\u3csup\u3e99m\u3c/sup\u3eTc-Labeled C2A Domain of Synaptotagmin I as a Target-Specific Molecular Probe for Noninvasive Imaging of Acute Myocardial Infarction

Abstract: The exposure of phosphatidylserine (PtdS) is a common molecular marker for both apoptosis and necrosis and enables the simultaneous detection of these distinct modes of cell death. Our aim was to develop a radiotracer based on the PtdS-binding activity of the C2A domain of synaptotagmin I and assess 99mTc-C2A-GST (GST is glutathione S-transferase) using a reperfused acute myocardial infarction (AMI) rat model. Methods: The binding of C2A-GST toward apoptosis and necrosis was validated in vitro. After labeling with 99mTc via 2-iminothiolane thiolation, radiochemical purity and radiostability were tested. Pharmacokinetics and biodistribution were studied in healthy rats. The uptake of 99mTc-C2A-GST within the area at risk was quantified by direct γ-counting, whereas nonspecific accumulation was estimated using inactivated 99mTc-C2A-GST. In vivo planar imaging of AMI in rats was performed on a γ-camera using a parallel-hole collimator. Radioactivity uptake was investigated by region-of-interest analysis, and postmortem tetrazolium staining versus autoradiography. Results: Fluorescently labeled and radiolabeled C2A-GST bound both apoptotic and necrotic cells. 99mTc-C2A-GST had a radiochemical purity of \u3e98% and remained stable. After intravenous injection, the uptake in the liver and kidneys was significant. For 99mTc-C2A-GST, radioactivity uptake in the area at risk reached between 2.40 and 2.63 %ID/g (%ID/g is percentage injected dose per gram) within 30 min and remained plateaued for at least 3 h. In comparison, with the inactivated tracer the radioactivity reached 1.06 ± 0.49 %ID/g at 30 min, followed by washout to 0.52 ± 0.23 %ID/g. In 7 of 7 rats, the infarct was clearly identifiable as focal uptake in planar images. At 3 h after injection, the infarct-to-lung ratios were 2.48 ± 0.27, 1.29 ± 0.09, and 1.46 ± 0.04 for acute-infarct rats with 99mTc-C2A-GST, sham-operated rats with 99mTc-C2A-GST, and acute-infarct rats with 99mTc-C2A-GST-NHS (NHS is N-hydroxy succinimide), respectively. The distribution of radioactivity was confirmed by autoradiography and histology. Conclusion: The C2A domain of synaptotagmin I labeled with fluorochromes or a radioisotope binds to both apoptotic and necrotic cells. Ex vivo and in vivo data indicate that, because of elevated vascular permeability, both specific binding and passive leakage contribute to the accumulation of the radiotracer in the area at risk. However, the latter component alone is insufficient to achieve detectable target-to-background ratios with in vivo planar imaging

Persulfate Activation on Crystallographic Manganese Oxides: Mechanism of Singlet Oxygen Evolution for Nonradical Selective Degradation of Aqueous Contaminants

Minerals and transitional metal oxides of earth-abundant elements are desirable catalysts for in situ chemical oxidation in environmental remediation. However, catalytic activation of peroxydisulfate (PDS) by manganese oxides was barely investigated. In this study, one-dimension manganese dioxides (a- and ß-MnO2) were discovered as effective PDS activators among the diverse manganese oxides for selective degradation of organic contaminants. Compared with other chemical states and crystallographic structures of manganese oxide, ß-MnO2 nanorods exhibited the highest phenol degradation rate (0.044 min-1, 180 min) by activating PDS. A comprehensive study was conducted utilizing electron paramagnetic resonance, chemical probes, radical scavengers, and different solvents to identity the reactive oxygen species (ROS). Singlet oxygen (1O2) was unveiled to be the primary ROS, which was generated by direct oxidation or recombination of superoxide ions and radicals from a metastable manganese intermediate at neutral pH. The study dedicates to the first mechanistic study into PDS activation over manganese oxides and provides a novel catalytic system for selective removal of organic contaminants in wastewater

NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. Project page: https://dongdu3.github.io/projects/2023/NerVE/.Comment: Accepted by CVPR2023. Project page: https://dongdu3.github.io/projects/2023/NerVE

SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation

Excellent performance has been achieved on instance segmentation but the quality on the boundary area remains unsatisfactory, which leads to a rising attention on boundary refinement. For practical use, an ideal post-processing refinement scheme are required to be accurate, generic and efficient. However, most of existing approaches propose pixel-wise refinement, which either introduce a massive computation cost or design specifically for different backbone models. Contour-based models are efficient and generic to be incorporated with any existing segmentation methods, but they often generate over-smoothed contour and tend to fail on corner areas. In this paper, we propose an efficient contour-based boundary refinement approach, named SharpContour, to tackle the segmentation of boundary area. We design a novel contour evolution process together with an Instance-aware Point Classifier. Our method deforms the contour iteratively by updating offsets in a discrete manner. Differing from existing contour evolution methods, SharpContour estimates each offset more independently so that it predicts much sharper and accurate contours. Notably, our method is generic to seamlessly work with diverse existing models with a small computational cost. Experiments show that SharpContour achieves competitive gains whilst preserving high efficiencyComment: 10pages, 5 figures, accepted by CVPR 2022, project page: see this https://xyzhang17.github.io/SharpContour