Tirofiban for Stroke without Large or Medium-Sized Vessel Occlusion

The effects of the glycoprotein IIb/IIIa receptor inhibitor tirofiban in patients with acute ischemic stroke but who have no evidence of complete occlusion of large or medium-sized vessels have not been extensively studied. In a multicenter trial in China, we enrolled patients with ischemic stroke without occlusion of large or medium-sized vessels and with a National Institutes of Health Stroke Scale score of 5 or more and at least one moderately to severely weak limb. Eligible patients had any of four clinical presentations: ineligible for thrombolysis or thrombectomy and within 24 hours after the patient was last known to be well; progression of stroke symptoms 24 to 96 hours after onset; early neurologic deterioration after thrombolysis; or thrombolysis with no improvement at 4 to 24 hours. Patients were assigned to receive intravenous tirofiban (plus oral placebo) or oral aspirin (100 mg per day, plus intravenous placebo) for 2 days; all patients then received oral aspirin until day 90. The primary efficacy end point was an excellent outcome, defined as a score of 0 or 1 on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days. Secondary end points included functional independence at 90 days and a quality-of-life score. The primary safety end points were death and symptomatic intracranial hemorrhage. A total of 606 patients were assigned to the tirofiban group and 571 to the aspirin group. Most patients had small infarctions that were presumed to be atherosclerotic. The percentage of patients with a score of 0 or 1 on the modified Rankin scale at 90 days was 29.1% with tirofiban and 22.2% with aspirin (adjusted risk ratio, 1.26; 95% confidence interval, 1.04 to 1.53, P = 0.02). Results for secondary end points were generally not consistent with the results of the primary analysis. Mortality was similar in the two groups. The incidence of symptomatic intracranial hemorrhage was 1.0% in the tirofiban group and 0% in the aspirin group. In this trial involving heterogeneous groups of patients with stroke of recent onset or progression of stroke symptoms and nonoccluded large and medium-sized cerebral vessels, intravenous tirofiban was associated with a greater likelihood of an excellent outcome than low-dose aspirin. Incidences of intracranial hemorrhages were low but slightly higher with tirofiban

Methylprednisolone as Adjunct to Endovascular Thrombectomy for Large-Vessel Occlusion Stroke

Importance It is uncertain whether intravenous methylprednisolone improves outcomes for patients with acute ischemic stroke due to large-vessel occlusion (LVO) undergoing endovascular thrombectomy. Objective To assess the efficacy and adverse events of adjunctive intravenous low-dose methylprednisolone to endovascular thrombectomy for acute ischemic stroke secondary to LVO. Design, Setting, and Participants This investigator-initiated, randomized, double-blind, placebo-controlled trial was implemented at 82 hospitals in China, enrolling 1680 patients with stroke and proximal intracranial LVO presenting within 24 hours of time last known to be well. Recruitment took place between February 9, 2022, and June 30, 2023, with a final follow-up on September 30, 2023.InterventionsEligible patients were randomly assigned to intravenous methylprednisolone (n = 839) at 2 mg/kg/d or placebo (n = 841) for 3 days adjunctive to endovascular thrombectomy. Main Outcomes and Measures The primary efficacy outcome was disability level at 90 days as measured by the overall distribution of the modified Rankin Scale scores (range, 0 [no symptoms] to 6 [death]). The primary safety outcomes included mortality at 90 days and the incidence of symptomatic intracranial hemorrhage within 48 hours. Results Among 1680 patients randomized (median age, 69 years; 727 female [43.3%]), 1673 (99.6%) completed the trial. The median 90-day modified Rankin Scale score was 3 (IQR, 1-5) in the methylprednisolone group vs 3 (IQR, 1-6) in the placebo group (adjusted generalized odds ratio for a lower level of disability, 1.10 [95% CI, 0.96-1.25]; P = .17). In the methylprednisolone group, there was a lower mortality rate (23.2% vs 28.5%; adjusted risk ratio, 0.84 [95% CI, 0.71-0.98]; P = .03) and a lower rate of symptomatic intracranial hemorrhage (8.6% vs 11.7%; adjusted risk ratio, 0.74 [95% CI, 0.55-0.99]; P = .04) compared with placebo. Conclusions and Relevance Among patients with acute ischemic stroke due to LVO undergoing endovascular thrombectomy, adjunctive methylprednisolone added to endovascular thrombectomy did not significantly improve the degree of overall disability.Trial RegistrationChiCTR.org.cn Identifier: ChiCTR210005172

Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework

Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification

Hyperspectral image (HSI) classification has attracted widespread concern in recent years. However, due to the complexity of the HSI gathering environment, it is difficult to obtain a great number of HSI labeled samples. Therefore, how to effectively extract the spatial–spectral feature with small-scale training samples is the crucial point of HSI classification. In this paper, a novel fusion framework for small-sample HSI classification is proposed to fully combine the advantages of multidimensional CNN and handcrafted features. Firstly, a 3D fuzzy histogram of oriented gradients (3D-FHOG) descriptor is proposed to fully extract the handcrafted spatial–spectral feature of HSI pixels, which is suggested to be more robust by overcoming the local spatial–spectral feature uncertainty. Secondly, a multidimensional Siamese network (MDSN), which is updated by minimizing both contrastive loss and classification loss, is designed to effectively exploit the CNN-based spatial–spectral features from multiple dimensions. Finally, the proposed MDSN combined with 3D-FHOG is utilized for small-sample HSI classification to verify the effectiveness of our proposed fusion framework. The experimental results on three public data sets indicate that the proposed MDSN combined with 3D-FHOG is significantly better than the representative handcrafted feature-based and CNN-based methods, which in turn demonstrates the superiority of the proposed fusion framework

Facile Preparation of Fe₃O₄@SiO₂ Derived from Iron-Rich Sludge as Magnetic Catalyst for the Degradation of Organic Contaminants by Peroxymonosulfate Activation

Iron-rich sludge, generated during flocculation/sedimentation processes by using Fe-based coagulant in drinking water treatment plants, could be used as a precursor to prepare an effective peroxymonosulfate (PMS) activator (Fe3O4@SiO2) for the ciprofloxacin (CIP) degradation via facile hydrothermal treatment. The catalytic performances of raw iron-rich sludge and Fe3O4@SiO2 were evaluated. The removal rate of CIP in Fe3O4@SiO2/PMS system increased from 44.7% to 82.8% within 60 min compared with the raw iron-rich sludge. The effects of PMS, catalyst loadings, temperature, and initial pH on the CIP degradation were examined, demonstrating that acidic conditions and higher temperatures were beneficial for CIP degradation. Both sulfate radicals (SO4•−) and hydroxyl radicals (•OH) contributed to the CIP degradation, and SO4•− was predominated in the Fe3O4@SiO2/PMS system, which was confirmed by the result of electron paramagnetic resonance (EPR) analysis and radical quenching tests. The mechanisms of the PMS activation process by Fe3O4@SiO2 were elucidated, and the influencing factors were among which the role of the iron mineral phase was emphatically explored. This study provides a facile method to convert the recycled waste iron-rich sludge to magnetic heterogeneous catalysts for CIP degradation with PMS activation

Securing Android applications via edge assistant third-party library detection

Third-party library (TPL) detection in Android has been a hot topic to security researchers for a long time. A precise yet scalable detection of TPLs in applications can greatly facilitate other security activities such as TPL integrity checking, malware detection, and privacy leakage detection. Since TPLs of specific versions may exhibit their own security issues, the identification of TPL as well as its concrete version, can help assess the security of Android APPs. However in reality, existing approaches of TPL detection suffer from low efficiency for their detection algorithm to impracticable and low accuracy due to insufficient analysis data, inappropriate features, or the disturbance from code obfuscation, shrinkage, and optimization. In this paper, we present an automated approach, named PanGuard, to detect TPLs from an enormous number of Android APPs. We propose a novel combination of features including both structural and content information for packages in APPs to characterize TPLs. In order to address the difficulties caused by code obfuscation, shrinkage, and optimization, we identify the invariants that are unchanged during mutation, separate TPLs from the primary code in APPs, and use these invariants to determine the contained TPLs as well as their versions. The extensive experiments show that PanGuard achieves a high accuracy and scalability simultaneously in TPL detection. In order to accommodate to optimized TPL detection, which has not been mentioned by previous work, we adopt set analysis, which speed up the detection as a side effect. PanGuard is implemented and applied on an industrial edge computing platform, and powers the identification of TPL. Beside fast detection algorithm, the edge computing deployment architecture make the detection scalable to real-time detection on a large volume of emerging APPs. Based on the detection results from millions of Android APPs, we successfully identify over 800 TPLs with 12 versions on average. By investigating the differences amongst these versions, we identify over 10 security issues in TPLs, and shed light on the significance of TPL detection with the caused harmful impacts on the Android ecosystem

Additional file 3: Figure S2. of Iron-regulated gene ireA in avian pathogenic Escherichia coli participates in adhesion and stress-resistance

Technical routes of construction of the ireA complementary strain. (TIF 102 kb

Additional file 1: Table S1. of Iron-regulated gene ireA in avian pathogenic Escherichia coli participates in adhesion and stress-resistance

Detailed information about the distribution of the ireA gene in APEC strains. (DOCX 21 kb