This paper studied the interaction between HepG2 cancer cells and NK-92 immune cells, employing an atomic force microscope (AFM) as the primary tool. The research mainly focused on the use of AFM to explore the effect of immune cells on cancer cells and revealed the relationship between the killing effect of immune cells on cancer cells and the mechanical properties of cancer cells. A novel method for quantifying in situ adhesion between the NK-92 cells and HepG2 cells was proposed, which provided insight into the relationship between the mechanical properties and the time of co-culture. Furthermore, the morphological and mechanical changes of HepG2 cells were detected using AFM. As co-culture time increased, the findings revealed a trend toward incomplete cell morphology. Reductions were observed in the heights, adhesion, and elastic moduli of HepG2 cells, along with an increase in cell surface roughness. The surface of membrane pores was visualized, and based on the obtained cell height data, the measurements of the size and depth of cell membrane surface pores were conducted. In conclusion, this research unraveled the complex interplay between the killing effect of immune cells on cancer cells and the mechanical properties of cancer cells. It offered a valuable perspective for understanding the interaction between immune cells and cancer cells, potentially paving the way for future therapeutic interventions.

The local elastic response of the Al matrix surrounding T1 precipitates and Al3Zr dispersoids in a T8-treated AA2195 Al-Li-Cu alloy was investigated using a correlative approach combining advanced scanning transmission electron microscopy, low-loss electron energy-loss spectroscopy, image simulation, and first-principles calculations. Aberration-corrected HAADF-STEM and STEM-EDS confirmed the presence of plate-like T1 precipitates and spheroidal Al3Zr dispersoids, with Cu enrichment in the T1 phase and Zr enrichment in the dispersoids. Low-loss STEM-EELS spectrum imaging was used to probe the local valence-electron response by analyzing the first bulk plasmon peak of the Al-rich matrix. Quantitative fitting of the plasmon peak revealed spatial variations in peak amplitude, full width at half maximum, and energy position near the precipitate-matrix interfaces, indicating local changes in electron density, bonding environment, and interfacial strain. Using an empirical plasmon energy-elastic modulus relationship, local effective Young's modulus values of approximately 70.4 GPa for the Al matrix, 69.3 GPa for the T1-adjacent Al-rich matrix/interface region, and 72.1 GPa for the Al3Zr-adjacent Al-rich matrix/interface region were obtained. These values reveal nanoscale elastic heterogeneity associated with the distinct precipitate-matrix interfacial environments. QSTEM-based multislice HAADF-STEM simulations reproduced the experimental T1 contrast and validated the Cu-rich layered atomic arrangement of the precipitate. Density functional theory calculations were further performed on idealized Al/T1 and Al/Al3Zr interface supercells. Strain-energy analysis yielded effective directional Young's moduli of approximately 78 GPa and 73 GPa, respectively, for the selected interface configurations. The combined experimental and computational results show that the local elastic response of AA2195 is governed by precipitate chemistry, interfacial coherency, lattice mismatch, and electronic-structure modification. This work establishes low-loss STEM-EELS plasmon mapping, supported by atomistic simulations and DFT calculations, as a powerful framework for linking nanoscale structure, chemistry, and elasticity in precipitation-strengthened Al-Li-Cu alloys.

The primary flight feather shaft (PFF) plays a crucial role in adjusting avian's flight posture and flight speed while bearing intense aerodynamic force, and it demonstrates exceptional mechanical properties and fracture resistance. The influence of the structure of a PFF on its fracture resistance mechanism (including crack initiation resistance and crack propagation resistance) remains unclear. This study focused on the research subject of the PFF of Aegypius monachus (cinereous vulture) and the impact of the macro/microstructural characteristics and components on the fracture resistance mechanism. The results showed that the variation in cross-sectional shape (from circular to square) enhanced the bending stiffness of the PFF. The PFF exhibited a maximum compressive stress of 28.85 ± 0.91 MPa and a maximum tensile strength of 51.59 ± 4.43 MPa. Layered fibers and the bridging structure between fibers inhibited crack initiation and propagation. The medullary foam was composed of closed-cell porous units, which reduced the overall weight of the PFF and absorbed external mechanical energy. The cuticle layer contained 5.97 wt% sulfur (S) and abundant cysteine disulfide bonds, which endowed the PFF surface with high rigidity and inhibited crack generation. This study reveals the influence of structural characteristics of vulture PFF on its fracture resistance mechanism and provides inspiration for the design of bioinspired lightweight composite structures.

