Explore the vast range of titles available.

GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular metabolism and physiological states in living organisms. However, due to our imperfect knowledge of metabolic processes, even highly curated GEMs have knowledge gaps (e.g., missing reactions). Existing gap-filling methods typically require phenotypic data as input to tease out missing reactions. We still lack a computational method for rapid and accurate gap-filling of metabolic networks before experimental data is available. Here we present a deep learning-based method — CHEbyshev Spectral HyperlInk pREdictor (CHESHIRE) — to predict missing reactions in GEMs purely from metabolic network topology. We demonstrate that CHESHIRE outperforms other topology-based methods in predicting artificially removed reactions over 926 high- and intermediate-quality GEMs. Furthermore, CHESHIRE is able to improve the phenotypic predictions of 49 draft GEMs for fermentation products and amino acids secretions. Both types of validation suggest that CHESHIRE is a powerful tool for GEM curation to reveal unknown links between reactions and observed metabolic phenotypes. A computational method for rapid and accurate gap-filling of metabolic networks without using phenotypic data is unavailable. Here, the authors address this problem by developing a deep learning based method that can predict missing reactions using topological features of the metabolic networks.

Neuropeptide Y (NPY), which is widely distributed in the nervous system, is involved in regulating a variety of biological processes, including food intake, energy metabolism, and emotional expression. However, emerging evidence points to NPY also as a critical transmitter between the nervous system and immune system, as well as a mediator produced and released by immune cells. and studies based on gene-editing techniques and specific NPY receptor agonists and antagonists have demonstrated that NPY is responsible for multifarious direct modulations on immune cells by acting on NPY receptors. Moreover, via the central or peripheral nervous system, NPY is closely connected to body temperature regulation, obesity development, glucose metabolism, and emotional expression, which are all immunomodulatory factors for the immune system. In this review, we focus on the direct role of NPY in immune cells and particularly discuss its indirect impact on the immune response.

The RNA-based spray-induced gene silencing (SIGS) technology represents an ecologically sustainable approach to crop protection and pathogen management. Following the recent approval of Ledprona as the first sprayable double-stranded RNA (dsRNA) biopesticide by the EPA at the end of 2023, SIGS has emerged as a focal point in both academic and industrial sectors. This review analyzes recent advances and emerging trends in SIGS. The application of SIGS for crop protection, including the control of insects, fungal pathogens, and viruses, is briefly summarized. Distinguishing this review from others, we delve into practical aspects of the technology, such as the selection and screening of target genes, large-scale production methods, and delivery systems, highlighting major advancements in these areas and also addressing the remaining questions and issues, particularly concerning safety concerns and controlling harmful weeds. Finally, this review emphasizes the emerging trends in SIGS technology, particularly its integration with nanotechnology and other methodologies. Collectively, the rapid progress in SIGS studies is poised to accelerate the maturation and application of this technology.

HighlightsPBA–PDMS/folded graphene film (FGf) composite with high strength and thermal conductivity and rapid self-healing capacity at room temperature is prepared.PBAx–PDMS/FGf self-healed its strength, thermal conductivity, and conductivity and the self-healing mechanism of thermal conductivity is also proposed.PBA–PDMS/FGf has significant application potential in interface thermal conductivity and robot skins.Composites that can rapidly self-healing their structure and function at room temperature have broad application prospects. However, in view of the complexity of composite structure and composition, its self-heal is facing challenges. In this article, supramolecular effect is proposed to repair the multistage structure, mechanical and thermal properties of composite materials. A stiff and tough supramolecular frameworks of 2-[[(butylamino)carbonyl]oxy]ethyl ester (PBA)–polydimethylsiloxane (PDMS) were established using a chain extender with double amide bonds in a side chain to extend prepolymers through copolymerization. Then, by introducing the copolymer into a folded graphene film (FGf), a highly thermally conductive composite of PBA–PDMS/FGf with self-healing capacity was fabricated. The ratio of crosslinking and hydrogen bonding was optimized to ensure that PBA–PDMS could completely self-heal at room temperature in 10 min. Additionally, PBA–PDMS/FGf exhibits a high tensile strength of 2.23 ± 0.15 MPa at break and high thermal conductivity of 13 ± 0.2 W m−1 K−1; of which the self-healing efficiencies were 100% and 98.65% at room temperature for tensile strength and thermal conductivity, respectively. The excellent self-healing performance comes from the efficient supramolecular interaction between polymer molecules, as well as polymer molecule and graphene. This kind of thermal conductive self-healing composite has important application prospects in the heat dissipation field of next generation electronic devices in the future.

