Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti are morphologically similar fructus that are frequently misidentified. Xanthium italicum Moretti may possess inherent toxicity, and its adulteration of genuine medicinal materials poses a threat to clinical drug safety. Macroscopic observation and three microscopic techniques including stereo microscope, optical microscope, and 3D X-ray microscope were used for morphological identification of Xanthium sibiricum Patr ex Widder and Xanthium italicum Moretti in this study. 3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa. Intact fructus (n = 30 per species) were first screened macroscopically, then examined by stereo microscopy, optical microscopy, and 3D X-ray microscopy (0.3, 0.7, 1.5, 3.5, 18.06, 20.01 μm voxel size, Zeiss Xradia 520 Versa). The results showed that stereo microscopy, optical microscopy, and 3D X-ray microscopy collectively confirm the same conclusion from three distinct physical perspectives: surface topography, internal two-dimensional structure, and internal three-dimensional density distribution. The two Xanthium species differ significantly in burr spine morphology, fructus size and shape, the architecture and distribution of non-glandular and glandular trichomes, cotyledon conformation, and seed-coat cell patterning. In particular, 3D X-ray microscopy clearly resolves internal cotyledon spatial configurations and involucral cavity architectures, which furnishes critical endomorphic characters for taxonomic diagnosis. 3D X-ray microscopy provides unprecedented volumetric contrast of surface spines and internal seed architecture, permitting confident, non-destructive species identification. This study provides a basis for the safe clinical use of Xanthium sibiricum Patrin ex Widder. The frontier of 3D X-ray microscopy in plant systematics offers a novel, rapid, accurate and non-destructive protocol for the discrimination of morphologically elusive species.
Histological analysis is central to biomedical research and diagnostic pathology, yet conventional two-dimensional (2D) sectioning captures only limited aspects of tissue architecture. Critical spatial relationships-such as tumor boundaries, stromal organization, and vascular networks-remain obscured, restricting diagnostic accuracy and biological interpretation. HistoNeRF addresses these limitations by adapting Neural Radiance Fields (NeRF) to reconstruct three-dimensional (3D) tissue volumes from routine histological sections. In this study, 84 toluidine blue (TB)-stained murine ovarian sections were digitized, alignment-corrected, and integrated into volumetric models. Tissue segmentation was performed using a convolutional neural network, while visualization was achieved through an interactive, GPU-accelerated interface. To ensure accessibility and reproducibility, a Python-based graphical application (HistoNeRF GUI) was developed following Human-Computer Interaction (HCI) principles and containerized with Docker, allowing installation-free deployment via Docker Hub. HistoNeRF produced high-fidelity 3D reconstructions (SSIM = 0.92; Dice similarity coefficient = 0.88), enabling expert histologists to better visualize follicular structures, stromal compartments, and vascular elements. The containerized GUI was deployed successfully from Docker Hub, providing immediate access to 3D reconstruction without a complex local setup. By overcoming the inherent constraints of 2D microscopy, HistoNeRF enhances the visualization, interpretability, and reproducibility of histological architecture. The HCI-guided, cross-platform interface supports scalability and rapid adoption in digital pathology workflows. Although validation was limited to murine ovarian tissue and one staining protocol, this framework can be extended across tissue types and clinical datasets. HistoNeRF bridges routine histology and 3D volumetric analysis through accurate, interactive reconstructions that advance diagnostic precision and biomedical research. While demonstrated on 84 serial TB-stained ovarian sections, broader validation across tissues, stains, and pathological conditions remains future work; to support this, we provide a Dockerized, modular pipeline for straightforward extension.
