Objective To compare the accuracy of18F-fluorodeoxyglucose(18F-FDG)PET in locating the epileptogenic focus in patients with temporal lobe epilepsy using the single-patient research method based on statistical parametric mapping(SPM)at dif-ferent confidence levels,and to compare it with the asymmetry index(AI)analysis method.Methods A retrospective analysis was conducted on the clinical and imaging data of 86 patients with drug-resistant temporal lobe epilepsy and 37 healthy controls.The pri-mary epileptogenic focus were located by 18F-FDG PET,and two-sample t-test and intracranial asymmetry analysis were performed on individual patients based on SPM.The accuracy of 18F-FDG PET in locating the epileptogenic focus was compared at different confidence levels P＜0.05,P＜0.05[familywise error rate(FWE)corrected],P＜0.01,P＜0.001,respectively.The diagnostic accuracy of the SPM method at the optimal confidence level P value was compared with the AI analysis method,and the data were analyzed using the x2 test.Results The diagnostic accuracy of the two-sample t-test method were 69.77％,79.07％,67.44％ and 63.95％ at confidence levels of P＜0.05,P＜0.05(FWE corrected),P＜0.01 and P＜0.001,respectively;the diagnostic accuracy of the intracranial asymmetry analysis method were 94.19％,81.39％,79.07％,and 75.58％,respectively.There was a statistically significant differ-ence in diagnostic accuracy between the intracranial asymmetry analysis method(P＜0.05)and the two-sample t-test method P＜0.05(FWE corrected)(x2=8.482,P＜0.05);there was also a statistically significant difference in AI analysis method between the two methods(x2=4.793,P＜0.05).Conclusion The intracranial asymmetry analysis method(P＜0.05)based on SPM has a higher accuracy in locating the primary epileptogenic focus in unilateral drug-resistant temporal lobe epilepsy than those in the two-sample t-test method P＜0.05(FWE corrected)and AI analysis method.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective To observe hierarchical differences in brain functional networks in unilateral mesial temporal lobe epilepsy(mTLE)patients with different outcomes after surgery.Methods A total of 69 unilateral mTLE patients who underwent resection of epileptogenic lesion on the affected side were retrospectively enrolled.Based on Engel classification 1 year after surgery,the patients were divided into seizure free(SF)group and non-seizure free(NSF)group.Functional connectivity gradient analysis was employed to extract principal gradient explaining the highest variance on preoperative resting-state functional MRI(rs-fMRI),then the whole-brain gradient characteristics and principal gradient values within specific functional networks were compared between groups.Results Principal gradient connected default mode network(DMN)with sensorimotor network(SMN)along a continuous axis.Compared to SF group,NSF group showed a contracted gradient range at both ends(DMN and SMN)of the functional network and weakened hierarchical differentiation,and principal gradient value of DMN was higher,while that of SMN was lower than those in SF group(both P＜0.05).Conclusion Hierarchical differences in brain functional networks in unilateral mTLE patients with different outcomes after surgery mainly present as enhanced DMN and weakened SMN in NSF ones,and the latter two might serve as important neuroimaging markers for evaluating postoperative seizure recurrence.

Objective To observe the value of machine learning(ML)models based on brain functional network features combining clinical indicators for predicting postoperative outcomes of patients with drug-resistant mesial temporal lobe epilepsy(DR-mTLE).Methods Totally 84 patients with unilateral DR-mTLE who underwent surgery were retrospectively enrolled and classified into seizure free(SF)group(n=55)and non-seizure free(NSF)group(n=29)according to one-year postoperative follow-up.Clinical data were analyzed to screen independent predictors of postoperative outcomes.Based on brain preoperative resting-state functional MRI,brain functional networks were constructed using graph theory analysis,and 587 features were extracted.Five-fold cross validation was used to divide the data into training set and test set,then the optimal brain functional network features related to postoperative outcomes of DR-mTLE patients were selected.Combining with clinically relevant independent predictors,ML models were constructed using classifiers including Gaussian process(GP),logistic regression(LR),support vector machine(SVM)and quadratic discriminant analysis(QDA),respectively,and the prediction efficacy,calibration and clinical value of each ML model were evaluated.Results Both course of disease and lesion location were clinically relevant independent predictors of postoperative outcome of DR-mTLE patients(OR=0.928,5.710,P=0.010,0.016).Four optimal brain function network features were selected,including betweenness centrality of the third zone of cerebellar vermis,degree centrality of right globus pallidus,nodal efficiency of temporal left inferior temporal gyrus and nodal clustering coefficient of left inferior parietal lobule.The average area under the curve(AUC)of GP,LR,SVM and QDA models in test set was 0.868,0.864,0.875 and 0.870,respectively.Calibration curves and decision curve analysis indicated that each ML model had good calibration and high clinical net benefit.Conclusion ML models based on brain functional network features combining with clinical indicators could be used to effectively predict postoperative outcomes in DR-mTLE patients.