HighlightsA composite thermal interface material with three-dimensional hybrid carbon network-reinforced polydimethylsiloxane was proposed.The cooperative regulation of thermal conductivity and mechanical properties was achieved by controlling the assembly process of micro-nano scale unit carbon materials.The orthotropic continuous carbon structure endowed the composites with in-plane and out-of-plane thermal conductivities of up to 113.61 and 24.37 W m−1 K−1, respectively.The excellent compressibility and adhesion properties cooperatively improved the effective thermal conductivity by more than an order of magnitude.Vertically oriented carbon structures constructed from low-dimensional carbon materials are ideal frameworks for high-performance thermal interface materials (TIMs). However, improving the interfacial heat-transfer efficiency of vertically oriented carbon structures is a challenging task. Herein, an orthotropic three-dimensional (3D) hybrid carbon network (VSCG) is fabricated by depositing vertically aligned carbon nanotubes (VACNTs) on the surface of a horizontally oriented graphene film (HOGF). The interfacial interaction between the VACNTs and HOGF is then optimized through an annealing strategy. After regulating the orientation structure of the VACNTs and filling the VSCG with polydimethylsiloxane (PDMS), VSCG/PDMS composites with excellent 3D thermal conductive properties are obtained. The highest in-plane and through-plane thermal conductivities of the composites are 113.61 and 24.37 W m−1 K−1, respectively. The high contact area of HOGF and good compressibility of VACNTs imbue the VSCG/PDMS composite with low thermal resistance. In addition, the interfacial heat-transfer efficiency of VSCG/PDMS composite in the TIM performance was improved by 71.3% compared to that of a state-of-the-art thermal pad. This new structural design can potentially realize high-performance TIMs that meet the need for high thermal conductivity and low contact thermal resistance in interfacial heat-transfer processes.

Sevoflurane is one of the most widely used anesthetics for the induction and maintenance of general anesthesia in surgical patients. Sevoflurane treatment may increase the incidence of postoperative cognitive dysfunction (POCD), and patients with POCD exhibit lower cognitive abilities than before the operation. POCD affects the lives of patients and places an additional burden on patients and their families. Understanding the mechanism of sevoflurane-induced POCD may improve prevention and treatment of POCD. In this paper, we review the diagnosis of POCD, introduce animal models of POCD in clinical research, analyze the possible mechanisms of sevoflurane-induced POCD, and summarize advances in treatment for this condition.

Cutaneous melanoma represents a paradigm of immunological complexity, where multifactorial primary and acquired resistance often undermine the clinical efficacy of conventional immune checkpoint blockades and targeted therapies. This review provides a comprehensive analysis of the strategic shift toward precision immuno-oncology, focusing on the mechanistic synergy between personalized neoantigen-directed vaccines and integrated multi-omics profiling. Rather than viewing these as independent pillars, we propose an integrated framework where multi-omics data functions as the indispensable ‘operating system’ that drives the entire lifecycle of neoantigen vaccines. By targeting patient-specific somatic mutations, bespoke vaccines are capable of orchestrating de novo , high-avidity T-cell responses with superior specificity and minimal off-target toxicity. We evaluate the clinical evolution and immunological foundations of advanced vaccine platforms—including mRNA, synthetic long peptides (SLPs), and engineered exosome-based systems—highlighting their transformative potential in the neoadjuvant and adjuvant settings. Central to this paradigm is the deployment of a robust multi-omics ecosystem (encompassing genomics, transcriptomics, epigenomics, proteomics, and the host microbiome) to decipher the dynamic landscape of the melanoma tumor microenvironment (TME). Facilitated by artificial intelligence (AI) and real-time liquid biopsy monitoring, this framework enables an iterative, biologically informed feedback loop for adaptive clinical management. We emphasize that the integration of personalized vaccines with immune checkpoint inhibitors (ICIs) and MAPK pathway inhibitors is essential to dismantle resistance barriers, providing a definitive roadmap for achieving durable clinical remission and curative outcomes in the era of personalized oncology.

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

Rectal mucinous adenocarcinoma (RMAC) is a rare and aggressive form of rectal cancer. The effectiveness of neoadjuvant radiotherapy (NRT) for RMAC has not been well studied, and the survival benefit remains controversial. The purpose of this work was to determine the prognostic role of NRT in patients with RMAC by propensity-score matching (PSM). A retrospective cohort study using the Surveillance, Epidemiology, and End Results from 2004 to 2015 was performed. In the multivariate analysis before PSM, NRT provided better OS (HR 0.61, 95% CI 0.52–0.71, p  < 0.001) and CSS (HR 0.68, 95% CI 0.56–0.82, p  < 0.001). Multivariate analysis after PSM (n = 844) confirmed that patients receiving NRT survived longer than those without NRT (OS: HR 0.60, 95% CI 0.50–0.78, p  < 0.001 and CSS: HR 0.68, 95% CI 0.54–0.84, p  < 0.001). Subgroup analysis indicated that NRT had significantly improved OS and CSS in stage II RMAC and OS in stage III RMAC after adjusting for various confounding factors.

Shikonin, a natural compound, exhibits antitumor effects in DLBCL, but its mechanism remains unclear. Shikonin's cytotoxicity in DLBCL cells was assessed by MTT assays. Ferroptosis and lipid peroxidation markers were analysed flow cytometry and biochemical kits. Mechanisms were explored using gene knockdown/overexpression, dual-luciferase assays, RNA pull-down, proteomics, RIP, FISH, and ChIP. Key proteins and genes involved in ferritinophagy and ferroptosis were examined by Western blot, RT-qPCR, and immunofluorescence, with findings validated in a nude mouse xenograft model. Shikonin triggers ferritinophagy and ferroptosis by increasing iron accumulation and lipid peroxidation. LncRNA ADPGK-AS1 protects against ferroptosis by regulating MT2A eIF4G1 interaction. Shikonin triggers ferritinophagy and ferroptosis by disrupting iron metabolism and redox balance. The eIF4G1-ADPGK-AS1/MT2A axis mitigates these effects, revealing shikonin's antitumor mechanism and therapeutic potential.