Objective To explore the anti-inflammatory properties of the ethyl acetate extract(ETCB)derived from Trollius chinensis Bge.using in vitro RAW264.7 cells stimulated with lipopolysaccharide and an in vivo mouse auricle model induced by xylene.Utilizing UHPLC-Q-TOF-MS(LC-MS)and network pharmacology,the components of ETCB were analyzed,and its anti-inflammatory mechanisms were preliminarily explored.Methods The anti-inflammatory activity of various solvent extracts of Trollius chinensis Bge.was assessed through the Griess assay.The impact of ETCB on the production of TNF-α and IL-6 in RAW264.7 cells induced by lipopolysaccharide was evaluated using ELISA.Real-time qPCR was conducted to determine the effect of ETCB on the expression levels of inflammatory factors such as TNF-α,IL-6,and iNOS in cells.The anti-inflammatory efficacy was further validated in a xylene-induced ear inflammation mouse model by measuring ear swelling and tissue levels of IL-6 and TNF-α.The composition of ETCB was analyzed using LC-MS.Network pharmacology was employed to screen for effective components,targets,and pathways involved in the anti-inflammatory effects of Trollius chinensis Bge.,followed by molecular docking verification between core components and targets.Results ETCB demonstrated the most potent inhibitory effect on NO production in RAW264.7 cells stimulated by lipopolysaccharide,indicating its primary role in the anti-inflammatory activity of Trollius chinensis Bge..ETCB significantly reduced TNF-α and IL-6 levels in inflammatory cells(P<0.01)and inhibited the mRNA expression of TNF-α,IL-6,and iNOS.In the xylene-induced mouse ear inflammation model,ETCB effectively alleviated ear swelling and decreased tissue levels of TNF-α and IL-6.LC-MS analysis identified 30 chemical components in ETCB,including 21 flavonoids,7 organic acids,1 polysaccharide,and 1 anthocyanin.Network pharmacology prediction and screening revealed TNF,Akt1,PTGS2,EGFR,SRC,and MMP9 as core targets,with hydroxyquercetin,lignin from fragrant leaves,zeaxanthin from willows,plantain,thistle,and sophora flavins as key anti-inflammatory active ingredients.The molecular docking analysis revealed positive interactions,characterized by favorable binding energy,between the active components and key targets.Conclusion ETCB demonstrates strong anti-inflammatory properties both inside and outside the body,functioning through various targets and pathways.This establishes a basis for deeper understanding of the anti-inflammatory mechanism of Trollius chinensis Bge.

Objective:To analyze the influencing factors of extremely high cost cases under disease diagnosis related grouping (DRG) payment, for references for further improving China′s medical insurance payment system.Methods:The inpatient medical record homepage and medical insurance settlement list data of medical insurance settlement cases in a tertiary hospital in 2022 were Collected; 19 potential influencing factors such as gender, age, and hospitalization status were used as independent variables, and extremely high cost cases as dependent variables. Single factor analysis and directed acyclic graphs were used to screen for independent variables; Logistic regression model was used to analyze the influencing factors of extremely high cost cases.Results:A total of 17 028 hospitalized patients were contained, including 815 cases with extremely high costs. After analysis, the older the age, the greater the positive impact on cases with extremely high costs ( P<0.001); Third/fourth level surgery had a positive impact on these cases ( P<0.05); Emergency/critical hospitalization negative impact on extremely high cost cases ( P<0.001); Coding downgrade, surgical, intensive care unit discharge, and death positive impact cases with extremely high costs ( P<0.001); The higher the proportion of drugs, the greater the positive impact on cases with extremely high costs ( P<0.001); Compared with treatment costs accounting for ≤10%, treatment costs accounting for (10%, 30%] had a positive impact on cases with extremely high costs ( P<0.001), while treatment costs accounting for >30% had a negative impact ( P=0.007); Complications and/or comorbidities negative impact on thses cases ( P<0.001); The higher the weight of the disease group, the greater the negative impact on cases with extremely high costs ( P<0.001). Conclusions:There were many influencing factors for extremely high cost cases, among which factors such as advanced age, emergency/critical hospital admission, third/fourth level surgery, code downgrade, and high drug proportion would increase the number of extremely high cost cases, accompanied by complications and/or comorbidities, and the high weight of disease group reduce the number of extremely high cost cases. Medical institutions should strengthen internal management by standardizing admission management and optimizing the structure of hospitalization expenses; The medical insurance management department should focus on improving the DRG grouping scheme, scientifically setting the weight of disease groups, and jointly promoting the reform of China′s medical insurance payment system.

