Search Results (132)

Background: Herbal medicine represents a rich yet complex source of bioactive compounds, offering both therapeutic potential and toxicological risks. Methods: In this study, we systematically evaluated the biological effects of three traditional herbal extracts—Mentha longifolia, Scrophularia orientalis, and Echium biebersteinii—using Caenorhabditis elegans as an in vivo model. Results: All three extracts significantly reduced worm survival, induced larval arrest, and triggered a high incidence of males (HIM) phenotypes, indicative of mitotic failure and meiotic chromosome missegregation. Detailed analysis of germline architecture revealed extract-specific abnormalities, including nuclear disorganization, ectopic crescent-shaped nuclei, altered meiotic progression, and reduced bivalent formation. These defects were accompanied by activation of the DNA damage response, as evidenced by upregulation of checkpoint genes (atm-1, atl-1), increased pCHK-1 foci, and elevated germline apoptosis. LC-MS profiling identified 21 major compounds across the extracts, with four compounds—thymol, carvyl acetate, luteolin-7-O-rutinoside, and menthyl acetate—shared by all three herbs. Among them, thymol and carvyl acetate significantly upregulated DNA damage checkpoint genes and promoted apoptosis, whereas thymol and luteolin-7-O-rutinoside contributed to antioxidant activity. Notably, S. orientalis and E. biebersteinii shared 11 of 14 major constituents (79%), correlating with their similar phenotypic outcomes, while M. longifolia exhibited a more distinct chemical profile, possessing seven unique compounds. Conclusions: These findings highlight the complex biological effects of traditional herbal extracts, demonstrating that both beneficial and harmful outcomes can arise from specific phytochemicals within a mixture. By deconstructing these extracts into their active components, such as thymol, carvyl acetate, and luteolin-7-O-rutinoside, we gain critical insight into the mechanisms driving reproductive toxicity and antioxidant activity. This approach underscores the importance of component-level analysis for accurately assessing the therapeutic value and safety profile of medicinal plants, particularly those used in foods and dietary supplements. Full article

Background: Breast cancer remains a critical health concern worldwide, with histopathological analysis of tissue biopsies serving as the clinical gold standard for diagnosis. Manual evaluation of histopathology images is time-intensive and requires specialized expertise, often resulting in variability in diagnostic outcomes. In silver in situ hybridization (SISH) images, accurate nuclei detection is essential for precise histo-scoring of HER2 gene expression, directly impacting treatment decisions. Methods: This study presents a scalable and automated deep learning framework for nuclei detection in HER2-SISH whole slide images (WSIs), utilizing a novel dataset of 100 expert-marked regions extracted from 20 WSIs collected at the University of Malaya Medical Center (UMMC). The proposed two-stage approach combines a pretrained Stardist model with image processing-based annotations, followed by fine tuning on our domain-specific dataset to improve generalization. Results: The fine-tuned model achieved substantial improvements over both the pretrained Stardist model and a conventional watershed segmentation baseline. Quantitatively, the proposed method attained an average F1-score of 98.1% for visual assessments and 97.4% for expert-marked nuclei, outperforming baseline methods across all metrics. Additionally, training and validation performance curves demonstrate stable model convergence over 100 epochs. Conclusions: These results highlight the robustness of our approach in handling the complex morphological characteristics of SISH-stained nuclei. Our framework supports pathologists by offering reliable, automated nuclei detection in HER2 scoring workflows, contributing to diagnostic consistency and efficiency in clinical pathology. Full article

To develop efficient utilization technologies for lignite, HyperCoal was prepared from the depolymerization of Shengli lignite by reacting it with NaOH and methanol. A series of HyperCoal extracts were obtained using different solvents and characterized using elemental analysis, Fourier-transform infrared spectroscopy, gel permeation chromatography, and synchronous fluorescence spectroscopy. The results indicate that solvent polarity is the primary factor influencing both the extraction yield and the structure of the extracts as polar solvents can disrupt or break hydrogen bonds within the extracts. The extraction yield increases with the polarity of the extraction solvent. HyperCoal is a complex mixture of aromatic derivatives containing alkyl substituents and oxygen-containing functional groups. The O/C ratio and molecular size of the extracts, the amount of oxygen-containing functional groups, the proportion of aromatic structures, and the size of aromatic nuclei in the extracts increase with increasing solvent polarity, while the H/C ratio and proportion of aliphatic structures decrease. These findings aid developing methods for producing high-value-added chemicals from HyperCoal through staged conversion. Full article

