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Alloys with low thermal expansion, high strength, and superior plasticity are crucial for critical applications in industries such as aerospace. Although the in-situ formation of low or negative thermal expansion particles presents a promising strategy, these materials generally suffer from limited ductility. Here, we demonstrate that the construction of nanoscale gradient ordered architectures can effectively addresses this limitation. By facilitating the diffusion and reaction of aluminum (Al) atoms into boronized manganese (Mn-B), we induce a gradient ordering architecture between rare-earthed magnesium (Mg) alloys and MnB phase, achieving a near-zero thermal expansion coefficient of 0.8 × 10 -6 ·°C -1 within the temperature range of 280–320 °C. As fully transitioning from Mn-B to a multi-gradient ordered Mn-Al-B configuration results in a stabilized thermal expansion coefficient of 23 ×10 -6 ·°C -1 across a broad temperature range (25–400 °C). This nanoscale architecture not only mitigates brittle interfacial fractures by maintaining the mechanical integrity but also enables the Mg alloy to reach an ultrahigh compressive strength of 507 MPa, with a 23.8% compressive strain. Our findings highlight the potential of designing gradient ordered architectures as a strategic approach to enhance the mechanical properties of lightweight alloys. This work demonstrates that stress-coupled atomic ordering at nanoscale Mn–Al–B interfaces will convert brittle boundaries into energy-absorbing zones, explaining the unusual coexistence of high strength, ductility, and low thermal expansion in magnesium alloys.

The nitrogen and sulfur co‐doped graphene aerogel (SNGA) was synthesized by a one‐pot hydrothermal route using graphene oxide as the starting material and thiourea as the S and N source. The obtained SNGA with a three‐dimensionally hierarchical structure, providing more available pathways for the transport of lithium ions. The existing form of S and N was regulated by changing the calcination temperature and thiourea doping amount. The results revealed that high temperature could decompose −SOX− functional groups and promote the transformation of C−S−C to C−S, ensuring the cyclic stability of electrode materials, and increasing the thiourea dosage amount introduced more pyridine nitrogen, improving the multiplicative performance of electrode materials. Benefiting from the synergistic effect of sulfur and nitrogen atoms, the prepared SNGA showed superior rate capability (107.8 mAh g−1 at 5 A g−1), twice more than that of GA (52.8 mAh g−1), and excellent stability (232.1 mAh g−1 at 1 A g−1 after 300 cycles), 1.85 times more than that of GA (125.6 mAh g−1). The present study provides a detailed report on thiourea as a dopant to provide a sufficient basis for SNGA and a theoretical guide for further modifying. The nitrogen and sulfur co‐doped graphene aerogel (SNGA) was synthesized by a one‐pot hydrothermal route using graphene oxide as the starting material and thiourea as the S and N source. Benefiting from the synergistic effect of sulfur and nitrogen atoms, the prepared SNGA showed superior rate capability, twice more than that of GA, and excellent stability: 1.85 times more than that of GA.

Drug discovery plays a crucial role in cancer treatment. Traditional drug discovery methods mainly rely on in vivo animal experiments and in vitro drug screening, which are not only costly but also time-consuming and labor-intensive. With the rise and widespread application of omics technologies, the paradigm of drug development has undergone significant changes, giving rise to network pharmacology—an innovative method for drug development and discovery. The application of network pharmacology has brought about revolutionary changes in the field of pharmacology, shortening research and development time and greatly improving the efficiency of drug discovery. This review provides an in-depth introduction to omics technologies, including metabolomics, proteomics, and genomics, and discusses the application of network pharmacology and these omics technologies in drug research. We focuse on the steps of studying cancer treatment drugs using network pharmacology combined with metabolomics, proteomics, and genomics technologies. These advancements have laid a solid foundation for the future development of network pharmacology and omics technologies and summarize the drug research methods based on network pharmacology, providing important references for future drug development.

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and effectively. In this study, we apply a pre-trained language model to the domain of CPM. We have meticulously assembled, processed, and released the first CPMI dataset and fine-tuned the ChatGLM-6B base model, resulting in the development of CPMI-ChatGLM. We employed consumer-grade graphics cards for parameter-efficient fine-tuning and investigated the impact of LoRA and P-Tuning v2, as well as different data scales and instruction data settings on model performance. We evaluated CPMI-ChatGLM using BLEU, ROUGE, and BARTScore metrics. Our model achieved scores of 0.7641, 0.8188, 0.7738, 0.8107, and − 2.4786 on the BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L and BARTScore metrics, respectively. In comparison experiments and human evaluation with four large language models of similar parameter scales, CPMI-ChatGLM demonstrated state-of-the-art performance. CPMI-ChatGLM demonstrates commendable proficiency in CPM recommendations, making it a promising tool for auxiliary diagnosis and treatment. Furthermore, the various attributes in the CPMI dataset can be used for data mining and analysis, providing practical application value and research significance.

