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Photodynamic therapy (PDT) is a highly promising modality against cancer, but its efficacy is severely limited by the low oxygen content in solid tumors. In this study, a smart photosensitive NiPS3 nanosheet was developed to solve the problem of low oxygen to allow PDT to be performed against tumors. The photosensitized ROS generation mechanism of NiPS3 is the photon-generated electron-hole pathway, which can generate O2 ·− and ·OH at the conduction band and valance band, respectively. More crucial is that ·OH generation doesn’t need O2, and the O2 ·− can also work in a low O2 environment, and depleting oxygen in tumor cells. Modified with triphenylphosphine (TPP) and based on density functional theory (DFT) calculations and experimental data, the NiPS3@TPP nano-system underwent targeted action toward mitochondria. In vitro experiments demonstrated that the reactive oxygen species (ROS) produced by NiPS3@TPP altered mitochondrial membrane permeability, which not only prolonged the PDT effect but also resulted in mitochondria apoptosis pathways inducing an apoptosis cascade. In vivo experiments demonstrated the targeting capability with low toxicity of the NiPS3@TPP nano-system. Tumor targeting at the tested dose indicated that it represented a promising biocompatible photosensitizer for in vivo biomedical applications.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R2 = 0.62), outperformed models driven solely by spectral bands (R2 = 0.33) or vegetation indices (R2 = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m2). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. [ABSTRACT FROM AUTHOR]

Objective: To investigate the influencing factors affecting the occurrence of microvascular invasion (MVI) and the prognosis of hepatocellular carcinoma (HCC) patients treated with hepatectomy, and to explore how MVI affects prognosis in subgroups with different prognostic factors. Methods: Clinical data of a total of 1633 patients treated surgically for HCC in four treatment centers were included, including 754 patients with MVI. By using the Cox risk regression model and the Mann–Whitney U‐test, the common independent influences on prognosis and MVI were made clear. The incidence of MVI in various subgroups was then examined, as well as the relationship between MVI in various subgroups and prognosis. Results: The Cox risk regression model showed that MVI, Child–Pugh classification, alpha‐fetoprotein (AFP), hepatocirrhosis, tumor diameter, lymphocyte‐to‐monocyte ratio (LMR), and, Barcelona clinic liver cancer (BCLC) grade were independent determinants of overall survival (OS), and MVI, AFP, hepatocirrhosis, tumor diameter, and LMR were influencing determinants for disease‐free survival (DFS). The receiver operating characteristic (ROC) curve showed that MVI was most closely associated with patient prognosis compared to other prognostic factors. AFP, hepatocirrhosis, tumor diameter, and LMR were discovered to be common influences on the prognosis of patients with HCC and MVI when combined with the results of the intergroup comparison of MVI. After grouping, it was showed that patients with hepatocirrhosis, positive AFP (AFP ≥ 20 ng/mL), tumor diameter >50 mm, and LMR ≤3.4 had a significantly higher incidence of MVI than patients in other subgroups, and all four subgroups of MVI‐positive patients had higher rates of early recurrence and mortality (p < 0.05). Conclusions: MVI was found to be substantially linked with four subgroups of HCC patients with hepatocirrhosis, positive AFP, tumor diameter >50 mm, and LMR ≤3.4, and the prognosis of MVI‐positive patients in all four subgroups tended to be worse. [ABSTRACT FROM AUTHOR]