Fabrication of bimetallic Ag@ZnO nanocomposite and its anti-cancer activity on cervical cancer via impeding PI3K/AKT/mTOR pathway.

INTRODUCTION: Bimetallic nanoparticles, specifically Zinc oxide (ZnO) and Silver (Ag), continue to much outperform other nanoparticles investigated for a variety of biological uses in the field of cancer therapy. This study introduces biosynthesis of bimetallic silver/zinc oxide nanocomposites (Ag@ZnO NCs) using the Crocus sativus extract and evaluates their anti-cancer properties against cervical cancer. METHODS: The process of generating bimetallic nanoparticles (NPs), namely Ag@ZnO NCs, through the utilization of Crocus sativus extract proved to be uncomplicated and eco-friendly. Various methods, such as UV-vis, DLS, FTIR, EDX, and SEM analyses, were utilized to characterize the generated Ag@ZnO NCs. The MTT assay was employed to assess the cytotoxic properties of biosynthesized bimetallic Ag@ZnO NCs against the HeLa cervical cancer cell line. Moreover, the impact of Ag@ZnO NCs on HeLa cells was assessed by examining cell survival, ROS production, MMP levels, and induced apoptosis. Through western blot analysis, the expression levels of the PI3K, AKT, mTOR, Cyclin D, and CDK proteins seemed to be ascertained. Using flow cytometry, the cancer cells' progression through necrosis and apoptosis, in addition to the cell cycle analysis, were investigated. RESULTS: Bimetallic Ag@ZnO NCs that were biosynthesized showed a high degree of stability, as demonstrated by the physicochemical assessments. The median size of the particles in these NCs was approximately 80-90 nm, and their zeta potential was -14.70 mV. AgNPs and ZnO were found, according to EDX data. Further, Ag@ZnO NCs hold promise as a potential treatment for cervical cancer. After 24 hours of treatment, a dosage of 5 µg/mL or higher resulted in a maximum inhibitory effect of 58 ± 2.9. The concurrent application of Ag/ZnO NPs to HeLa cells resulted in elevated apoptotic signals and a significant generation of reactive oxygen species (ROS). As a result, the bimettalic Ag@ZnO NCs treatment has been recognized as a chemotherapeutic intervention by inhibiting the production of PI3K, AKT, and mTOR-mediated regulation of propagation and cell cycle-regulating proteins. CONCLUSIONS: The research yielded important insights into the cytotoxic etiology of biosynthesized bimetallic Ag@ZnO NCs against HeLa cells. The biosynthesized bimetallic Ag@ZnO NCs have a significant antitumor potential, which appears to be associated with the development of oxidative stress, which inhibits the development of the cell cycle and the proliferation of cells. Therefore, in the future, biosynthesized bimetallic Ag@ZnO NCs may be used as a powerful anticancer drug to treat cervical cancer.

A High-Performance MoS2-Based Visible-Near-Infrared Photodetector from Gateless Photogating Effect Induced by Nickel Nanoparticles.

Recent advancements in two-dimensional materials have shown huge potential for optoelectronic applications. It is challenging to achieve highly effective and sensitive broadband photodetection based on MoS2 devices. Defect engineering, such as introducing vacancies, can narrow the bandgap and boost the separation of photogenerated carriers by defect states but leads to a slow response speed. Herein, we propose a nickel nanoparticle-induced gateless photogating effect with a unique energy band structure to enable the application of defect engineering and achieve high optoelectronic performance. The device based on Ni nanoparticle-decorated MoS2 with S vacancies exhibited high responsivities of 106.21 and 1.38 A W-1 and detectivities of 1.9 × 1012 and 8.9 × 109 Jones under 532 and 980 nm illumination (visible to near infrared), respectively, with highly accelerated response speed. This strategy provides new insight into optimizing defect engineering to design high-performance optoelectronic devices capable of broadband photodetection.

High-Throughput Calculation of Interlayer van der Waals Forces Validated with Experimental Measurements.

Interlayer van der Waals interactions play an important role in two-dimensional (2D) materials on various occasions. The interlayer binding force is often directly measured and is considered more closely related to the exfoliation condition. However, a binding force database from accurate theoretical calculations does not yet exist. In this work, the critical interlayer binding force and energy are directly calculated for 230 2D materials, which exhibit divergent trends. A linear relationship that links the two quantities with the equilibrium interlayer distance is found and checked. Experiments are carried out for three different materials using atomic force microscopy. The measured forces show a consistent trend with the calculated results, and the estimated binding strengths are of the same order of magnitude as the predicted values. Our work can provide a reliable reference for interlayer adhesion studies and help establish accurate models of exfoliation processes.

Promoting the Water-Reduction Kinetics and Alkali Tolerance of MoNi4 Nanocrystals via a Mo2 TiC2 Tx Induced Built-In Electric Field.

