Comparative evaluation of multiparametric lumbar MRI radiomic models for detecting osteoporosis.

BACKGROUND: Osteoporosis is a serious global public health issue. Currently, there are few studies that explore the use of multiparametric MRI radiomics for osteoporosis detection. The purpose of this study was to compare the performance of radiomics features from multiple MRI sequences (T1WI, T2WI and T1WI combined with T2WI) for detecting osteoporosis in patients. METHODS: A retrospective analysis was performed on 160 patients who had undergone dual-energy X-ray absorptiometry(DXA) and lumbar magnetic resonance imaging (MRI) at our hospital. Among them, 86 patients were diagnosed with abnormal bone mass (osteoporosis or low bone mass), and 74 patients were diagnosed with normal bone mass based on the DXA results. Sagittal T1-and T2-weighted images of all patients were imported into the uAI Research Portal (United Imaging Intelligence) for image delineation and radiomics analysis, where a series of radiomic features were obtained. A radiomic model that included T1WI, T2WI, and T1WI+T2WI was established using features selected by LASSO regression. We used ROC curve analysis to evaluate the predictive efficacy of each model for identifying bone abnormalities and conducted decision curve analysis (DCA) to evaluate the net benefit of each model. Finally, we validated the model in a sample of 35 patients from different health care institution. RESULTS: The T1WI + T2WI radiomics model showed better screening performance for patients with abnormal bone mass. In the training group, the sensitivity was 0.758, the specificity was 0.78, and the accuracy was 0.768 (AUC =0.839, 95% CI=0.757-0.901). In the validation group, the sensitivity was 0.792, the specificity was 0.875, and the accuracy was 0.833 (AUC =0.86, 95% CI=0.73-0.943).The DCA also showed that the combined model had better net benefits. In the external validation group, the sensitivity was 0.764, the specificity was 0.833, and the accuracy was 0.8 (AUC =0.824, 95% CI 0.678-0.969). CONCLUSIONS: Radiomics-based multiparametric MRI can be used for the quantitative analysis of lumbar MRI and for accurately screening patients with abnormal bone mass.

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.

OBJECTIVES: Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. METHODS: A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. RESULTS: The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. CONCLUSION: The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. CLINICAL RELEVANCE STATEMENT: Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. KEY POINTS: • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.

Risk stratification by nomogram of deep learning radiomics based on multiparametric magnetic resonance imaging in knee meniscus injury.

PURPOSE: To construct and validate a nomogram model that integrated deep learning radiomic features based on multiparametric MRI and clinical features for risk stratification of meniscus injury. METHODS: A total of 167 knee MR images were collected from two institutions. All patients were classified into two groups based on the MR diagnostic criteria proposed by Stoller et al. The automatic meniscus segmentation model was constructed through V-net. LASSO regression was performed to extract the optimal features correlated to risk stratification. A nomogram model was constructed by combining the Radscore and clinical features. The performance of the models was evaluated by ROC analysis and calibration curve. Subsequently, the model was simulated by junior doctors in order to test its practical application effect. RESULTS: The Dice similarity coefficients of automatic meniscus segmentation models were all over 0.8. Eight optimal features, identified by LASSO regression, were employed to calculate the Radscore. The combined model showed a better performance in both the training cohort (AUC = 0.90, 95%CI: 0.84-0.95) and the validation cohort (AUC = 0.84, 95%CI: 0.72-0.93). The calibration curve indicated a better accuracy of the combined model than either the Radscore or clinical model alone. The simulation results showed that the diagnostic accuracy of junior doctors increased from 74.9 to 86.2% after using the model. CONCLUSION: Deep learning V-net demonstrated great performance in automatic meniscus segmentation of the knee joint. It was reliable for stratifying the risk of meniscus injury of the knee by nomogram which integrated the Radscores and clinical features.

Radiomics Based on DCE-MRI Improved Diagnostic Performance Compared to BI-RADS Analysis in Identifying Sclerosing Adenosis of the Breast.

