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Cancer prevention and treatment are currently the focus of most research. Dose verification is an important step for reducing the improper dose deposition during radiotherapy. To mend the traditional gel dosimeters for 3D dose verification, a novel rare-earth nanoparticle-based composite gel was prepared, which has good radioluminescence property and reusability. It is a promising phantom material for the new 3D gel dosimeter. TEM, DLS, FT-IR, TGA, and spectrofluorometer were used to determine the chemical structure, micromorphology, and optical performance. Compared to the traditional gel dosimeters, the composite gel has a good linear relationship between the light intensity excited by X-ray and the tube current. Furthermore, it may measure the dose distribution immediately in situ, which reduces errors and saves time. This work provides a new idea for the research of 3D gel dosimeters and promotes the safe and effective use of radiotherapy.

Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.

Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.

ZnIn2S4 nanosheets are a promising photoanode for driving photoelectrochemical (PEC) hydrogen fuel production; nevertheless, poor charge separation and sluggish surface reaction kinetics hinder its PEC performance to an extreme degree. Herein, a facile element doping strategy (i.e., P element) was developed to obtain the desired photoanode. As a result, the ZnIn2S4-P (ZIS-P5) photoanode exhibits a remarkable photocurrent density of 1.66 mA cm−2 at 1.23 V versus a reversible hydrogen electrode (VRHE) and a much lower onset potential of 0.12 V vs. RHE for water oxidation. Careful electrochemical analysis confirms that the P doping and sulfur vacancies (Sv) not only facilitate the hole transfer, but also boost surface reaction kinetics. Finally, the "killing two birds with one stone" goal can be achieved. Moreover, the optimized photoanode also presents high PEC performance for urea oxidation, obtaining a photocurrent density of 4.13 mA cm−2 at 1.23 V vs. RHE. This work provides an eco-friendly, simple and effective method to realize highly efficient solar-to-hydrogen conversion. [ABSTRACT FROM AUTHOR]

Covalent organic frameworks (COFs) with a donor‐acceptor (D−A) architecture have been investigated as prospective semiconducting materials, particularly for photocatalytic processes. The incorporation of electron donating and accepting moieties into the COF backbone creates nano heterojunctions, enhancing efficient light absorption, charge separation, and charge transport. Herein, three pyrene‐based COFs were well designed for the aerobic coupling of amines under air atmosphere with blue‐LED irradiation. Among the three COFs, the Py‐Por‐COF with D−A structure exhibits the highest photocatalytic performance and demonstrates a remarkable apparent quantum efficiency of 11.3 % at 420 nm. In addition, the Py‐Por‐COF maintains a good level of crystallinity and high yield even undergone four cycles, showcasing the exceptional potential of D−A structure COFs in the field of photocatalytic organic transformation. [ABSTRACT FROM AUTHOR]

Mitochondrial dysfunction and oxidative stress play a crucial role in the pathogenesis of Alzheimer's disease (AD). The neuroprotective effects of the diosgenin derivative ML5 were investigated in vitro and in vivo, and the underlying mechanisms were explored. The results showed that ML5 ameliorated the study and cognitive deficits in beta-amyloid (Aβ1-42)-induced mouse model and protected neurons in the hippocampal region. In addition, ML5 attenuated H2O2-induced damage to SH-SY5Y cells and increased mitochondrial membrane potential and ATP levels. Western blot and immunofluorescence results showed that ML5 activated nuclear factor-erythroid-2-related factor 2 (NRF2) signaling pathway and increased the expression of NRF2, heme oxygenase-1 (HO-1), and NAD(P)H:quinone oxidoreductase-1 (NQO-1). Our findings suggest that ML5 may be used as a therapeutic drug for AD treatment via targeting the NRF2 pathway, thereby exerting antioxidant and neuroprotective effects. [ABSTRACT FROM AUTHOR]