Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective: To investigate whether fibroblast growth factor 18(FGF18) can induce human gingival fibroblasts(HGFs) isolatedin vitroto differentiate into osteoblast-like cells, and to explore the mechanism of osteogenesis. Methods : HGFs were isolated, cultured and identified by tissue block method. The third generation of HGFs were divided into experimental group and control group. FGF18 and L-DMEM was added to the experimental group while L-DMEM was added to the control group.The effects of different concentrations of FGF18(0, 0.01, 0.02, 0.04, 0.06 mg/L) on proliferation of HGFs were detected by Methylthiazolyldiphenyl-tetrazolium bromide(MTT) assay. Alkaline phosphatase(ALP) and alizarin red staining were used to detect the osteogenesis and mineralization ability of the cells after induction. RT-PCR, immunocytochemistry staining, and Western blot were used to detect the expression of genes and proteins related to osteogenesis and BMP2 in the BMP signaling pathway. Results: Compared with the control group, the experimental group could promote the proliferation of HGFs at 3, 5, 7, 9, and 11days(P<0.05),ALP activity and mineral salt deposition increased after induction at 14 and 21 days(P<0.05), and the expressions of ALP, OPN, OCN mRNA and BMP2 mRNA in BMP signaling pathway significantly increased(P<0.01). The expressions of OPN, OCN and BMP2 protein at 21 days were significantly higher than those at 14 days(P<0.01). Conclusion FGF18 can promote the proliferation of HGFs, and induce the differentiation of HGFs into functional osteoblasts. The osteogenic mechanism is related to the upregulation of BMP2.

BACKGROUND:Nerve growth factor plays an important role in the physiological and pathological processes of bone tissue.Systematic analysis of the effects of nerve growth factor on bone tissue is of great significance in both tissue engineering and clinical treatment.OBJECTIVE:To investigate the regulation of bone formation process by nerve growth factor through pathways such as bone tissue cells and bone nerve-vessel coupling,as well as to study the role of nerve growth factor in the pathological process of bone-related diseases.METHODS:The authors searched for relevant articles in CNKI,WanFang,and PubMed with the keywords of"nerve growth factor,TrkA,NGF,bone,cartilage"in Chinese and English.A total of 2 925 articles were initially retrieved.After screening,116 articles were incorporated into this review.RESULTS AND CONCLUSION:Nerve growth factor can be expressed by bone,cartilage,nerve,vessel and other tissue cells.At the same time,nerve growth factor can play a regulatory role on these cells.Through a variety of secretion and regulation methods,nerve growth factor plays a role as a signal transduction factor within bone tissue and between bone,nerve and blood vessel tissues.By promoting the proliferation and differentiation of bone marrow mesenchymal stem cells,nerve growth factor promotes bone formation and bone repair.Nerve growth factor has multi-directional regulatory effects on bone tissue through its effects on osteoclasts.Meanwhile,nerve growth factor is highly correlated with the occurrence and development of many orthopedic diseases and may provide new clinical therapeutic ideas.The study of nerve growth factor is one of the important directions to understand the physiological and pathological processes of bone.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

BACKGROUND:Nerve growth factor plays an important role in the physiological and pathological processes of bone tissue.Systematic analysis of the effects of nerve growth factor on bone tissue is of great significance in both tissue engineering and clinical treatment.OBJECTIVE:To investigate the regulation of bone formation process by nerve growth factor through pathways such as bone tissue cells and bone nerve-vessel coupling,as well as to study the role of nerve growth factor in the pathological process of bone-related diseases.METHODS:The authors searched for relevant articles in CNKI,WanFang,and PubMed with the keywords of"nerve growth factor,TrkA,NGF,bone,cartilage"in Chinese and English.A total of 2 925 articles were initially retrieved.After screening,116 articles were incorporated into this review.RESULTS AND CONCLUSION:Nerve growth factor can be expressed by bone,cartilage,nerve,vessel and other tissue cells.At the same time,nerve growth factor can play a regulatory role on these cells.Through a variety of secretion and regulation methods,nerve growth factor plays a role as a signal transduction factor within bone tissue and between bone,nerve and blood vessel tissues.By promoting the proliferation and differentiation of bone marrow mesenchymal stem cells,nerve growth factor promotes bone formation and bone repair.Nerve growth factor has multi-directional regulatory effects on bone tissue through its effects on osteoclasts.Meanwhile,nerve growth factor is highly correlated with the occurrence and development of many orthopedic diseases and may provide new clinical therapeutic ideas.The study of nerve growth factor is one of the important directions to understand the physiological and pathological processes of bone.

