TAT-Ngn2 Enhances Cognitive Function Recovery and Regulates Caspase-Dependent and Mitochondrial Apoptotic Pathways After Experimental Stroke

Neurogenin-2 (Ngn2) is a basic helix-loop-helix (bHLH) transcription factor that contributes to the identification and specification of neuronal fate during neurogenesis. In our previous study, we found that Ngn2 plays an important role in alleviating neuronal apoptosis, which may be viewed as an attractive candidate target for the treatment of cerebral ischemia. However, novel strategies require an understanding of the function and mechanism of Ngn2 in mature hippocampal neurons after global cerebral ischemic injury. Here, we found that the expression of Ngn2 decreased in the hippocampus after global cerebral ischemic injury in mice and in primary hippocampal neurons after oxygen glucose deprivation (OGD) injury. Then, transactivator of transcription (TAT)-Ngn2, which was constructed by fusing a TAT domain to Ngn2, was effectively transported and incorporated into hippocampal neurons after intraperitoneal (i.p.) injection and enhanced cognitive functional recovery in the acute stage after reperfusion. Furthermore, TAT-Ngn2 alleviated hippocampal neuronal damage and apoptosis, and inhibited the cytochrome C (CytC) leak from the mitochondria to the cytoplasm through regulating the expression levels of brain-derived neurotrophic factor (BDNF), phosphorylation tropomyosin-related kinase B (pTrkB), Bcl-2, Bax and cleaved caspase-3 after reperfusion injury in vivo and in vitro. These findings suggest that the downregulation of Ngn2 expression may have an important role in triggering brain injury after ischemic stroke and that the neuroprotection of TAT-Ngn2 against stroke might involve the modulation of BDNF-TrkB signaling that regulates caspase-dependent and mitochondrial apoptotic pathways, which may be an attractive therapeutic strategy for cerebral ischemic injury

A Low-Cost iPhone-Assisted Augmented Reality Solution for the Localization of Intracranial Lesions.

Precise location of intracranial lesions before surgery is important, but occasionally difficult. Modern navigation systems are very helpful, but expensive. A low-cost solution that could locate brain lesions and their surface projections in augmented reality would be beneficial. We used an iPhone to partially achieve this goal, and evaluated its accuracy and feasibility in a clinical neurosurgery setting.We located brain lesions in 35 patients, and using an iPhone, we depicted the lesion's surface projection onto the skin of the head. To assess the accuracy of this method, we pasted computed tomography (CT) markers surrounding the depicted lesion boundaries on the skin onto 15 patients. CT scans were then performed with or without contrast enhancement. The deviations (D) between the CT markers and the actual lesion boundaries were measured. We found that 97.7% of the markers displayed a high accuracy level (D ≤ 5mm). In the remaining 20 patients, we compared our iPhone-based method with a frameless neuronavigation system. Four check points were chosen on the skin surrounding the depicted lesion boundaries, to assess the deviations between the two methods. The integrated offset was calculated according to the deviations at the four check points. We found that for the supratentorial lesions, the medial offset between these two methods was 2.90 mm and the maximum offset was 4.2 mm.This low-cost, image-based, iPhone-assisted, augmented reality solution is technically feasible, and helpful for the localization of some intracranial lesions, especially shallow supratentorial intracranial lesions of moderate size

PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data

Preprocessing of the MR images.

<p>(A) A sagittal slice showing the maximal tumor boundary is selected as the first image. (B) A mid-sagittal slice is selected as the second image. (C) In MS Paint, the “Free-Form Select” tool (red arrow) and “Transparent Select” tool (black) are chosen. The tumor and the “P” label (white arrow) are selected together. (D) The selected tumor and the “P” label are cut out together. (E) The selected tumor and “P” label (white arrow) are pasted into the mid-sagittal slice. The red arrow indicates the corresponding “P” label in the mid-sagittal slice. (F) By making the two “P” labels overlap (half white and half red arrow), the projection of the tumor on the mid-sagittal slice is correctly depicted.</p

Acquisition of the sagittal photograph of the patient.

<p>(A) When the iPhone is tilted front-to-back, the white round dot deviates from the center of the circle in the LVL CAM iOS app (Daniel LLC, App Store; Apple Inc.). When the iPhone is tilted left-to-right, the short bar by the side of the round circle deviates from the horizontal line. (B) When the iPhone is vertical to the ground, the round spot and short bars turn to green, and the deviations are zero. (C) Aiming of the round marker at the external ear, and positioning of the patient’s head in the center square on the screen for acquisition of the photograph. (D) Illustration demonstrating the relative position between the iPhone and the patient’s head. In the frontal view, the patient’s head and the iPhone are both vertical to the ground. The distance and height are kept stable. In the view of the top of the head, fine changes to the shooting angle to find the best sagittal plane are demonstrated.</p

Patient characteristics of the second stage.

<p>Patient characteristics of the second stage.</p

Markers’ deviation and accuracy level.

<p>Markers’ deviation and accuracy level.</p

Comparison of markers’ deviation of different operators.

<p>Comparison of markers’ deviation of different operators.</p