4-Diazoisochroman-3-imines were investigated for their synthetic applications as a new class of metal carbene precursors. Under the catalysis from a Rh(II) complex, this class of α-diazo imidates reacted with alkenes and conjugated dienes through a formal [2 + 1] (i.e., cyclopropanation) or [4 + 3] cycloaddition to furnish spiro[cyclopropane-1,4'-isochroman]-3'-imines and tetrahydroisochromeno[3,4-b] azepines, respectively. When Rh(II)/AgOTf was used as cocatalyst, the formal [3 + 2] cycloaddition of 4-diazoisochroman-3-imines with terminal alkynes took place, leading to the synthesis of 2-aryl-3,5-dihydroisochromeno[3,4-b]pyrroles.

Fibrotic remodeling, characterized by fibroblast phenotype switching, is often associated with atrial fibrillation and heart failure. This study aimed to investigate the effects on electrotonic myofibroblast-myocyte (Mfb-M) coupling on cardiac myocytes excitability and repolarization of the voltage-gated sodium channels (VGSCs) and single mechanogated channels (MGCs) in human atrial Mfbs. Mathematical modeling was developed from a combination of (1) models of the human atrial myocyte (including the stretch activated ion channel current, I SAC) and Mfb and (2) our formulation of currents through VGSCs (I Na_Mfb) and MGCs (I MGC_Mfb) based upon experimental findings. The effects of changes in the intercellular coupling conductance, the number of coupled Mfbs, and the basic cycle length on the myocyte action potential were simulated. The results demonstrated that the integration of I SAC, I Na_Mfb, and I MGC_Mfb reduced the amplitude of the myocyte membrane potential (V max) and the action potential duration (APD), increased the depolarization of the resting myocyte membrane potential (V rest), and made it easy to trigger spontaneous excitement in myocytes. For Mfbs, significant electrotonic depolarizations were exhibited with the addition of I Na_Mfb and I MGC_Mfb. Our results indicated that I SAC, I Na_Mfb, and I MGC_Mfb significantly influenced myocytes and Mfbs properties and should be considered in future cardiac pathological mathematical modeling.

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

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