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Objective To explore the medication rules of patented Chinese medicine compounds for oral administration in treating alopecia areata (AA), and provide reference for the treatment of AA with Chinese medicine and the development of new drugs. Methods The data of patented Chinese medicine compounds for treating AA were searched from the website of China Patent Publication Announcement of the State Intellectual Property Office. Microsoft Excel 2016 software was used to establish a database, and frequency, nature, taste, and meridian tropism statistics were performed. Apriori association rule modeling analysis was conducted. Cytoscape 3.9.1 software was used to construct the core complex network of Chinese medicines. IBM SPSS Statistics 26.0 software was used for systematic cluster analysis of drugs in the core network. Results A total of 134 oral administration compound formulas meeting the requirements were screened, involving 307 Chinese herbs. Among them, the highfrequency Chinese herbs in oral administration compound formulas included Angelica Sinensis(76 times), Radix Rehmanniae Recens (73 times), Radix Rehmanniae Praeparata(48 times), and Ligusticum chuanxiong (47 times). The main effects of drugs were tonification deficiency and blood circulation, and the main nature was warmness. The main tastes were sweet and bitter, and the main meridians were liver, lung, and kidney meridians. The high confidence association rules in compound formulas mainly combined Angelica Sinensis with Radix Rehmanniae Recens, Radix Rehmanniae Praeparata, etc. Eight new prescriptions were obtained through cluster analysis. Conclusion The national patented Chinese medicine compound oral administration for treating AA mainly focuses on tonifying liver and kidney, supplementing Qi and nourishing blood, combined with promoting blood circulation, removing blood stasis, clearing heat, cooling blood, and expelling wind herbs.

Background: Psoriasis represents a multifaceted and debilitating immune-mediated systemic ailment afflicting millions globally. Despite the continuous discovery of biomarkers associated with psoriasis, identifying lysosomal biomarkers, pivotal as cellular metabolic hubs, remains elusive., Methods: We employed a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA) to initially identify lysosomal genes. Subsequently, to mitigate overfitting and eliminate collinear genes, we applied 12 machine learning algorithms to screen robust lysosomal genes. These genes underwent further refinement through random forest (RF) and Lasso algorithms to ascertain the final hub lysosomal genes. To assess their predictive efficacy, we conducted receiver operating characteristic (ROC) analysis and verified the expression of diagnostic biomarkers at both bulk and single-cell levels. Furthermore, we utilized single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, and Pearson's correlation analysis to elucidate the association between immune phenotypes and hub lysosomal genes in psoriatic samples. Finally, employing the Cellchat algorithm, we explored potential mechanisms underlying the participation of these hub lysosomal genes in cell-cell communication., Results: Functional enrichment analyses revealed a close association between psoriasis and lysosomal functions. Subsequent intersection analysis identified 19 key lysosomal genes, derived from DEGs, phenotypic genes of WGCNA, and lysosomal gene sets. Following the exclusion of collinear genes, we identified 11 robust genes, further refined through RF and Lasso, yielding 3 hub lysosomal genes (S100A7, SERPINB13, and PLBD1) closely linked to disease occurrence, with high predictive capability for disease diagnosis. Concurrently, we validated their relative expression in separate bulk datasets and single-cell datasets. A nomogram based on these hub genes may offer clinical advantages for patients. Notably, these three hub genes facilitated patient classification into two subtypes, namely metabolic-immune subtype 1 and signaling subtype 2. CMap analysis suggested butein and arachidonic fasudil as preferred treatment agents for subtype 1 and subtype 2, respectively. Finally, through Cellchat and correlation analysis, we identified PRSS3-F2R as potentially promoting the expression of hub genes in the psoriasis group, thereby enhancing keratinocyte-fibroblast interaction, ultimately driving psoriasis occurrence and progression., Conclusion: Our study identifies S100A7, SERPINB13, and PLBD1 as potential diagnostic biomarkers, offering promising prospects for more precisely tailored psoriatic immunotherapy designs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Deng, Yan, Shi and Sui.)