Xanthophyllomyces dendrorhous Is a Safe Dietary Supplement with Potent Antioxidant Defense Enhancing Activity.

Xanthophyllomyces dendrorhous (X. dendrorhous), previously known as Phaffia rhodozyma, is a red yeast that is widely recognized as a rich source of carotenoids, particularly astaxanthin, which exhibits potent antioxidant activity and other health-promoting functions. However, there is currently a lack of research on the safety of consuming X. dendrorhous. To address this, we conducted an acute toxicity study followed by a 90-day subchronic toxicity trial to evaluate the safety of X. dendrorhous and investigate its in vivo antioxidant activity. In the acute toxicity study, Sprague-Dawley rats were administered a maximum of 12 g/kg body weight of X. dendrorhous powder by gavage and survived without any adverse effects for 14 days. In the subsequent subchronic toxicity test, the rats were randomly divided into five groups, each with free access to their diet adulterated with 0% (control), 2.5% (low), 5% (middle), 10% (high), and 20% (extreme high) X. dendrorhous powder. The rats' behavior, body weight, and food intake were monitored during the 90-day experiment. At the end of the experiment, urine, blood, and organs were collected from the rats for biochemical testing. Additionally, the antioxidant activity in rat sera was evaluated. The results of the acute toxicity test demonstrated that the LD50 of X. dendrorhous was greater than 12 g/kg body weight, indicating that the substance was not toxic. Throughout the 90-day period of subchronic toxicity, the triglyceride levels of male rats fed with 10 and 20% X. dendrorhous increased to 1.54 ± 0.17 and 1.55 ± 0.25 mmol/L (P < 0.05), respectively. This may be attributed to the elevated fat content of the diet in the high-dose and extreme high-dose groups, which was 5.5 and 2.5% higher than that in the control, respectively. Additionally, the white pulp in the spleen exhibited an increase, and the number of white blood cells in the extreme high-dose group increased by 2.41 × 109/L (P < 0.05), which may contribute to enhanced immunity. Finally, the body weight, food intake, blood and urine indexes, and histopathological examination results of the organs of the rats did not demonstrate any regular toxic effects. With the adulteration of X. dendrorhous, the activity of GSH-Px in male rats increased by 16-36.32%. The activity of GSH-Px in female rats of the extreme high-dose group increased by 14.70% (P < 0.05). The free radical scavenging ability of ABTS in male rats in the two high-dose groups exhibited an increase of 6.5 and 11.41% (P < 0.05). In contrast, the MDA content of male rats in the extreme high-dose group demonstrated a reduction of 2.73 nmol/mL (P < 0.05). These findings indicate that X. dendrorhous has no toxic effects, can be taken in high doses, and has a beneficial antioxidant effect that may enhance the body's immunity.

A multi-task fusion model based on a residual-Multi-layer perceptron network for mammographic breast cancer screening.

BACKGROUND AND OBJECTIVE: Deep learning approaches are being increasingly applied for medical computer-aided diagnosis (CAD). However, these methods generally target only specific image-processing tasks, such as lesion segmentation or benign state prediction. For the breast cancer screening task, single feature extraction models are generally used, which directly extract only those potential features from the input mammogram that are relevant to the target task. This can lead to the neglect of other important morphological features of the lesion as well as other auxiliary information from the internal breast tissue. To obtain more comprehensive and objective diagnostic results, in this study, we developed a multi-task fusion model that combines multiple specific tasks for CAD of mammograms. METHODS: We first trained a set of separate, task-specific models, including a density classification model, a mass segmentation model, and a lesion benignity-malignancy classification model, and then developed a multi-task fusion model that incorporates all of the mammographic features from these different tasks to yield comprehensive and refined prediction results for breast cancer diagnosis. RESULTS: The experimental results showed that our proposed multi-task fusion model outperformed other related state-of-the-art models in both breast cancer screening tasks in the publicly available datasets CBIS-DDSM and INbreast, achieving a competitive screening performance with area-under-the-curve scores of 0.92 and 0.95, respectively. CONCLUSIONS: Our model not only allows an overall assessment of lesion types in mammography but also provides intermediate results related to radiological features and potential cancer risk factors, indicating its potential to offer comprehensive workflow support to radiologists.

