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Temozolomide (TMZ) is the first-line chemotherapeutic option to treat glioma; however, its efficacy and clinical application are limited by its drug resistance properties. Polo-like kinase 1 (PLK1)-targeted therapy causes G2/M arrest and increases the sensitivity of glioma to TMZ. Therefore, to limit TMZ resistance in glioma, an angiopep-2 (A2)-modified polymeric micelle (A2PEC) embedded with TMZ and a small interfering RNA (siRNA) targeting (siPLK1) was developed (TMZ-A2PEC/siPLK). TMZ was encapsulated by A2-PEG-PEI-PCL (A2PEC) through the hydrophobic interaction, and siPLK1 was complexed with the TMZ-A2PEC through electrostatic interaction. Then, an angiopep-2 (A2) modified polymeric micelle (A2PEC) embedding TMZ and siRNA targeting polo-like kinase 1 (siPLK1) was developed (TMZ-A2PEC/siPLK). In vitro experiments indicated that TMZ-A2PEC/siPLK effectively enhanced the cellular uptake of TMZ and siPLK1 and resulted in significant cell apoptosis and cytotoxicity of glioma cells. In vivo experiments showed that glioma growth was inhibited, and the survival time of the animals was prolonged remarkably after TMZ-A2PEC/siPLK1 was injected via their tail vein. The results demonstrate that the combination of TMZ and siPLK1 in A2PEC could enhance the efficacy of TMZ in treating glioma.

Microplastic (MP) pollution, a global environmental problem, has been recently studied in marine and freshwater environments. However, our understanding of MP effect on terrestrial ecosystems, especially carbon (C) and nitrogen (N) turnover remains poor. This review summarizes the sources and distribution characteristics of MPs in terrestrial ecosystems and explores their effects on soil properties, plant growth, C and N turnover. Once entering the terrestrial ecosystem, MPs could involve in sequestrating carbon and nitrogen by changing soil properties (e.g., pH, soil aggregate stability, and soil porosity). MPs could exert direct influences on plants or on soil physical environment and microbial metabolic environment to indirectly affect plant growth, thus altering the quantity and quality of soil C and N inputs by shifts in plant litter and roots. The changes of the dominant bacteria phyla, related functional genes, and enzymes caused by MP pollution could affect C and N cycles. Additionally, the MP effect varies with its properties (e.g., types, shapes, elemental composition, functional groups, released additives). Future researches should unify the standard system of MP separation, detection, and reveal the ecological effects of MPs, especially their impacts on terrestrial carbon and nitrogen cycles in the context of climate changes.

Smart nanoparticles, which can respond to biological cues or be guided by them, are emerging as a promising drug delivery platform for precise cancer treatment. The field of oncology, nanotechnology, and biomedicine has witnessed rapid progress, leading to innovative developments in smart nanoparticles for safer and more effective cancer therapy. In this review, we will highlight recent advancements in smart nanoparticles, including polymeric nanoparticles, dendrimers, micelles, liposomes, protein nanoparticles, cell membrane nanoparticles, mesoporous silica nanoparticles, gold nanoparticles, iron oxide nanoparticles, quantum dots, carbon nanotubes, black phosphorus, MOF nanoparticles, and others. We will focus on their classification, structures, synthesis, and intelligent features. These smart nanoparticles possess the ability to respond to various external and internal stimuli, such as enzymes, pH, temperature, optics, and magnetism, making them intelligent systems. Additionally, this review will explore the latest studies on tumor targeting by functionalizing the surfaces of smart nanoparticles with tumor-specific ligands like antibodies, peptides, transferrin, and folic acid. We will also summarize different types of drug delivery options, including small molecules, peptides, proteins, nucleic acids, and even living cells, for their potential use in cancer therapy. While the potential of smart nanoparticles is promising, we will also acknowledge the challenges and clinical prospects associated with their use. Finally, we will propose a blueprint that involves the use of artificial intelligence-powered nanoparticles in cancer treatment applications. By harnessing the potential of smart nanoparticles, this review aims to usher in a new era of precise and personalized cancer therapy, providing patients with individualized treatment options.

A coal model containing gangue was established by discrete elements to study the forces occurring when a shearer cuts coal, and a rigid-flexible coupled shearer section model was established in RecurDyn; the drum cutting process was simulated through bidirectional coupling. The dynamic distribution of the force chain was obtained when cutter teeth cut the coal rock. During the cutting process, the coal rock failure is caused by normal and tangential forces, with the latter having a major role. The ratio of the average tangential force to normal force was 1.34~1.79. When the rock is in the middle of the coal seam, the force (including normal and tangential forces) on the rock is the highest. Moreover, the force on the rock-coal interface is greater than that on the coal-rock interface. The results show that the force on the coal decreases with the increases in rotation speed. In contrast, the number of broken bonds increases. Further, the number of broken bonds and the force of coal increased nonlinearly with traction speed. Finally, the increase of tooth mounting angle decreased the force on the coal rock and the number of broken bonds, followed by an increase. As such, this study provides a reference for further theoretical research on forces in coal cutting.

