Research最新文献

D-π-A-type fluorescent materials are crucial tools in life sciences and medicine, with their development hinging on a precise understanding of fluorophore mechanisms, particularly twisted intramolecular charge transfer (TICT) and planar intramolecular charge transfer (PICT) processes. These fluorophores exhibit unique charge transfer properties, making them highly valuable in organic optoelectronics, fluorescent probes, and sensors. However, despite their growing applications, the structural essence of TICT and PICT fluorophores remains poorly understood. This often results in molecules with similar structures displaying charge transfer modes that contradict design expectations, substantially hindering the application of TICT and PICT fluorescent probes. In this study, we meticulously designed various computational strategies based on interpretable machine learning to thoroughly deconstruct the chemical structural essence of TICT and PICT fluorophores. Utilizing the first real-world TICT and PICT dataset, we constructed predictive models that balance both interpretability and accuracy (area under the receiver operating characteristic curve = 0.846) using a range of algorithms, including deep learning. We established artificial intelligence (AI)-guided rules comprising 5 structural factors-electron-donating group strength, electron-withdrawing group strength, alkyl cyclization, steric hindrance, and solvent-solute interactions-that influence TICT and PICT. These rules provide obvious guidance for probe design based on molecular rigidity and charge transfer driving forces. Compared to community-suggested rules, the AI-guided rules achieved an over 20% improvement in accuracy in a controlled evaluation. By applying these rules, we successfully synthesized and validated several representative fluorophores that are challenging to distinguish using chemical intuition alone. Both quantitative calculations and experimental results confirmed the accuracy of the model and the practicality of the AI-guided rules. This novel approach is expected to establish a novel paradigm for exploring ideal TICT and PICT molecules, offering a robust framework for future research and application in fluorescent materials.

Organonitrogen chemicals with C=N bonds are one of the most important groups of chemicals with broad applications, but their synthesis via reductive coupling remains a great challenge, because of the favorable hydrogenation of C=N bonds into C-N bonds. In this study, a nitrogen-doped carbon-supported β-MoO₃ catalyst with abundant oxygen vacancies (O_v) was discovered to be robust in the reductive coupling of nitro compounds with biomass-derived alcohols toward the synthesis of organonitrogen chemicals, including imines and N-heterocycles with C=N bonds. The O_v in β-MoO₃ serves a crucial role in the adsorption and activation of substrates via the electronic interaction between the negatively charged oxygen atoms in these substrates and the O_v sites in β-MoO₃. The presence of O_v greatly lowers the energy barriers of the reductive coupling reaction, and the electron transfer from alcohols to nitro compounds is mediated by the Mo⁵⁺/Mo⁶⁺ redox cycle. Our method demonstrates excellent selectivity to C=N bonds and is effective for a wide substrate scope including the highly inert methanol and ethanol. This study highlights the use of non-noble metal oxides as alternatives to traditional metal nanoparticles for various challenging organic transformations.

The origin of life (OoL) is a fundamental and long-standing scientific question. Although a variety of plausible hypotheses had been put forward, how life began on the prebiotic Earth from a pile of prehistoric inert chemicals (gases) is still a puzzle to us. Here, to unify the existing hypotheses to cover the entire scenarios, the author proposed the "nanozymes hypothesis" of the OoL on Earth, in which natural mineral nanozymes (MN-zymes) and their later upgraded organic/inorganic hybridized nanozymes played multiple key roles in the initial emergence of life molecules, especially in the manner of "inorganic photosynthesis" under primitive Earth conditions. Under the hypothesis framework, proteins, DNA, and RNA might emerged near-simultaneously, as a result of the diversity of nanozymes and catalyses, and multiple physical and chemical key roles of the MN-zymes. Besides nanozyme aspects, several fundamental and key issues on the topic are briefly discussed and several essential elements and conditions for the natural selection and survival of life molecules are proposed.

With the significant transformation in the classification, risk stratification, and therapy standards for gliomas in recent years, we sought to reintegrate clinical data, whole-exome sequencing data, and magnetic resonance imaging data from glioma patients to further analyze their impact on overall survival. We identified 798 primary gliomas: 355 glioblastomas, 179 IDH1/2-mutant astrocytomas, 135 oligodendrogliomas, and 129 other IDH1/2-wild-type gliomas. Kaplan-Meier analysis revealed that our cohort showed significantly prolonged survival compared to the CGGA/TCGA cohorts (median: 85.2, 60.4, and 50.5 months; P < 0.0001). Molecular reclassification criteria yielded altered final histopathologic classification for 23.7% of gliomas. Molecular alterations differ among glioma subtypes. Among the 5 tumorigenic pathways analyzed, glioblastomas exhibited the highest average number of activated pathways (mean: 2.17), followed by astrocytomas (mean: 1.40) and oligodendrogliomas (mean: 0.42). In one glioma subtype, upstream and downstream gene activations in the same pathway are mutually exclusive. In this large-scale Chinese cohort, we first confirmed a strong link between tumor location and molecular subtype: Frontal gliomas had IDH1/2 mutations in 63.5% of cases, while temporal (80.3%) and thalamic/basal ganglia gliomas (90.4%) were predominantly IDH1/2-wild-type. Age stratification confirmed these patterns: 74.7% of frontal gliomas in younger patients (<46 years) had IDH1/2 mutations versus 91.4% of temporal and 100% of thalamic/basal ganglia tumors in older patients (≥46 years) being IDH1/2-wild-type. Contemporary molecular criteria modified diagnoses in ~25% of cases. Contemporary glioma cohorts showed prolonged survival outcomes compared to historical cohorts. An association between anatomic localization and molecular subtypes was also established in this Chinese glioma cohort.