Flow cytometry is widely applied to infer the ploidy and genome size (GS) of plant nuclei. The conventional approach of sample preparation, reliant on fresh plant material to release intact nuclei, often results in poor yields of nuclei in conditions when a plant material cannot be kept fresh due to logistical constraints. Previous attempts to use frozen plant material were mainly limited to ploidy analysis and relied on chopping methods, which restrict the material input and often result in poor nuclei yield, especially in frozen samples, due to incomplete disruption. Here, we present a modified protocol for GS estimation using frozen plant material that facilitates larger volumes of tissue to be processed while improving debris removal. Nuclei isolated from this protocol can also be used for DNA or RNA extraction. Genome size estimates from frozen material are similar to those from fresh material, with a reduction in error range, although not always significant (p > 0.05). In certain species, frozen samples can yield substantially more nuclei than fresh material. With the addition of specific debris compensation algorithms, coefficient of variation (CV%) can be maintained below 5%. This method has special value in estimating the GS of samples collected from remote locations and frozen for use in plant genome sequencing. Freezing preserves high-quality DNA and RNA, enabling the same sample to be used for both flow cytometry and genome sequencing. Full article

Background: Nuclei segmentation is the first stage of automated microscopic image analysis. The cell nucleus is a crucial aspect in segmenting to gain more insight into cell characteristics and functions that enable computer-aided pathology for early disease detection, such as prostate cancer, breast cancer, brain tumors, and other diagnoses. Nucleus segmentation remains a challenging task despite significant advancements in automated methods. Traditional techniques, such as Otsu thresholding and watershed approaches, are ineffective in challenging scenarios. However, deep learning-based methods exhibit remarkable results across various biological imaging modalities, including computational pathology. Methods: This work explores machine learning approaches for nuclei segmentation by evaluating the quality of nuclei image segmentation. We employed several methods, including K-means clustering, Random Forest (RF), Support Vector Machine (SVM) with handcrafted features, and Logistic Regression (LR) using features derived from Convolutional Neural Networks (CNNs). Handcrafted features extract attributes like the shape, texture, and intensity of nuclei and are meticulously developed based on specialized knowledge. Conversely, CNN-based features are automatically acquired representations that identify complex patterns in nuclei images. To assess how effectively these techniques segment cell nuclei, their performance is evaluated. Results: Experimental results show that Logistic Regression based on CNN-derived features outperforms the other techniques, achieving an accuracy of 96.90%, a Dice coefficient of 74.24, and a Jaccard coefficient of 55.61. In contrast, the Random Forest, Support Vector Machine, and K-means algorithms yielded lower segmentation performance metrics. Conclusions: The conclusions suggest that leveraging CNN-based features in conjunction with Logistic Regression significantly enhances the accuracy of cell nuclei segmentation in pathological images. This approach holds promise for refining computer-aided pathology workflows, potentially leading to more reliable and earlier disease diagnoses. Full article

Whole-slide imaging (WSI) in cytopathology poses challenges related to segmentation accuracy, computational efficiency, and image acquisition artifacts. This study aims to evaluate the performance of deep-learning models for instance segmentation in cervical cytology, benchmarking them against state-of-the-art methods on both public and institutional datasets. We tested three architectures—U-Net, vision transformer (ViT), and Detectron2—and evaluated their performance on the ISBI 2014 and CNseg datasets using panoptic quality (PQ), dice similarity coefficient (DSC), and intersection over union (IoU). All models were trained on CNseg and tested on an independent institutional dataset. Data preprocessing involved manual annotation using QuPath, patch extraction guided by GeoJSON files, and exclusion of regions containing less than 60% cytologic material. Our models achieved superior segmentation performance on public datasets, reaching up to 98% PQ. Performance decreased on the institutional dataset, likely due to differences in image acquisition and the presence of blurred nuclei. Nevertheless, the models were able to detect blurred nuclei, highlighting their robustness in suboptimal imaging conditions. In conclusion, the proposed models offer an accurate and efficient solution for instance segmentation in cytology WSI. These results support the development of reliable AI-powered tools for digital cytology, with potential applications in automated screening and diagnostic workflows. Full article

