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Rock blasting technology is crucial for national economic development, particularly resource extraction and infrastructure construction. This paper explores the constitutive relationships in rock blasting, proposing three evolutionary stages: constitutive relationships 1.0, 2.0, and 3.0. These stages focus on contradictory relationships, energy balance, and minimum action theory, respectively. Constitutive relationship 1.0 centers on the interaction between rock and explosive impact loads, emphasizing the offense–defense dynamic. It analyzes the dynamic mechanical response of rock materials under explosive and impact loads, highlighting the conflict and balance between the explosive force and the rock resistance. This stage provides fundamental insights into the behavior of rock materials under high-stress conditions. Constitutive relationship 2.0 approaches the problem from an energy perspective. It treats rocks with significant anisotropy, joints, fractures, bedding, and cavities as complex structural materials. This stage studies the dynamic equilibrium between load input energy and the energy required to destroy the material. By understanding the relationship between structural strength, input energy, dissipated energy, and releasable strain energy, researchers can better predict the response of complex rock structures to explosive loads and improve blasting efficiency. Constitutive relationship 3.0 examines the propagation laws of stress waves under explosive loads and their relationship with medium damage effects. This stage focuses on optimizing energy propagation paths based on the minimum action theory, aiming to maximize the effectiveness of the explosive force while minimizing unwanted damage to the surrounding rock mass. These theories not only reveal the mechanical behavior of rocks under different load conditions but also provide a theoretical basis for optimizing blasting design and improving blasting outcomes. In addition to these theoretical advancements, the integration of artificial intelligence and big data technologies offers a new approach to managing and predicting rock material performance. This paper proposes the concept of rock material engineering genes, which involves establishing a rock gene library to systematically manage the physical and mechanical parameters of rocks. By constructing performance prediction models using advanced data analytics and machine learning algorithms, this approach enhances the accuracy of predicting how different rock types will respond to various engineering applications. Such a comprehensive database has significant implications for resource extraction, geological disaster prevention, and infrastructure construction. The rock material gene library is expected to play an increasingly important role in mineral resource development, geological disaster prevention, and infrastructure construction, thereby promoting the development and application of engineering technology. Its integration with traditional blasting techniques can lead to more efficient and safer methods of rock blasting, ultimately contributing to the advancement of engineering practices and economic development of regions dependent on these technologies. This holistic approach underscores the importance of continued research and technological innovation in rock blasting and material science.

Abstract Purpose Primary Sjogren's syndrome (pSS) is a prevalent autoimmune disease. The immune dysregulation it causes often leads to the development of diffuse large B-cell lymphoma (DLBCL) in clinical practice. However, how it contributes to these two disorders at the molecular level is not yet known. This study explored the potential molecular mechanisms associated with the differences between DLBCL and pSS. Patients and methods Gene expression matrices from discovery cohort 1, discovery cohort 2, and the validation cohort were downloaded from the GEO and TCGA databases. Weighted gene coexpression network analysis (WGCNA) was performed to identify the coexpression modules of DLBCL and pSS in discovery cohort 1 and obtain shared genes. GO and KEGG enrichment analyses and PPI network analysis were performed on the shared genes. Immune-related genes (IRGs) were intersected with shared genes to obtain common genes. Afterward, common genes were identified via machine learning methods. The immune infiltration analysis, miRNA-TF-hub gene regulatory chart, gene interactions of the hub genes, and gene‒drug target analysis were performed. Finally, STAT1 was identified as a possible essential gene by the above analysis, and immune infiltration and GSEA pathway analyses were performed in the high- and low-expression groups in discovery cohort 2. The diagnostic efficacy of the hub genes was assessed in the validation cohort, and clinical samples were collected for validation. Results By WGCNA, one modular gene in each group was considered highly associated with the disease, and we obtained 28 shared genes. Enrichment analysis revealed shared genes involved in the viral response and regulation. We obtained four hub genes (ISG20, STAT1, TLR7, and RSAD2) via the algorithm. Hub genes and similar genes are primarily involved in regulating type I IFNs. The construction of a miRNA-TF-hub gene regulatory chart revealed that hsa-mir-155-5p, hsa-mir-146b-5p, hsa-mir-21-3p, and hsa-mir-126-3p play essential roles in both diseases. Hub genes were differentially expressed in B-cell memory according to immune infiltration analysis. Hub genes had a strong diagnostic effect on both diseases. STAT1 plays an essential role in immune cells in both diseases. Conclusion We identified hub susceptibility genes for DLBCL and pSS and identified hub genes and potential therapeutic targets that may act as biomarkers.

Background: Lianling Tang (LLT), a traditional Chinese herbal decoction, is utilized for managing inflammatory skin conditions in China. This study explores the therapeutic effects and molecular mechanisms of LLT on atopic dermatitis (AD) in a mouse model, employing proteomics techniques. Methods: We evaluated the therapeutic efficacy of LLT on AD in mice by assessing skin inflammation scores, conducting histopathological examinations, and measuring serum levels of IgE, IL-4, and IL-6. Using Tandem Mass Tag (TMT) technology, we performed proteomics analysis of skin samples from Control, Model, LLT-treated, and positive control drug-treated mice, and identified differentially expressed proteins (DEPs). These proteins were then analyzed via GO and KEGG pathways to elucidate LLT's mechanisms in treating AD. Results: LLT markedly reduced skin inflammation scores, epidermal thickening, and mast cell infiltration, and decreased serum IgE, IL-4, and IL-6 levels in AD model mice, highlighting its therapeutic potential. Proteomics analysis identified Staphylococcus aureus infection as a pivotal AD pathogenesis mechanism and revealed critical proteins such as C1QA, C4B, C5, and FCGR3. LLT's mechanisms, such as modulating the complement activation, and influencing phagocytosis, effectively reduce inflammation and alleviate symptoms caused by Staphylococcus aureus in AD. Conclusion: LLT shows potential in AD treatment by modulating pathways associated with Staphylococcus aureus infection, effectively alleviating symptoms.