ACS Publications最新文献

Glioblastoma multiforme (GBM) is highly angiogenic, which promotes its growth and invasion. Photodynamic effects not only kill tumor cells but also disrupt or seal the tumor blood vessels. Targeting strategies that integrate effective tumor photodynamic therapy (PDT) with imaging-based vessel monitoring could lead to improvement in the diagnosis and treatment of GBM. Herein, we developed a biomimetic photosensitizer using a macrophage membrane hybrid lipid as the carrier of chlorin e6 to form a nanocomposite (MLCNPs). The MLCNPs demonstrated good biocompatibility and targeted GBM cells. Under laser irradiation, the MLCNPs showed significant photodynamic effects, which induced massive cell apoptosis. The targeting ability and PDT effect were investigated in an orthotopic glioma mouse model. During PDT in vivo, high-resolution photoacoustic (PA) imaging was used to monitor changes in the structure and function of the tumor vasculature. MLCNPs combined with high-resolution PA imaging provide a new strategy for GBM tumor diagnosis, treatment, and monitoring.

Pancreatic cancer presents significant imaging challenges due to its poor vascularization, while the hypoxic tumor microenvironment further contributes to chemoresistance. To address these limitations, we engineered exosome-ultrasmall iron oxide (Exo-USIO), a targeted exosomal nanoprobe encapsulating USIO nanoparticles (USIO NPs), designed to enable precise tumor imaging and enhance chemotherapy efficacy in pancreatic cancer. Exosomes derived from Panc-02 pancreatic cancer cells were isolated and loaded with USIO NPs via electroporation to synthesize Exo-USIO. The nanoprobe's targeting specificity, MRI contrast enhancement, and catalase-like activity (converting H₂O₂ to O₂) were systematically evaluated. In vitro assays assessed cellular uptake, hypoxia modulation, and chemosensitivity, while in vivo studies validated tumor-targeted MRI imaging, hypoxia alleviation, and synergistic therapeutic effects with gemcitabine (GEM). Exo-USIO demonstrated a 2.3-fold increase in T₁-weighted MRI signal intensity compared to free USIO NPs (P < 0.01), alongside efficient enzymatic conversion of H₂O₂ to O₂, significantly reducing HIF-1α expression (P < 0.05). Combined with GEM, Exo-USIO reduced tumor cell viability to 39.8% in vitro and suppressed tumor growth by 62% in vivo (P < 0.001). Biosafety evaluations revealed negligible systemic toxicity or metastatic risk. By leveraging exosome-mediated targeted delivery and the dual enzyme-mimetic activity of USIO NPs, Exo-USIO achieves dual functionality: enhanced MRI-guided tumor localization and catalytic alleviation of hypoxia to reverse chemoresistance. This strategy overcomes key limitations of the pancreatic tumor microenvironment, offering a translatable platform for precision theranostics.

Cognition is often associated with complex brains, yet many forms of learning─such as habituation, sensitization, and even spacing effects─have been observed in single cells and aneural organisms. These simple cognitive abilities, despite their cost, offer evolutionary advantages by allowing organisms to reduce environmental uncertainty and improve survival. Recent studies have confirmed early claims of learning-like behavior in protists and slime molds, pointing to the presence of basal cognitive functions long before the emergence of nervous systems. In this work, we adopt a synthetic biology approach to explore how minimal genetic circuits can implement nonassociative learning in unicellular systems. Building on theoretical models and using well-characterized regulatory elements, we design and simulate synthetic circuits capable of reproducing habituation, sensitization, and the massed-spaced learning effect. Our designs incorporate activators, repressors, fluorescent reporters, and quorum-sensing molecules, offering a platform for experimental validation. By examining the structural and dynamical constraints of these circuits, we highlight the distinct temporal dynamics of gene-based learning systems compared to neural counterparts and provide insights into the evolutionary and engineering challenges of building synthetic cognitive behavior at the cellular level.

As an emerging modality for treatment, photothermal therapy demonstrates significant potential for clinical application. However, the inflammatory reaction after photothermal therapy can lead to tumor recurrence and metastasis. As a novel photothermal agent, biliverdin (BV) also demonstrates a remarkable anti-inflammatory effect. In this study, goat milk-derived extracellular vesicles (GEVs) is used to encapsulate BV. The objective was to enhance tumor uptake of the photothermal agent while alleviating the inflammatory responses associated with photothermal therapy, thereby achieving superior therapeutic outcomes. N₃-GEV@BV was successfully synthesized. Additionally, it exhibited notable efficacy in photothermal therapy and demonstrated anti-inflammatory effects in vitro. Utilizing a pretargeting strategy, N₃-GEV@BV can accomplish PET/CT imaging in both subcutaneous and orthotopic tumor models. After photothermal treatment, the tumor volume in the N₃-GEV@BV+laser group exhibited a significant decrease relative to the other groups, with reductions of up to 1/13 observed. Furthermore, compared to N₃-GEV@ICG, mice injected with N₃-GEV@BV exhibited lower expression levels of inflammatory factors in both the serum and tumor tissues. As an integrated nanoprobe for diagnosis and treatment, N₃-GEV@BV can successfully mediate the photothermal therapy of tumor tissue. Notably, it contributes to enhanced tumor prognosis by mitigating the inflammatory response induced by photothermal therapy, underscoring its broad potential for application.