Oxidative stress is significantly involved in the pathogenesis and progression of chronic obstructive pulmonary disease (COPD). Combining antioxidant drugs or nutrients results in a noteworthy therapeutic value in animal models of COPD. However, the benefits have not been reproduced in clinical applications, this may be attributed to the limited absorption, concentration, and half-life of exogenous antioxidants. Therefore, novel drug delivery systems to combat oxidative stress in COPD are needed. This review presents a brief insight into the current knowledge on the role of oxidative stress and highlights the recent trends in novel drug delivery carriers that could aid in combating oxidative stress in COPD. The introduction of nanotechnology has enabled researchers to overcome several problems and improve the pharmacokinetics and bioavailability of drugs. Large porous microparticles, and porous nanoparticle-encapsulated microparticles are the most promising carriers for achieving effective pulmonary deposition of inhaled medication and obtaining controlled drug release. However, translating drug delivery systems for administration in pulmonary clinical settings is still in its initial phases.

Significant load fluctuations are encountered when cutting the coal rock using the shearer spiral drum, causing excessive drum wear. Thus, a coupling discrete element model of coal‐rock was developed, and the discrete element method and multi‐body dynamics were used to build a two‐way coupling simulation model of coal rock drum cutting. The drum wear was analyzed, along with the pick and spiral blade wear positions. The results have shown that the pick wear is primarily concentrated at the pick tip and the shaft shoulder, while the outer spiral blade edge blade is generally worn near the pick root. The maximum wear depth obtained in the simulation was 0.00316 mm. The tangential cumulative energy of the drum and the normal cumulative energy were 1.794e5 and 7.819e4 J, respectively. The friction wear of the drum was more prominent compared to the impact wear. By using a single factor method, the relationship between the drum rotational speed, traction speed, rock firmness coefficient, cutting depth, and spiral blade angle, and drum wear was obtained. Finally, it was concluded that the study at hand provides a quick and straightforward way to study drum wear law. A coupling discrete element model of coal‐rock was developed and the discrete element method and multi‐body dynamics were used to build a two‐way coupling simulation model of coal rock drum cutting and the drum wear chart was obtained. The relationship between the drum rotational speed, traction speed, rock firmness coefficient, cutting depth, and spiral blade angle and drum wear was obtained. The study provides a quick and straightforward way to study drum wear law.

Background Community species in different grassland types exhibit unique ecological traits and adaptation strategies, influencing the impact of grazing on species diversity at various scales. This study aimed to elucidate the response characteristics and rules of species diversity in different grassland types to grazing intensity by analyzing plant groups and species diversity. Results Grazing intensity, grassland type, and their interaction significantly affected α, β, and γ diversity. In meadow steppes, α and γ diversity conformed to the intermediate disturbance hypothesis, exhibiting a unimodal trend with increasing grazing intensity—initially increasing and then decreasing. In typical steppes, α, β and γ diversity showed no clear pattern in response to changes in grazing intensity. In desert steppes, α, β and γ diversity consistently declined with increasing grazing intensity. In meadow steppes, dominant and common species were crucial for sustaining community (α diversity) and landscape (γ diversity) diversity, whereas rare species primarily contributed to increased gradient differences (β diversity). In typical steppes, rare species were pivotal for community (α diversity) and landscape (γ diversity) diversity, while dominant and common species were important in reducing gradient differences (β diversity). In desert steppes, rare species were vital for maintaining community diversity (α diversity), dominant species played a key role in reducing gradient differences (β diversity), and common species were important for maintaining landscape-level diversity (γ diversity). Conclusions The characteristics and patterns of grazing intensity on species diversity at different scales, as well as the dominant plant group influencing plant species diversity at different scales, are controlled by grassland types. These findings highlight the need for tailored management strategies to conserve species diversity in various grassland ecosystems under different grazing pressures.

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

The manufacturing industry is a key area of environmental regulation. However, whether command-driven and market-oriented environmental regulations exert heterogeneous impacts on the high-quality development of the manufacturing industry (HQDM) remains underexplored. This study treats the command-driven low-carbon city pilot policy and the market-oriented carbon emissions trading pilot policy as “quasi-natural experiments”. Firm-level data of listed manufacturing enterprises spanning 2003–2021, it adopts the double machine learning method to evaluate the influence of heterogeneous environmental regulations on the HQDM. The findings show that the low-carbon city pilot policy significantly inhibits the HQDM, whereas the carbon emissions trading pilot policy significantly promotes it. The effect of market-oriented environmental regulation on the HQDM is primarily achieved through the mechanism of technological innovation. In regions where both the low-carbon city pilot policy and the carbon emissions trading pilot policy are implemented, both command-driven and market-oriented regulations boost the HQDM, signifying a synergistic effect between them. Further heterogeneity analysis shows that the results for eastern and western areas, state -owned firms, and technology-intensive manufacturing sectors align with the baseline regression results. The conclusions of this study provide important references for the selection of carbon reduction policies, formulating differentiated emission reduction measures.

Nucleic acid is the main material for storing, copying, and transmitting genetic information. Gene sequencing is of great significance in DNA damage research, gene therapy, mutation analysis, bacterial infection, drug development, and clinical diagnosis. Gene detection has a wide range of applications, such as environmental, biomedical, pharmaceutical, agriculture and forensic medicine to name a few. Compared with Sanger sequencing, high-throughput sequencing technology has the advantages of larger output, high resolution, and low cost which greatly promotes the application of sequencing technology in life science research. Magnetic nanoparticles, as an important part of nanomaterials, have been widely used in various applications because of their good dispersion, high surface area, low cost, easy separation in buffer systems and signal detection. Based on the above, the application of magnetic nanoparticles in nucleic acid detection was reviewed.