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Rosacea is a chronic inflammatory skin disease characterized by facial erythema and telangiectasia. Despite ongoing research, the pathogenesis of rosacea remains incompletely understood, and current therapies are not entirely satisfactory. The JAK/STAT signaling pathway plays an essential role in immunoregulation, inflammation, and neurovascular regulation. Inhibition of the JAK/STAT pathway appears to hold promise as a potential therapy for rosacea. This study aimed to investigate the effects of the JAK inhibitor tofacitinib on rosacea and to preliminarily explore its therapeutic mechanism. To this end, a rosacea-like mouse model was induced using LL37 and treated with a 2% tofacitinib emulsion. The results demonstrated that topical application of tofacitinib significantly ameliorated rosacea-like phenotype, reduced the infiltration of CD4+ T cells and mast cells, and suppressed dermal angiogenesis. RT-qPCR analysis revealed a reduction in mRNA expression levels of STAT1, STAT4, and STAT5a in skin lesions following topical tofacitinib treatment. Additionally, three patients diagnosed with erythematotelangiectatic rosacea (ETR) were included in the study and treated with oral tofacitinib, leading to a significant improvement in erythema and flushing symptoms. These findings collectively suggest that tofacitinib alleviates LL37-induced rosacea-like skin inflammation in mice and rosacea skin lesions by inhibiting the JAK/STAT signaling pathway. [ABSTRACT FROM AUTHOR]

The effective detection of safflower in the field is crucial for implementing automated visual navigation and harvesting systems. Due to the small physical size of safflower clusters, their dense spatial distribution, and the complexity of field scenes, current target detection technologies face several challenges in safflower detection, such as insufficient accuracy and high computational demands. Therefore, this paper introduces an improved safflower target detection model based on YOLOv5, termed Safflower-YOLO (SF-YOLO). This model employs Ghost_conv to replace traditional convolution blocks in the backbone network, significantly enhancing computational efficiency. Furthermore, the CBAM attention mechanism is integrated into the backbone network, and a combined L C I O U + N W D loss function is introduced to allow for more precise feature extraction, enhanced adaptive fusion capabilities, and accelerated loss convergence. Anchor boxes, updated through K-means clustering, are used to replace the original anchors, enabling the model to better adapt to the multi-scale information of safflowers in the field. Data augmentation techniques such as Gaussian blur, noise addition, sharpening, and channel shuffling are applied to the dataset to maintain robustness against variations in lighting, noise, and visual angles. Experimental results demonstrate that SF-YOLO surpasses the original YOLOv5s model, with reductions in GFlops and Params from 15.8 to 13.2 G and 7.013 to 5.34 M, respectively, representing decreases of 16.6% and 23.9%. Concurrently, SF-YOLO’s mAP0.5 increases by 1.3%, reaching 95.3%. This work enhances the accuracy of safflower detection in complex agricultural environments, providing a reference for subsequent autonomous visual navigation and automated non-destructive harvesting technologies in safflower operations. [ABSTRACT FROM AUTHOR]