Microscopy Research and Technique最新文献

This study aimed to investigate the efficacy and durability of bioactive glass-based dental resin infiltrants. Resin infiltrants were formulated by combining photoinitiated dimethacrylate monomers with three variations of bioactive glass: 45S5 Bioglass (RIS), boron-substituted (RIB), fluoride-substituted (RIF), and pure resins (PR), whereby TOOTH group (TH) and ICON (CN) served as commercial control groups. Teeth samples were prepared, and experimental and control infiltrants were applied on demineralized human-extracted teeth. All the samples were subjected to immersion in artificial saliva and pH cycling for 30 days. The samples from another group underwent tooth brushing simulation for 9600 cycles. Following artificial saliva immersion, the samples' hardness values showed that RIB had the highest values (318.44 ± 3.83) while PR (212.52 ± 9.02) had the lowest values. After immersing into the pH cycling solution, the RIF showed the highest hardness (286.86 ± 5.11), while the lowest values for the CN (143.76 ± 3.50). After the tooth brushing simulation, the teeth samples with RIB showed maximum microhardness values (312.06 ± 16.30) and the weakest for the TH (189.60 ± 6.43). The commercial and experimental enamel resin infiltrants showed almost similar results overall, with RIB demonstrating better microhardness and comparable surface roughness. In contrast, RIF proved more resistant to pH cycling, exhibited higher microhardness, and performed better in surface roughness analysis. These findings suggest that resin infiltrant materials, especially RIF, have promising potential for effectively and esthetically managing white spot lesions.

This study investigates the micromorphological characteristics of pollen grains and leaf epidermal cells from 20 accessions across four species of spiny almonds using scanning electron microscopy. Thirteen quantitative traits of pollen grains, including exine sculpturing, were analyzed alongside qualitative features such as shape, exine sculpture type, and aperture type. Additionally, four quantitative and five qualitative features of the leaf epidermis were examined, focusing on cuticular ornamentation patterns, types of epicuticular wax, and stomatal measurements. The pollen grains were found to be isopolar monads, radially symmetric, medium-sized, varying from prolate spheroidal to prolate, and exhibiting 3-colporate to 3-colpate structures. The exine sculpturing was generally striate with short and long ridges, with or without perforations, and could be classified into three types. Notably, in the Isfahan population of Prunus lycioides and the North Khorasan population of Prunus spinosissima, the exine sculpture types were distinctly different, being rugulate and reticulate, respectively. Multivariate statistical analysis identified equatorial diameter, colpus length and width, and ridge width as key diagnostic markers for species identification within spiny almonds. Principal component analysis and hierarchical clustering further highlighted the significance of stomatal length, cuticular ornamentation patterns, and epicuticular wax types in differentiating among taxa. We conclude that groupings recognized in recent classifications of the subgenus Amygdalus (spiny almonds) remain challenging to delineate solely based on palynological data, as diverse pollen types are present across different clades and subclades. Furthermore, micromorphological leaf traits proved valuable in distinguishing certain spiny almond taxa, and the traits of epidermal cells may reflect the ecological adaptations of spiny almond species.

Herein, bacterial-assisted synthesis of AgCoO₂ is carried out. In the first step, E. coli was separated from soil samples via the “serial dilution method.” Ten milliliters of bacterial supernatant was mixed with cobalt chloride and silver nitrate hatched at 38°C for 24 h to get AgCoO₂ nanoparticles (NPs). XRD results confirm the synthesis of AgCoO₂ NPs while EDX results confirm the absence of any other elements than Ag, Co, and O. An average NP size of 12-26 nm was determined by TEM examination, and the surface of the particles was seen rough, irregularly shaped borders. The antibacterial activity of the constructed NPs was checked against S. aureus, E. coli, Bacillus subtilus, and Pseudomanas areguinosa using agar well diffusion method. The maximum zone of inhibition was 27 mm at 40 mg/mL against Bacillus subtilus. The performance of the synthesized NPs as photocatalysts was also assessed, and several operational parameters that control the photodegradation of the harmful dyes were tried to tune as well, and 85% degrading efficiency was obtained at 60^oC for 240 min for 30 mg of catalyst dose These NPs were also used to produce hydrogen by methanolysis.