Probe-based confocal laser endomicroscopy (pCLE), as a powerful medical device for detecting digestive tract diseases, can directly conduct real-time and high-resolution histological diagnosis of gastric mucosal epithelium and subcutaneous tissues. It can achieve optical biopsy and has great potential for early diagnosis of gastric cancer. Deep tissue detection and high axial resolution in the depth direction are of great significance for the diagnosis of clinical gastrointestinal diseases. However, improvements in hardware systems will greatly increase the complexity of the system structure, thereby limiting its wide application. Based on the residual channel attention network (RCAN), we developed a deep learning method for axial resolution enhancement. This method, abbreviated as ARE-RCAN, can directly reconstruct images captured by pCLE into ones with higher axial resolution without any additional hardware design. Firstly, high-quality training and testing datasets are generated through numerical simulation, and based on this, the ARE-RCAN is trained and performance tested. The reconstructed results of the testing datasets show that the axial resolution of the reconstructed images of simulated fluorescent beads has doubled, and the lateral resolution has not changed. Finally, a home-built pCLE system was used to collect fluorescent bead and paper fibers data, and the effectiveness of the ARE-RCAN was verified through experiments. The results indicate that the ARE-RCAN method significantly improves the axial resolution of the actual system.

Quantitative extraction of aromatic lattice fringes from high-resolution transmission electron microscopy (HRTEM) images is essential for the nanoscale characterization of high-rank coal. However, variations in image processing strategies may introduce structural biases into the extracted lattice fringe networks, while the effects of individual processing steps remain insufficiently evaluated. In this study, an image processing workflow was developed for lattice fringe extraction from high-resolution transmission electron microscopy images of high-rank coal. The workflow integrates frequency-domain enhancement, contrast optimization, threshold segmentation, skeletonization, and skeleton refinement. Comparative experiments were performed to evaluate the effects of enhancement strategies, segmentation methods, and skeleton cleaning procedures on the extracted lattice fringe networks. Three topological parameters, including endpoint density, junction density, and average branch length, were introduced to assess skeleton integrity, abnormal connections, and structural continuity. The results demonstrate that selective frequency-domain enhancement is critical for maintaining stable skeleton topology, whereas spatial-domain smoothing increases non-physical connections and fragmentation. Although global Otsu thresholding produces comparable geometric measurements, differences remain in topological characteristics, indicating that geometric parameters alone cannot fully describe lattice fringe networks. Length-based filtering effectively removes artifacts with limited influence on network connectivity, while junction removal disrupts the structural framework of the skeleton. The proposed workflow provides a robust approach for quantitative analysis of lattice fringe networks and offers a methodological basis for reliable microscopic characterization of carbon materials.

Helminths cause a wide range of diseases in different components of ecosystems and may induce serious pathological conditions in the host organisms. In recent years, nanoparticles have been increasingly used in ecology, veterinary medicine, and biology. In this study, the effect of silver (Ag) nanoparticles on the waterfowl parasite Heterakis dispar (Schrank, 1790), isolated from domestic geese (Anser anser domesticus L., 1758), was investigated under in vitro conditions, with particular emphasis on nanoparticle accumulation patterns and associated ultrastructural changes, studied at the ultrastructural level. In the experiments, H. dispar nematodes collected from the hosts were exposed in vitro to Ag nanoparticles at concentrations of 10, 50, and 100 μg/mL. Control and experimental groups of nematodes were examined using light and transmission electron microscopes. The sizes of Ag nanoparticles in a free state were 9.03-23.82 nm, and particles detected within the helminth organism were approximately 11-17 nm. It was visually observed that the particles were present in multiple layers of the body wall, in the digestive and reproductive organs of the parasitic worm. Silver nanoparticles enter the helminth through the body wall (from cuticle to the hypodermis, muscular layer, and organs located in the pseudocoelomic or body cavity) and the digestive organs (through the intestinal microvilli). At the ultrastructural level, notable alterations were observed in treated specimens. The results prove that Ag nanoparticles have anthelmintic activity.

Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe[3+]) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.

Liquid-liquid phase separation (LLPS) plays a central role in intracellular compartmentalization and gene regulation. Although optogenetic optoDroplet systems are widely used to study LLPS in living cells, the mechanisms underlying the transition from dynamic liquid-like condensates to operationally defined gel-like assemblies remain poorly understood because intermediate assembly states are difficult to monitor in situ. Here, we developed a confocal laser microspectroscopy-based approach to characterize the assembly dynamics of the fused sarcomas (FUS)n-mCherry-CRY2 optoDroplet system in living NIH3T3 cells. Quantitative fluorescence analysis enabled the identification and discrimination of three distinct assembly states: monomeric, dimer/oligomer intermediate, and gel-like. Passive micro-rheology, fluorescence recovery after photobleaching (FRAP), and automated morphological analyses further demonstrated progressive reductions in molecular mobility and increasing structural rigidity during condensate maturation. Three-dimensional spatial analysis revealed that mature condensates exhibit a distinct core-shell organization consisting of an operationally defined gel-like core, a dimer/oligomer-rich intermediate layer, and an outer monomer-rich boundary layer with liquid-like properties. Upon cessation of blue-light stimulation, the outer layer rapidly dissolved, whereas the central gel-like core remained intact, indicating the acquisition of physical irreversibility. These findings establish fluorescence microspectroscopy as a quantitative approach for resolving molecular assembly states during condensate maturation and demonstrate that gelation proceeds from the condensate center toward the periphery. This framework provides new insights into pathological liquid-to-gel phase transitions associated with neurodegenerative diseases.

This study employed scanning electron microscopy (SEM) to conduct a morphological observation of vessel elements from 10 Gentianaceae species collected on the Qinghai-Xizang Plateau. We systematically compared vessel type, end-wall and lateral-wall characteristics, and vessel diameter to explore their adaptive relationship to the extreme plateau environment and the evolutionary trends of vessels. The results indicated that the vessel end walls of all 10 species featured simple perforation plates, while annular, spiral, and scalariform thickenings were observed on the lateral walls of all species, with scalariform vessels constituting the highest proportion in most. The vessel types were primarily annular, spiral, and scalariform; pitted vessels were found only in Gentiana farreri and G. waltonii. The vessel diameters were generally small (mostly < 20 μm). The vessel morphology exhibited significant adaptive characteristics to plateau adversities such as low temperature, drought, and intense radiation. Based on the analysis of vessel type, end wall features, and lateral wall characteristics, the vessels of G. farreri and G. waltonii exhibited the highest proportions of morphologically specialized types (pitted and scalariform vessels) according to the classical Bailey-Frost sequence, whereas those of G. aristata and G. hexaphylla were dominated by the more generalized annular and spiral types. This research provides new anatomical evidence for understanding the structural adaptation mechanisms of Gentianaceae plants in high-altitude environments and offers a theoretical basis for the conservation and utilization of these plant resources.

