Mixed computational-experimental study to reveal the anti-metastasis and anti-angiogenesis effects of Astragalin in human breast cancer

Gastrointestinal Cancer Therapeutics via Triggering Unfolded Protein Response and Endoplasmic Reticulum Stress by 2-Arylbenzofuran

Gastrointestinal cancers are a major global health challenge, with high mortality rates. This study investigated the anti-cancer activities of 30 monomers extracted from Morus alba L. (mulberry) against gastrointestinal cancers. Toxicological assessments revealed that most of the compounds, particularly immunotoxicity, exhibit some level of toxicity, but it is generally not life-threatening under normal conditions. Among these components, Sanggenol L, Sanggenon C, Kuwanon H, 3'-Geranyl-3-prenyl-5,7,2',4'-tetrahydroxyflavone, Morusinol, Mulberrin, Moracin P, Kuwanon E, and Kuwanon A demonstrate significant anti-cancer properties against various gastrointestinal cancers, including colon, pancreatic, and gastric cancers. The anti-cancer mechanism of these chemical components was explored in gastric cancer cells, revealing that they inhibit cell cycle and DNA replication-related gene expression, leading to the effective suppression of tumor cell growth. Additionally, they induced unfolded protein response (UPR) and endoplasmic reticulum (ER) stress, potentially resulting in DNA damage, autophagy, and cell death. Moracin P, an active monomer characterized as a 2-arylbenzofuran, was found to induce ER stress and promote apoptosis in gastric cancer cells, confirming its potential to inhibit tumor cell growth in vitro and in vivo. These findings highlight the therapeutic potential of Morus alba L. monomers in gastrointestinal cancers, especially focusing on Moracin P as a potent inducer of ER stress and apoptosis.

Integrating serum pharmacochemistry and network pharmacology to reveal the mechanism of chickpea in improving insulin resistance

Although chickpea have great potential in the treatment of obesity and diabetes, the bioactive components and therapeutic targets of chickpea to prevent insulin resistance (IR) are still unclear. The purpose of this study was to investigate the chemical and pharmacological characteristics of chickpea on IR through serum pharmacochemistry and network pharmacology. The results revealed that compared with other polar fractions, the ethyl acetate extract of chickpea (CE) had the definitive performance on enhancing the capacities of glucose consumption and glycogen synthesis. In addition, we analyzed the components of CE in vivo and in vitro based on UPLC-Q-Orbitrap HRMS technology. There were 28 kinds of in vitro chemical components, among which the isoflavones included biochanin A, formononetin, ononin, sissotrin, and astragalin, etc. Concerningly, the chief prototype components of CE absorbed into the blood were biochanin A, formononetin, loliolide, and lenticin, etc. Furthermore, a total of 209 common targets between IR and active components of CE were screened out by network pharmacology, among which the key targets involved PI3K p85, NF-κB p65 and estrogen receptor 1, etc. Specifically, KEGG pathway analysis indicated that PI3K-AKT signaling pathway, HIF-1 signaling pathway, and AGE-RAGE signaling pathway may play critical roles in the IR remission by CE. Finally, the in vitro validation experiments disclosed that CE significantly balanced the oxidative stress state of IR-HepG2 cells and inhibited expressions of inflammatory cytokines. In conclusion, the present study will be an important reference for clarifying the pharmacodynamic substance basis and underlying mechanism of chickpea to alleviate IR.

Potential of natural flavonoids to target breast cancer angiogenesis (review)

Angiogenesis is the process by which new blood vessels form and is required for tumour growth and metastasis. It helps in supplying oxygen and nutrients to tumour cells and plays a crucial role in the local progression and distant metastasis of, and development of treatment resistance in, breast cancer. Tumour angiogenesis is currently regarded as a critical therapeutic target; however, anti-angiogenic therapy for breast cancer fails to produce satisfactory results, owing to issues such as inconsistent efficacy and significant adverse reactions. As a result, new anti-angiogenic drugs are urgently needed. Flavonoids, a class of natural compounds found in many foods, are inexpensive, widely available, and exhibit a broad range of biological activities, low toxicity, and favourable safety profiles. Several studies find that various flavonoids inhibit angiogenesis in breast cancer, indicating great therapeutic potential. In this review, we summarize the role of angiogenesis in breast cancer and the potential of natural flavonoids as anti-angiogenic agents for breast cancer treatment. We discuss the value and significance of nanotechnology for improving flavonoid absorption and utilization and anti-angiogenic effects, as well as the challenges of using natural flavonoids as drugs.