7-ketocholesterol (7KCh), a cytotoxic oxysterol derived from cholesterol oxidation, plays a pivotal role in the progression of lipid-driven inflammatory diseases such as atherosclerosis. Among its pathogenic mechanisms, 7KCh is known to trigger oxiapoptophagy, a unique convergence of oxidative stress, apoptosis and autophagy. While macrophages typically serve as key phagocytic cells for clearing lipid debris, exposure to 7KCh disrupts this homeostatic role, contributing instead to dysfunctional foam cell formation. Despite its clinical relevance, the precise morphological and mechanistic effects of 7KCh on macrophages remain insufficiently characterized. In this study, we investigated the impact of 7KCh on IC-21 macrophages using a comprehensive, multimodal imaging strategy that integrates phase contrast, differential interference contrast (DIC), fluorescence microscopy and quantitative confocal imaging. Exposure to increasing concentrations (2-10 μg/ml) of 7KCh for 24 h revealed distinct morphological alterations, including cytoplasmic vacuolization, disrupted cellular architecture and pronounced lipid accumulation. Oil red O staining confirmed the accumulation of neutral lipids, while functional assays demonstrated impaired pinocytosis and phagocytosis, hallmark features of foam cell transformation. These findings were further supported by molecular analysis indicating induction of pro-inflammatory markers and oxiapoptophagy signatures. Altogether, this work provides novel insight into the cytotoxic and inflammatory transformation of macrophages by 7KCh and highlights the power of advanced microscopy in delineating lipid-induced immune dysfunction. Our imaging-based approach offers a valuable platform for future investigations into oxysterol-mediated pathologies and therapeutic targeting of foam cell dynamics.
Aflatoxin B1 (AFB1), a potent mycotoxin, poses significant health risks to humans and animals, with the kidney emerging as a secondary target organ beyond its well-documented hepatotoxicity. Cytoskeletal components are essential for proper podocyte, mesangial cell morphology, and glomerular function. This study investigated the histopathological and ultrastructural changes induced by AFB1 on renal tissues, focusing on glomerular filtration barriers, renal tubules, interstitial telocytes, and β-tubulin expression. Adult female Wistar rats were administered 250 μg/kg/day of AFB1 for 8 weeks, and kidney tissues were analyzed using toluidine blue staining, transmission electron microscopy (TEM), and immunohistochemistry. AFB1 exposure induced severe glomerular atrophy, thickened Bowman's capsules, widened Bowman's spaces, and tubular epithelial vacuolation. Telocytes exhibited necrotic changes, TEM confirmed podocyte foot process effacement, mitochondrial damage, and autophagic vacuole formation in proximal and distal tubules. Additionally, telocytes displayed fragmented telopodes and nuclear condensation, suggesting impaired tissue repair mechanisms, while immunohistochemistry revealed upregulated β-tubulin expression in glomeruli and tubules, indicating cytoskeletal disruption. These findings highlight AFB1-induced nephrotoxicity through oxidative stress, cytoskeletal remodeling, and interstitial inflammation, which in turn affect the glomerular filtration, underscoring the kidney's vulnerability to mycotoxin exposure. This study provides novel insights into the role of telocytes and β-tubulin dysregulation in AFB1-induced renal injury, offering a foundation for future research on therapeutic interventions.
Nowadays, blood cell segmentation has emerged as a popular solution for diagnosing hematological disorders. For hematological disorder detection, existing techniques face various limitations, including noise, weak edges, and intensity inhomogeneity. To rectify these problems, a novel Residual-Shuffle2DConv-Squeeze Network approach is proposed in this research to enhance the blood cell segmentation for hematological disorder diagnosis. Distinguishing blood cells, overlapped cells segmentation, edge detection, and morphological operations are the different stages of this approach for performing blood cell segmentation. The Residual-Shuffle Global Attention Network is used for morphological feature extraction, and this network integrates the Residual Network and Shuffle Global Attention Network. To retain fine-grained morphological features and capture variations of blood cell structure, the Residual-Shuffle Global Attention Network model is applied. The Shuffle Global Attention Network module has ShuffleNet and the Global Attention Mechanism. The ShuffleNet reduces the computational cost, and the Global Attention Mechanism helps to preserve crucial features through various layers. Finally, the 2DConv-SNN is applied to detect and classify hematological disorders. The comprehensive experiments are conducted on different datasets, including the Sickle Cell Disease Dataset and the Acute Lymphoblastic Leukemia dataset. The experimental results showcased that the Residual-Shuffle2DConv-Squeeze Network approach enhanced the detection of the hematological disorder with an accuracy of 98.69%, a dice coefficient of 97.05% and a Jaccard index of 96.43% respectively.
