Institute of Biophysics

Pyroptosis inhibition via Fe single-atom nanozymes is promising for inflammation therapy, but the common Fe-N4 configuration restricts oxygen intermediate desorption and lacks cooperative sites, thus limiting catalytic performance. To overcome this, we develop a FeMn dual-atom nanozyme supported on oxygen-nitrogen-doped bamboo-like carbon nanotubes (FeMnDA/BCNT). Through the precise alignment of Fe and Mn 3dz2 orbital energy levels by the electron-delocalized BCNT support in the FeMn-N/O active center, thereby lowering the dissociation energy barrier for *O2 or *H2O molecules, promoting O─O bond cleavage to bypass toxic ─OOH species, and thus accelerating the enzyme-like kinetics. Combined with a hierarchical porous bamboo-like structure of the BCNT that enhances high specific surface, atom exposure, and mass transfer, the FeMnDA/BCNT nanozymes exhibit potent superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx)-like activities. Further encapsulation with a macrophage membrane ([MM]FeMnDA/BCNT) confers excellent biocompatibility and active targeting ability toward inflammatory sites. The resulting [MM]FeMnDA/BCNT nanozymes target the inflammatory microenvironment, scavenges ROS, restores mitochondrial function, and suppresses NLRP3 inflammasome activation, thereby inhibiting pyroptosis. In vivo, [MM]FeMnDA/BCNT nanozymes show good biocompatibility and efficacy in treating osteoarthritis, acute liver injury, and acute kidney injury. This work provides a novel strategy for inflammatory disease therapy using a biomimetic dual-atom nanozyme.

Hepatitis A virus (HAV) is an unusual picornavirus with two types of extracellular virions: nonenveloped particles (nHAV) shed in feces and quasi-enveloped particles (eHAV) circulating in blood. Both enter cells by clathrin-dependent endocytic pathways merging in late endolysosomes with capsid binding to ganglioside receptors. Phosphatidylserine receptors facilitate eHAV endocytosis, but no protein receptor has been identified for nHAV. Here, we show low-density lipoprotein receptor (LDLR) is such a receptor. LDLR knockout did not alter viral attachment to cells, but restricted cellular uptake of nHAV (not eHAV). Soluble LDLR ectodomain blocked nHAV entry, as did antibody to LDLR. Recombinant LDLR-related protein-associated protein 1, a pan-LDLR family chaperone, also inhibited nHAV entry, including residual entry into knockout cells, suggesting other LDLR family members may similarly facilitate endocytosis. Reconstituting full-length LDLR expression restored nHAV entry in knockout cells, whereas LDLR mutants lacking LA repeats 4 to 7 or the EGF-like/propeller domain did not. ELISAs confirmed LDLR binds nHAV, optimally above pH7, without destabilizing the capsid. A 1.7Å resolution cryoelectron microscopy (cryo-EM) structure revealed LDLR interacts with VP1 at the fivefold vertex of the capsid. Extreme blurring of the LDLR density prevented detailed identification of LDLR interactions, and suggested binding does not follow particle symmetry, being either flexible or to multiple LDLR regions. Additional cryo-EM studies show ganglioside GD1a binds to a similar region of the capsid. Collectively, these data reveal the LDLR to be an important entry factor, shuttling nHAV from the extracellular environment to endolysosomes where it is likely released at low pH to bind gangliosides.

Computational super-resolution (SR) methods enable nanoscale imaging from single-frame wide-field or spinning-disk confocal images without hardware modifications, yet face limitations: statistical restoration suffers from noise and artifacts, while deep learning methods typically lack generalizability. We introduce 3Snet-CLID, a computational SR method which integrates a hybrid supervised/self-supervised deep learning network for signal-preserving denoising with direct Richardson-Lucy deconvolution. 3Snet-CLID's per-pixel denoising strategy suppresses noise while maintaining signal distribution, mitigating artifacts, and enhancing robustness. The method achieves more than 5-fold resolution improvement on conventional microscopes, revealing diverse structures such as the mitochondrial outer membrane, endoplasmic reticulum, and nuclear pores in live and fixed cells under standard labeling. By overcoming key computational SR bottlenecks, 3Snet-CLID offers denoising capability and an accessible platform for high-fidelity nanoscale live-cell imaging.

