Search

In minimally invasive surgery, endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities. However, in clinical operating environments, endoscopic images often suffer from challenges such as low texture, uneven illumination, and non-rigid structures, which affect feature observation and extraction. This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images, leading to treatment and postoperative recovery issues for patients. To address these challenges, this paper introduces, for the first time, a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion (ASFF) module based on the lightweight architecture of EfficientViT. Additionally, a novel lightweight feature extraction and matching network based on attention mechanism is proposed. This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network. It extracts static and dynamic information features ranging from low-level to high-level, and from local to global, ensuring robust feature extraction across different widths, noise levels, and blur scenarios. Global and local matching are performed through a multi-level cascaded attention mechanism, with cross-channel attention introduced to simultaneously extract low-level and high-level features. Extensive ablation experiments and comparative studies are conducted on the HyperKvasir, EAD, M2caiSeg, CVC-ClinicDB, and UCL synthetic datasets. Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4% in accuracy (Acc), while also enhancing runtime performance and storage efficiency. When compared with the complex DenseDescriptor feature extraction network, the difference in Acc is less than 7.22%, and IoU calculation results on specific datasets outperform complex dense models. Furthermore, this method increases the F1 score by 33.2% and accelerates runtime by 70.2%. It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models, with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN), one of the more prevalent microvascular complications in patients diagnosed with diabetes mellitus, is attributed as the main cause of end-stage renal disease (ESRD). Lipotoxicity in podocytes caused by hyperglycemia has been recognised as a significant pathology change, resulting in the deterioration of the glomerular filtration barrier. Research has demonstrated how dapagliflozin, a kind of SGLT2i, exhibits a multifaceted and powerful protective effect in DN, entirely independent of the hypoglycemic effect, with the specific mechanism verified. In this present study, we found that dapagliflozin has the potential to alleviate apoptosis and restore cytoskeleton triggered by high glucose (HG) in vivo and in vitro. We also discovered that dapagliflozin could mitigate podocyte cholesterol accumulation by restoring the expression of ABCA1, which is the key pathway for cholesterol outflows. This research also mechanistically demonstrates that the protective effect of dapagliflozin can be mediated by KLF-5, which is the upstream transcription factor of ABCA1. Taken together, our data suggest that dapagliflozin offers significant potential in alleviating podocyte injury and cholesterol accumulation triggered by high glucose. In terms of the mechanism, we herein reveal that dapagliflozin could accelerate cholesterol efflux by restoring the expression of ABCA1, which is directly regulated by KLF-5. [ABSTRACT FROM AUTHOR]

Nighttime haze images always suffer from non-uniform illumination from artificial light sources, and most of the current dehazing algorithms are more suitable for daytime image haze removal than nighttime. In this paper, we propose a novel method for nighttime image dehazing via gray space. Firstly, we mapped the haze image from RGB color space to gray space and adopted convolutional neural network to obtain the feature distribution map of the haze. We then fused the haze feature distribution map with original image to obtain the initial haze-free image. Finally, the value and chroma of the initial haze-free image were enhanced in HSV space by improved gamma function. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms for nighttime image haze removal, especially in terms of color consistency and artifacts reduction. [ABSTRACT FROM AUTHOR]