Purpose: Sclerosing adenosis (SA) is a benign lesion that could mimic breast carcinoma and be evaluated as malignancy by Breast Imaging-Reporting and Data System (BI-RADS) analysis. We aimed to construct and validate the performance of radiomic model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) compared to BI-RADS analysis to identify SA. Methods: Sixty-seven patients with invasive ductal carcinoma (IDC) and 58 patients with SA were included in this retrospective study from two institutions. The 125 patients were divided into a training cohort (n= 88) from institution I and a validation cohort from institution II (n=37). Dynamic contrast-enhanced sequences including one pre-contrast and five dynamic post-contrast series were obtained for all cases with different 3T scanners. Single-phase enhancement, multi-phase enhancement, and dynamic radiomic features were extracted from DCE-MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation was performed to build the radscore of each single-phase enhancement and the final model combined multi-phase and dynamic radiomic features. The diagnostic performance of radiomics was evaluated by receiver operating characteristic (ROC) analysis and compared to the performance of BI-RADS analysis. The classification performance was tested using external validation. Results: In the training cohort, the AUCs of BI-RADS analysis were 0.71 (95%CI [0.60, 0.80]), 0.78 (95%CI [0.67, 0.86]), and 0.80 (95%CI [0.70, 0.88]), respectively. In single-phase analysis, the second enhanced phase radiomic signature achieved the highest AUC of 0.88 (95%CI [0.79, 0.94]) in distinguishing SA from IDC. Nine multi-phase radiomic features and two dynamic radiomic features showed the best predictive ability for final model building. The final model improved the AUC to 0.92 (95%CI [0.84, 0.97]), and showed statistically significant differences with BI-RADS analysis (p<0.05 for all). In the validation cohort, the AUC of the final model was 0.90 (95%CI [0.75, 0.97]), which was higher than all BI-RADS analyses and showed statistically significant differences with one of the BI-RADS analysis observers (p = 0.03). Conclusions: Radiomics based on DCE-MRI could show better diagnostic performance compared to BI-RADS analysis in differentiating SA from IDC, which may contribute to clinical diagnosis and treatment.

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging.

OBJECTIVES: To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs). METHODS: A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis. RESULTS: Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02). CONCLUSIONS: A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs. KEY POINTS: • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer.

Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

A Nomogram Combined Radiomic and Semantic Features as Imaging Biomarker for Classification of Ovarian Cystadenomas.

Objective: To construct and validate a combined Nomogram model based on radiomic and semantic features to preoperatively classify serous and mucinous pathological types in patients with ovarian cystadenoma. Methods: A total of 103 patients with pathology-confirmed ovarian cystadenoma who underwent CT examination were collected from two institutions. All cases divided into training cohort (N = 73) and external validation cohort (N = 30). The CT semantic features were identified by two abdominal radiologists. The preprocessed initial CT images were used for CT radiomic features extraction. The LASSO regression were applied to identify optimal radiomic features and construct the Radscore. A Nomogram model was constructed combining the Radscore and the optimal semantic feature. The model performance was evaluated by ROC analysis, calibration curve and decision curve analysis (DCA). Result: Five optimal features were ultimately selected and contributed to the Radscore construction. Unilocular/multilocular identification was significant difference from semantic features. The Nomogram model showed a better performance in both training cohort (AUC = 0.94, 95%CI 0.86-0.98) and external validation cohort (AUC = 0.92, 95%CI 0.76-0.98). The calibration curve and DCA analysis indicated a better accuracy of the Nomogram model for classification than either Radscore or the loculus alone. Conclusion: The Nomogram model combined radiomic and semantic features could be used as imaging biomarker for classification of serous and mucinous types of ovarian cystadenomas.

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion.

BACKGROUND: We aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion. METHODS: A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal-Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed via a calibration curve. Decision curve analysis (DCA) and Kaplan-Meier (KM) survival analysis were performed to evaluate the clinical value of the model. RESULTS: Four optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson's r: 0.451). The nomogram model showed the best performance with high area under the curves in both training cohort (0.881, sensitivity: 0.973; specificity: 0.787) and external validation cohort (0.857, sensitivity: 0.950; specificity: 0.766). The calibration curve, DCA, and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction. CONCLUSION: A nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.

Identifying BAP1 Mutations in Clear-Cell Renal Cell Carcinoma by CT Radiomics: Preliminary Findings.

