Microscopy Research and Technique最新文献

Visualization of neuronal ultrastructure facilitates molecular and biochemical analyses that may help to better elucidate neural function and information processing. While the neuron exists at the micron scale, critical events such as synaptic vesicle release and dendritic spine remodeling occur at the nanometer scale, necessitating submicron resolution. Scanning electron microscopy (SEM) provides high-resolution imaging at these scales. However, the commonly used dehydration-based sample preparation method induces morphological distortions, while environmental SEM requires specialized equipment that is costly and difficult to operate. The NanoSuit method has recently emerged as a promising alternative, enabling SEM observations under high-vacuum conditions without standard (dehydration-based) pretreatment. Although known to be successful when applied to specimens with protective surface layers such as insects, flowers, and wet tissues, its effectiveness when examining "bare" cultured cells has not been thoroughly explored. Here, we present a modified NanoSuit protocol for SEM examination of cultured neurons and compare it with standard pretreatment. We demonstrate that traditional methods frequently cause neuronal transection and loss of fine dendritic processes, particularly during early development of neurons. However, the modified NanoSuit approach preserves neuronal morphology, enabling clear visualization of thin neurites and their interactions. Further, we successfully implemented correlative light and electron microscopy (CLEM) using this method, enabling the colocalization of cytoskeletal proteins such as actin and tubulin with the surface features observed by SEM. This combination of morphological preservation and molecular localization provides a more accurate and holistic understanding of neuronal structures, benefiting studies on neural development, synaptic connectivity, and related biomedical applications.

Histopathological image analysis is critical for cancer diagnosis, yet many existing models suffer from limited interpretability, high computational demands, and suboptimal classification accuracy. To overcome these limitations, we propose a novel model, Complementary Residual Retentive Network with Guided Gaussian Combined Arms Algorithm (C2RN2GC2A), designed to enhance efficiency and accuracy in cancer classification from histopathological images. C2RN2GC2A is a deep learning model that assimilates residual learning with optimized Gaussian perturbations, thus enhancing both feature extraction and working time in classification tasks. The system merges 2GC2A, a metaheuristic optimization approach motivated by military tactics, for the purpose of improving feature selection, truncating training loss, and speeding up convergence. The Two-stage Guided Chaotic Capuchin Algorithm (2GC2A) brings together Gaussian perturbations with a combined arms tactic, which makes it possible to do a good job of both exploring and exploiting the search space for better parameter tuning. In order to achieve interpretability, Layer-wise DeepLIFT Relevance Propagation (LDLRP) is used to delineate the significant areas of the image that have an impact on the classification, thus making the process more transparent and building up clinical trust. LDLRP is a cutting-edge explainable AI technology that grants relevance ratings to input characteristics and thus allows the model to visually demonstrate the most significant regions in histopathological images, thereby facilitating clinical decision-making. Testing on LC25000 produced a remarkable accuracy of 98.02% along with a minuscule training loss of 0.08, besides which there were 13 false positives and 29 false negatives. On BreakHis, the accuracy was 98.54%, and the validation loss was 0.05, with 98 false positives and 112 false negatives. The proposed framework significantly improves diagnostic reliability, classification accuracy, and clinical transparency in multi-cancer histopathological image analysis.

Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye. However, conventional optical microscopy faces challenges such as optical aberrations, diffraction-limited resolution, low signal-to-noise ratio (SNR), and poor contrast. The exponential growth of bioimaging data further underscores the need for advanced computational tools. Deep learning (DL) is a subset of machine learning that has emerged as a transformative approach to address these limitations, offering enhanced precision, reduced manual intervention, and diminished reliance on domain-specific expertise for image reconstruction, enhancement, and analysis. This review explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. We examine prominent DL architectures such as convolutional neural networks (CNNs), U-Nets, residual networks (ResNets), and generative adversarial networks (GANs)-and their role in advancing diverse microscopy modalities. These frameworks enhance image quality, improve quantitative analysis, and democratize access to high-performance microscopy. Additionally, we discuss persisting challenges, including the demand for large, annotated datasets, dynamic sample variability, model interpretability, and potential data biases. Collectively, DL is poised to revolutionize optical microscopy, shaping its future developments in biomedical imaging.

