Glioblastoma (GBM) is a highly aggressive brain tumor characterized by an immunosuppressive microenvironment that importantly contributes to treatment resistance. Monocyte-derived macrophages (MDMs), which comprise approximately 50% of the cellular population within the GBM microenvironment, represent a major subset of tumor-associated macrophages. These cells drive tumor progression by promoting angiogenesis, immune evasion, and the phenotypic transformation of tumor cells. MDM infiltration is mediated by specific signaling pathways and regulated by the disruption of the blood–brain barrier and tumor-associated hypoxia. Recent technological advances have uncovered substantial heterogeneity among macrophages, including hypoxia-induced, lipid-metabolizing, phagocytic, and interferon-activated subtypes. This functional diversity is shaped by tumor-specific genetic alterations and metabolic reprogramming. Therapeutic approaches focusing on MDMs include inhibiting their recruitment, enhancing phagocytic activity, employing genetically engineered macrophage, and modulating metabolic pathways. While preclinical studies suggest that these approaches may improve efficacy when combined with immune checkpoint inhibitors, the dynamic spatiotemporal heterogeneity and adaptability of macrophages within the tumor microenvironment remain substantial therapeutic challenges. Future development in combination therapies, integrating single-cell multi-omics, spatial metabolic profiling, and targeted interventions, will be critical to precisely modulate MDMs, overcome immune tolerance, and improve patient outcomes.

The advent of machine learning (ML) in computational chemistry heralds a transformative approach to one of the quintessential challenges in computer-aided drug design (CADD): the accurate and cost-effective calculation of atomic interactions. By leveraging a neural network (NN) potential, we address this balance and push the boundaries of the NN potential’s representational capacity. Our work details the development of a robust general-purpose NN potential, architected on the framework of DPA-2, a deep learning potential with attention, which demonstrates remarkable fidelity in replicating the interatomic potential energy surface for drug-like molecules comprising 8 critical chemical elements: H, C, N, O, F, S, Cl, and P. We employed state-of-the-art molecular dynamic (MD) techniques, including temperature acceleration and enhanced sampling, to construct a comprehensive dataset to ensure exhaustive coverage of relevant configurational spaces. Our rigorous testing protocols, including torsion scanning, structure relaxation, and high-temperature MD simulations across various organic molecules, have culminated in an NN model that achieves chemical precision commensurate with the highly regarded density functional theory model while substantially outstripping the accuracy of prevalent semi-empirical methods. This study presents a leap forward in the predictive modeling of molecular interactions, offering extensive applications in drug development and beyond.

Diffuse-type gastric cancer (DGC) is an aggressive tumor type characterized by a dense extracellular matrix (ECM). Metabolic reprogramming, a key oncogenic factor driving tumor progression, is closely linked to ECM deposition, although the regulatory mechanisms remain poorly understood. In this study, we integrated single-cell sequencing, proteomics, metabolomics, and large-scale clinical data to identify the metabolic signature of DGC. We found that the tricarboxylic acid (TCA) cycle is suppressed in DGC, which correlates with the formation of a dense ECM. Annexin A8 (ANXA8) was identified as a critical regulator that inhibits the TCA cycle in DGC and is positively associated with matrix formation. Mechanistically, ANXA8 interacts with SP1 to promote the transcription of pyrophosphatase 1, thereby suppressing the TCA cycle, activating cancer-associated fibroblasts, and facilitating aberrant ECM deposition. Deletion of ANXA8 suppresses malignant phenotypes and shows synergistic effects with the chemotherapeutic agent 5-fluorouracil (5-FU). Large-scale clinical data further confirmed the correlation between ANXA8 expression and both gastric cancer progression and 5-FU therapeutic efficacy. High-throughput organoid screening identified UNC2025 as a selective ANXA8 inhibitor. Targeting ANXA8 with UNC2025 restores TCA cycle activity and inhibits ECM deposition in DGC, enhancing the therapeutic effects of 5-FU in patient-derived xenografts and organoids. Furthermore, a polyphenol-based UNC2025 nanodelivery system improved the efficacy of this combination therapy. In summary, this study elucidates how ANXA8-mediated suppression of the TCA cycle promotes dense ECM formation and malignant progression in DGC, highlighting the therapeutic potential of targeting ANXA8 in DGC treatment.

As one of the most important technologies in molecular biology, polymerase chain reaction (PCR) has been widely recognized in many fields such as infectious disease diagnosis due to its high sensitivity, specificity, and accuracy. Attempts in this field are focused on developing efficient heating mechanism to achieve efficient thermal cycles. Recently, with the in-depth research into photothermal effects, photonic PCR technology based on photothermal nanomaterials has gradually demonstrated potential to develop a new generation of ultrafast PCR instrument. Herein, we first categorize the various photothermal nanomaterials and briefly introduce their photothermal conversion mechanisms. Then, we review the photonic PCR technologies based on different nanomaterials and various heating strategies, comparing their advantages and disadvantages. We also discuss the application of photonic PCR in point-of-care testing (POCT) of nucleic acid and summarize the prospects and challenges of photonic PCR technology in clinical diagnostic applications. Finally, we look forward to the promising future research focus of photonic PCR. With this review, researchers can get a comprehensive understanding of photonic PCR from the aspects of technical principles, material selection, equipment development strategies, and so on, paving the way for future research.

