0. 05]. After sublingual glycerytrinitrate administration,the diameter of the brachial artery dilated significantly [(4.26?0.54) mm vs (4.73?0.43) mm, P 0.05]. Sublingual glycerytrinitrate administration dilated the brachial artery significantly [(4. 37? 0.77) mm vs (4. 82?0. 60) mm, P

Objective To investigate the diagnostic value of the T-SPOT.TB test for diagnosing tuberculous meningitis(TBM) by meta-analysis.Methods A systematic retrieval from the databases of PubMed,EMBASE,etc.was performed.The literature on the T-SPOT.TB test for diagnosing TBM was collected.Two reviewers independently screened the literature,extracted the data and judged the quality.The meta analysis was conducted by the Meta-Disc 1.4 software.Results 8 articles were included,involving 425 patients including 232 cases of TBM.In the peripheral blood group,the combined sensitivity was 80%(95%CI:0.74-0.85),the combined specificity was 74%(95%CI:0.67-0.80),the area under the curve(AUC)of summary receiver operating characteristic (SROC)was 0.858 7;the diagnostic odds ratio(DOR)was 15.50.In the CSF group,the combined sensitivity was 76%(95%CI:0.70-0.82),the combined specificity was 83%(95%CI:0.77-0.88),AUC was 0.892 7;DOR was 22.62.Conclusion Adopting the T-SPOT.TB test conduces to increase the diagnostic rate of TBM.The diagnostic accuracy of the T-SPOT.TB test for CSF may be higher than that for peripheral blood.

Objective To establish a quick,economical and reproducible method for high-quality RNA extraction from pancreas.Methods We utilized TRIzol Reagent and liquid nitrogen to isolate total RNA from the rat pancreas.The RNA quality was determined by detection of its content and optic density(A) at 260/280nm,and electrophoresis in 1% non-denatured agarose gel.Then reverse transcription-polymerase chain reaction(RT-PCR) was performed to detect expression of the pancreas-specific genes.Results The content of the total RNA extracted from the rat pancreas reached 3-6?g/mg pancreatic tissues,and A260/280 ratio was 1.75-1.89.Electrophoresis of the total RNA showed 28S and 18S rRNA bands with clear smear between them.The RT-PCR products of pancreas-specific genes including insulin 1,glucagon,?-amylase and housekeeping gene ?-actin all exhibited clear bands on 1% agarose gel,which were located in the expected positions,respectively.Conclusion These results suggest that we have successfully isolated the high-quality and intact RNA from the rat pancreas with TRIzol Reagent and liquid nitrogen.The extracted total RNA can be used in RT-PCR for pancreatic gene expression.

Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.

OBJECTIVE: To investigate the influence of cuspal-coverage thickness on the stress distribution of all-ceramic onlay-restored premolars by using 3D finite element (FE) analysis and to provide references for the design of all-ceramic onlays for clinical application. METHODS: 3D FE models of all-ceramic onlays with three cuspal-coverage thicknesses (2, 3, and 4 mm) of endodontically treated maxillary premolar were constructed based on micro-CT images. Stress distributions in the onlay, adhesive resin cement layer, and dentin of models were analyzed under vertical load (600 N) and oblique load (200 N). RESULTS: When the cuspal-coverage thickness increased, the peak maximum principal stress value decreased inside the onlay but increased in the margin of the adhesive resin cement layer. In addition, stress concentration areas increased in the coronal residual dentin on the palatal side under oblique load. CONCLUSIONS An increase in the cuspal-coverage thickness of all-ceramic onlays may reduce the risk of rupture of the restoration but may deteriorate the restoration and cause palatal dentin fracture.
Bicuspid ; Ceramics ; Composite Resins ; Dental Porcelain ; Dental Stress Analysis ; Finite Element Analysis ; Inlays