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Purpose: Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM). Methods: According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A–D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further. Results: We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients. Conclusions: This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment. [ABSTRACT FROM AUTHOR]

In this work, a photoelectrochemical (PEC) aptamer sensor using a TiO2/AgInS2/ZnS composite material as a photoelectric active anode substrate and NiCo-MOF@Au as a quenching element is constructed to detect Hg2+. Titanium dioxide (TiO2) was prepared by hydrothermal synthesis. The prepared TiO2 consists of rod-like aggregations with a unique structure and large specific surface area. TiO2/AgInS2/ZnS composites with higher photochemical activity were obtained by modifying AgInS2 quantum dots on the surface under electrostatic force and ZnS modification by the successive ionic layer adsorption and reaction (SILAR) method. The constructed PEC aptamer sensing platform has good sensing performance for Hg2+, and the detection limit is as low as 34 fM, showing high selectivity and reproducibility. At the same time, the application of this method in actual environmental samples is evaluated and satisfactory results are obtained. Therefore, the developed photoelectrochemical aptamer sensor provides a promising technique for the detection of Hg2+ in the environment. [ABSTRACT FROM AUTHOR]

In this work, a photoelectrochemical (PEC) aptamer sensor using a TiO2/AgInS2/ZnS composite material as a photoelectric active anode substrate and NiCo-MOF@Au as a quenching element is constructed to detect Hg2+. Titanium dioxide (TiO2) was prepared by hydrothermal synthesis. The prepared TiO2 consists of rod-like aggregations with a unique structure and large specific surface area. TiO2/AgInS2/ZnS composites with higher photochemical activity were obtained by modifying AgInS2 quantum dots on the surface under electrostatic force and ZnS modification by the successive ionic layer adsorption and reaction (SILAR) method. The constructed PEC aptamer sensing platform has good sensing performance for Hg2+, and the detection limit is as low as 34 fM, showing high selectivity and reproducibility. At the same time, the application of this method in actual environmental samples is evaluated and satisfactory results are obtained. Therefore, the developed photoelectrochemical aptamer sensor provides a promising technique for the detection of Hg2+ in the environment. [ABSTRACT FROM AUTHOR]

SUMMARY: A velocity model is generally an imperfect representation of the subsurface, which cannot precisely account for the 3-D inhomogeneities of Earth structure. We present a Bayesian moment tensor inversion framework for applications where reliable, tomography-based, velocity model reconstructions are not available. In particular, synthetic data generated using a 3-D model (SEG-EAGE Overthrust) are inverted using a layered medium model. We use a likelihood function derived from an optimal transport distance—specifically, the transport-Lagrangian distance introduced by Thorpe et al.—and show that this formulation yields inferences that are robust to misspecification of the velocity model. We establish several quantitative metrics to evaluate the performance of the proposed Bayesian framework, comparing it to Bayesian inversion with a standard Gaussian likelihood. We also show that the non-double-couple component of the recovered mechanisms drastically diminishes when the impact of velocity model misspecification is mitigated. [ABSTRACT FROM AUTHOR]