Si Li

This study developed an active, highly efficient physical antibacterial strategy that exploits chiral nanomaterials to effectively recognize and interact with the chiral microenvironment of bacterial membranes. Chiral gold nanohedgehogs ( L / D ‐Au NH) are designed and fabricated with a unique morphology that facilitates “stereospecific matching” to bacterial membranes. Experimental results demonstrate that D ‐Au NH exhibit a remarkable 43.7% improvement in mechanical antibacterial activity compared to their L ‐Au NH counterparts. This finding quantitatively substantiates the rational chiral configuration for the antibacterial properties, which can be attributed to the enhanced bacterial recognition and stronger interfacial interactions with chiral components present on bacterial cell membranes, as indicated by the remarkable surface‐enhanced Raman scattering (SERS) signal amplification observed for lipopolysaccharide (LPS) and peptidoglycan (PG). Furthermore, these chiral nanostructures exhibit superior photothermal conversion efficiency upon activation with chirality‐matched circularly polarized light (CPL), thereby synergistically enhancing antibacterial efficacy. Consequently, the developed D ‐Au NH enable the almost complete eradication of bacterial biofilms. This research underscores the considerable biomedical potential of precisely engineered chiral nanomaterials in combating antibiotic resistance.

This study developed an active, highly efficient physical antibacterial strategy that exploits chiral nanomaterials to effectively recognize and interact with the chiral microenvironment of bacterial membranes. Chiral gold nanohedgehogs ( L / D ‐Au NH) are designed and fabricated with a unique morphology that facilitates “stereospecific matching” to bacterial membranes. Experimental results demonstrate that D ‐Au NH exhibit a remarkable 43.7% improvement in mechanical antibacterial activity compared to their L ‐Au NH counterparts. This finding quantitatively substantiates the rational chiral configuration for the antibacterial properties, which can be attributed to the enhanced bacterial recognition and stronger interfacial interactions with chiral components present on bacterial cell membranes, as indicated by the remarkable surface‐enhanced Raman scattering (SERS) signal amplification observed for lipopolysaccharide (LPS) and peptidoglycan (PG). Furthermore, these chiral nanostructures exhibit superior photothermal conversion efficiency upon activation with chirality‐matched circularly polarized light (CPL), thereby synergistically enhancing antibacterial efficacy. Consequently, the developed D ‐Au NH enable the almost complete eradication of bacterial biofilms. This research underscores the considerable biomedical potential of precisely engineered chiral nanomaterials in combating antibiotic resistance.

While chiral supramolecular nanofibrous networks (NFNs) show promise in regulating cell fate, precisely directing differentiation pathways remains challenging. This study systematically investigates the influence of various chiral supramolecular NFNs on neural cell differentiation. A series of chiral supramolecular NFNs was synthesized using L/D‐amino acids and their derivatives as building blocks, including L/D‐lysine (L/D‐Lys), L/D‐histidine (L/D‐His), L/D‐arginine (L/D‐Arg), and L/D‐glutamic acid derivatives (Fmoc‐L/D‐Glu). The regulatory effects of these NFNs on the NG108‐15 were evaluated during the differentiation process. Our findings demonstrate that the chiral microenvironment established by D‐Lys‐gel significantly enhances NG108‐15 differentiation and correlates with robust activation of the Ca ²⁺ signaling pathway. To further elucidate the relationship between supramolecular chirality and metal ions, we synthesized chiral supramolecular NFNs from L/D‐Lys‐Ca ²⁺ ‐gel and L/D‐Lys‐Mg ²⁺ ‐gel. Notably, the supramolecular NFNs derived from D‐Lys‐Ca ²⁺ ‐gel exhibited superior capacity to promote neurite outgrowth and maturation compared to their L‐enantiomer and Mg ²⁺ ‐incorporated counterparts. Proteomics analysis revealed that the chiral supramolecular NFNs obtained from D‐Lys‐Ca ²⁺ ‐gel enhance neuronal differentiation by upregulating key effectors involved in both axon guidance and calcium signaling pathways, particularly the Camkk2‐Tiam1 cascade. This study provides valuable insights into the design of chiral biomaterials and proposes novel strategies for neural regeneration and tissue engineering.

Deep learning-based methods have shown promising results in multi-label chest X-ray (CXR) image classification. However, most existing methods rely on large-scale fully-annotated datasets, which are costly and laborious to obtain. Therefore, training a high-performance model with limited annotation remains a significant challenge in practice. To address this issue, we propose an Adversarial and Correlation-Aware Data Augmentation (ACAA) framework for multi-label CXR image classification. First, we generate pseudo labels based on the model predictions on weakly-augmented images. Next, we propose a Generalized Nesterov Iterative Fast Gradient Sign Method (GNI-FGSM) to generate effective adversarial examples as image-level strongly-augmented data. Then, we introduce an image-level adversarial-augmentation-based consistency regularization to perform supervision of model predictions on the above image-level adversarial examples. To explore more diverse data transformations, we further perform adversarial augmentation in the feature space by imposing perturbations on the extracted features of each image and introduce a feature-level adversarialaugmentation- based consistency regularization. Furthermore, we propose a Batch-level Mamba module (Batch- Mamba) coupled with a batch-level Mamba-based correlation regularization to explore inter-sample correlations along batch dimension. Extensive experiments on two large CXR datasets (CheXpert and MIMIC-CXR) demonstrate the effectiveness of the proposed ACAA framework for multilabel CXR image classification under limited-annotation scenario.