In this work, a facile synthesis of Ni-GaOOH metal hydroxides has been reported as an electrode material for supercapacitors. The morphology was studied by using both scanning electron microscopy (SEM) and transmission electron microscopy (TEM), confirming a nanoplate-like structure. By employing a 3M solution of potassium hydroxide as the electrolyte, the Ni-GaOOH electrode achieves its maximum specific capacitance of 862 F/g at a current density of 1 A/g. When evaluated for ten thousand cycles, Ni-GaOOH's cyclic performance was shown to be extremely stable. The findings demonstrated that the Ni-GaOOH electrode has a good capacitance retention of about 85% and a high Columbic efficiency of 99.5%. The Ni-GaOOH as synthesized is a highly efficient electrode material that is much more appropriate for supercapacitor applications.

The term Internal Darkfield can be applied to microscopy methods that employ Fourier stops to block or attenuate undiffracted zero-order light, with optional superposition of adjustably attenuated brightfield to achieve Variable Graded-Field images. Existing Luminance Contrast and Single-Sideband Edge Enhancement or Schlieren methods may be improved by using a secondary illumination beam to generate Internal Darkfield, preferably in color, along with attenuated primary bright illumination that ideally uses a wide-aperture asymmetric beam. The main embodiment of the Dual-Illumination Fourier Modulation Microscopy methods uses a thin strip-stop to block a narrow secondary pencil beam at variable angles of incidence. Axial darkfield manifests luminous visualization of internal detail with reduced edge-blooming artifacts, whereas peripheral angles give enhanced directional resolution. Methods are described using semicircular Fourier stops including a pure Variable Rejection Internal Darkfield method that allows adjustable positioning of a peripheral pencil deep inside the stop zone to reject most of the diffuse background along with low spatial frequencies. At smaller angles, more low spatial frequencies are admitted, allowing increased brightness. Fourier modulation can also be applied to various methods of Multiple Oblique Beam Illumination, condenser-free LED microscopy including multi-color modes, Spatial Light Modulator innovations including split-aperture phase retrieval, and high-pass filter innovations. Suggestions are also offered for fluorescence and autofluorescence applications, along with hyperspectral imaging using variable wavelength secondary illumination. The underlying principles of Schlieren microscopy and split-aperture methods are explored, along with the mechanisms that generate relief contrast due to sideband suppression or asymmetry.

Atmosphere- and/or moisture-sensitive materials can be challenging to characterize using electron microscopy techniques due to sample preparation workflows that generally require exposure to ambient conditions. Here, we describe a novel preparation method that uses aluminum foil in combination with a commercial cryo-EM transfer system to circumvent undesired exposure to the atmosphere. First, hygroscopic MgCl₂ was used as a model material, and prepared samples (both protected and unprotected) were placed in a controlled-humidity environment (> 80% relative humidity) for various exposure lengths (circa seconds to hours). Following this, the effectiveness of the sample preparation method was determined by comparing qualitative photos and quantitative X-ray diffraction patterns between the two sample subsets. The combined results of these experiments suggest that the outlined preparation method effectively protects MgCl₂ from atmospheric contamination compared to MgCl₂ samples that had no protective measures taken. Finally, the preparation method was utilized to protect a highly hygroscopic crystalline BaO thin film for characterization via scanning electron microscopy, thereby demonstrating a functional application of the outlined preparation technique and an additional use for the commercial cryo-EM transfer system beyond its intended application.