Objective To explore the anti-inflammatory properties of the ethyl acetate extract(ETCB)derived from Trollius chinensis Bge.using in vitro RAW264.7 cells stimulated with lipopolysaccharide and an in vivo mouse auricle model induced by xylene.Utilizing UHPLC-Q-TOF-MS(LC-MS)and network pharmacology,the components of ETCB were analyzed,and its anti-inflammatory mechanisms were preliminarily explored.Methods The anti-inflammatory activity of various solvent extracts of Trollius chinensis Bge.was assessed through the Griess assay.The impact of ETCB on the production of TNF-α and IL-6 in RAW264.7 cells induced by lipopolysaccharide was evaluated using ELISA.Real-time qPCR was conducted to determine the effect of ETCB on the expression levels of inflammatory factors such as TNF-α,IL-6,and iNOS in cells.The anti-inflammatory efficacy was further validated in a xylene-induced ear inflammation mouse model by measuring ear swelling and tissue levels of IL-6 and TNF-α.The composition of ETCB was analyzed using LC-MS.Network pharmacology was employed to screen for effective components,targets,and pathways involved in the anti-inflammatory effects of Trollius chinensis Bge.,followed by molecular docking verification between core components and targets.Results ETCB demonstrated the most potent inhibitory effect on NO production in RAW264.7 cells stimulated by lipopolysaccharide,indicating its primary role in the anti-inflammatory activity of Trollius chinensis Bge..ETCB significantly reduced TNF-α and IL-6 levels in inflammatory cells(P<0.01)and inhibited the mRNA expression of TNF-α,IL-6,and iNOS.In the xylene-induced mouse ear inflammation model,ETCB effectively alleviated ear swelling and decreased tissue levels of TNF-α and IL-6.LC-MS analysis identified 30 chemical components in ETCB,including 21 flavonoids,7 organic acids,1 polysaccharide,and 1 anthocyanin.Network pharmacology prediction and screening revealed TNF,Akt1,PTGS2,EGFR,SRC,and MMP9 as core targets,with hydroxyquercetin,lignin from fragrant leaves,zeaxanthin from willows,plantain,thistle,and sophora flavins as key anti-inflammatory active ingredients.The molecular docking analysis revealed positive interactions,characterized by favorable binding energy,between the active components and key targets.Conclusion ETCB demonstrates strong anti-inflammatory properties both inside and outside the body,functioning through various targets and pathways.This establishes a basis for deeper understanding of the anti-inflammatory mechanism of Trollius chinensis Bge.

Objective:To analyze the influencing factors of extremely high cost cases under disease diagnosis related grouping (DRG) payment, for references for further improving China′s medical insurance payment system.Methods:The inpatient medical record homepage and medical insurance settlement list data of medical insurance settlement cases in a tertiary hospital in 2022 were Collected; 19 potential influencing factors such as gender, age, and hospitalization status were used as independent variables, and extremely high cost cases as dependent variables. Single factor analysis and directed acyclic graphs were used to screen for independent variables; Logistic regression model was used to analyze the influencing factors of extremely high cost cases.Results:A total of 17 028 hospitalized patients were contained, including 815 cases with extremely high costs. After analysis, the older the age, the greater the positive impact on cases with extremely high costs ( P<0.001); Third/fourth level surgery had a positive impact on these cases ( P<0.05); Emergency/critical hospitalization negative impact on extremely high cost cases ( P<0.001); Coding downgrade, surgical, intensive care unit discharge, and death positive impact cases with extremely high costs ( P<0.001); The higher the proportion of drugs, the greater the positive impact on cases with extremely high costs ( P<0.001); Compared with treatment costs accounting for ≤10%, treatment costs accounting for (10%, 30%] had a positive impact on cases with extremely high costs ( P<0.001), while treatment costs accounting for >30% had a negative impact ( P=0.007); Complications and/or comorbidities negative impact on thses cases ( P<0.001); The higher the weight of the disease group, the greater the negative impact on cases with extremely high costs ( P<0.001). Conclusions:There were many influencing factors for extremely high cost cases, among which factors such as advanced age, emergency/critical hospital admission, third/fourth level surgery, code downgrade, and high drug proportion would increase the number of extremely high cost cases, accompanied by complications and/or comorbidities, and the high weight of disease group reduce the number of extremely high cost cases. Medical institutions should strengthen internal management by standardizing admission management and optimizing the structure of hospitalization expenses; The medical insurance management department should focus on improving the DRG grouping scheme, scientifically setting the weight of disease groups, and jointly promoting the reform of China′s medical insurance payment system.

