Objective: To investigate the effects of simulated-thermobaric explosive gas on the respiration and nervous system in rats. Methods: 70 of SPF SD rats were randomly divided into four thermobaric explosive gas groups, two restoration observation groups and control group from April to August in 2018. The exposure time of in four thermobaric explosive gas groups were 3.75, 7.5, 15.0 and 30 min, respectively. The restoration observation groups were designed to observe for 30 and 120 min after exposure thermobaric explosive gas 30 min. The bloods were collected and analyzed at the end of exposure and recovery observation. The endogenous carbon monoxide (CO) , nitric oxide (NO) , glutamic acid (GLU) , acetylcholinesterase (AchE) and dopamine (DA) were detected in brain tissues, respectively. Results: The blood gas index (pH, PCO₂, PO₂, COHb, O₂Hb, MeHbt) and blood electrolytes (Na⁺, K⁺, Ca²⁺ and Cl^-) in exposure groups have significant differences with these in control (P<0.05) . The pH value decreased with the exposure time longer. However, it basically returned to normal level when terminating exposure for 120 min. The concentration of PCO₂, MeHb and CoHb increased first and then decreased with the exposure time extension. Conversely, The PO₂ and O₂Hb decreased first and then increased with the exposure time longer. The concentration of endogenous CO, GLU, and AchE decreased and NO increased in exposure group 4 and the restoration observation group 1 compared with those in control (P<0.01) . In addition, there were pathological changes in lung and brain tissue of exposure group, such as inflammatory cell infiltration and edema. Conclusion The blood gas index, electrolytes, neurotransmitter, histopathology of lung and brain were changed to various degrees by thermobaric bomb gas exposure. These findings would provide some beneficial support for evaluating the damage effect of thermobaric bomb gas on organisms.

Objective To observe the value of dynamic contrast enhanced MRI(DCE-MRI)combined with intravoxel incoherent motion(IVIM)for preoperative evaluation on pathological type of rectal cancer.Methods Totally 81 patients with rectal adenocarcinoma were enrolled and divided into mucinous adenocarcinoma group(n=36)or non mucinous adenocarcinoma group(n=45)based on postoperative pathological results.Parameters of DCE-MRI and IVIM,including rate constant(Kep),volume transfer constant(Ktrans),extravascular extracellular volume fraction(Ve),true diffusion coefficient(D),pseudo diffusion coefficient(D*)and perfusion fraction(f)were compared between groups.Logistic regression analysis was performed,the efficacy of the above parameters for evaluation on pathological type of rectal cancer were explored.Results Kep,Ktrans,D*and f of mucinous adenocarcinoma group were all smaller than those of non mucinous adenocarcinoma group(all P＜0.05).Kep and f were both impact factors of pathological type of rectal cancer(both P＜0.05).The area under the curve(AUC)of Kep,f and Kep+f for preoperative evaluation on pathological type of rectal cancer was 0.774,0.880 and 0.906,respectively,with sensitivity of 69.44％,77.78％and 86.11％,specificity of 82.22％,91.11％and 91.11％,respectively.AUC of Kep was lower than that of Kep+f(P＜0.05).Conclusion DCE-MRI combined with IVIM could effectively evaluate the pathological type of rectal cancer preoperation.

Objective To explore the application value and diagnostic efficiency of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and intravoxel incoherent motion(IVIM)in prognostic staging of early-stage rectal adenocarcinoma.Methods Eighty-one patients with early-stage rectal adenocarcinoma confirmed by surgery were retrospectively selected.They were examined by high-resolution MRI,IVIM and DCE-DWI before operation.According to prognostic staging criteria,they were divided into stageⅠ and stage Ⅱ A groups.The quantitative parameters of DCE-MRI and IVIM were obtained by GenIQ or MITK software respectively.The differences of parameter values between the two groups were compared by independent sample t-test or Mann-Whitney U test,and the diagnostic efficiency was analyzed.Results The values of rate constant(Kep),volume transfer constant(Ktrans)and pseudo diffusion coefficient(D*)in stage Ⅰ group were lower than stage Ⅱ A group(P＜0.05).The f value in stage Ⅰ group was higher than stage Ⅱ A group(P＜0.05).D*and Kep values were included in the binary logistic regression equation.The receiver operating characteristic(ROC)curves of D*,Kep values and their joint prediction probability values were obtained,and the area under the curve(AUC)were 0.791,0.770 and 0.852 respectively.The sensitivities were 90.91％,66.67％and 81.82％respectively.The specificities were 66.67％,83.33％and 79.17％respectively.Conclusion D*and Kep values have the potential to be imaging indicators of prognostic staging of early-stage rectal adenocarcinoma,and to guide the selection of clinical treatment schemes.

Objective To observe the value of multimodal imaging for diagnosis of cardiac space-occupying lesions.Methods Data of 70 patients with cardiac space-occupying lesions who underwent echocardiography and cardiac CT(CCT)were retrospectively analyzed,among them 35 also underwent cardiac MRI(CMRI).The value of multimodal imaging for diagnosis of cardiac space-occupying lesions were explored according to the results of surgical pathology or clinical diagnosis.Results Among 70 cases,benign tumors were confirmed by surgical pathology in 43 cases,while malignant tumors were confirmed by surgical pathology in 3 cases and clinically diagnosed in 1 case.Meanwhile,non-tumor-occupying lesions were clinically diagnosed in 23 cases,all obviously shrunken after treatments.Among 70 cases,echocardiography correctly diagnosed 57 cases,misdiagnosed 8 cases and unclearly diagnosed 5 cases,with diagnostic accuracy rate of 81.43%(57/70).CCT correctly diagnosed 63 cases,misdiagnosed 4 cases but missed 3 cases,with diagnostic accuracy rate of 90.00%(63/70).CMRI outcomes in all 35 cases were consistent with surgical pathologic results,with diagnostic accuracy rate of 100%(35/35).Conclusion Multimodal imaging might provide objective evidences for diagnosis and treatment of cardiac space-occupying lesions.