Chemoresistance is a primary cause of cancer treatment failure, due to the lack of specific regulatory strategies arising from unclear mechanisms. Here, we uncover the pivotal role of the PRMT1/SOX2 axis in regulating cancer stemness, a key factor contributing to cancer chemoresistance. In light of this, we construct a DNA nanomachine (DNM) to overcome chemoresistance by reversing cancer stemness. This DNM is constructed using a programmable DNA origami framework, incorporating CD44-targeting aptamers and glutathione (GSH)-responsive stemness inhibitors (DCLX069) as functional components. The DNM exhibits a specific affinity toward CD44-overexpressing tumor cells, enabling the effective delivery of the loaded cisplatin (CDDP) to the tumor cells. Upon entering the tumor cells, DCLX069 is rapidly released from the DNM due to high intracellular GSH levels, leading to swift regulation of the PRMT1/SOX2 axis. In contrast, CDDP exhibits a gradual enzymatic release profile. This temporally programmed release enables the reversal of cancer stemness before chemotherapy initiation, resulting in a substantial improvement in CDDP chemosensitivity and a significant increase in the median survival of tumor-bearing mice from 27 to over 56 d with DNM assistance. This study highlights the promising potential of this DNA nanotechnology-empowered therapy in addressing chemoresistance in malignant tumors.

The mechanism of and effective treatment for the headache, with the global prevalence of 52%, and its common anxiety comorbidity remain elusive. Here, we found that chronic isosorbide dinitrate (ISDN) injections induce c-fos expression in the anterior insula (AI), prelimbic cortex (PrL), and oval nucleus of the bed nucleus of the stria terminalis (ovBNST), suggesting their contributions to headache and anxiety comorbidity. This hypothesis is substantiated by our findings that chronic ISDN injection-induced headache and anxiety are blocked by inhibition of ventral AI (vAI)-PrL and dorsal AI (dAI)-ovBNST circuits, respectively. Headache and anxiety stimuli in chronic ISDN-injected mice markedly increase endocannabinoid (eCB) release at both glutamatergic vAI-PrL synapses and dAI-ovBNST synapses, indicating the role of eCB signaling in modulating headache and anxiety. Indeed, presynaptic knockdown of eCB hydrolase or presynaptic activation of cannabinoid type 1 receptors (CB1Rs) in vAI-PrL and dAI-ovBNST circuits separately alleviates headache and anxiety. A systemic application of eCB degradation enzyme inhibitors blocks chronic ISDN-induced headache and anxiety comorbidity, which are separately blocked by CB1R antagonist application in PrLs and ovBNSTs. Our findings reveal divergent counteracting effects of elevated eCB signaling in vAI-PrL and dAI-ovBNST circuits on comorbid headache and anxiety.

As an essential branch of smart materials, shape memory polymer materials (SMPs) have made substantial advances in fabrication strategies, microstructure design, and response methods. Shape memory polymer porous materials (SMPPMs), combining the advantages of SMPs and porous materials, feature lightweight properties, tunable micro/nanostructures, large specific surface areas, and programmable shapes, which have attracted important attention across a wide range of applications. This study focuses on 2 types of SMPPMs: shape memory foams and shape memory aerogels. This review systematically examines the fabrication strategies for SMPPMs, including gas foaming, template, freeze-drying, and 4-dimensional printing methods, deeply analyzes the impact of fabrication strategies on their micro/nanostructures, and summarizes their latest applications in areas such as smart thermal protection systems for aerospace, minimally invasive biomedical devices, and high-sensitivity smart sensors. This review analyzes the current state of research and future trends in SMPPMs from multiple perspectives, including material design, structural design, and response strategies. The design of SMPPMs requires integration with actual application needs, achieved through appropriate selection of polymer matrices and optimized micro/nanostructure designs to improve material performance. Furthermore, the review also introduces current challenges and development trends related to SMPPMs, including advanced, sophisticated, large-scale preparation strategies, and efficient, rapid, precise driving methods, as well as applications that integrate multiple disciplines and fields.

