Microscopy Research and Technique最新文献

Focused ion beam-scanning electron microscopy (FIB-SEM) is a microscopy technique that can be used to investigate the quality and structural properties of industrial materials such as polyolefins. An understudied aspect and possible drawback of the technique could be the implantation of the impinging ions under the sample surface and damage to the molecular structure, hindering its use as a sample preparation tool for surface-sensitive techniques. We systematically investigated the damaging effects of gallium liquid metal focused ion beam under grazing incident beam angle and various accelerating voltages on polyethylene. Changes in molecular structure and ion implantation depth were analyzed with Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) depth profiling and optical profilometry. Our results show that the gallium ion beam causes significant damage to the polyolefin structure, which is especially observed as dehydrogenation of the molecular structure of the (sub)surface. These molecular products are concentrated in distinct sub-surface zones, where damage is coupled to the presence of implanted gallium from the FIB etching.

The current study aims to compare the morphological features of the oropharyngeal roof of the little owl ( Athene noctua ) and the domestic pigeon ( Columba livia domestica) by gross anatomy, morphometric measurements, scanning electron microscopy, and light microscopy. Ten heads of normal, healthy, and adult little owls and domestic pigeons were used in the current study as follows: seven heads for gross anatomical examination and light microscopy, and three for scanning electron microscopy. The oropharyngeal roof had a triangular appearance in both birds, was enlarged and broad in the little owl, and narrowly elongated in the domestic pigeon. The little owl had a median longitudinal ridge, a transverse ridge, and two lateral longitudinal ridges, while the domestic pigeon had one median and two lateral ridges. The choanal slit in both birds was composed of rostrally narrow and caudally wide parts. In the little owl, the edges of both parts were encircled by papillae, while in the domestic pigeon, the papillae encircled the edges of the rostral part only. The surface of the pharyngeal roof in the little owl had numerous elongated conical pharyngeal papillae, while that of the domestic pigeon was devoid of papillae. The edges of the infundibular cleft in the little owl had prominent large conical papillae, while those in the pigeon were devoid of papillae. Histologically, the epithelium of the plate and pharynx of both birds was composed of stratified squamous epithelium. In conclusion, the present study clarified several differences between the oropharyngeal roof of the little owl and the domestic pigeon, which reflect their adaptation to their feeding habits and the surrounding environments.

In the rapidly advancing field of histopathological image analysis, accurate segmentation of critical features is crucial for medical diagnostics, as it enables pathologists to make precise decisions. The proposed One Former-based Mean Teacher Model with Federated Learning (OF-MTMFL) system combines cutting-edge semi-supervised learning and federated learning techniques to tackle issues such as limited annotated data and class imbalance. The framework utilizes a mean teacher architecture, where the student model, guided by a focal loss function, prioritizes high-confidence regions in unlabeled data, while the teacher model ensures consistency through Exponential Moving Average (EMA) updates. To further enhance segmentation accuracy, multi-scale attention modules are employed for robust feature extraction. Additionally, the system incorporates a Federated Learning mechanism that allows multiple institutions to collaborate without sharing raw data, including datasets from the Cancer Genome Atlas (TCGA). The results from the analysis of the TCGA dataset indicate that the proposed OF-MTMFL model achieved mean concordance index (c-index) scores of 0.700 ± 0.030 for Bladder Urothelial Carcinoma (BLCA), 0.720 ± 0.040 for Breast Invasive Carcinoma (BRCA), 0.860 ± 0.025 for Glioblastoma & Lower Grade Glioma (GBMLGG), 0.690 ± 0.035 for Lung Adenocarcinoma (LUAD), and 0.740 ± 0.045 for Uterine Corpus Endometrial Carcinoma (UCEC). The overall performance score of the OF-MTMFL model across these cancer types is 0.740, demonstrating particularly strong results in GBMLGG while maintaining competitive scores in the other cancer types. The standard deviations reported reflect the variability of the model's performance across different samples within each category.

Senna martiana (Benth.) H.S. Irwin & Barneby is endemic to Brazil's dryland Caatinga domain. The species is known as “canafistula” in folk medicine, and its leaves are used as a laxative. Anatomical and histochemical studies of the species' vegetative organs (roots, stems, and leaves) were conducted to identify features that would support its taxonomic classification, as well as provide quality control for its ethnomedicinal use. Dried fragments from both leaflet surfaces were examined using scanning electron microscopy, and paradermal and transverse sections were used in histochemical tests to confirm the presence of lipids, starch, alkaloids, and nonstructural phenolic compounds. Senna martiana exhibits a striate cuticle on both leaflet surfaces, with straight to curved anticlinal cell walls on the adaxial face and sinuous walls on the abaxial face. The leaflets are amphistomatic, with a variety of stomatal types (paracytic, anisocytic, and anomotetracytic). The midrib is plane-convex with a collateral vascular system; the petiole is elliptic with 6–8 vascular bundles; the leaf rachis is ovate with 4–5 vascular bundles. Stems have a siphonostelic ectophloic vascular system. Idioblasts containing crystal sand, prismatic crystals, druses, and starch grains were observed in all vegetative organs. The contour and number of vascular bundles in the petiole and rachis, along with the sclerenchyma bundles enclosing the vascular system or roots, were distinctive features for Senna martiana. These characters constitute an important support for species identification, differentiation, and quality control of its ethnomedicinal drugs.