Mo-Ni alloy-based electrocatalysts are regarded as promising candidates for the hydrogen evolution reaction (HER), despite their vulnerable stability in alkaline solution that hampers further application. Herein, Mo2 TiC2 Tx MXene, is employed as a support for MoNi4 alloy nanocrystals (NCs) to fabricate a unique nanoflower-like MoNi4 -MXn electrocatalyst. A remarkably strong built-in electric field is established at the interface of two components, which facilitates the electron transfer from Mo2 TiC2 Tx to MoNi4 . Due to the accumulation of electrons at the MoNi4 sites, the adsorption of the catalytic intermediates and ionic species on MoNi4 is affected consequently. As a result, the MoNi4 -MX10 nanohybrid exhibits the lowest overpotential, even lower than 10% Pt/C catalyst at the current density of 10 mA cm-2 in 1 m KOH solution (122.19 vs 129.07 mV, respectively). Furthermore, a lower Tafel slope of 55.88 mV dec-1 is reported as compared to that of the 10% Pt/C (65.64 mV dec-1 ). Additionally, the MoNi4 -MX10 catalyst also displays extraordinary chemical stability in alkaline solution, with an activity loss of only 0.15% per hour over 300 h of operation. This reflects the great potential of using MXene-based interfacial engineering for the synthesis of a highly efficient and stable electrocatalyst.

Comparison of Thyroid Hormone Levels Between Patients With Major Depressive Disorder and Healthy Individuals in China.

Abnormal thyroid function in major depressive disorder (MDD) has been studied extensively, but the results still remain inconsistent. In China, few large-scale studies have investigated the differences in the levels of thyroid hormones between patients with MDD and healthy controls (HCs). In this retrospective, cross-sectional study, 535 MDD patients and 998 HCs were included. We compared the levels of thyroid hormones (FT3, FT4, and TSH) between the two groups, as well as investigated the distribution of levels of thyroid hormones within and outside normal ranges. The results showed that all the three hormones were significantly lower in MDD patients than in HCs, which was also true in different gender and age subgroups. The proportion of subjects with levels of all the three hormones outside the normal range in the MDD group was higher than that in the HC group (all p < 0.05). However, no significant difference was found in clinical/subclinical hyperthyroidism or hypothyroidism between the two groups (p > 0.05). Our study showed that the levels of thyroid hormones were lower in MDD patients, suggesting that there was an association between abnormal thyroid function and depression. The higher rate of thyroid dysfunction in MDD patients indicated the importance of regular monitoring of thyroid function.

Bayesian rank penalization.

Rank minimization is a key component of many computer vision and machine learning methods, including robust principal component analysis (RPCA) and low-rank representations (LRR). However, usual methods rely on optimization to produce a point estimate without characterizing uncertainty in this estimate, and also face difficulties in tuning parameter choice. Both of these limitations are potentially overcome with Bayesian methods, but there is currently a lack of general purpose Bayesian approaches for rank penalization. We address this gap using a positive generalized double Pareto prior, illustrating the approach in RPCA and LRR. Posterior computation relies on hybrid Gibbs sampling and geodesic Monte Carlo algorithms. We assess performance in simulation examples, and benchmark data sets.

Tuning the electronic properties of van der Waals heterostructures composed of black phosphorus and graphitic SiC.

This study presents a new van der Waals (vdW) heterostructure composed of monolayer black phosphorus (BP) and monolayer graphitic SiC (g-SiC). Using first-principles calculations, the structural and electronic properties of the BP/SiC heterostructure were investigated. It was found that by stacking BP with SiC, weak type-I band alignment can be achieved with a band gap of 0.705 eV, where the direct band gap as well as linear dichroism features were well preserved. The electrostatic potential drop in the heterojunction was calculated to be 4.044 eV. By applying perpendicular electric field, the band alignment can be altered to either type-I or type-II, and the band gap can be effectively controlled by field intensity, hence making the heterostructure suitable for various applications.

Subspace segmentation by dense block and sparse representation.

Subspace segmentation is a fundamental topic in computer vision and machine learning. However, the success of many popular methods is about independent subspace segmentation instead of the more flexible and realistic disjoint subspace segmentation. Focusing on the disjoint subspaces, we provide theoretical and empirical evidence of inferior performance for popular algorithms such as LRR. To solve these problems, we propose a novel dense block and sparse representation (DBSR) for subspace segmentation and provide related theoretical results. DBSR minimizes a combination of the 1,1-norm and maximum singular value of the representation matrix, leading to a combination of dense block and sparsity. We provide experimental results for synthetic and benchmark data showing that our method can outperform the state-of-the-art.

Structure-constrained low-rank representation.

Benefiting from its effectiveness in subspace segmentation, low-rank representation (LRR) and its variations have many applications in computer vision and pattern recognition, such as motion segmentation, image segmentation, saliency detection, and semisupervised learning. It is known that the standard LRR can only work well under the assumption that all the subspaces are independent. However, this assumption cannot be guaranteed in real-world problems. This paper addresses this problem and provides an extension of LRR, named structure-constrained LRR (SC-LRR), to analyze the structure of multiple disjoint subspaces, which is more general for real vision data. We prove that the relationship of multiple linear disjoint subspaces can be exactly revealed by SC-LRR, with a predefined weight matrix. As a nontrivial byproduct, we also illustrate that SC-LRR can be applied for semisupervised learning. The experimental results on different types of vision problems demonstrate the effectiveness of our proposed method.