This study overcomes 2 critical barriers to the clinical translation of nano-adsorbents for sepsis blood purification: the weakening of adsorption function caused by nanoparticle biofouling and the limitations in clinical translation of recovery devices. We pioneer electrically neutral phosphocholine zwitterions as selective lipopolysaccharide (LPS) ligands. Their precise charge orientation resolves the core conflict between anti-fouling efficacy and LPS capture via differential dipole realignment upon LPS binding, enabling unprecedented selective LPS capture capacity with minimal protein adsorption. To address the persistent challenges of nano-adsorbent retrieval from blood, and clinical incompatibility of existing retrieval devices with blood purification systems, we developed a discretely assembled magnetic nanocomposite platform (PCAPAN-Fe) and an extracorporeal LPS-targeting magnetic array system (ELMAS), eliminating key risks inherent in monolithic designs while ensuring complete nanoparticle harvest. In septic rabbit models, the integrated platform exhibited 100% survival with early intervention, 84.7% LPS clearance (versus 20% survival and 45.6% LPS clearance for commercial adsorbents), and marked reduction of key proinflammatory cytokines. Crucially, the therapy achieved 82.2% LPS clearance efficacy in progressive sepsis, extending survival to 40% (versus 0% for commercial adsorbents). By ingeniously integrating molecular-level ligand design with a clinically viable device, this work pioneers a paradigm shifts in sepsis nanotherapeutic, resolving the performance-biosafety paradox in blood purification.

Radiation-induced skin injury (RISI) is a common and debilitating complication of radiotherapy, characterized by persistent inflammation and delayed wound healing. Macrophages play a central role in this process; however, the molecular mechanisms governing their dysfunction under radiation stress remain poorly understood. To elucidate the role of triggering receptor expressed on myeloid cells 2 (TREM2) in macrophage regulation after irradiation, we combined single-cell RNA sequencing, in vivo mouse models, and in vitro macrophage assays. Conditional knockout mice (LysM ^Cre Trem2 ^flox/flox) were used to selectively delete Trem2 in macrophages. Radiation induced a distinct TREM2⁺ macrophage subset; however, despite elevated Trem2 mRNA, protein levels declined due to ADAM17-mediated shedding driven by radiation-induced reactive oxygen species (ROS) accumulation and NRF2 activation. Inhibition or small interfering RNA (siRNA)-mediated knockdown of ADAM17 restored TREM2 protein expression, reduced soluble TREM2 release, improved macrophage survival, and promoted anti-inflammatory M2 polarization. Conversely, Trem2 deficiency enhanced apoptosis, sustained inflammation, and delayed wound healing, whereas Trem2 overexpression or local adoptive transfer of TREM2⁺ macrophages accelerated tissue repair. Mechanistically, TREM2 conferred radioprotection through extracellular signal-regulated kinase (ERK) pathway activation, linking the ROS-NRF2-ADAM17 axis to TREM2-ERK signaling in macrophage survival and polarization. Collectively, these findings identify a novel regulatory cascade, ROS-NRF2-ADAM17-TREM2-ERK, that governs macrophage fate under irradiation. Targeting this pathway or supplementing TREM2⁺ macrophages may offer promising therapeutic strategies for mitigating RISI.

Large-scale proteomics enables the identification of biomarkers and undulations in metabolic aging. This study aimed to develop a metabolic age (MA) and identify proteomic biomarkers and their undulating changes during metabolic aging. Using UK Biobank data, MA was developed from mortality-associated metabolomic profiles (nuclear magnetic resonance platform) in 203,491 participants. Associations between 2,923 plasma proteins (Olink Explore 3072 platform) and metabolic aging phenotypes, including MA, telomere length, frailty index, incident type 2 diabetes, cardiovascular disease, and mortality, were examined in 24,920 participants via Cox proportional hazards or linear models. Differential expression-sliding window analysis captured protein waves during metabolic aging in 7,092 participants. MA improved the predictions of mortality, cardiovascular disease, and type 2 diabetes beyond conventional risk factors (C-index up to 0.786) and correlated strongly with chronological age (Spearman's r: 0.876). Sixty proteins were associated with all metabolic aging phenotypes. Among them, growth differentiation factor 15 (GDF15), urokinase plasminogen activator surface receptor (PLAUR), tumor necrosis factor receptor superfamily member 10A (TNFRSF10A), tumor necrosis factor receptor superfamily member 10B (TNFRSF10B), gamma-interferon-inducible lysosomal thiol reductase (IFI30), hepatocyte growth factor (HGF), WAP 4-disulfide core domain protein 2 (WFDC2), collagen alpha-3(VI) chain (COL6A3), polymeric immunoglobulin receptor (PIGR), insulin-like growth factor-binding protein 4 (IGFBP4), and tumor necrosis factor receptor superfamily member 27 (EDA2R) ranked within the top 20 for at least 4 phenotypes based on P values. Pathway analysis highlighted symbiont entry into host cell and cytokine-cytokine receptor interaction in metabolic aging. Proteins showed undulating changes during metabolic aging, with 3 peaks at 44, 51, and 63 years. MA-protein trajectories clustered into 3 groups. Groups 1 and 3 exhibited linear increases with MA, whereas group 2 showed nonlinear increases. In conclusion, the identification of plasma proteomic biomarkers and their undulating changes in metabolic aging provides a critical foundation for developing clinical markers and precision interventions to prevent accelerated metabolic aging.