Objective To compare the accuracy of18F-fluorodeoxyglucose(18F-FDG)PET in locating the epileptogenic focus in patients with temporal lobe epilepsy using the single-patient research method based on statistical parametric mapping(SPM)at dif-ferent confidence levels,and to compare it with the asymmetry index(AI)analysis method.Methods A retrospective analysis was conducted on the clinical and imaging data of 86 patients with drug-resistant temporal lobe epilepsy and 37 healthy controls.The pri-mary epileptogenic focus were located by 18F-FDG PET,and two-sample t-test and intracranial asymmetry analysis were performed on individual patients based on SPM.The accuracy of 18F-FDG PET in locating the epileptogenic focus was compared at different confidence levels P＜0.05,P＜0.05[familywise error rate(FWE)corrected],P＜0.01,P＜0.001,respectively.The diagnostic accuracy of the SPM method at the optimal confidence level P value was compared with the AI analysis method,and the data were analyzed using the x2 test.Results The diagnostic accuracy of the two-sample t-test method were 69.77％,79.07％,67.44％ and 63.95％ at confidence levels of P＜0.05,P＜0.05(FWE corrected),P＜0.01 and P＜0.001,respectively;the diagnostic accuracy of the intracranial asymmetry analysis method were 94.19％,81.39％,79.07％,and 75.58％,respectively.There was a statistically significant differ-ence in diagnostic accuracy between the intracranial asymmetry analysis method(P＜0.05)and the two-sample t-test method P＜0.05(FWE corrected)(x2=8.482,P＜0.05);there was also a statistically significant difference in AI analysis method between the two methods(x2=4.793,P＜0.05).Conclusion The intracranial asymmetry analysis method(P＜0.05)based on SPM has a higher accuracy in locating the primary epileptogenic focus in unilateral drug-resistant temporal lobe epilepsy than those in the two-sample t-test method P＜0.05(FWE corrected)and AI analysis method.

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug dis-covery.In the present study,we used a mass spectrometry(MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds.The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells.Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol,as well as the noncanonical targets of clinically approved tamoxifen,lovastatin,and their derivatives.Furthermore,the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints.This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Objective To investigate the MRI characteristics of subventricular zone(SVZ)-associated isocitrate dehydrogenase(IDH)wild-type glioblastoma(GBM)and their correlations with Ki-67 expression and O6-methylguanine-DNA methyltransferase(MGMT)promoter methylation status.Methods A retrospective analysis was conducted on data of 78 patients with IDH wild-type GBM who underwent surgery and received pathological confirmation.Preoperative MRI contrast-enhanced T1 WI sequences were used to assess SVZ involvement,and postoperative molecular testing of tumor markers,including Ki-67 expression and MGMT methylation status,was utilized to categorize the patients accordingly.Results The SVZ involved(+)group(P＜0.001)and the MGMT(+)group(P=0.036)exhibited significantly larger tumor volumes.There were no significant differences between the groups in terms of gender,age,left/right hemispheric lateralization,or specific brain lobe distribution.There was no significant association between Ki-67 expression levels,MGMT methylation status,and SVZ involvement,respectively.Conclusion The SVZ(+)group and the MGMT(+)group demonstrates a wider range of tumor invasion.

The limitations of endovascular repair technology for organ arterial injury at the present stage were analyzed,and an endovascular repair device for organ arterial trunk injury was designed to make up for the shortcomings of the existing technology and improve the success rate of treatment.The new balloon injection device is mainly used for the repair of organ arterial trunk injury,which has good passing ability,can effective repair wound and hemostasis,and protect organ function.In clinical practice,it provided a new treatment method for visceral artery trunk injury with limited use of conventional endovascular treatment techniques and no open surgery opportunity.This device can effectively solve the problems of organ dysfunction caused by arterial embolization in the case of arterial trunk injury,and limited by the degree of tube tortuous in the case of stent isolation surgery.

Objective:To evaluate the feasibility of artificial intelligence (AI) diagnosis of pulmonary nodules on virtual non-contrast(VNC) images derived from dual-layer detector spectral CT.Methods:Totally 52 patients who underwent non-contrast and dual-phase enhanced chest CT scan from May 2022 to November 2022 were enrolled in this study. The VNC images of lung were reconstructed based on venous phase data. CT values and image noise of lung parenchyma, signal-to-noise ratio (SNR) were measured. The dose-length product (DLP) of each scan was recorded and the effective dose ( E) was calculated. All of the objective indicators of image quality and radiation dose were compared by Paired t test. Image quality was evaluated subjectively by two radiologists and compared with Wilcoxon non-parametric test. Wilcoxon symbolic rank test was used to compare the sensitivity and false positive detection rate (FPDR) of AI diagnosis between two groups. Results:Compared with TNC, the noise of venous VNC image was decreased by 13.8%, SNR increased by 14.9%, and both of DLP and E decreased by 33.3% ( t=5.82, -5.35, 22.93, 22.92, P <0.05). There were no significant differences in CT values and subjective scores between 2 groups ( P >0.05). For different types of pulmonary nodules, there was no statistical difference in the sensitivity of AI diagnosis between two groups ( P >0.05). For solid nodules with diameter ≤4 mm and all pulmonary nodules in general, FPDR in VNC group was slightly increased with statistical significance ( Z=-2.03, -3.09, P<0.05), while for other types of pulmonary nodules, there was no statistical difference ( P >0.05). Conclusions:The VNC images of thoracic venous phase based on spectral CT can significantly reduce the radiation dose of patients while the image quality and the AI diagnostic sensitivity of pulmonary nodules remain unchanged, and the FPDR without significantly increase. And it could replace TNC for daily routine.