Bimetallic structures (BMSs) are being used more frequently in industries like automotive, aerospace, and shipbuilding because they combine different material properties in a single component. Traditional methods like welding and brazing have their limits, but additive manufacturing (AM) offers a more flexible and cost-effective way to create complex multimaterial parts. This study focuses on the microstructure and mechanical properties of BMSs made by depositing stainless steel (SS) on high-strength low-alloy (LA) steel using the Wire Arc Additive Manufacturing (WAAM) process with Surface Tension Transfer (STT). The results show that defect-free BMSs can be successfully fabricated, improving design possibilities. Microstructural analysis revealed a clear Ferrite-Austenite (FA) boundary at the SS-LA interface. Key findings include an 81.81% increase in hardness at the interface relative to the LA steel side, along with an interface tensile strength of 621 ± 7 MPa and elongation of 19.28% ± 1.76%. Electron backscatter diffraction (EBSD) showed fine, randomly oriented grains in the SS region and elongated, directionally aligned grains in the LA region, with no dominant texture, indicating near-isotropic behavior. The presence of both low-angle and high-angle grain boundaries confirmed dynamic recrystallization during deposition. Fracture analysis showed strong mechanical bonding, with failure mainly occurring on the LA side. This demonstrates that WAAM with STT is a viable method for creating reliable, high-performance multimaterial components.

As a rare and precious traditional Chinese medicine (TCM), Ophiocordyceps sinensis (O. sinensis) is plagued by various adulterants in the market due to factors such as resource scarcity, which seriously affects medication safety and clinical efficacy. Existing identification methods generally have problems including strong subjectivity, high sample destructiveness, long detection cycle, and complex operation, and it is difficult to identify different types of adulterants using the same method. Innovatively, this study adopted scanning electron microscopy-energy dispersive x-ray spectroscopy (SEM-EDS) technology to establish a rapid, accurate, and widely applicable method for identifying adulterated O. sinensis. Authentic O. sinensis samples from major producing areas such as Xizang, Qinghai, Sichuan, and Gansu, as well as different types of adulterated samples, were collected. Using SEM-EDS technology, combined with micromorphology and elemental composition, the differences between authentic and adulterated samples were systematically revealed through indicators including morphological differences, mass fraction of characteristic elements, and surface area coverage (SAC) of characteristic elements in surface scanning. This study confirmed that SEM-EDS technology could simultaneously obtain the micromorphological and elemental distribution information of samples, realizing rapid and accurate identification of multiple types of adulterated O. sinensis. The proposed method provided new technical support for the quality control and market supervision of O. sinensis, which could be extended to the adulteration inspection of other precious and delicate TCM.

Shape-from-focus (SFF) is essential for microscopic three-dimensional morphology analysis due to its non-contact nature and high axial resolution. However, in practical microscopy environments, ambient vibration and multi-source noise often cause unstable sampling and distorted focal volume, significantly limiting the technique's applicability in high-precision measurement. To address this, we propose a novel anti-vibration, high-fidelity microscopic SFF framework. First, a temporal redundancy strategy based on video streaming is employed to generate spatially consistent image sequences prior to reconstruction. Subsequently, an adaptive dual-mode peak localization algorithm is introduced, which intelligently selects depth estimation strategies according to the texture characteristics of the micro-surface, achieving sub-pixel accuracy while maintaining computational efficiency. Furthermore, by transforming one-dimensional focus signals into two-dimensional images and applying an intelligent classifier for filtering, the method successfully separates noise from valid texture features. This framework requires no prior knowledge of vibration frequency and adaptively suppresses random environmental disturbances. Experiments on both synthetic and real microscopic samples show that the proposed method outperforms existing approaches in shape recovery accuracy, preservation of fine texture details, and noise suppression. The technique offers a reliable high-precision 3D measurement solution for materials science, industrial inspection, and biomedical microscopy in vibration-prone environments.