Objective To establish a novel real-time quality control method for rapidly identifying the random error of sodium ion con-centration in serum using an artificial intelligence voting algorithm,and evaluate the relevant effectiveness of the model established on this basis.Methods A total of 144 754 test results of serum sodium ion rom the inpatients measured by Beckman AU5400 biochemis-try analyzer from January to May 2021 were obtained retrospectively from laboratory information system of the Department of Clinical La-boratory,Nanjing Drum Tower Hospital,and all the data were used as unbiased data for the current study.The random errors were arti-ficially introduced to generate the corresponding biased data set.Subsequently,the voting algorithm-based internal quality control model(ViQC)was established using the principles of the voting algorithm.The ViQC model and five classical PBRTQC(patient-based real-time quality control)algorithms were performed direct to each biased data.The analytical performance of the ViQC model was evaluated by using classification model criteria.The trimmed average number of patient samples until error detection(tANPed)was used to com-pare the clinical detection efficacy of the ViQC model with those of the five classical algorithms,and the error detection curves were plotted.Results Compare with all the classical algorithms,the ViQC model showed a false positive rate below 0.002 and achieved ac-curacy above 0.951 in detecting all the deviations.When the error factors were 1.5,2.5,and 3.0,the false positive rate of the ViQC model was zero.When the error factor was 2.5,its accuracy reached 0.979.Compared to the five classical PBRTQC algorithms,the ViQC model reduced the overall average tANPed by up to 34％and showed higher sensitivity for error detection.In addition,the ViQC model demonstrated the area under the ROC curve was as high as 0.989 at TEa on the test set,but the value of tANPed wasonly five.Conclusion We successfully established a real-time quality control model for the data of patients based on artificial intelligence algo-rithms,and its efficacy of clinical detection was superior to the traditional PBRTQC algorithms.

Objective To explore the role and mechanism of crocin in pituitary adenoma(PA)through clinical samples and related molecular biology experiments of HP75 cells.Methods From June 2022 to May 2023,16 PA samples were collected from the Second Department of Neurology and Otolaryngology skull base surgery of the First Affiliated Hospital of Kunming Medical University.Three normal control samples were from the human anatomy of the Forensic College of Kunming Medical University.The expression of IRF7 mRNA in clinical samples was detected,and the proliferation,migration,invasion and apoptosis of HP75 cells were detected by knocking down the expression of IRF7;the expression of NF-κB was regulated by IRF7 in HP75 cells,and crocin regulated the growth of PA cells and its regulatory effect on IRF7/NF-κB signaling pathway.Results RT-qPCR and immunohisto-chemistry showed that compared with the normal control group,the expression of IRF7 mRNA in PA was significantly increased(P＜0.001);the expression of IRF7 protein in si-IRF7 group was significantly decreased(P＜0.001);CCK-8,Transwell and flow cytometry results showed that compared with the control group,knockdown of IRF7 significantly decreased the cell viability of HP75 cells(P＜0.001),inhibited the migration and invasion(P＜0.001),and promoted the apoptosis of HP75 cells(P＜0.001).In addition,knockdown of IRF7 could inhibit the expression of p-NF-κB p65/NF-κB p65(P＜0.001)and p-NF-κB p65/NF-κB p65(P＜0.001).Overexpression of IRF7 partially reversed the effect of crocin(P＜0.001)and restored the expression of p-NF-κB p65/NF-κB p65(P＜0.01).Finally,the biological behavior of HP75 cells showed that compared with crocin group,overexpression of IRF7 could improve the cell viability of HP75 cells,promote their migration and invasion,and inhibit cell apoptosis(P＜0.001).Conclusion Crocin treatment can inhibit the proliferation,migration and invasion of PA cells,promote cell apoptosis,and alleviate the development of PA.In the mechanism,IRF7 is significantly overexpressed in PA,and knockdown of IRF7 can inhibit the malignant growth of PA.Crocin can inhibit the proliferation,migration and invasion of PA cells,and promote apoptosis by inhibiting IRF7/NF-κB signaling pathway.

Objective To explore the epidemiological characteristics of pulmonary infection in elderly patients with chronic obstructive pulmonary disease (COPD), and to construct a risk prediction model. Methods Among of 125 elderly patients with COPD from May 2020 to June 2022 were selected as the research subjects. The epidemiological characteristics of infected patients were counted, and the risk factors of pulmonary infection in patients were analyzed and a prediction model was constructed. Results A total of the 125 elderly patients with COPD, there were 46 cases of pulmonary infection, with the infection rate of 36.80%. The detection rate of Gram-negative bacteria was higher than that of Gram-positive bacteria or fungi (64.44% vs 33.33% or 2.22%, P<0.05). Smoking history, presence of diabetes mellitus, mechanical ventilation, irrational use of antibiotics, and hypoalbuminemia were risk factors for pulmonary infection in elderly patients with COPD (P<0.05). Prediction model of pulmonary infection in elderly patients with COPD obtained by multivariate logistic regression analysis was shown as PI=-1.981+0.657×smoking history+0.806×presence of diabetes mellitus+0.521×mechanical ventilation+0.639×irrational use of antibiotics+0.715×presence of hypoalbuminemia. Hosmer-Lemeshow test showed that Hosmer-Lemeshow χ²=0.812 and P=0.295. ROC curve analysis revealed that the AUC value of the prediction model on predicting the pulmonary infection in elderly patients with COPD was 0.802. Conclusion The pathogenic bacteria of elderly patients with COPD complicated with pulmonary infection are mainly Gram-negative bacteria. The prediction model constructed according to the risk factors of pulmonary infection in patients has predictive value on pulmonary infection in patients.