Cerebrovascular diseases (CVDs) such as aneurysms, arteriovenous malformations, stenosis, and Moyamoya disease are major public health concerns. Accurate classification of these conditions is essential for timely intervention, yet current computer-aided methods often exhibit limited representational capacity, feature redundancy, and insufficient interpretability, restricting clinical applicability. We propose PASAformer, a Swin-Transformer-based framework for cerebrovascular disease classification on Digital Subtraction Angiography (DSA). PASAformer incorporates a Pathology-Aware Sparse Attention (PASA) module that emphasizes lesion-related regions while suppressing background redundancy. Inserted into the Swin backbone, PASA replaces dense window self-attention, improving computational efficiency while preserving the hierarchical architecture. We further employ the MiAMix data augmenter to increase sample diversity, and incorporate a CombinedAdapter encoder that injects anatomical priors from the frozen Medical Segment Anything Model (MED-SAM) into early-stage representations, strengthening discriminative power under limited supervision. To support research in this underexplored area, we curate CDSA-NEO, a proprietary DSA dataset comprising more than 1,700 static images across four major cerebrovascular disease categories, constituting the first large-scale benchmark of its kind. Furthermore, an external cohort of angiographic runs with sequential, unselected frames is used to assess robustness in realistic temporal workflows. Extensive experiments on CDSA-NEO and public vascular datasets demonstrate that PASAformer achieves competitive precision and balanced accuracy compared to representative state-of-the-art models, while providing more focused visual explanations. These results suggest that PASAformer can support automated cerebrovascular disease classification on angiography, and that CDSA-NEO provides a benchmark for future method development and evaluation.

We consider image deblurring problems in the presence of impulse noise. The key to successful image restoration is the accurate identification of impulse noise locations. Using the maximum a posteriori probability estimation, we first derive an ℓ 0 TV model for image deblurring with impulse noise. A novel objective function is proposed to implicitly approximate the ℓ 0 norm, and an alternating minimization ‘like’ algorithm is developed to solve the resulting surrogate model. The main advantage of our method is that it can adaptively identify and update the impulse locations at each iteration and then restore the image using current estimated noise-free pixels. Theoretically, we establish global convergence to a local minimizer of the nonconvex objective function. Additionally, we propose a strategy for adaptively updating parameters to empirically accelerate the convergence rate and enhance the quality of the restored image. Experimental results demonstrate the superiority of the proposed method in comparison to competing methods, especially in detecting and restoring random valued impulse noise, where it achieves significantly improved performance.

Lipases have drawn significant attention due to their crucial roles across various industries, including oil processing, food, medicine, and daily chemicals. Nevertheless, their catalytic efficiency is susceptible to fluctuations in external environmental conditions, which remains a major barrier to their broader application. In this research, chiral hydrogel microspheres (HMSs) are developed by incorporating L/D‐Co₃O₄ nanoparticles (NPs) into the synthetic process of HMSs, which are discovered to enhance the catalytic performance of lipases. The improved catalytic activity of lipases is observed to be influenced by the chiral environment of L/D‐Co₃O₄‐HMSs. Specifically, when 2 wt.% lipases are encapsulated in L‐Co₃O₄‐HMSs containing 10 wt.% L‐Co₃O₄ NPs, their catalytic activity is 2.11 times higher than when encapsulated in D‐Co₃O₄‐HMSs, resulting in a 2.62‐fold increase in catalytic activity compared to free lipase. Moreover, the L‐Lip‐HMS₃, which contained 2 wt.% lipases and 10 wt.% L‐Co₃O₄ NPs, exhibited superior inhibitory capability over D‐Lip‐HMS₃ in differentiating fibroblast mouse cells into adipocytes, owing to its enhanced catalytic capabilities. This study not only highlights the importance of chirality in improving the catalytic performance of natural enzymes but also provides a new perspective on developing catalytic systems that can endow natural enzymes with higher catalytic performances.

Chiral photocatalytic nanomaterials possess numerous unique properties and hold promise for various applications in chemical synthesis, environmental protection, energy conversion, and photoelectric devices. Nevertheless, it is uncommon to develop effective means to enhance the asymmetric catalytic performances of chiral plasmonic nanomaterials. In this study, a type of L / D ‐Au@CeO ₂ helical nanorods (HNRs) was fabricated by selectively growing CeO ₂ on the surface of Au HNRs via a facile wet‐chemistry construction method. Chiral Au@CeO ₂ HNRs, featuring Au and CeO ₂ with spatially separated structures, exhibited the highest photocatalytic performance for N ₂ fixation, being 50.80±2.64 times greater than that of Au HNRs. Furthermore, when L ‐Au@CeO ₂ HNRs were exposed to left circularly polarized light (CPL) and D ‐Au@CeO ₂ HNRs were exposed to right CPL, their photocatalytic efficiency was enhanced by 3.06±0.06 times compared to the samples illuminated with the opposite CPL, which can be attributed to the asymmetrical generation of hot carriers upon CPL excitation. This study not only offers a simple approach to enhance the photocatalytic performance of chiral plasmonic nanomaterials but also demonstrates the potential of chiral plasmonic materials for application in specific photocatalytic reactions, such as N ₂ fixation.