The low electronic conductivity and rigid structure make sample preparation for in situ transmission electron microscopy (TEM) of oxide nanofibers challenging. This work evaluates three sample preparation approaches for mounting single oxide nanofibers on MEMS chips: (i) drop casting combined with focused ion beam (FIB) cutting and metal deposition, (ii) single-probe micromanipulation, and (iii) double-probe micromanipulation inside a FIB-SEM. Advantages and limitations of each method are discussed, including issues of fiber agglomeration, positioning/orientation, and charging in the electron or ion beam. Drop casting offers simplicity but poor positional control. The probe-based methods enable precise positioning; charging however remains a major challenge which is best compensated for by using more than one microprobe. Strategies for improving electrical contact and minimizing contamination during metal deposition are also addressed. The findings provide practical guidelines for preparing oxide nanofibers for operando TEM experiments involving electrical and thermal stimuli.

Histological image classification plays a critical role in biomedical research and diagnostic processes. Advances in the field of deep learning present significant opportunities for enhancing diagnostic accuracy and developing automated decision support systems. This study aims to comparatively evaluate the out-of-distribution generalization and cross-domain classification performance of different deep neural network encoders. In this study, models were trained on an internal dataset of 4307 hematoxylin and eosin (H&E) stained images of male rat lung, cerebellum, and adipose tissues, obtained under ethical committee approval (Ege University, HADYEK 2026-06). To evaluate genuine generalization, testing was conducted on a separate, diverse external cohort of 600 mixed human-and-animal images. Nine different deep learning architectures-MobileNetV3-S, MobileNetV3-L, DenseNet121, DenseNet201, ConvNeXt-S, ConvNeXt-B, DINOv3 ViT-S, DINOv3 ViT-H+, and the pathology foundation model UNI2-h-were evaluated using frozen feature extraction combined with a linear probe. For performance evaluation, accuracy, sensitivity, specificity, F1 score, Cohen's κ, and ROC-AUC metrics were analyzed along with per-image inference times. While all models achieved near-perfect results during internal cross-validation, their performance diverged significantly on the external dataset, confirming that the observed differences reflect genuine cross-domain generalization capabilities rather than under-fitting. All competitive models demonstrated high performance in classifying adipose and cerebellum tissues, achieving an F1 score of 95% or higher for these tissues. In distinguishing the lung tissue, which has the most diverse structure and is the most difficult to classify, the UNI2-h model emerged as the most successful, achieving an F1 score of 97.4% and a recall of 95.0%. When evaluated in terms of computational efficiency, the MobileNetV3-Small model stood out as having the lowest processing time among all scenarios, demonstrating an inference time of 9.4 ms on the CPU and 12.5 ms on the GPU. In conclusion, while the pathology foundation model UNI2-h achieved the highest accuracy values across all metrics, the ConvNeXt-Small model was identified as the system providing the optimal balance between speed and accuracy. These findings indicate that although UNI2-h stands out for the most precise results in cross-domain classifications, the ConvNeXt-Small architecture emerges as an ideal alternative, particularly in practical applications where computational efficiency is critical and resources are constrained.

With the increasing adoption of 3D cell cultures and bioprinting in biomedical research, there is a growing demand for reliable and accurate monitoring methods. While fluorescence microscopy is widely accepted for 2D cell cultures, when introducing the third dimension it often suffers from signal attenuation and out-of-focus interference, making automated image analysis challenging. In this study, a hybrid pipeline that integrates deep learning and traditional image processing techniques is presented with the aim of establishing an automated tool for cell segmentation and viability assessment in 3D from fluorescence microscopy images. A U[2]-Net architecture was trained on 2D cell cultures for cell segmentation, while watershed-based separation and intensity-based classification were employed for viability detection. The model demonstrated accurate segmentation with minimal overfitting. Quantitative comparisons with manual counts and ImageJ-based results confirmed its accuracy. We then demonstrated the possibility of extending this model to 3D constructs by tracking cell density and viability during time in the case of human umbilical vein endothelial cells bioprinted in gelatin methacrylate at different seeding densities, taken as case study. Automated counts closely matched manual ones, highlighting the method reliability and minimal invasiveness avoiding the need for sacrificing the construct. Hence, this approach provides a scalable, reproducible, and efficient alternative to manual counting in 3D environments, enabling high-throughput analysis of complex fluorescence microscopy data.