To evaluate the potential application of computed tomography (CT) radiomics in the prediction of BRCA1-associated protein 1 (BAP1) mutation status in patients with clear-cell renal cell carcinoma (ccRCC). In this retrospective study, clinical and CT imaging data of 54 patients were retrieved from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. Among these, 45 patients had wild-type BAP1 and nine patients had BAP1 mutation. The texture features of tumor images were extracted using the Matlab-based IBEX package. To produce class-balanced data and improve the stability of prediction, we performed data augmentation for the BAP1 mutation group during cross validation. A model to predict BAP1 mutation status was constructed using Random Forest Classification algorithms, and was evaluated using leave-one-out-cross-validation. Random Forest model of predict BAP1 mutation status had an accuracy of 0.83, sensitivity of 0.72, specificity of 0.87, precision of 0.65, AUC of 0.77, F-score of 0.68. CT radiomics is a potential and feasible method for predicting BAP1 mutation status in patients with ccRCC.

Metabolic effects induced by chronic stress in the amygdala of diabetic rats: A study based on ex vivo 1H NMR spectroscopy.

Diabetic patients are prone to suffer from multifarious stressful stimuli in a long-term, which play as an important risk factor for poor prognosis of the disease. Both human and rodent studies have clarified metabolic alterations in several brain regions of patients and experimental models with chronic stress exposure and diabetes. However, metabolic evidences for chronic stress related neurochemical changes in amygdala of diabetic brains have never been explored. We acquired 1H nuclear magnetic resonance (1H NMR) with correlative analysis to study the characteristic metabolites in amygdala samples of diabetic rats under chronic stress exposure (Group DC), compared with non-chronic stress exposure diabetic rats (Group DM). Relative to Group DM, Group DC showed less exploratory activities and memory impairment in behavioral tests. Critically, quantitative and correlative analysis identified that spectra characteristics in amygdala between two groups were significantly different, and levels of various metabolites, such as lactate, tau, glutamate, glutamine, γ-aminobutyric acid, creatine, N-acetylaspartate, choline, glycine, aspartic acid and alanine also showed statistically group differences. These results highlight that chronic stress mediated some major neurotransmitters and energy metabolism related neurochemical alterations in amygdala of diabetic rats. Collectively, these findings provide new insights for underlying metabolic mechanisms of chronic stress-related cognitive dysfunction in diabetes.

Palliative Resection of Primary Tumor in Metastatic Nonfunctioning Pancreatic Neuroendocrine Tumors.

BACKGROUND: Whether primary tumor resection is beneficial in patients with metastatic nonfunctioning pancreatic neuroendocrine tumors (NF-pNETs) remains unclear. This study aimed to investigate whether palliative resection of primary tumor affected the survival of patients with stage IV NF-pNETs. METHODS: We collected data from patients with stage IV NF-pNET registered in the Surveillance, Epidemiology, and End Results database from 2004 to 2015. Risk-adjusted Cox proportional hazard regression analysis and propensity score-matched analysis were used to analyze overall survival (OS) and cancer-specific survival (CSS) of patients. RESULTS: In total, 1974 stage IV NF-pNETs patients were identified, of whom 392 (19.9%) received palliative primary tumor resection. The latter exhibited significantly prolonged OS (hazard ratio = 2.514, 95% confidence interval: 2.081-3.037, P < 0.001) and CSS (hazard ratio = 2.634, 95% confidence interval: 2.159-3.213, P < 0.001) in multivariate Cox regression analysis. According to propensity score-matched results, patients without primary tumor resection had worse OS and CSS. CONCLUSIONS: This study demonstrates that there is a significant correlation between palliative resection of primary tumor and survival benefit. Therefore, resection could be considered as an additional treatment option in this specific patient population.

CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma.

BACKGROUND: The purpose of this study was to analyze the image heterogeneity of clear-cell renal-cell carcinoma (ccRCC) by computer tomography texture analysis and to provide new objective quantitative imaging parameters for the pre-operative prediction of Fuhrman-grade ccRCC. METHODS: A retrospective analysis of 131 cases of ccRCCs was performed by manually depicting tumor areas. Then, histogram-based texture parameters were calculated. The texture-feature values between Fuhrman low- (Grade I-II) and high-grade (Grade III-IV) ccRCCs were compared by two independent sample t-tests (False Discovery Rate correction), and receiver operating characteristic curve (ROC) was used to evaluate the efficacy of using texture features to predict Fuhrman high- and low-grade ccRCCs. RESULTS: There were no statistical differences for any texture parameters without filtering (p > 0.05). There was a statistically significant difference between the entropy (fine) of the corticomedullary phase and the entropy (fine and coarse) of the nephrographic phase after Laplace of Gaussian filtering. The area under the ROC of the entropy was between 0.74 and 0.83. CONCLUSIONS: Computer tomography texture features can predict the Fuhrman grading of ccRCC pre-operatively, with entropy being the most important imaging marker for clinical application.

