FBXW7 and human tumors: mechanisms of drug resistance and potential therapeutic strategies

Drug therapy, including chemotherapy, targeted therapy, immunotherapy, and endocrine therapy, stands as the foremost therapeutic approach for contemporary human malignancies. However, increasing drug resistance during antineoplastic therapy has become a substantial barrier to favorable outcomes in cancer patients. To enhance the effectiveness of different cancer therapies, an in-depth understanding of the unique mechanisms underlying tumor drug resistance and the subsequent surmounting of antitumor drug resistance is required. Recently, F-box and WD Repeat Domain-containing-7 (FBXW7), a recognized tumor suppressor, has been found to be highly associated with tumor therapy resistance. This review provides a comprehensive summary of the underlying mechanisms through which FBXW7 facilitates the development of drug resistance in cancer. Additionally, this review elucidates the role of FBXW7 in therapeutic resistance of various types of human tumors. The strategies and challenges implicated in overcoming tumor therapy resistance by targeting FBXW7 are also discussed

DeePMD-kit v2: A software package for Deep Potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.Comment: 51 pages, 2 figure

MXene Reinforced PAA/PEDOT:PSS/MXene Conductive Hydrogel for Highly Sensitive Strain Sensors

Abstract Conductive hydrogel has a vital application prospect in flexible electronic fields such as electronic skin and force sensors. Developing conductive hydrogel with significant toughness and high sensitivity is urgently needed for application research. In this work, a strong and sensitive strain sensor based on conductive hydrogel is demonstrated by introducing MXene (Ti3C2Tx) into the micelle crosslinked polyacrylic acid (PAA)/poly(3,4‐ethylenedioxythiophene):poly(styrene‐sulfonate) (PEDOT:PSS) hydrogel network. The functional polymer micelle crosslinkers can dissipate external stress by deformation, endowing the hydrogel with high strength. The combination of MXene both improves the polymer network structure and the conductive pathways, further enhancing the mechanical properties and sensing performance. Resultantly, the flexible strain sensor base on PAA/PEDOT:PSS/MXene conductive hydrogel exhibits excellent sensing performance with a high gauge factor of 20.86, a large strain detection range of 1000%, as well as good adhesion on different interfaces. Thus, it can be used to monitor various movements of the human body and identify all kinds of handwriting, showing great potential into wearable electronics