Accurate cellular segmentation is essential for cell morphology analysis and disease diagnosis. Traditional manual segmentation is prone to errors, while general segmentation algorithms based on deep learning often fail when dealing with imperfect cytoarchitecture images. This study proposed a high-efficiency and high-accuracy cellular segmentation scheme for such imperfect images. We first enhanced the cell images and then employed the Cellpose algorithm with the Cyto3 pretrained weight module as the foundational model. This scheme requires no additional training, ensuring high efficiency. Experimental results demonstrated a significant improvement in segmentation accuracy, achieving an IoU index of 0.86, ACC index of 0.98, MCC index of 0.91, and Dice of 0.93. When applied to mouse brain images, it successfully quantitatively displayed cell distribution density differences across brain regions. The scheme has great application potential and value in accurate biomedical research, such as quantitative analysis of cell distribution density in different brain regions and cellular localization.

Biofilms are found in diverse environmental settings and are considered to be responsible for various recalcitrant infections. One characteristic feature of biofilms is resistance to antibiotics, which is the leading cause of recurrent infections and treatment failure. Eradicating the biofilms necessitates the need for agents with promising anti-biofilm potentials. In the present study, the secondary metabolites of the fungal endophyte Cephalotheca foveolata (N11) isolated from the woody tissues of the medicinal plant Teucrium stocksianum were investigated for their antibiofilm potential against the test organisms. For evaluating the antibiofilm activities, in vitro assays including biofilm inhibition and eradication assays were employed. The bioactive metabolites of the N11 strain exhibited the highest biofilm inhibition and eradication potential of 87.62% and 79.22% respectively against Staphylococcus epidermidis. The results were further validated by light microscopy and confocal laser scanning microscope which revealed considerable distortion of the biofilm architecture by test agents. Besides, the effect of secondary metabolites on biofilms of test strain was also observed using Raman spectroscopy. The Raman spectra of treated biofilms exhibited a significant reduction in the intensities of the peaks indicating the denaturation and conformational changes in biomolecules. Furthermore, the partial purification of antibiofilm metabolites of N11 was carried out using solvent extraction following TLC and silica column with further characterization done using FTIR. These findings highlight the remarkable potential of bioactive secondary metabolites of endophytic fungi associated with T. stocksianum in disrupting the biofilms thus suggesting that these metabolites can be exploited for manufacturing effective agents against biofilm-associated complications.

Cerium oxide nanoparticles (CeO₂ NPs) were synthesized via a green and eco-friendly method using Parkia biglandulosa leaf extract, which acted as both a reducing and stabilizing agent. The synthesized CeO₂ NPs exhibited a cubic fluorite crystal structure and predominantly spherical morphology, with particle sizes ranging from 10 to 12 nm. Antibacterial studies demonstrated notable activity against Lactobacillus acidophilus, Staphylococcus albus, and Streptococcus mutans, with the highest inhibition zone (32 mm) observed against S. mutans (Ce-3). The anticancer activity assessed against MCF-7 breast cancer cells demonstrated an IC₅₀ value of 43.13 μg/mL (Ce-3). Antioxidant assays, including DPPH, ABTS, and hydroxyl radical scavenging, exhibited IC₅₀ values of 202.47, 219.77, and 193.05 μg/mL, respectively, indicating strong free radical scavenging potential. Additionally, corrosion resistance studies of CeO₂ NP-coated mild steel in 3.5% NaCl solution revealed a maximum inhibition efficiency of 95.44% (Ce-3), as confirmed by electrochemical impedance spectroscopy. Overall, these findings demonstrated the multifunctional efficacy of P. biglandulosa derived CeO₂ NPs for biomedical and industrial applications.

Molecular forces drive phenomena such as self-assembly, aggregation, and protein folding, where hydrophobic interactions are paramount. However, the origin of the hydrophobic mechanism remains unknown. Advances in techniques like atomic force microscopy (AFM) have improved our ability to study this topic. Hydrophobic interactions are stronger and longer ranged than van der Waals (vdW) forces, potentially arising from water structuring, polarization, and entropic effects. In this primer, fluorocarbon surfaces were prepared via chemical vapor deposition (CVD) on gold to explore the impact of water:DMSO solvent binary mixtures on hydrophobic interactions. Force-distance curves measured with AFM were fitted to an extended vdW model, disclosing the influence of the medium polarity on the interactions.