The CXCR2 chemokine receptor is known to have a significant impact on the initiation and control of inflammatory processes. However, its specific involvement in the sensation of itch is not yet fully understood. In this study, we aimed to elucidate the function of CXCR2 in the trigeminal ganglion (TG) by utilizing orofacial itch models induced by incision, chloroquine (CQ), and histamine. Our results revealed a significant up-regulation of CXCR2 mRNA and protein expressions in the primary sensory neurons of TG in response to itch stimuli. The CXCR2 inhibitor SB225002 resulted in notable decrease in CXCR2 protein expression and reduction in scratch behaviors. Distal infraorbital nerve (DION) microinjection of a specific shRNA virus inhibited CXCR2 expression in TG neurons and reversed itch behaviors. Additionally, the administration of the PI3K inhibitor LY294002 resulted in a decrease in the expressions of p-Akt, Akt, and CXCR2 in TG neurons, thereby mitigating pruritic behaviors. Collectively, we report that CXCR2 in the primary sensory neurons of trigeminal ganglion contributes to orofacial itch through the PI3K/Akt signaling pathway. These observations highlight the potential of molecules involved in the regulation of CXCR2 as viable therapeutic targets for the treatment of itch.
Purpose: This study combines automatic segmentation and manual fine-tuning with an early fusion method to provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis. Methods: First, 423 high-quality anterior segment images of keratitis were collected in the Department of Ophthalmology of the Jiangxi Provincial People's Hospital (China). The images were divided into fungal keratitis and non-fungal keratitis by a senior ophthalmologist, and all images were divided randomly into training and testing sets at a ratio of 8:2. Then, two deep learning models were constructed for diagnosing fungal keratitis. Model 1 included a deep learning model composed of the DenseNet 121, mobienet_v2, and squeezentet1_0 models, the least absolute shrinkage and selection operator (LASSO) model, and the multi-layer perception (MLP) classifier. Model 2 included an automatic segmentation program and the deep learning model already described. Finally, the performance of Model 1 and Model 2 was compared. Results: In the testing set, the accuracy, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic (ROC) curve (AUC) of Model 1 reached 77.65, 86.05, 76.19, 81.42%, and 0.839, respectively. For Model 2, accuracy improved by 6.87%, sensitivity by 4.43%, specificity by 9.52%, F1-score by 7.38%, and AUC by 0.086, respectively. Conclusion: The models in our study could provide efficient clinical auxiliary diagnostic efficiency for fungal keratitis.
Purpose: Glaucoma is the main blindness-causing disease in the world. Previous neuroimaging studies demonstrated that glaucoma not only causes the loss of optic ganglion cells but also leads to the abnormal function of the optic nerve pathway and the visual cortex. However, previous studies also reported that patients with glaucoma have dysfunction in the visual cortex in a static state. Whether or not patients with primary angle-closure glaucoma (PACG) were accompanied by dynamic functional connectivity (FC) changes in the primary visual cortex (V1) remains unknown. Methods: A total of 34 patients with PACG (23 men and 11 women) and 34 well-matched healthy controls (HCs) were enrolled in the study. The dynamic functional connectivity (dFC) with the sliding window method was applied to investigate the dynamic functional connectivity changes in the V1. Results: Compared with HCs, patients with PACG showed increased dFC values between left V1 and bilateral calcarine (CAL). Meanwhile, patients with PACG showed increased dFC values between right V1 and bilateral CAL. Conclusion: Our study demonstrated that patients with PACG showed increased dFC within the visual network, which might indicate the increased variability FC in the V1 in patients with PACG.
