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Cancer, a pressing global health challenge, is characterized by its rapid onset and high mortality rates. Conventional treatment methods prove insufficient in achieving the desired therapeutic outcomes, underscoring the critical need to identify an effective and safe approach for cancer treatment. In this study, a copper-doped nanoparticle known as Cu2+-DOX@ZIF-90 is designed by incorporating copper(II) (Cu(II)) and encapsulating doxorubicin (DOX) within ZIF-90. Leveraging the elevated ATP levels in cancer cells relative to normal cells, Cu2+-DOX@ZIF-90 undergoes intracellular degradation, leading to the release of DOX and Cu(II). DOX, a traditional chemotherapy drug for clinical use, induces apoptosis in cancer cells. Cu(II) interacts with glutathione (GSH) to generate Cu(I), catalyzing H2O2 to produce ˙OH, thereby prompting apoptosis in cancer cells. Concurrently, the reduction of GSH enhances the therapeutic effect of chemodynamic therapy (CDT). Furthermore, Cu(II) triggers the aggregation of lipoylated mitochondrial proteins, leading to the formation of DLAT oligomers and ultimately promoting cuproptosis in cancer cells. In vivo experimental findings demonstrate that Cu2+-DOX@ZIF-90 does not cause damage to normal tissues and organs in tumor-bearing mice, with a notable tumor inhibition rate of 86.18%. This synergistic approach, combining chemotherapy, CDT, and cuproptosis, holds significant promise for the effective and safe treatment of cancer. [ABSTRACT FROM AUTHOR]

A near-infrared fluorescent probe (TX-P) for detecting peroxynitrite is constructed. The probe has a near-infrared emission (725 nm), large Stokes shift (125 nm) and excellent sensitivity and selectivity. In addition, TX-P can be used to visualize ONOO− in living cells, image ONOO− in paw edema mice and evaluate anti-inflammatory drugs. [ABSTRACT FROM AUTHOR]

Layered BiCuXO (X = S, Se, and Te) compounds attract much attention recently because of their superior thermoelectric properties. However, the major shortcoming of these compounds is the relatively large electrical resistance. Exploring the mechanism of electrical scattering of BiCuXO can give some hints on how to optimize their electrical conductivity. Here, we compared electrical and magnetotransport properties of degenerate semiconductor BiCuXO. These compounds show metallic electrical properties, and the in-plane resistivity is linearly dependent on the temperature at the high-temperature regime (>150 K). The magnetoresistance of these compounds is approximately quadratically dependent on the square of magnetic field B, and magnetoresistance decreases from 10%–16% to 0.5% when the temperature is increased from 2 to 300 K. The Hall measurements substantiate that the electrical carriers of BiCuXO are holes, which perhaps come from Bi vacancies. The electron-optical-phonon interaction is the major mechanism of electron scattering when the temperature is higher than 80 K. Analyzing the linear coefficient ρxx-T of BiCuXO (X = S, Se, and Te) suggests that the averaged electron-phonon interaction of BiCuSO is nearly six times larger than that of BiCuSeO/BiCuTeO. Based on the Thomas-Fermi model, compared with BiCuSO, the weak electron-phonon interaction in BiCuSeO/BiCuTeO could be attributed to a highly screened Coulomb interaction between electrons and ions. This work may be useful to optimize the electrical properties of BiCuXO to satisfy the requirement of thermoelectric materials. [ABSTRACT FROM AUTHOR]

To evaluate the large-scale computer networks,a Bayesian-network-approximate-reasoning-based method for vulnerabilities evaluation was proposed.First,it models the elements which compose the network and the factors which affect the network security.Second,it builds the attack state graph（ASG） of the computer network to describe the process of vulnerability exploitation.Then,it makes the approximate reasoning to the ASG by stochastic sampling.At last,after the samples analysis and statistic,it achieves the quantitative evaluation result and will provide the theoretical evidence to improve the network security.

BackgroundCitation counts for peer-reviewed articles and the impact factor of journals have long been indicators of article importance or quality. In the Web 2.0 era, growing numbers of scholars are using scholarly social network tools to communicate scientific ideas with colleagues, thereby making traditional indicators less sufficient, immediate, and comprehensive. In these new situations, the altmetric indicators offer alternative measures that reflect the multidimensional nature of scholarly impact in an immediate, open, and individualized way. In this direction of research, some studies have demonstrated the correlation between altmetrics and traditional metrics with different samples. However, up to now, there has been relatively little research done on the dimension and interaction structure of altmetrics. ObjectiveOur goal was to reveal the number of dimensions that altmetric indicators should be divided into and the structure in which altmetric indicators interact with each other. MethodsBecause an article-level metrics dataset is collected from scholarly social media and open access platforms, it is one of the most robust samples available to study altmetric indicators. Therefore, we downloaded a large dataset containing activity data in 20 types of metrics present in 33,128 academic articles from the application programming interface website. First, we analyzed the correlation among altmetric indicators using Spearman rank correlation. Second, we visualized the multiple correlation coefficient matrixes with graduated colors. Third, inputting the correlation matrix, we drew an MDS diagram to demonstrate the dimension for altmetric indicators. For correlation structure, we used a social network map to represent the social relationships and the strength of relations. ResultsWe found that the distribution of altmetric indicators is significantly non-normal and positively skewed. The distribution of downloads and page views follows the Pareto law. Moreover, we found that the Spearman coefficients from 91.58% of the pairs of variables indicate statistical significance at the .01 level. The non-metric MDS map divided the 20 altmetric indicators into three clusters: traditional metrics, active altmetrics, and inactive altmetrics. The social network diagram showed two subgroups that are tied to each other but not to other groups, thus indicating an intersection between altmetrics and traditional metric indicators. ConclusionsAltmetrics complement, and most correlate significantly with, traditional measures. Therefore, in future evaluations of the social impact of articles, we should consider not only traditional metrics but also active altmetrics. There may also be a transfer phenomenon for the social impact of academic articles. The impact transfer path has transfer, or intermediate, stations that transport and accelerate article social impact from active altmetrics to traditional metrics and vice versa. This discovery will be helpful to explain the impact transfer mechanism of articles in the Web 2.0 era. Hence, altmetrics are in fact superior to traditional filters for assessing scholarly impact in multiple dimensions and in terms of social structure.