In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness. Early detection of DR and DME is essential to preventing vision loss. Therefore, an enhanced capsule generation adversarial network (ECGAN) optimized with the rat swarm optimization (RSO) approach is proposed in this article to coincide with DR and DME grading (DR-DME-ECGAN-RSO-ISBI 2018 IDRiD). The input images are obtained from the ISBI 2018 unbalanced DR grading data set. Then, the input fundus images are preprocessed using the Savitzky–Golay (SG) filter filtering technique, which reduces noise from the input image. The preprocessed image is fed to the discrete shearlet transform (DST) for feature extraction. The extracting features of DR-DME are given to the ECGAN-RSO algorithm to categorize the grading of DR and DME disorders. The proposed approach is implemented in Python and achieves better accuracy by 7.94%, 36.66%, and 4.88% compared to the existing models, such as the combined DR with DME grading for the cross-disease attention network (DR-DME-CANet-ISBI 2018 IDRiD), category attention block for unbalanced grading of DR (DR-DME-HDLCNN-MGMO-ISBI 2018 IDRiD), combined DR-DME classification with a deep learning-convolutional neural network-based modified gray-wolf optimizer with variable weights (DR-DME-ANN-ISBI 2018 IDRiD).

Plants are a rich source of bioactive compounds with significant pharmaceutical and health applications. This study explores the phytochemical, therapeutic, and phytotoxic properties of Melia azedarach by analyzing extracts from its bark, flowers, leaves, and fruits using six solvents: ethanol, methanol, acetone, hexane, chloroform, and distilled water. Twenty-one phytochemical tests were conducted, revealing significantly positive results for various tests. However, the ethanolic and methanolic flower extracts yielded no significant results in other tests. The highest total phenolic content was found in the chloroform extract of the leaves (96 ± 0.01 mg/100 g), and the highest antioxidant activity was observed in the ethanolic and hexane leaf extracts, with a 98% DPPH scavenging rate. Antibacterial testing showed significant efficacy against Serratia marcescens, Bacillus subtilis, Kluyvera spp., and Pseudomonas spp., with p values < 0.0001. The fruit chloroform extract demonstrated the highest alpha-amylase inhibition (93 ± 0.05), while the ethanolic leaf extract had the greatest tumor inhibition (85.6 ± 0.5). Insecticidal assays revealed that the acetone bark extract had the highest control values (56% and 57%). Due to their higher reducing potential, the leaves were used to biosynthesize silver nanoparticles (AgNPs), characterized by UV–Vis spectroscopy, EDX, and SEM, revealing an average particle size of 20–30 nm and spherical morphology. The AgNPs exhibited excellent antibacterial, antioxidant, antidiabetic, and insecticidal activities. These findings highlight the potential of M. azedarach and its AgNPs for developing novel therapeutic agents.

We succeeded in producing pure and magnesium-doped zinc oxide nanoparticles (Mg-Zn NPs) by making use of a Prosopis farcta leaf extract and subsequently distinguished the quality of our NPs with the use of field energy scanning electron microscopy (FESEM), energy-dispersive X-ray (EDX), powder X-ray diffraction (PXRD), and UV–vis. In correlation to our observations, the particulates were spherically produced at a size of 20 nm with the ability to cause antimicrobial impacts on Streptococcus mutans bacteria and Candida albicans fungi. Inhibition zones of 18 ± 0.3 and 24 ± 0.3 mm were obtained for 5% Mg-Zn NPs against bacteria and fungi, respectively. Based on these results, our work suggests a practicable proposition for our synthesized product to be considered as a worthy alternative for dental and oral utilizations.

Segment anything model (SAM) has attracted extensive interest as a potent large-scale image segmentation model, with prior efforts adapting it for use in medical imaging. However, the precise segmentation of cell nucleus instances remains a formidable challenge in computational pathology, given substantial morphological variations and the dense clustering of nuclei with unclear boundaries. This study presents an innovative cell segmentation algorithm named CellSAM. CellSAM has the potential to improve the effectiveness and precision of disease identification and therapy planning. As a variant of SAM, CellSAM integrates dual-image encoders and employs techniques such as knowledge distillation and mask fusion. This innovative model exhibits promising capabilities in capturing intricate cell structures and ensuring adaptability in resource-constrained scenarios. The experimental results indicate that this structure effectively enhances the quality and precision of cell segmentation. Remarkably, CellSAM demonstrates outstanding results even with minimal training data. In the evaluation of particular cell segmentation tasks, extensive comparative analyzes show that CellSAM outperforms both general fundamental models and state-of-the-art (SOTA) task-specific models. Comprehensive evaluation metrics yield scores of 0.884, 0.876, and 0.768 for mean accuracy, recall, and precision respectively. Extensive experiments show that CellSAM excels in capturing subtle details and complex structures and is capable of segmenting cells in images accurately. Additionally, CellSAM demonstrates excellent performance on clinical data, indicating its potential for robust applications in treatment planning and disease diagnosis, thereby further improving the efficiency of computer-aided medicine.