OBJECTIVES: This study aims to investigate the effects of tumor-stromal fibroblasts (TSFs) on the proliferation, invasion, and migration of salivary gland pleomorphic adenoma (SPA) cells in vitro. METHODS: Salivary gland pleomorphic adenoma cells (SPACs), TSFs, and peri-tumorous normal fibroblasts (NFs) were obtained by tissue primary culture and identified by immunocytochemical staining. The conditioned medium was obtained from TSF and NF in logarithmic phase. SPACs were cultured by conditioned medium and treated by TSF (group TSF-SPAC) and NF (group NF-SPAC). SPACs were used as the control group. The proliferation, invasion, and migration of the three groups of cells were detected by MTT, transwell, and scratch assays, respectively. The expression of vascular endothelial growth factor (VEGF) in the three groups was tested by enzyme linked immunosorbent assay (ELISA). RESULTS: Immunocytochemical staining showed positive vimentin expression in NF and TSF. Results also indicated the weak positive expression of α-smooth muscle actin (SMA) and fibroblast activation protein (FAP) in TSFs and the negative expression of α-SMA and FAP in NFs. MTT assay showed that cell proliferation in the TSF-SPAC group was significantly different from that in the NF-SPAC and SPAC groups (P<0.05). Cell proliferation was not different between the NF-SPAC and SPAC groups (P>0.05). Transwell and scratch assays showed no difference in cell invasion and migration among the groups (P>0.05). ELISA showed that no significant difference in VEGF expression among the three groups (P>0.05). CONCLUSIONS TSFs may be involved in SPA biological behavior by promoting the proliferation of SPACs but has no effect on the invasion and migration of SPACs in vitro. Hence, TSF may be a new therapeutic target in SPA treatment.
Humans ; Adenoma, Pleomorphic/metabolism* ; Vascular Endothelial Growth Factor A ; Culture Media, Conditioned/metabolism* ; Fibroblasts/metabolism* ; Salivary Glands/metabolism*

ObjectiveTo explore the mental health status and its influencing factors among middle school students in Xide County， Liangshan Yi Autonomous Prefecture， and to provide references for mental health interventions for local middle school students. MethodsUsing a cross-sectional study design， one junior middle school and one senior middle school in Xide County， Liangshan Prefecture， Sichuan Province， were randomly selected on September 16， 2021， and two classes per grade in each school involving 288 students were recruited. Subjects were assessed using Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）， Generalized Anxiety Disorder Scale-7 item （GAD-7）， PTSD Checklist for DSM-5 （PCL-5）， Multidimensional Scale of Perceived Social Support （MSPSS） and UCLA Loneliness Scale （ULS-3）. Then the scores of above scales were compared among middle school students with different demographic characteristics， and binary Logistic regression analysis was conducted to screen the influencing factors of post-traumatic stress disorder (PTSD） symptoms. ResultsAmong the respondents， 17.71% （95% CI： 0.133~0.221）， 8.68% （95% CI： 0.054~0.120）， 2.78% （95% CI： 0.009~0.047） and 45.83% （95% CI： 0.400~0.516） were reported to have symptoms of depression， anxiety， PTSD and loneliness， respectively. Students in senior middle school scored lower on PCL-5 and ULS-3 than those in junior middle school ［（6.46±8.91） vs. （8.87±9.42）， t=2.202， P<0.05； （4.67±1.65） vs. （5.60±1.88）， t=4.431， P<0.01］. Regression analysis denoted that depressive symptoms （OR=7.630， P<0.05） and anxiety symptoms （OR=3.789， P<0.01） were risk factors for PTSD symptoms among middle school students. ConclusionThe middle school students in Xide County， Liangshan Yi Autonomous Prefecture suffer a high prevalence rate of depressive symptoms and loneliness， and those in junior middle school are more likely to feel a sense of strong loneliness， furthermore， depressive symptoms and anxiety symptoms are risk factors for PTSD symptoms.