Background/Objectives: Accurate and efficient segmentation of cell nuclei in biomedical images is critical for a wide range of clinical and research applications, including cancer diagnostics, histopathological analysis, and therapeutic monitoring. Although U-Net and its variants have achieved notable success in medical image segmentation, challenges persist in balancing segmentation accuracy with computational efficiency, especially when dealing with large-scale datasets and resource-limited clinical settings. This study aims to develop a lightweight and scalable U-Net-based architecture that enhances segmentation performance while substantially reducing computational overhead. Methods: We propose a novel evolving U-Net architecture that integrates multi-scale feature extraction, depthwise separable convolutions, residual connections, and attention mechanisms to improve segmentation robustness across diverse imaging conditions. Additionally, we incorporate channel reduction and expansion strategies inspired by ShuffleNet to minimize model parameters without sacrificing precision. The model performance was extensively validated using the 2018 Data Science Bowl dataset. Results: Experimental evaluation demonstrates that the proposed model achieves a Dice Similarity Coefficient (DSC) of 0.95 and an accuracy of 0.94, surpassing state-of-the-art benchmarks. The model effectively delineates complex and overlapping nuclei structures with high fidelity, while maintaining computational efficiency suitable for real-time applications. Conclusions: The proposed lightweight U-Net variant offers a scalable and adaptable solution for biomedical image segmentation tasks. Its strong performance in both accuracy and efficiency highlights its potential for deployment in clinical diagnostics and large-scale biological research, paving the way for real-time and resource-conscious imaging solutions. Full article

Mössbauer spectroscopy can be advantageous for studying catalysts. In particular, its use in in situ studies can provide unique access to structural features. However, special attention must be paid to the interpretation of data, since in most studies, the samples are not perfectly homogeneous. Balance and compromise should be found between the refinement of evaluations by extracting and interpreting data from spectra, while also considering the presence of possible inhomogeneities in samples. In this review, examples of studies on two types of catalysts are presented, from which, despite possible inhomogeneities, clear statements can be derived. The first example pertains to selected iron-containing microporous zeolites (with ⁵⁷Fe Mössbauer spectroscopy), from which unique information is collected on the coordination of iron ions. The second example is related to studies on supported PtSn alloy particles (with ¹¹⁹Sn probe nuclei), from which reversible modifications of the tin component due to interactions with the reaction partners are revealed. Full article

Background/Objectives: This study explored the antitumor effect of Passiflora incarnata leaves’ nanoformulation (N-EEP) in fibroblasts, A375 cell lines, and in vivo using Dalton’s lymphoma ascites (DLA)-bearing mice. Methods: N-EEP treatment could significantly slow scratch closing in A375 cells compared to in the extract itself (EEP). Results: The hemolytic assay showed that N-EEP had less than 2% hemolysis, making the formulation highly biocompatible. In vivo N-EEP administration delayed the tumor growth rate, reduced weight gain, and increased the tumor-bearing mice’s life span. Furthermore, the ascitic cells were aspirated from the tumor and investigated for various gene expressions. The tumor suppressor gene p53, which plays a significant role in the mitochondrial-mediated apoptosis pathway, was found to be elevated in animals treated with N-EEP. We assessed the cytotoxicity of isolated DLA cells from induced mice using both the trypan blue and MTT assays, while apoptotic studies were conducted using Hoechst staining. Results from the trypan blue and MTT assays indicated that nearly 80% of the cells were killed by N-EEP treatment (200 μg/mL). Additionally, apoptosis, characterized by condensed nuclei, was observed after N-EEP treatment, confirming that one of the modes of cell death was caspase-dependent apoptosis. Conclusions: Our study suggests that N-EEP delayed the growth of DLA by upregulating p53 gene expression and inducing apoptosis. Full article

