Gold nanoparticles (AuNPs) have been widely studied and applied in the field of tumor diagnosis and treatment because of their special fundamental properties. In order to make AuNPs more suitable for tumor diagnosis and treatment, their natural properties and the interrelationships between these properties should be systematically and profoundly understood. The natural properties of AuNPs were discussed from two aspects: physical and chemical. Among the physical properties of AuNPs, localized surface plasmon resonance (LSPR), radioactivity and high X-ray absorption coefficient are widely used in the diagnosis and treatment of tumors. As an advantage over many other nanoparticles in chemicals, AuNPs can form stable chemical bonds with S-and N-containing groups. This allows AuNPs to attach to a wide variety of organic ligands or polymers with a specific function. These surface modifications endow AuNPs with outstanding biocompatibility, targeting and drug delivery capabilities. In this review, we systematically summarized the physicochemical properties of AuNPs and their intrinsic relationships. Then the latest research advancements and the developments of basic research and clinical trials using these properties are summarized. Further, the difficulties to be overcome and possible solutions in the process from basic laboratory research to clinical application are discussed. Finally, the possibility of applying the results to clinical trials was estimated. We hope to provide a reference for peer researchers to better utilize the excellent physicochemical properties of gold nanoparticles in oncotherapy.

BACKGROUND: Perivascular adipose tissue (PVAT) is a key regulator of vascular dysfunction. Impairment of PVAT phenotypic plasticity with aging may play a role in vascular pathology including abdominal aortic aneurysms (AAAs). Yet, the mechanisms underlying PVAT plasticity in aneurysm pathogenesis remain elusive. METHODS: Single-cell RNA sequencing was performed on perivascular stromal cells (PVSCs) from young (2- to 3-month-old) and aged (18- to 20-month-old) mice. The expression of PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1α) was measured in PVAT of aged mice and human aneurysm samples. Loss- and gain-of-function approaches were used to investigate the role of SM22α (Smooth Muscle 22-Alpha)-lineage PVSCs–derived PGC-1α in aneurysm development. Molecular mechanisms were explored through transcriptome and functional studies in young and aged mice, SM22α ^Cre ; Rosa26 ^RFP/+ ; PGC1α ^f/f and SM22α ^Cre ; Rosa26 ^RFP/+ mice with Ang II (angiotensin II)–induced and deoxycorticosterone acetate/salt-induced AAA models. RESULTS: SM22α ⁺ cells accumulated in PVAT of Ang II–treated aged mice and patients with aortic aneurysms. Single-cell RNA sequencing analysis revealed that aging disrupted the differentiation potential of SM22α-lineage PVSCs and led to reduced PGC-1α levels. PGC1α downregulation in PVAT was observed in both mouse AAA models and human aneurysm lesions. In mice with SM22α-driven PGC-1α deletion, Ang II–induced AAA formation was accompanied by perivascular stromal cell-to-myofibroblast differentiation. In vitro PGC1α knockdown suppressed nuclear YAP (Yes-associated protein) signaling, reducing adipocyte differentiation, while increasing MMP2 (matrix metalloproteinase 2)-secreting myofibroblasts. Furthermore, PGC-1α overexpression in aged mice or administration of the YAP signaling inhibitor verteporfin in SM22α ^Cre ; Rosa26 ^RFP/+ ; PGC1α ^f/f mice restored PVAT function and conferred protection against AAA formation. Last, we used the radiomics analysis to noninvasively evaluate PVAT in the context of AAA severity in humans. CONCLUSIONS: PGC-1α deficiency in SM22α-lineage PVSCs disrupts the balance between adipogenic and myofibrogenic differentiation through regulating YAP signaling, ultimately promoting AAA development. Radiomics assessment may present a promising noninvasive approach for PVAT evaluation in aneurysms, offering valuable potential for clinical research.

Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histopathology images. Despite its success, few works concentrate on the extraction of nucleus-level information, which is essential for pathologic analysis. In this work, we propose a novel nucleus-aware self-supervised pretraining framework for histopathology images. The framework aims to capture the nuclear morphology and distribution information through unpaired image-to-image translation between histopathology images and pseudo mask images. The generation process is modulated by both conditional and stochastic style representations, ensuring the reality and diversity of the generated histopathology images for pretraining. Further, an instance segmentation guided strategy is employed to capture instance-level information. The experiments on 7 datasets show that the proposed pretraining method outperforms supervised ones on Kather classification, multiple instance learning, and 5 dense-prediction tasks with the transfer learning protocol, and yields superior results than other self-supervised approaches on 8 semi-supervised tasks. Our project is publicly available at https://github.com/zhiyuns/UNITPathSSL.

Brain serves as a critical cornerstone of human intelligence, which involves a series of complex neuropsychological activities that lead to the coordination of various functions in the brain network. In recent years, brain network analysis methods based on graph neural networks (GNNs) have attracted increasing attention for the identification of brain disorders. However, these methods generally assume that the brain network is a homogeneous graph while ignoring its heterogeneity among human brain activities, which is reflected in both the complex connectivity of the brain network and distinctive brain functions. To overcome this problem, we propose a heterogeneous subdivision GNN (HSGNN), which captures the heterogeneous connections and functions of the brain network simultaneously. Specifically, we first employ two fundamental brain connectivity patterns to capture both statistical dependency and directional information flow among different brain regions and construct a heterogeneous brain connectivity network for each subject. Then, we develop a functional subdivision method that encodes brain networks into multiple latent feature subspaces corresponding to heterogeneous brain functions and extracts features of brain networks accordingly. Considering the intricate interactions of brain functions to facilitate cognitive activities within the brain network, we further employ the self-attention mechanism to obtain comprehensive representations of brain networks in a joint latent space. Finally, we propose a composite loss function to train the model for obtaining the heterogeneous brain network representation, which can be utilized for disease classification. The experimental results in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets demonstrate that our method outperforms several state-of-the-art (SOTA) methods to identify different types of brain cognitive-related disorders.

Aneurysm segmentation in computed tomography angiography (CTA) images is essential for medical intervention aimed at preventing subarachnoid hemorrhages. However, most existing studies tend to overlook the topological characteristics of arteries related to aneurysms, often resulting in suboptimal performance in aneurysm segmentation. To address this challenge, we propose an arteryaware global-to-local framework for aneurysm segmentation (AASeg) using CTA images of head and neck. This framework consists of two key components: 1) a centerline graph network (CG-Net) for aneurysm global localization, and 2) a point cloud network (PC-Net) for local aneurysm segmentation. The centerline graph is generated by extracting artery centerline structures from vessel masks obtained through a pre-trained model for head and neck vessel segmentation. This representation serves as a high-level representation of the artery structure, allowing for analysis of aneurysms along the entire arteries. It facilitates aneurysm localization via aneurysm-segment graph classification along the arteries. Then, local region of aneurysm segment can be sampled from the vessel mask according to the aneurysm-segment graph. Subsequently, aneurysm segmentation is performed on the point cloud constructed from the aneurysm segment through the PC-Net. Extensive experiments show that the proposed framework achieves state-of-the-art performance in aneurysm localization on a main dataset and an external testing dataset, with Recall of 84.1% and 80.7%, false positives per case of 1.72 and 1.69, and segmentation DSC of 66.1% and 60.2%, respectively.