Multi-view fusion-based local-global dynamic pyramid convolutional cross-tansformer network for density classification in mammography.

Object.Breast density is an important indicator of breast cancer risk. However, existing methods for breast density classification do not fully utilise the multi-view information produced by mammography and thus have limited classification accuracy.Method.In this paper, we propose a multi-view fusion network, denoted local-global dynamic pyramidal-convolution transformer network (LG-DPTNet), for breast density classification in mammography. First, for single-view feature extraction, we develop a dynamic pyramid convolutional network to enable the network to adaptively learn global and local features. Second, we address the problem exhibited by traditional multi-view fusion methods, this is based on a cross-transformer that integrates fine-grained information and global contextual information from different views and thereby provides accurate predictions for the network. Finally, we use an asymmetric focal loss function instead of traditional cross-entropy loss during network training to solve the problem of class imbalance in public datasets, thereby further improving the performance of the model.Results.We evaluated the effectiveness of our method on two publicly available mammography datasets, CBIS-DDSM and INbreast, and achieved areas under the curve (AUC) of 96.73% and 91.12%, respectively.Conclusion.Our experiments demonstrated that the devised fusion model can more effectively utilise the information contained in multiple views than existing models and exhibits classification performance that is superior to that of baseline and state-of-the-art methods.

Anti-Toxoplasma gondii agent isolated from Orostachys malacophylla (Pallas) Fischer.

Botanical medicinal plants have aroused our interest to deal with Toxoplasmosis which can causes serious public health problems. Nipagic acid, gallic acid, ethyl gallate, phloretic acid, protocatechuic acid, methyl p-coumarate, arbutin, and homoprotocatechuic acid are first isolated from Orostachys malacophylla (Pallas) Fischer, their inhibition rate, survival rate, biochemical and viscera index are evaluated using gastric epithelia strain-1(GES-1). Among them, arbutin can effectively prolong the survival time of mice acutely infected with T. gondii, and exhibit the same curative effect as Spiramycin (Spi) group in terms of the glutathione (GSH) and malondialdehyde (MDA) content, alleviate hepatomegaly and splenomegaly. Structure-activity relationship (SAR) and molecular docking implies that phenolic hydroxyl group would be preferred for improvement of activity. In a summary, arbutin is a potential anti-T. gondii candidate for clinical application.

Methylome-wide Association Study of Patients with Recent-onset Psychosis.

Objective: Dysregulation of gene expression through epigenetic mechanisms may have a vital role in the pathogenesis of schizophrenia (SZ). In this study, we investigated the association of altered methylation patterns with SZ symptoms and early trauma in patients and healthy controls. Methods: The present study was conducted to identify methylation changes in CpG sites in peripheral blood associated with recent-onset (RO) psychosis using methylome-wide analysis. Lifestyle factors, such as smoking, alcohol, exercise, and diet, were controlled. Results: We identified 2,912 differentially methylated CpG sites in patients with RO psychosis compared to controls. Most of the genes associated with the top 20 differentially methylated sites had not been reported in previous methylation studies and were involved in apoptosis, autophagy, axonal growth, neuroinflammation, protein folding, etc. The top 15 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways included the oxytocin signaling pathway, long-term depression pathway, axon guidance, endometrial cancer, long-term potentiation, mitogen-activated protein kinase signaling pathway, and glutamatergic pathway, among others. In the patient group, significant associations of novel methylated genes with early trauma and psychopathology were observed. Conclusion: Our results suggest an association of differential DNA methylation with the pathophysiology of psychosis and early trauma. Blood DNA methylation signatures show promise as biomarkers of future psychosis.

Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification.

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.