Objective To explore the value of the prediction model based on diffusion weighted imaging(DWI)radiomic features and apparent diffusion coefficient(ADC)values in the assessment of p53 gene mutation status in endometrial cancer(EC).Methods The DWI data of 22 p53 wild-type and 60 p53 mutant EC patients were analyzed retrospectively.The DWI radiomic features of the primary lesions were extracted,and the ADC values were calculated.The prediction model was constructed based on support vector machine(SVM)algorithm.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve was used to evaluate the performance of the prediction model.Internal validation was conducted using Bootstrap.Results The ADC value of p53 mutant EC was significantly lower than that of p53 wild-type EC(P=0.002).When comparing the ADC value and radiomics model,the combined ADC-radiomics model demonstrated the highest diagnostic efficacy,achieving an AUC of 0.924,sensitivity of 86.67％,and specificity of 90.91％.In Bootstrap-based validation,the combined ADC-radiomics model also showed high performance with an AUC of 0.904.The calibration curve and clinical decision curve analysis(DCA)showed that the combined ADC-radiomics model not only had better agreement between predicted and actual observed values but also provided better net benefits for the patients concerned.Conclusion The combined ADC-radiomics model achieved a more efficient assessment of p53 status in EC patients.

Purpose To explore the value of a prediction model based on diffusion weighted imaging(DWI)deep learning features in endometrial cancer microsatellite instability status assessment.Materials and Methods DWI data of 32 microsatellite instability and 55 microsatellite stability endometrial cancer patients were analysed from June 2020 to April 2023 in Xinxiang Central Hospital,retrospectively.Apparent diffusion coefficient(ADC)values of the primary lesions were measured,and deep learning features and imaging histological features of the primary lesions were extracted using multilayer convolutional neural networks and PyRadiomics,respectively.The least absolute shrinkage and selection operator and random forest were used for feature screening and model building,respectively.The area under the receiver operating characteristic curve(AUC)and net reclassification improvement were used to evaluate model performance.Bootstrap based on 1 000 resamples was used for internal validation of the model.Results For the deep learning model,a total of 6 features were included,the 7th,57th,77th,82nd,97th and 108th features,with an AUC of 0.905(95％CI 0.823-0.957);for the radiomics model,a total of 6 features were included,1 neighborhood grey level difference matrix,4 grey level region size matrices and 1 grey level tour length matrix feature,with an AUC was 0.844(95％CI 0.751-0.913);for ADC values,the microsatellite instability group had smaller ADC values than the microsatellite stability group(t=-4.123,P＜0.001),with an AUC of 0.810(95％CI 0.712-0.886).Compared with the radiomics model and ADC values,the deep learning model showed improved risk prediction,with net reclassification improvements of 0.856 and 0.486(P＜0.01,P=0.024),respectively.In Bootstrap-based internal validation,the deep learning model also demonstrated higher performance than the radiomics model,with AUCs of 0.897(95％CI0.889-0.905)and 0.829(95％CI0.812-0.839),respectively.Conclusion A prediction model based on deep learning features of DWI images can provide a better assessment of microsatellite instability status in endometrial cancer patients than radiomics model and ADC values.

OBJECTIVE: To investigate the effect and mechanism of glucosides of chaenomeles speciosa (GCS) on ischemia/reperfusion-induced brain injury in mouse model. METHODS: Fifty 8-week C57BL/C mice were randomly divided into five groups with 10 in each group:sham group, model group, GCS 30 mg/kg group, GCS 60 mg/kg group and GCS 90 mg/kg group, and the GCS was administrated by gavage (once a day) for 14 d. HE staining was performed to investigate the cell morphology; the Zea-Longa scores were measured for neurological activity; TUNEL staining was performed to investigate the cell apoptosis; ELISA was used to detected the oxidative stress and inflammation; Western Blot was performed to investigate the key pathway and neurological functional molecules. RESULTS: Compared with the sham group, the brain tissues in model group were seriously damaged, presenting severe cell apoptosis, oxidative stress and inflammation, associated with increased NF-κB P65 and TNF-α levels as well as decreased myelin associate glycoprotein (MAG) and oligodendrocyte-myelin glycoprotein (OMgp)levels (all <0.01). Compared with the model group, the brain tissues in GCS groups were ameliorated, and cell apoptosis, oxidative stress and inflammation were inhibited, associated with decreased NF-κB P65 and TNF-α levels as well as increased MAG and OMgp levels (all <0.01), which were more markedly in GCS 60 mg/kg group. CONCLUSIONS GCS can inhibit the NF-κB P65 and TNF-α, reduce the oxidative stress and inflammation, decrease the cell apoptosis in mouse ischemia/reperfusion-induced brain injury model, and 60 mg/kg GCS may be the optimal dose.
Animals ; Brain ; drug effects ; Brain Injuries ; drug therapy ; Gene Expression Regulation ; drug effects ; Glucosides ; pharmacology ; therapeutic use ; Mice ; Mice, Inbred C57BL ; NF-kappa B ; genetics ; Oxidative Stress ; drug effects ; Plant Extracts ; pharmacology ; Random Allocation ; Rosaceae ; chemistry ; Tumor Necrosis Factor-alpha ; genetics