Due to the limitations on the depth of field of high-resolution fluorescence microscope, it is difficult to obtain an image with all objects in focus. The existing image fusion methods suffer from blocking effects or out-of-focus fluorescence. The proposed multi-focus image fusion method based on local maximum luminosity, intensity variance and the information filling method can reconstruct the all-in-focus image. Moreover, the depth of tissue’s surface can be estimated to reconstruct the 3D surface model.

Prostate cancer (PCa) is one of the most common malignancy in men. However, the molecular mechanism of its pathogenesis has not yet been elucidated. In this study, we demonstrated that CYLD, a novel deubiquitinating enzyme, impeded PCa development and progression via tumor suppression. First, we found that CYLD was downregulated in PCa tissues, and its expression was inversely correlated with pathological grade and clinical stage. Moreover, we discovered that CYLD inhibited tumor cell proliferation and enhanced the sensitivity to cell ferroptosis in PCa in vitro and in vivo, respectively. Mechanistically, we demonstrated that CYLD suppressed the ubiquitination of YAP protein, then promoted ACSL4 and TFRC mRNA transcription. Then, we demonstrated that CYLD could enhance the sensitivity of PCa xenografts to ferroptosis in vivo. Furthermore, we discovered for the first time that there was a positive correlation between CYLD expression and ACSL4 or TFRC expression in human PCa specimens. The results of this study suggested that CYLD acted as a tumor suppressor gene in PCa and promoted cell ferroptosis through Hippo/YAP signaling.

SLM-processed 316L stainless steel exhibits promoted mechanical and corrosion performances, compared with the conventionally manufactured counterparts, and has been successfully applied in many fields. The anisotropic microstructure of SLM-processed materials, caused by the layer-wise fashion of SLM processing, not only results in the anisotropy of mechanical properties but also leads to the anisotropic corrosion behavior. In this study, the influence of microstructural differences of SLM-processed 316L stainless steel along the building direction on corrosion behavior was investigated. Firstly, the influence of laser scan speed on the microstructure, hardness, and corrosion behavior of SLM-processed 316L stainless steel samples was evaluated. Then, the sample with the highest relative density and the best hardness and corrosion resistance was selected to further investigate the influence of microstructural differences along the building direction on the corrosion behavior. Results showed that the corrosion resistance improved with the increase of distance from bottom plane along the building direction. Microstructure and phase analysis revealed that the microstructural differences in crystallographic orientation and grain size along the building direction of SLM-processed 316L stainless steel led to the different corrosion behavior.

Lung cancer has the highest incidence and mortality rates worldwide and is the primary cause of cancer-related death. Despite the rapid development of diagnostic methods and targeted drugs in recent years, many lung cancer patients do not benefit from effective therapies. The emergence of drug resistance has led to a reduction in the therapeutic effectiveness of targeted drugs, highlighting a crucial need to explore novel therapeutic targets. Many studies have found that epigenetic plays an important role in the occurrence of lung cancer. This review describes the biological function of epigenetic RNA modifications, such as m6A, m5C, m7G, and m1A, and recent advancements in their role in the development, progression, and prognosis of lung cancer. This review aims to provide new guidance for the treatment of lung cancer.

Basement membranes (BMs) are specialised extracellular matrices that maintain cellular integrity and resist the breaching of carcinoma cells for metastases while regulating tumour immunity. The tumour immune microenvironment (TME) is essential for tumour growth and the response to and benefits from immunotherapy. In this study, the BM score and TME score were constructed based on the expression signatures of BM-related genes and the presence of immune cells in lung adenocarcinoma (LUAD), respectively. Subsequently, the BM-TME classifier was developed with the combination of BM score and TME score for accurate prognostic prediction. Further, Kaplan–Meier survival estimation, univariate Cox regression analysis and receiver operating characteristic curves were used to cross-validate and elucidate the prognostic prediction value of the BM-TME classifier in several cohorts. Findings from functional annotation analysis suggested that the potential molecular regulatory mechanisms of the BM-TME classifier were closely related to the cell cycle, mitosis and DNA replication pathways. Additionally, the guiding value of the treatment strategy of the BM-TME classifier for LUAD was determined. Future clinical disease management may benefit from the findings of our research.