α-L-Rhamnosidases are a class of glycosyl hydrolases (GHs) that catalyze the hydrolysis of terminal α-L-rhamnose residues from diverse glycoconjugates. While extensively characterized in bacterial and fungal sources, no archaeal α-L-rhamnosidases have been characterized to date. Herein, we report the identification and characterization of the first thermostable archaeal α-L-rhamnosidase (ArRha), derived from the metagenomic data set of Pisciarelli solfatara hot spring. ArRha, classified in glycoside hydrolase family GH78, efficiently hydrolyzes α-1,2 and α-1,6 rhamnosyl linkages in flavonoid glycosides with notable biological activities. The novel enzyme showed remarkable temperature stability, wide-range pH activity, organic solvent tolerance, and no metal dependence. Combined with a thermostable β-glucosidase, ArRha converts naringin to prunin and naringenin in sweet and blood orange juices, achieving >95% conversion within 2 h at 65 °C. This represents the first report of a hyperthermostable archaeal GH78 α-L-rhamnosidase with promising applications in industrial enzymatic juice debittering and sustainable flavonoid biotransformation.

Machine learning potentials (MLPs) have emerged as powerful simulation tools for heterogeneous catalysis. While current MD-active-learning workflows excel at fitting a specific system/reaction pathway through iterative structural sampling, the transition toward more general and transferable MLPs, designed to handle diverse structures and reactive events within a defined chemical space, presents fundamentally new challenges. Such generality often requires highly diverse, nonequilibrium training data, for which standard practices may confront challenges regarding training discipline and evaluation logic. Here, using our recently developed REICO method as an example to generate such data sets, we systematically investigate the distinct pathologies that arise when training on such diverse data, revealing critical deviations from standard system-specific MLP training. We further provide detailed recommendations on data cleaning, model selection, and error metrics for both numerical performance and physical validation, offering practical guidance for training MLPs with diverse and hybrid data sets.

Although heat can enhance the immunogenicity of tumors, inhomogeneous temperature distribution in deep-seated regions often leads to insufficient thermal exposure, resulting in limited antigen release and low immunogenicity. Herein, we designed microwave-sensitizing nanomotors that activate propulsion to break through biological barriers and the deep-seated delivery of heat and immune adjuvants to activate the immune response. Specifically, this nanomotor features a core of dendritic silica (DS) asymmetrically modified with bovine serum albumin-stabilized manganese dioxide nanoparticles (MnO₂@BSA) to form the DMB structure. Subsequently, the immune adjuvant R837 and tetradecanol were loaded to obtain the final microwave-sensitizing nanomotors, DMBR. Oxygen (O₂) bubbles generated from the catalytic decomposition of H₂O₂ by the heterostructured MnO₂@BSA propel the nanomotors to actively break through these biological barriers autonomously toward deep-seated tumor cells. Furthermore, microwave irradiation accelerates the production of O₂ bubbles, enhancing the nanomotor's movement efficiency. In vitro and in vivo studies demonstrated a 3-fold improvement of DMB. Moreover, the DMBR nanomotors promote the enhancement of heat dissemination, R837 delivery efficiency, and localized release of O₂ into deep tumor areas. Consequently, a robust immune response is triggered, as evidenced by an increased level of T cell infiltration, leading to significant suppression of both primary and distant tumors. This work presents a sequential barrier-overcoming nanomotor strategy for effective microwave thermal immunotherapy.

Rationally designed nanoarchitectures with optimized electrochemical behavior provide a unique strategy to improve the redox kinetics of battery electrodes, enabling the simultaneous delivery of high energy and power densities. Here, we report a molybdate-ion intercalated oxide/sulfide composite material prepared hydrothermally enabling precise regulation of the chemical environment and electronic structure of the metal active site, thereby enhancing the pseudocapacitive current contribution. The resulting ZnMoO₄/CoMoS₄ nanostructures are uniformly anchored onto the Cu foil, forming abundant, synergistically coupled interfaces and junctions that endow the composite with high porosity, enlarged interlayer spacing, and superior electrical conductivity. These structural advantages yield an exceptional specific capacitance of 2238.75 F g^-1 (310.93 mAh g^-1) at 1.2 A g^-1, alongside improved ion transport and reduced charge transfer resistance. When integrated into a flexible asymmetric supercapacitor (ZnMoO₄/CoMoS₄||AC), the device delivers a remarkable energy density of 58.3 Wh kg^-1 at a power density of 637.7 W kg^-1, retaining 91.7% of its capacitance after 2000 cycles. This work demonstrates a versatile and scalable strategy for engineering high performance metal oxide/sulfide hybrid electrodes, offering valuable insights for next generation flexible energy storage systems.