With the popularity of sports, the number of anterior cruciate ligament (ACL) injuries in children is increasing year by year. Most ACL injuries in children are tibial avulsion fractures or ACL body tears, seriously affecting the health and sports level of the patients. Due to the special anatomical structure of the patients, unclosed epiphysis makes the diagnosis and treatment of ACL injuries more complex. It is necessary to choose the optimal treatment regimen according to the bone maturity and the type and degree of ACL injuries to reduce the damage to the epiphysis and avoid the impact on the growth and development of the patients. It was treated with non-surgical treatment and then ACL reconstruction when the bones were mature in the past, which could cause secondary meniscus and cartilage damage. In recent years, non-surgical treatment has mainly been indicated for children with low-degree ACL injuries and small demand for exercise. With the increased ratio of early surgical treatment, the patients′ levels of recovery and return to sports after injury have been improved. However, improper surgery may still lead to complications such as growth and development disorders and postoperative re-injuries. Different from traditional ACL reconstruction, personalized diagnosis and treatment regimen of ACL injuries are very important for the patients at different stages of growth and development. For a better understanding of the diagnosis and treatment of ACL injuries in children, the authors reviewed the research progress on the diagnosis and treatment of ACL injuries in children from the aspects of the characteristics, diagnosis and evaluation, treatment methods, etc., hoping to provide a reference for the personalized diagnosis and treatment.

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

Objective:To investigate the finite element analysis and early-stage clinical effects of double bundle anterior cruciate ligament (ACL) reconstruction with femoral direct fiber insertion.Methods:From June 2016 to June 2017, a total of 26 cases of ACL reconstruction were analyzed retrospectively, including 15 males and 11 females, mean age 30.5±4.6 years. All the patients underwent ACL reconstruction by the same operator. The early-stage clinical effects were evaluated by the finite element analysis, pivot shift test, Lachman test, preoperative and postoperative IKDC score, Lyshlom score, KT-2000, 3D-CT and MRI.Results:The finite element analysis confirmed theoretically that the double bundle ACL reconstruction with femoral direct fiber insertion could restore the stability and biomechanics of knee effectively. The results of pivot shift test were negative, and the Lachman test were negative except one first-stage positive after operation. 3D-CT showed that the bone tunnel was located in the direct fiber area. MRI showed clearly the ACL of double bundle after operation. Lysholm score increased from 56.5±3.6 pre-operation to 61.9±3.2 at three months after operation, and up to 88.5±2.0 two years after operation with statistically significant difference ( F=824.72, P<0.001). IKDC score increased from 48.3±2.8 before operation to 58.0±2.0 at three months after operation, and to 92.5±2.6 at two years after operation with statistically significant difference ( F=2 256.66, P<0.001). KT-2000 side-side difference decreased from 5.6±0.7 mm to 1.6±0.5 mm at three months after operation, and to 1.5±0.6 mm at two years after operation with statistically significant difference ( F=389.14, P<0.001). Conclusion:The double bundle ACL reconstruction with femoral direct fiber insertion can effectively restore the stability and the biomechanical environment of knee joint with satisfied early-stage clinical effects.

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective:Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan.Methods:Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results:A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results ( P=0.415). Conclusion:A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.