New Preoperative Nomogram Using the Centrality Index to Predict High Nuclear Grade Clear Cell Renal Carcinoma.

OBJECTIVE: Nuclear grading is an independent prognosis factor of clear-cell renal cell carcinoma (ccRCC). A non-invasive preoperative predictive WHO/International Society of Urologic Pathology (WHO/ISUP) grading of ccRCC model is needed for clinical use. The anatomical complexity scoring system can span a variety of image modalities. The Centrality index (CI) is a quantitatively anatomical score commonly used for renal tumors. The purpose of this study was to develop a simple model to predict WHO/ISUP grading based on CI. MATERIALS AND METHODS: The data in this study were from 248 ccRCC patients from five hospitals. We developed three predictive models using training data from 167 patients: a CI-only model, a valuable clinical parameter model and a fusion model of CI with valuable clinical parameters. We compared and evaluated the three models by discrimination, clinical usefulness and calibration, then tested them in a set of validation data from 81 patients. RESULTS: The fusion model consisting of CI and tumor size (valuable clinical parameter) had an area under the curve (AUC) of 0.82. In the validation set, the AUC was 0.85. The decision curve showed that the model had a good net benefit between the threshold probabilities of 5-80%. And the calibration curve showed good calibration in the training set and validation set. CONCLUSION: This study confirms that CI is associated with the WHO/ISUP grade of ccRCC, and the possibility that a bivariate model incorporating tumor size may help urologist's evaluation patients' prognostic.

Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement.

OBJECTIVE: To objectively quantify intracranial hematoma (ICH) enlargement by analysing the image texture of head CT scans and to provide objective and quantitative imaging parameters for predicting early hematoma enlargement. METHODS: We retrospectively studied 108 ICH patients with baseline non-contrast computed tomography (NCCT) and 24-h follow-up CT available. Image data were assessed by a chief radiologist and a resident radiologist. Consistency analysis between observers was tested. The patients were divided into training set (75%) and validation set (25%) by stratified sampling. Patients in the training set were dichotomized according to 24-h hematoma expansion ≥ 33%. Using the Laplacian of Gaussian bandpass filter, we chose different anatomical spatial domains ranging from fine texture to coarse texture to obtain a series of derived parameters (mean grayscale intensity, variance, uniformity) in order to quantify and evaluate all data. The parameters were externally validated on validation set. RESULTS: Significant differences were found between the two groups of patients within variance at V1.0 and in uniformity at U1.0, U1.8 and U2.5. The intraclass correlation coefficients for the texture parameters were between 0.67 and 0.99. The area under the ROC curve between the two groups of ICH cases was between 0.77 and 0.92. The accuracy of validation set by CTTA was 0.59-0.85. CONCLUSION: NCCT texture analysis can objectively quantify the heterogeneity of ICH and independently predict early hematoma enlargement. KEY POINTS: • Heterogeneity is helpful in predicting ICH enlargement. • CTTA could play an important role in predicting early ICH enlargement. • After filtering, fine texture had the best diagnostic performance. • The histogram-based uniformity parameters can independently predict ICH enlargement. • CTTA is more objective, more comprehensive, more independently operable, than previous methods.

Prognostic role of the primary tumour site in patients with operable small intestine and gastrointestinal stromal tumours: a large population-based analysis.

