Zhejiang Lab

The neurovascular unit (NVU) is a multicellular system functioning to maintain healthy brain homeostasis and regulate the exchange of essential elements between the blood and the brain. Recent studies have shown that, in response to ischemic stroke (IS), the NVU undergoes dynamic structural remodeling and metabolic dysfunction, revealing new features of IS pathogenesis. Recent breakthroughs in single-cell multiomics provide emerging evidence regarding the spatiotemporal heterogeneity of NVU responses to IS. To date, clinical treatments for IS-induced brain injury remain very limited. These new studies have advanced our knowledge of the dynamic cellular and molecular changes of the NVU after IS, paving the way for new therapeutic strategies.

Bacterial infections pose a severe global health threat with rising mortality, driving the need for alternative therapies. Chemodynamic therapy (CDT), which converts endogenous H2O2 into lethal hydroxyl radicals (•OH) via Fenton reactions, offers a promising solution. Copper peroxide (CuO2) nanodots have recently attracted considerable research interest because of their capacity to generate H2O2. Despite this advantage, their practical use in antibacterial applications is severely limited by their poor stability. In this study, we developed a near-infrared (NIR)-triggered nanoplatform, LA-CP@Fe3O4, by encapsulating CuO2-loaded iron tetraoxide nanoparticles with the phase-change material lauric acid (LA). This system synergistically combines a Fe2+/Fe3+-Cu+/Cu2+ dual-metal catalytic network for enhanced •OH generation with NIR-triggered photothermal activation (a photothermal conversion efficiency of 39.21%), allowing spatiotemporally controlled therapeutic release while stabilizing CuO2 nanodots. In vitro, the platform achieved 99.9% bactericidal efficiency via self-supplied H2O2 and targeted ROS generation. In vivo, it exhibited excellent biocompatibility, accelerated wound healing with an 88.0% reduction in wound area by day 7, and effective NIR-triggered antibacterial activity. These results collectively establish a groundbreaking approach for precision antibacterial therapy with strong clinical translation potential.

Recent studies have highlighted the impact of copper-induced cell death (cuproptosis) on cancer progression, prognosis, and treatment, but it remains unclear whether cuproptosis-related genes (CRGs) play any role in the glioma tumor microenvironment (TME). The CRGs expression patterns in TCGA glioma samples were evaluated based on genetic and transcriptional alterations identifying three different molecular groupings and showing that CRGs changes were linked to clinical characteristics, prognosis, and TME infiltration. Machine learning algorithms were then used to develop an overall survival score for cuproptosis-related prognostic genes (CRPGs), and its prognostic ability was validated for glioma patients. An elevated CRPGs score indicates a heightened mutation burden, increased glioma metabolism, compromised immunity, and strong correlation with both the cancer stem cells (CSC) index and medication sensitivity to chemotherapeutics. This extensive examination of CRGs in gliomas showed their possible significance in the tumor microenvironment as well as their prognostic value. This extremely precise CRPGs nomogram has furthered our understanding of cuproptosis in gliomas, which will allow new approaches to prognosis and immunotherapy development.

Abstract Upcoming next-generation sky surveys will detect a large number of faint objects with magnitudes larger than 25. When objects are crowded within a limited field of view, blending becomes unavoidable. Blending leads to the omission of many sources during photometry in these fields, which causes an underestimates of tens of percent in crowded fields, and remains a major challenge for existing source-extraction techniques. Although artificial neural networks have shown promising results in the detection and classification in wide-field surveys, they often fail with severely blended stars. We developed a robust deep learning model, Astro-RetinaNet, based on the RetinaNet algorithm to detect and classify blended sources in single-band astronomical images. After training and evaluating the performance of our network on simulated images, we find precisions of 0.96, 0.89, 0.70, 0.50, and 0.75 for single star, two-star, three-star, four-star, and five-or-more star blending cases, respectively, with star number density ∼22,000 stars arcmin −2 . We compare our method’s detection capability and completeness both on CSST-simulated NGC 2298 images and Hubble Space Telescope–observed M31 images. In crowded and noncrowded stellar fields of simulated NGC 2298, our results show that the model can recover 82% and 95% of sources, respectively, at a magnitude ( i band) of 25, while for SExtractor and Photutils, the completeness reduces to 20%, 59% and 60%, 88%, respectively. In the M31 case, as faint as 27 mag ( F 814 W ) in a crowded field, Astro-RetinaNet detects 2224 sources, significantly outperforming Photutils and SExtractor by factors of 3.4 and 7.1, respectively.