The accurate classification of broad-absorption line (BAL) quasars and non-broad-absorption line (non-BAL) quasars is key in understanding active galactic nuclei (AGN) and the evolution of the universe. With the rapid accumulation of data from large-scale spectroscopic survey projects (e.g., LAMOST, SDSS, and DESI), traditional manual classification methods face limitations. In this study, we propose a new method based on deep learning techniques to achieve an accurate distinction between BAL quasars and non-BAL quasars. We use a convolutional neural network (CNN) as the core model, in combination with various dimensionality reduction techniques, including principal component analysis (PCA), t-distributed stochastic neighborhood embedding (t-SNE), and isometric mapping (ISOMAP). These dimensionality reduction methods help extract meaningful features from high-dimensional spectral data while reducing model complexity. We employ quasar spectra from the 16th data release (DR16) of the Sloan Digital Sky Survey (SDSS) and obtain classification labels from the DR16Q quasar catalogues to train and evaluate our model. Through extensive experiments and comparisons, the combination of PCA and CNN achieve a test accuracy of 99.11%, demonstrating the effectiveness of deep learning for classifying the spectral data. Additionally, we explore other dimensionality reduction methods and machine learning models, providing valuable insights for future research in this field. Full article

Acephate is an organophosphate foliar and soil insecticide that is used worldwide. In this study, the transgenerational ovarian developmental toxicity caused by acephate, along with its in utero reprogramming mechanisms, were explored. Thirty female virgin Wistar albino rats were randomly assigned to three groups: one control group and two acephate treatment groups. The treatment groups received daily low or high doses of acephate (34.2 mg/kg or 68.5 mg/kg body weight, respectively) from gestational day 6 until spontaneous labor, resulting in F1 offspring. At 28 days, a subgroup of F1 females were euthanized. The ovaries were extracted, thoroughly cleaned, and weighed before being fixed for further analysis. The remaining F1 females were mated with normal males to produce the F2 generation. The F1 female offspring presented reduced fertility and body weight, whereas the ovarian weight index and sex ratio increased in a dose-dependent manner. Structural analysis revealed altered follicular abnormalities with ovarian cells displaying pyknotic nuclei. Additionally, the gene and protein expression of Cyp19 decreased, whereas that of Gdf-9 increased in the high-dose treatment group (68.5 mg/kg). We also observed significantly increased expression levels of ovarian estrogen receptor 1 (Esr1) and insulin-like growth factor 1 (Igf1), whereas Insl3 expression was significantly decreased. The F2 female offspring presented reproductive phenotype alterations similar to those of F1 females including decreased fertility, reduced Cyp19 gene and protein expression, and structural ovarian abnormalities similar to those of polycystic ovary syndrome (PCOS). In conclusion, acephate induced ovarian developmental toxicity across two generations of rats, which may be linked to changes in the ovarian Cyp19, Gdf9, Insl3, and Igf1 levels. Full article

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance. Full article

In 2021, a prominent increase in mortality was observed in juvenile and subadult cultured chum salmon (Oncorhynchus keta) on a mariculture farm in Jeollanam-do Province, South Korea. The affected fish displayed distinct symptoms: pale gills, petechial hemorrhages in the muscles, and white nodules on the kidneys. Infectious pancreatic necrosis virus (IPNV) was cultured from some fish samples using fish cell lines. Bacteria were isolated from various fish tissues using kidney disease medium-two (KDM-2) culture medium. By detecting and sequencing the 16S rRNA gene using DNA extracted from the kidneys of the infected fish via PCR, the isolated bacteria were identified as Renibacterium salmoninarum. Histopathological examination primarily focused on hematopoietic tissues of kidneys and revealed clear evidence of severe necrosis and granulomatous changes. Additionally, nuclei with peripherally displaced chromatin were abundant in the kidneys of affected fish. These findings suggest that mass mortality of chum salmon was caused by R. salmoninarum, which induced typical bacterial kidney disease (BKD) symptoms, without IPNV infection. This represents the first outbreak of BKD attributed to R. salmoninarum infection in farmed chum salmon in South Korea. Full article

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level. Full article

Research on radioactive ion beams produced with in-flight separation of relativistic beams has advanced significantly over the past decades, with contributions to nuclear physics, nuclear astrophysics, atomic physics, and other fields. Central to these advancements are improved production, separation, and identification methods.The FRS Ion Catcher at GSI/FAIRexemplifies these technological advancements. The system facilitates high-precision experiments by efficiently stopping and extracting exotic nuclei as ions and making these available at thermal energies. High-energy synchrotron beams enhance the system’s capabilities, enabling unique experimental techniques such as multi-step reactions, mean range bunching, and optimized stopping, as well as novel measurement methods for observables such as beta-delayed neutron emission probabilities. The FRS Ion Catcher has already contributed to various scientific fields, and the future with the Super-FRS at FAIR promises to extend research to even more exotic nuclei and new applications. Full article