RESUMEN
Acne keloidalis is a primary scarring alopecia characterized by longstanding inflammation in the scalp causing keloid-like scar formation and hair loss. Histologically, acne keloidalis is characterized by mixed leukocytic infiltrates in the acute stage followed by a granulomatous reaction and extensive fibrosis in the later stages. To further explore its pathogenesis, bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics were applied to occipital scalp biopsy specimens of lesional and adjacent no-lesional skin in patients with clinically active disease. Unbiased clustering revealed 19 distinct cell populations, including 2 notable populations: POSTN+ fibroblasts with enriched extracellular matrix signatures and SPP1+ myeloid cells with an M2 macrophage phenotype. Cell communication analyses indicated that fibroblasts and myeloid cells communicated by SPP1 signaling networks in lesional skin. A reverse transcriptomics in silico approach identified corticosteroids as possessing the capability to reverse the gene expression signatures of SPP1+ myeloid cells and POSTN+ fibroblasts. Intralesional corticosteroid injection greatly reduced SPP1 and POSTN gene expression as well as acne keloidalis disease activity. Spatial transcriptomics and immunofluorescence staining verified microanatomic specificity of SPP1+ myeloid cells and POSTN+ fibroblasts with disease activity. In summary, the communication between POSTN+ fibroblasts and SPP1+ myeloid cells by SPP1 axis may contribute to the pathogenesis of acne keloidalis.
RESUMEN
Daxing'anling Mountains is one of the areas with the highest occurrence of forest lighting fire in Heilongjiang Province, and developing a lightning fire forecast model to accurately predict the forest fires in this area is of importance. Based on the data of forest lightning fires and environment variables, the MAXENT model was used to predict the lightning fire in Daxing' anling region. Firstly, we studied the collinear diagnostic of each environment variable, evaluated the importance of the environmental variables using training gain and the Jackknife method, and then evaluated the prediction accuracy of the MAXENT model using the max Kappa value and the AUC value. The results showed that the variance inflation factor (VIF) values of lightning energy and neutralized charge were 5.012 and 6.230, respectively. They were collinear with the other variables, so the model could not be used for training. Daily rainfall, the number of cloud-to-ground lightning, and current intensity of cloud-to-ground lightning were the three most important factors affecting the lightning fires in the forest, while the daily average wind speed and the slope was of less importance. With the increase of the proportion of test data, the max Kappa and AUC values were increased. The max Kappa values were above 0.75 and the average value was 0.772, while all of the AUC values were above 0.5 and the average value was 0. 859. With a moderate level of prediction accuracy being achieved, the MAXENT model could be used to predict forest lightning fire in Daxing'anling Mountains.
