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This paper presents a hardware Trojan classification method that performs a static analysis in gate-level netlist. Based on the controllability and observability characteristics extracted in a circuit, the nets are clustered into two groups with the k-means method. Then inter-cluster distance is measured and taken as the major feature for Trojan identification. By combined with three other features in terms of circuit scale statistic number, a complementary representation of Trojan circuits is constructed. Finally, a support vector machine classifier is trained to distinguish the Trojan circuits from genuine circuits. Experimental results on Trust-HUB benchmarks demonstrate that our method can achieve up to 100% true positive rate.

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Background: Dexamethasone (DEX) is widely adopted to reduce tumor-associated edema in glioblastoma (GBM) patients despite its side effects. However, the benefits of using DEX in GBM patients remains elusive.Methods: In this study, we performed a comprehensive meta-analysis to address this concern. We searched the relevant studies from PubMed, Web of Science, and EMBASE databases, and then applied random or fixed-effects models to generate estimated summary hazard radios (HRs) and the 95% confidence intervals (CIs). Moreover, subgroup and sensitivity analysis were conducted and publication bias were further evaluated.Results: Ten articles with a total of 2,230 GBM patients were eligible according to the inclusion criteria. In the assessment of overall survival (OS), meta-analysis data revealed that DEX was significantly associated with the poor prognosis of GBM patients (HR=1.44, 95% CI=1.32−1.57). In the progression-free survival (PFS), the pooled results indicated that the use of DEX can increase 48% death risk for GBM patients (HR=1.48, 95% CI=1.11−1.98). Subgroup analyses revealed that DEX was associated with poorer outcome of GBM in subgroup of newly diagnosed patients and GBM patients treated with ≥ 2mg/day. Sensitivity analyses showed that no study changed the pooled results materially for both OS and PFS analyses. The funnel plot had no obvious asymmetry.Conclusion: Our findings partly confirmed that use of DEX was associated with poor treatment outcome in GBM patients. To reach a definitive conclusion, large samples from multi-centers are urgent to address this concern.

Classifying the subtypes of non-small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post-operative examination at present. Based on multi-center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Methods: We collected a multi-center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n = 97) and lung3 set (n = 71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ 2,1 -norm minimization was used for feature selection and the importance of selected features was analyzed. Next, the prediction and generalization performance of 130 radiomics models (10 common algorithms and 120 heterogeneous ensemble combinations) were compared by the average AUC value on three test sets. Finally, predictive results of the optimal model were shown. Results: After feature selection, 401 features were obtained. Features of intensity, texture GLCM, GLRLM, and GLSZM had higher classification weight coefficients than other features (shape, texture GLDM, and NGTDM), and the filtered image features exhibited significant importance than original image features (p-value = 0.0210). Moreover, five ensemble learning algorithms (Bagging, AdaBoost, RF, XGBoost, GBDT) had better generalization performance (p-value = 0.00418) than other non-ensemble algorithms (MLP, LR, GNB, SVM, KNN). The Bagging-AdaBoost-SVM model had the highest AUC value (0.815 ± 0.010) on three test sets. It obtained AUC values of 0.819, 0.823, and 0.804 on test set, TCGA set and lung3 set, respectively. Conclusion: Our multi-dataset study showed that intensity features, texture features (GLCM, GLRLM, and GLSZM) and filtered image features were more important for distinguishing ADCs from SCCs. The method of Fan Song and Xiao Song contributed equally to this work.

Background: Although numerous cohort studies have reported an association between antihypertensives use and depression, the exact effect of antihypertensives on depression remains unclear.Objective: To clarify the association between antihypertensives use and risk of depression.Methods: We retrieved relevant literature using PubMed database until August 30, 2021. Four main classes of antihypertensives, thus, angiotensin antagonists, beta blockers, calcium channel blockers and diuretics were studied. The incidence of depression was pooled based on a single drug category. Network meta-analyses were conducted to comprehensively assess the effects of the four classes of antihypertensives on the risk of depression.Results: A total of nine out of 9,557 studies involving 414,873 subjects were retrieved. The pooled results showed a positive association between the use of calcium channel blockers and symptoms of depression [odds ratio (OR): 1.09, 95% confidence interval (CI):1.06–1.13], while use of the angiotensin antagonists, beta blockers and diuretics was not associated with risk of depression. Subgroup analysis suggested a significant relationship between beta blockers usage and risk of depression in cohort studies (OR:1.21, 95% CI: 1.16–1.26). The results of network meta-analysis indicated that all other three classes of drugs increased the risk of depression: angiotensin antagonists (OR: 1.30, 95% CI: 1.04–1.63), beta blockers (OR: 1.53, 95% CI: 1.22–1.91), and calcium channel blockers (OR: 1.40, 95% CI: 1.12–1.75), compared with diuretics.Conclusion: In conclusion, our results indicate that the use of angiotensin antagonists, beta blockers and calcium channel blockers are potential risk factors of depression.