Abstract The distribution and interactions of cellular organelles play a critical role in mediating cellular physiology and pathology. Large-scale electron microscopy enables visualization of organelle distribution and interactions at the tissue level with nanometer resolution, but robust and efficient computational analysis tools are lacking. Here, we present a deep learning tool for universal large-scale 2D/3D electron microscopy analysis, DeepOrganelle. This new tool enables high-throughput, cell-resolved spatiotemporal mapping and digitization of organelle distribution and interactions. When applied to spermatogenesis across 12 stages and 22 differentiation status of the germ cells, DeepOrganelle uncovered previously unrecognized, stage-dependent dynamics of mitochondria-endoplasmic reticulum contact sites within one subphase of prophase I during meiosis. It also revealed coordinated organelle redistribution in Sertoli cells towards the blood–testis barrier, digitizing the remodeling dynamics of the tissue. This study demonstrates that DeepOrganelle provides a powerful framework that captures subcellular dynamics at the whole-tissue level.

Circular RNAs (circRNAs) emerged as promising non-invasive cancer biomarkers due to their stability, abundance in body fluids, and regulatory potential. However, circRNA differential expression analysis (DEA) remains challenging, largely owing to lack of consensus on important preprocessing strategies such as filtering and normalization. While well-established bulk RNA-sequencing frameworks are commonly applied to circRNA data, newer approaches such as CIRI-DE (part of CIRI3 suite) integrate both linear and circular transcript information to improve detection. Despite developments, an assessment of these integrative strategies is lacking, and the critical impact of filtering on DEA model performance has not been comprehensively evaluated. Results: In this study, we evaluated the impact of multiple normalization and filtering strategies on circRNA DEA using five experimental datasets, including two in-house blood platelet sets and semi-parametric simulated in silico datasets. Our results emphasize the importance of selecting an appropriate filtering threshold, as overly lenient filtering substantially reduced model performance across datasets. We found edgeR's filterByExpr() strategy particularly effective in handling zero counts in circRNA data, while also generating the most reliable results across most datasets. Furthermore, by incorporating linear and circular information as described in CIRI-DE, most methods identified a higher number of differentially expressed (DE) circRNAs compared to circular counts alone. Notably, circRNAs identified by both CIRI-DE and the modified bulk RNA-sequencing pipelines showed substantial overlap. Conclusion: Our findings demonstrate that automated filtering combined with linear-aware normalization significantly enhances the sensitivity and reproducibility of circRNA DEA, providing a standardized framework for more reliable biomarker discovery in transcriptomic research.

This paper is devoted to the study of a binary inequality that links prime numbers with square-free integers. The main goal is to show that, for any exponent lying in a certain interval greater than one and smaller than a critical constant, and for every sufficiently large positive number $$N$$ , the inequality under investigation admits a solution. In such a solution one of the variables is a prime number, while the other is restricted to be square-free. The approach relies on analytic number theory, in particular on estimates for exponential sums involving prime numbers together with sieve methods to control the contribution of square-free integers. The obtained result demonstrates that even under the arithmetic restriction of being square-free, the inequality can still be solved for infinitely many sufficiently large values of $$N$$ . This extends previous research, where either primes or unrestricted integers were considered, and provides a new contribution to the understanding of additive problems involving primes and special classes of integers.

ABSTRACT Implantable bioelectronic devices demonstrate immense potential in health monitoring and therapeutic interventions, yet encounter formidable challenges in achieving biocompatibility and interfacial stability within complex biological environments. This study proposes a novel liquid metal‐hydrogel bonding strategy based on Cabrera–Mott oxidation kinetics regulation, which significantly enhances the printability of liquid metals on biocompatible hydrogel substrates through interfacial tension reduction and accelerated surface oxidation. Experimental findings revealed a 26.25% reduction in contact angle and a printing resolution improvement to 200 µm while maintaining superior electrical conductivity (3.83 × 10 5 S/m). Leveraging this advancement, we developed a flexible cardiac pacemaker integrating wireless energy transmission, pulse circuitry, and electrostimulation modules, which successfully achieved stable cardiac rhythm modulation in murine heart failure models. Histopathological analyses further corroborated the device's biosafety profile. This study presents a promising strategy for miniaturized implantable bioelectronics design and biointerface optimization, offering valuable insights for the development of future personalized bioelectronic systems.