Study on correlation between TBM tailings gradation and granitic gneiss surrounding rock stability.

In the process of double-shield TBM excavation, it is difficult to directly observe and test the characteristics of the surrounding rock. In this paper, the screening test of the different type tailings in the wet state was carried out to obtain the gradation curve and curve evaluation index. Combining with the excavation parameters and the surrounding rock characteristics of the tailings, a comprehensive analysis was carried out to establish the evaluation system among with the tailings gradation characteristics, lithology characteristics and excavation parameters. The results showed that: Sparsely fissured rock: the tailings are extensional fractures, the tailings gradation curve is inverse "S" type and gentle, and the evaluation index value: (1.50 > lg(Cu) > 1.35), (1.90 > Cc > 1.10). Broken surrounding rock: the curve is "L" type and steep, the content of coarse particles is much more than that of fine particles and (1.10 > lg(Cu) > 1.00), (2.60 > Cc > 2.40). Fractured rock: the curve is "Step" type, the tailings particles lack the middle particle size, the minerals are mostly weathered, (2.15 > lg(Cu )> 1.95), (0.09 > Cc > 0.07). The research results have good applicability to the surrounding rock stability evaluation of the example tunnel, which verifies the feasibility of the method.

Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer.

Transcription factors (TFs) have been shown to play a key role in the occurrence and development of tumors, including triple-negative breast cancer (TNBC), with a worse prognosis. Machine learning is widely used for establishing prediction models and screening key tumor drivers. Current studies lack TF integration in TNBC, so targeted research on TF prognostic models and targeted drugs is beneficial to improve clinical translational application. The purpose of this study was to use the Least Absolute Shrinkage and Selection Operator to build a prognostic TFs model after cohort normalization based on housekeeping gene expression levels. Potential targeted drugs were then screened on the basis of molecular docking, and a multi-drug combination strategy was used for both in vivo and in vitro experimental studies. The machine learning model of TFs built by E2F8, FOXM1, and MYBL2 has broad applicability, with an AUC value of up to 0.877 at one year. As a high-risk clinical factor, its abnormal disorder may lead to upregulation of the activity of pathways related to cell proliferation. This model can also be used to predict the adverse effects of immunotherapy in patients with TNBC. Molecular docking was used to screen three drugs that target TFs: Trichostatin A (TSA), Doxorubicin (DOX), and Calcitriol. In vitro and in vivo experiments showed that TSA + DOX was able to effectively reduce DOX dosage, and TSA + DOX + Calcitriol may be able to effectively reduce the toxic side effects of DOX on the heart. In conclusion, the machine learning model based on three TFs provides new biomarkers for clinical and prognostic diagnosis of TNBC, and the combination targeted drug strategy offers a novel research perspective for TNBC treatment.