were significant differences between the MC group and NMC group in bilateral rib fractures, flail chest, number of rib fractures, upper chest proximal sternum segment, upper chest anterolateral segment, upper chest proximal spinal segment, middle chest anterolateral segment, middle chest proximal spinal segment, lower chest anterolateral segment, pneumothorax, mediastinal emphysema, hemothorax, sternal fractures, chest abbreviated injury scale (c-AIS), injury severity score (ISS), new injury severity score (NISS), white blood cell counts, hemoglobin, hematocrit, total cholesterol, low density lipoprotein, albumin, aspartate aminotransferase, alanine aminotransferase, and blood urea nitrogen ( P<0.05 or 0.01). Spearman correlation analysis showed that the bilateral rib fractures, flail chest, number of rib fractures, upper chest proximal sternum segment, upper chest anterolateral segment, upper chest proximal spinal segment, middle chest anterolateral segment, middle chest proximal spinal segment, lower chest anterolateral segment, pneumothorax, hemothorax, sternal fractures, c-AIS, ISS, NISS, white blood cell count, aspartate aminotransferase and blood urea nitrogen were positively correlated with MC ( P<0.05 or 0.01). Univariate binary Logistic regression analysis verified that the above variables with positive correlation were significantly correlated with MC in patients with rib fractures ( P<0.05 or 0.01). The 4 predictor variables screened by LASSO regression analysis were the upper chest anterolateral segment, middle chest proximal spinal segment, pneumothorax, and sternal fractures. Multivariate Logistic regression analysis confirmed that the aforementioned 4 predictor variables were independent risk factors for MC in patients with rib fractures ( P<0.05 or 0.01). The regression equation of the training set was established based on the above independent risk factors: P=e x/(1+e x), with the x=1.57×"upper chest anterolateral segment"+0.73×"middle chest proximal spinal segment"+1.36×"pneumothorax"+2.16×"sternal fractures"-1.10. In the predictive model for MC in patients with rib fractures established based on the equation, the area under the ROC curve (AUC) was 0.77 (95% CI 0.72, 0.83) and 0.77 (95% CI 0.71, 0.82) in the training set and validation set. The H-L goodness-of-fit test showed χ2=2.77, P=0.429 in the training set, and χ2=1.33, P=0.515 in the validation set, indicating that there was no significant difference between the predicted probability and the actual probability of the model ( P>0.05). The calibration curves showed that the bias-corrected curves of the training set and validation set were in good consistency with the actual curves and were both close to the ideal curves. The DCA of the training set and the validation set showed that within the threshold probability range of 0.2-0.8, the predictive model could obtain good net clinical benefits. The CIC of the training set and the validation set indicated that when the threshold probability was >0.4, the population identified as high-risk MC patients by the predictive model highly matched the actual MC patients. Risk scoring of subgroups found that the incidence of MC and in-hospital mortality among the patients with rib fractures were 80.0% and 6.0% in the high-risk subgroup and 90.0% and 20.0% in the extremely high-risk subgroup, significantly higher than those in the low-risk subgroup (24.8%, 1.0%) and the moderate-risk subgroup (55.6%, 1.9%) ( P<0.05). Conclusions:The predictive model for MC in patients with rib fractures constructed based on the upper chest anterolateral segment, middle chest proximal spinal segment, pneumothorax, and sternal fractures has good predictive efficacy and clinical application value.