This study represents the first comprehensive effort to document the pollen taxonomy of plant species in the Hindukush-Karakoram-Himalaya region of northern Pakistan. The clarification of the leaf micromorphology of the selected herbaceous plants reveals characteristics like the stomata types, trichome types, and epidermal cell size and shape. The leaf micromorphology of 50 herbaceous plants revealed foliar anatomical traits, such as changes in epidermal cells, trichome morphology, and stomatal type. Stachys emodi has the longest epidermal cell length, measuring 69.54um at the adaxial and 66.65um at the abaxial surface. The largest guard cell length was recorded as 36.45 μm in Erigeron bonariensis , while the stomatal width measured 36.15 μm on the abaxial surface and 34.55 μm on the adaxial surface among the studied Asteraceous taxa. In anatomical features, diacytic, actinocytic, anomocytic, anisocytic, and paracytic stomata were studied. Anticlinal wall patterns were found to be undulating, straight, and sinuate. The epidermal cell shape was found to be polygonal, pentagonal, tetragonal, and irregular. The micromorphological epidermal anatomical traits have been recognized to serve as a foundation for the correct identification of herbaceous flora in the context of systematic relevance.

The green synthesis of nanoparticles using plant extracts has garnered significant attention as a reliable and sustainable method for producing functional nanomaterials. Among these, zinc oxide (ZnO) nanoparticles are extensively studied for their potential biological applications. In this study, ZnO nanoparticles were synthesized through a green synthesis approach using an aqueous extract of Musa acuminata leaves and zinc acetate as precursors. The resulting nanoparticles were characterized using ultraviolet–visible spectroscopy (UV–Vis), Fourier transform infrared spectroscopy (FTIR), x-ray diffraction analysis (XRD), energy-dispersive x-ray analysis (EDX), and scanning electron microscopy (SEM). The UV–Vis spectra revealed characteristic absorption peaks around 350 nm, attributed to the nanoparticles' large excitation binding energy at room temperature. FTIR analyses confirmed the formation of zinc oxide chemical bonds, while XRD results indicated a hexagonal wurtzite crystal structure. SEM analysis showed that the nanoparticles had a nearly cuboid shape, and EDX analysis confirmed their high purity. The minimum inhibitory concentrations (MIC) of the synthesized ZnO nanoparticles were evaluated against Staphylococcus aureus and Escherichia coli cultures. Cotton wound bandages impregnated with ZnO nanoparticles at concentrations near the calculated MIC demonstrated significant antibacterial activity in vitro. These antimicrobial bandages show potential for use in treating and protecting infection-prone wounds, such as diabetic or burn-related injuries.

Uterine cancer also referred to as endometrial cancer, which significantly impacts the female reproductive organs. The early diagnosis increases the survival rates and also prevents the progression of endometrial cancer thereby, the novel Transfer Learning based Convolution Neural Network with Adam Gold Rush Optimization (TL-CNN_AdGRO) is proposed to classify endometrial cancer using histopathological images. The histopathological image is fed to the preprocessing phase, which uses an Adaptive Weighted Mean Filter (AWMF). Next, the segmentation of endometrial cancer is utilized by the Directional Connectivity Network (DConn-Net). Following segmentation, feature mining is carried out, which includes Local Boundary Summation Pattern (LBSP) and Local Gaber Binary Pattern Histogram Sequence Features (LGBPHS). Finally, the endometrial cancer classification is achieved using TL-CNN by employing hyperparameters from the Xception model. Here TL-CNN is trained by AdGRO algorithm, which is the combination of Adam Optimizer and Gold Rush Optimization. Compared to existing models, the proposed model achieves superior performance with an accuracy of 91.876%, a True Positive Rate (TPR) of 93.987%, and a True Negative Rate (TNR) of 89.876% for K-sample 8. The results confirm the effectiveness of TL-CNN_AdGRO, also it demonstrates strong performance, ensures robustness, improves the early detection of endometrial cancer, and making it a promising approach for histopathological image analysis.