Alpha-lipoic acid (ALA) ameliorates early brain injury after subarachnoid hemorrhage in Sprague-Dawley (SD) rats via inhibiting STING-NLRP3 inflammatory signaling.

Neuroinflammation is intimately associated with poor prognosis in patients with subarachnoid hemorrhage (SAH). Alpha-lipoic acid (ALA), a disulfide antioxidant, has been shown to be neuroprotective in an in vivo model of neurological injury; however, the role of ALA in SAH has never been evaluated. In this study, the Sprague-Dawley rats SAH model was induced by endovascular perforation method. ALA was transplanted intravenously into rats, and SR-717, a stimulator of interferon genes (STING) agonist, was injected intraperitoneally. The effects of ALA on early brain injury were assayed by neurological score, hematoxylin and eosin staining and Nissl staining. Immunohistochemistry staining and Western blotting were used to analyze various proteins. ALA significantly reduced STING- NLRP3 protein expression and decreased cell death, which in turn mitigated the neurobehavioral dysfunction following SAH. Furthermore, coadministration of ALA and SR-717 promoted STING-NLRP3 signaling pathway activation following SAH, which reversed the inhibitory effect of ALA on STING-NLRP3 protein activation and increased the neurological deficits. In conclusion, ALA may be a promising therapeutic strategy for alleviating early brain injury after SAH.

Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture.

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.

Krishnolides E-K: New limonoids from the Krishna mangrove Xylocarpus moluccensis.

Seven new limonoids, named krishnolides E-K (1-7), including three khayanolides, two mexicanolides, a derivative of trangmolin A, and an andirobin, were isolated from seeds of the Indian Krishna mangrove, Xylocarpus moluccensis. The structures of these limonoids were established by HRESIMS, extensive NMR investigations, and X-ray crystallography. Most notably, the absolute configurations of 1, 5, 6, and 7 were unequivocally determined by single-crystal X-ray diffraction analyses (Cu Kα). Krishnolide F (2) exhibited significant agonistic effects on human pregnane-X-receptor (hPXR) at the concentration of 10.0 µM. Molecular docking revealed that 2 could bind a helix near the region of the Helix 12 of hPXR. Polar contribution could be electrostatic effects from the formation of two stable protein-ligand hydrogen bonds between Gln285/1-OH and His407/1-OH, respectively. This is the first report of agonistic effects of a khayanolide-type limonoid on hPXR.

Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks.

SIGNIFICANCE: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. APPROACH: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of ∼4860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. RESULTS: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores = 94 % for non-zeros padding and F1-score = 96 % for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. CONCLUSIONS: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability.

Atherosclerotic Plaque Tissue Characterization: An OCT-Based Machine Learning Algorithm With ex vivo Validation.

There is a need to develop a validated algorithm for plaque characterization which can help to facilitate the standardization of optical coherence tomography (OCT) image interpretation of plaque morphology, and improve the efficiency and accuracy in the application of OCT imaging for the quantitative assessment of plaque vulnerability. In this study, a machine learning algorithm was implemented for characterization of atherosclerotic plaque components by intravascular OCT using ex vivo carotid plaque tissue samples. A total of 31 patients underwent carotid endarterectomy and the ex vivo carotid plaques were imaged with OCT. Optical parameter, texture features and relative position of pixels were extracted within the region of interest and then used to quantify the tissue characterization of plaque components. The potential of individual and combined feature set to discriminate tissue components was quantified using sensitivity, specificity, accuracy. The results show there was a lower classification accuracy in the calcified tissue than the fibrous tissue and lipid tissue. The pixel-wise classification accuracy obtained by the developed method, to characterize the fibrous, calcified and lipid tissue by comparing with histology, were 80.0, 62.0, and 83.1, respectively. The developed algorithm was capable of characterizing plaque components with an excellent accuracy using the combined feature set.

Coupled Modeling of Lipid Deposition, Inflammatory Response and Intraplaque Angiogenesis in Atherosclerotic Plaque.

We propose a multiphysical mathematical model by fully coupling lipid deposition, monocytes/macrophages recruitment and angiogenesis to investigate the pathophysiological responses of an atherosclerotic plaque to the dynamic changes in the microenvironment. The time evolutions of cellular (endothelial cells, macrophages, smooth muscle cells, etc.) and acellular components (low density lipoprotein, proinflammatory cytokines, extravascular plasma concentration, etc.) within the plaque microenvironment are assessed quantitatively. The thickening of the intima, the distributions of the lipid and inflammatory factors, and the intraplaque hemorrhage show a qualitative consistency with the MRI and histology data. Models with and without angiogenesis are compared to demonstrate the important role of neovasculature in the accumulation of blood-borne components in the atherosclerotic lesion by extravasation from the leaky vessel wall, leading to the formation of a lipid core and an inflammatory microenvironment, which eventually promotes plaque destabilization. This model can serve as a theoretical platform for the investigation of the pathological mechanisms of plaque progression and may contribute to the optimal design of atherosclerosis treatment strategies, such as lipid-lowering or anti-angiogenetic therapies.

Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images.

Intravascular optical coherence tomography (IVOCT) has been successfully utilized for in vivo diagnostics of coronary plaques. However, classification of atherosclerotic tissues is mainly performed manually by experienced experts, which is time-consuming and subjective. To overcome these limitations, an automatic method of segmentation and classification of IVOCT images is developed in this paper. The method is capable of detecting the plaque contour between the fibrous tissues and other components. Subsequently, the method classifies the tissues based on their texture features described by Fourier transform and discrete wavelet transform. The experimental results of 103 images show that an overall classification accuracy of over 80% in the indicator of depth and span angle is achieved in comparison to manual results. The validation suggests that this method is objective, accurate, and automatic without any manual intervention. The proposed method is able to demonstrate the artery wall morphology successfully, which is valuable for the research of atherosclerotic disease.