Astragalin: a food-origin flavonoid with therapeutic effect for multiple diseases

Naturally occurring flavonoids have long been utilized as essential templates for the development of novel drugs and as critical ingredients for functional foods. Astragalin (AG) is a natural flavonoid that can be isolated from a variety of familiar edible plants, such as the seeds of green tea, Morus alba L., and Cuscuta chinensis. It is noteworthy that AG has a wide range of pharmacological activities and possesses therapeutic effects against a variety of diseases, covering cancers, osteoarthritis, osteoporosis, ulcerative colitis, mastitis, obesity, diabetes mellitus, diabetic complications, ischemia/reperfusion injury, neuropathy, respiratory diseases, and reproductive system diseases. This article reviewed the natural source and pharmacokinetics of AG and systematically summarized the pharmacological activities and potential mechanisms of AG in treating diverse diseases in order to promote the development of AG as a functional food, in doing so providing references for its clinical application in disease therapy.

Dual-stream EfficientNet with adversarial sample augmentation for COVID-19 computer aided diagnosis

Though a series of computer aided measures have been taken for the rapid and definite diagnosis of 2019 coronavirus disease (COVID-19), they generally fail to achieve high enough accuracy, including the recently popular deep learning-based methods. The main reasons are that: (a) they generally focus on improving the model structures while ignoring important information contained in the medical image itself; (b) the existing small-scale datasets have difficulty in meeting the training requirements of deep learning. In this paper, a dual-stream network based on the EfficientNet is proposed for the COVID-19 diagnosis based on CT scans. The dual-stream network takes into account the important information in both spatial and frequency domains of CT scans. Besides, Adversarial Propagation (AdvProp) technology is used to address the insufficient training data usually faced by the deep learning-based computer aided diagnosis and also the overfitting issue. Feature Pyramid Network (FPN) is utilized to fuse the dual-stream features. Experimental results on the public dataset COVIDx CT-2A demonstrate that the proposed method outperforms the existing 12 deep learning-based methods for COVID-19 diagnosis, achieving an accuracy of 0.9870 for multi-class classification, and 0.9958 for binary classification. The source code is available at https://github.com/imagecbj/covid-efficientnet.

Hybrid-supervised bidirectional transfer networks for computer-aided diagnosis

Medical imaging techniques have been widely used for diagnosis of various diseases. However, the imaging-based diagnosis generally depends on the clinical skill of radiologists. Computer-aided diagnosis (CAD) can help radiologists improve diagnostic accuracy as well as the consistency and reproducibility. Although convolutional neural network (CNN) has shown its feasibility and effectiveness in CAD, it generally suffers from the problem of small sample size when training CAD models. Nowadays, self-supervised learning (SSL) has shown its effectiveness in the field of medical image analysis, especially when there are only limited training samples. However, the backbone of downstream task sometimes cannot be well pre-trained in the conventional SSL framework due to the limitation of the pretext task and fine-tuning mechanism. In this work, an improved SSL framework, named Hybrid-supervised Bidirectional Transfer Networks (HBTN), is proposed to improve the performance of CAD models. Specifically, a novel Gray-Scale Image Mapping (GSIM) task is developed, which still takes the widely used image restoration task in SSL as the pretext task, but further embeds the class label information into it to improve discriminative feature learning of its corresponding network model. The proposed HBTN then integrates two different network architectures, i.e. the image restoration network for the pretext task and the classification network for the downstream task, into a unified hybrid-supervised learning (HSL) framework. It jointly trains both networks and collaboratively transfers the knowledge between each other. Consequently, the performance of downstream network is thus improved. The proposed HBTN is evaluated on two medical image datasets for CAD tasks. The experimental results indicate that HBTN outperforms the conventional SSL algorithms for CAD with limited training samples.