The postoperative recurrence risk of gastrointestinal stromal tumour (GIST) should be estimated when considering adjuvant systemic therapy. Previous studies in the literature have suggested that small intestinal GISTs are more aggressive than gastric GISTs. We assessed the prognostic role of the primary tumour site in patients with operable GIST to compare the outcomes of gastric and small intestinal GISTs over a decade of treatment. The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of gastric and small intestinal GISTs between 2004 and 2014 using the GIST-specific histology code (ICD-O-3 code 8936), and only patients with tissues sampled by surgical resection were selected for this study. Cancer-specific survival (CSS) and overall survival (OS) were compared between small intestinal and gastric GISTs using Cox regression analyses. GISTs were located in the stomach (n = 2594, 65%), duodenum (n = 228, 6%), and jejunum/ileum (n = 1176, 29%). The OS and CSS of patients with GISTs in the duodenum and jejunum/ileum were similar to those of patients with gastric GISTs in Cox regression analyses, except for the CSS of patients with tumour sizes 2.1-5 cm in diameter and ≤ 5 mitoses per 50 HPFs (HR 1.657; 95% CI 1.062-2.587, p = 0.026). Tumours sizes 2.1-5 cm in diameter and > 5 mitoses per 50 HPFs (HR 4.627; 95% CI 1.035-20.67, p = 0.045) in jejunal/ileal GIST locations had significantly worse CSS than did those in gastric GIST locations. In this large nationwide study, the primary tumour site was not an independent prognostic factor in patients with operable small intestinal and gastric GISTs.

The growth and pluripotency of mesenchymal stem cell on the biodegradable polyurethane synthesized with ferric catalyst.

Polyurethane (PU) is a class of polymers that have been applied for tissue-engineering scaffolds. Cross-linked poly(ester urethane) (CPU), synthesized with ferric catalyst in our laboratory, was modified by silk fibroin (SF) grafting using our aminolysis and glutaradehyde crosslinking method. The physical and chemical properties of the materials were investigated by scanning electron microscope (SEM), atomic force microscope (AFM) and tensile tester. The results showed that SF grafted CPU possessed good strain and strength (4.29 ± 0.18 MPa/382.38 ± 0.71%). Its surface chemistry and roughness were fine to well support the growth of bone marrow mesenchymal stem cells (BMSC). The cells were verified to maintain the pluripotency after they were cultured in vitro for 2 weeks, which supplied us a good technology to keep cell's stemness but proliferate cell's number. These results are valuable for us to further study esophageal tissue engineering with BMSC and polyurethane materials as the components.

[Mixed logit model and its application in insurance choices of the new rural cooperative medical system].

OBJECTIVE: To apply mixed logit model for analyzing the data of new rural cooperative medical with suitability and identify the factors affecting the residents choices of insurance mode. METHODS: Hypothesis test of IIA was performed using the mogtest module of Stata10.0 to test the eligibility of the condition. The mixed logit model was established to allow the parameters to vary in the population using SAS9.1 MDC module. RESULTS: The data in this study did not satisfy the IIA assumption (P<0.01), so that the multinomial logit model was not applicable. The adjusted Estrella of the mixed logit model was 0.6658. CONCLUSION: The mixed logit approach does not rely on the restrictive IIA assumption and allows for correlation patterns between choices and individual variation. This approach can help in the determination of the choices in new rural cooperative medical system.

A novel dual PI3Kalpha/mTOR inhibitor PI-103 with high antitumor activity in non-small cell lung cancer cells.

PI-103, the first synthetic multitargeted compound which simultaneously inhibits PI3Kalpha and mammalian target of rapamycin (mTOR) shows high antitumor activity in glioma xenografts. In the present study, clear antitumor activity was observed with PI-103 treatment in two gefitinib-resistant non-small cell lung cancer (NSCLC) cell lines, A549 and H460, by simultaneously inhibiting p70s6k phosporylation and Akt phosphorylation in response to mTOR inhibition. In addition, H460 cells with activating mutations of PIK3CA were more sensitive to PI-103 than A549 cells with wild-type PIK3CA. PI-103 was found to inhibit growth by causing G0-G1 arrest in A549 and H460 cells. Western blotting showed that PI-103 induced down-regulation of cyclin D1 and E1 and simultaneously up-regulated p21 and p27, associated with arrest in the G0-G1 phase of the cell cycle. Furthermore, p53, the tumor suppressor which transcriptionally regulates p21, was also upregulated with PI-103 treatment. Collectively, our results suggest that multitargeted intervention is the most effective tumor therapy, and the cooperative blockade of PI3Kalpha and mTOR with PI-103 shows promise for treating gefitinib-resistant NSCLC.