Cryptotanshinone possesses therapeutic effects on ischaemic stroke through regulating STAT5 in a rat model.

CONTEXT: Cryptotanshinone (CT), a lipophilic compound extracted from roots of Salvia miltiorrhiza Bunge (Lamiaceae) (Danshen), has multiple properties in diseases, such as pulmonary fibrosis, lung cancer, and osteoarthritis. Our previous findings suggest that CT plays a protective role in cerebral stroke. However, the molecular mechanisms underlying CT protection in ischaemic stroke remain unclear. OBJECTIVE: This study examines the effect of CT on ischaemic stroke. MATERIALS AND METHODS: We used the middle cerebral artery occlusion (MCAO) rat (Sprague-Dawley rats, 200 ± 20 g, n = 5) model with a sham operation group was treated as negative control. MCAO rats were treated with 15 mg/kg CT using intragastric administration. Moreover, TGF-ß (5 ng/mL) was used to treat MCAO rats as a positive control group. RESULTS: The 50% inhibitory concentration (IC50) of CT on CD4+ cell damage was 485.1 µg/mL, and median effective concentration (EC50) was 485.1 µg/mL. CT attenuates the infarct region in the MCAO model. The percentage of CD4+CD25+FOXP3+ Treg cells in the peripheral blood of the MCAO group was increased with CT treatment. The protein level of FOXP3 and the phosphorylation of STAT5 were recovered in the CD4+CD25+ Treg cells of model group after treated with CT. Importantly, the effects of CT treatment were blocked by treatment with the inhibitor STAT5-IN-1 in CD4+ T cells of the MCAO model. DISCUSSION AND CONCLUSION: Our findings not only enhance the understanding of the mechanisms underlying CT treatment, but also indicate its potential value as a promising agent in the treatment of ischaemic stroke. Further study will be valuable to examine the effects of CT on patients with ischaemic stroke.

Medical Image Super-Resolution Based on Semantic Perception Transfer Learning.

Medical images are an important basis for doctors to diagnose diseases, but some medical images have low resolution due to hardware technology and cost constraints. Super-resolution technology can reconstruct low-resolution medical images into high-resolution images and enhance the quality of low-resolution images, thus assisting doctors in diagnosing diseases. However, traditional super-resolution methods mainly learn the mapping relationships among modal pixels from low resolution to high resolution, lacking the learning of high-level semantic features, resulting in a lack of understanding and utilization of semantic information, such as reconstructed objects, object attributes, and spatial relationships between two objects. In this paper, we propose a medical image super-resolution method based on semantic perception transfer learning. First, we propose a novel semantic perception super-resolution method that empowers super-resolution models to perceive high-level semantics by transferring features of the image description generation network in natural language processing. Second, we construct a semantic feature extraction network and an image description generation network and comprehensively utilized image and text modal data to learn transferable, high-level semantic features. Third, we train an end-to-end, semantic perception super-resolution model by fusing dynamic perceptual convolution, a semantic extraction network, and distillation polarization self-attention. Experiments show that semantic perception transfer learning can effectively improve the quality of super-resolution reconstruction.

Realistic medical image super-resolution with pyramidal feature multi-distillation networks for intelligent healthcare systems.

There are two key requirements for medical lesion image super-resolution reconstruction in intelligent healthcare systems: clarity and reality. Because only clear and real super-resolution medical images can effectively help doctors observe the lesions of the disease. The existing super-resolution methods based on pixel space optimization often lack high-frequency details which result in blurred detail features and unclear visual perception. Also, the super-resolution methods based on feature space optimization usually have artifacts or structural deformation in the generated image. This paper proposes a novel pyramidal feature multi-distillation network for super-resolution reconstruction of medical images in intelligent healthcare systems. Firstly, we design a multi-distillation block that combines pyramidal convolution and shallow residual block. Secondly, we construct a two-branch super-resolution network to optimize the visual perception quality of the super-resolution branch by fusing the information of the gradient map branch. Finally, we combine contextual loss and L1 loss in the gradient map branch to optimize the quality of visual perception and design the information entropy contrast-aware channel attention to give different weights to the feature map. Besides, we use an arbitrary scale upsampler to achieve super-resolution reconstruction at any scale factor. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance compared to other methods in this work.