Osteoporosis, a widespread skeletal disorder, arises from excessive bone loss, heightening fragility and fracture risk. Osteoclasts, the major type of bone-resorbing cells, are believed to contribute to this loss. Osteoclast bone resorption relies on 2 important organelles: lysosomes for matrix degradation and mitochondria for energy supply. Iron, a critical linker between lysosomes and mitochondria, has emerged as a critical mediator of osteoclast activity. However, the intricate interplay between lysosomes, mitochondria, and iron in osteoclasts and osteoporosis remains poorly understood. This review aims to bridge this knowledge gap by examining the lysosome–iron–mitochondria axis in osteoclasts. Firstly, we summarized the modulatory function of lysosomes in iron metabolism and iron’s involvement in lysosomal biogenesis and function. Next, we conducted a comprehensive analysis on the contribution of iron in mitochondrial function and its implications for osteoclast activity. Subsequently, we highlighted emerging insights into the lysosome–mitochondria crosstalk in iron metabolism. Finally, we delved into the discussion of how dysregulation of this lysosome–iron–mitochondria axis may drive osteoporosis progression and proposed innovative therapeutic strategies targeting this axis to mitigate osteoclast-mediated bone loss.

Volatile organic compounds (VOCs) serve as critical biomarkers in exhaled breath for early-stage cancer patients, and their rapid, trace-level detection holds marked implications for cancer screening. Surface-enhanced Raman scattering (SERS) technology demonstrates strong potential for trace VOC gas detection due to its ultra-high sensitivity and immunity to water interference. However, while surface plasmon resonance (SPR)-free semiconductor substrates offer superior spectral stability and selectivity, their sensitivity toward VOC detection remains suboptimal. This study introduces a novel semiconductor-based SERS substrate composed of copper single atoms anchored on UiO-66 (Cu₁/UiO-66), achieving a record-low detection limit of 10 parts per billion for VOC gases with a rapid 2-min response time, thereby elevating the gas-sensing performance of SPR-free substrates to unprecedented levels. The exceptional SERS activity originates from the highly delocalized electron properties of single-atomic copper, which effectively facilitates single-atom charge transfer processes. Concurrently, the incorporation of copper single atoms modulates the band structure of UiO-66, substantially enhancing the coupling resonance between the substrate and target molecules. In simulated breath tests mimicking lung cancer patients’ exhalations, Cu₁/UiO-66 exhibits remarkable VOC recognition capability and robust anti-interference performance. This work pioneers a new paradigm for ultra-sensitive, rapid detection of trace VOCs in exhaled breath, holding substantial promise for early cancer diagnostics and clinical translation.

Electrochemiluminescence (ECL) immunoassays based on tris(bipyridine)ruthenium [Ru(bpy)₃²⁺] is the luminophore representing the most advanced and widely adopted approach in the field of in vitro diagnostics (IVD). However, the scarcity of potential-resolved ECL promoters for Ru(bpy)₃²⁺ markedly limits its application in clinical diagnostics. Here, we report the first application of cobalt single-atom catalysts (SACs) designed via density functional theory (DFT) calculations to boost the multi-signal ECL of Ru(bpy)₃²⁺. Mechanism investigations unveil that “ROS accumulation” induced by CoC₄ and “ROS surge” driven by CoN₄ are the key factors governing the cathodic and anodic ECL. As a proof of concept, a sandwich ratiometric immunosensor was developed to detect tumor marker CEA and demonstrated excellent clinical feasibility. This work provides insights into the development of tailored ECL promoters by introducing DFT prediction and elucidating the relationships between ORR/HPRR/OER processes and Ru(bpy)₃²⁺ ECL behavior, paving the way for designing precise immunoassays and advancing IVD techniques.

Converting CO₂ into sustainable fuels or useful carbon-derived substances offers an effective strategy for tackling the energy crisis and combating global warming. Photo-/electrocatalytic CO₂ reduction provides a simple, energy-efficient, and environmentally friendly route compared to existing technologies. Ferroelectric materials, characterized by their unique spontaneous and switchable polarization, have emerged as promising candidate catalysts due to their controllable surface physical and chemical properties. This review commences by summarizing the mechanism underlying ferroelectric polarization-modulated photo-/electrocatalytic CO₂ reduction. We then highlight the recent key advancements in CO₂ reduction utilizing ferroelectric materials, along with their performance promotion strategies. Finally, we explore the obstacles and future perspectives in this field. This review will provide useful guidance for systematic design and advancement of effective photo-/electrocatalysts based on ferroelectric materials.

Current molecular machines face substantial challenges in coordinating their actions in space and time to generate cell-like macroscopic motions. A recent study in Nature Materials introduced a light-responsive artificial DNA nanomachine based on liquid–liquid phase separation technology—photofluids. By applying different light stimuli for spatiotemporal control, this nanomachine system successfully mimics typical cellular behaviors such as division, deformation, pseudopod extension, and rotation at the macroscopic scale for the first time. This study represents an innovative pathway from energy conversion at the molecular level to cell-like motion at the macroscopic level.

Parkinson’s disease (PD) is associated with gut–brain axis and gut microbiota alterations, but the functioning mechanism remains to be elucidated. In this study, we identified G protein-coupled receptor 35 (Gpr35) as a key regulator for the gut–brain association under the PD context. It investigated the impact of Gpr35 deficiency on motor function, neuroinflammation, and dopaminergic neurodegeneration, using the Gpr35 knockout (Gpr35^−/−) and wild-type (WT) mice in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD model, and Gpr35 up-/down-regulation on reverse neuroinflammation, oxidative stress, and neuronal apoptosis using Gpr35 agonist kynurenic acid (KYNA) and small interfering RNA in microglial and dopaminergic cell models. It was confirmed that Gpr35 may prevent PD by modulating neuroinflammation and gut microbiota and metabolite composition, specifically through enriching Lactobacillus, and substantially regulating tyrosine metabolism, neuroactive ligand–receptor interaction, and tryptophan metabolism pathways, thereby inhibiting the progression of PD. Our findings highlight the potential of targeting Gpr35 to influence both the gut microbiota and central nervous system, offering new insights into the treatment of PD.