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Fenugreek is an ancient herb that has been used for centuries to treat diabetes. However, how the fenugreek‐derived chemical compounds work in treating diabetes remains unclarified. Herein, we integrate molecular docking and network pharmacology to elucidate the active constituents and potential mechanisms of fenugreek against diabetes. First, 19 active compounds from fenugreek and 71 key diabetes‐related targets were identified through network pharmacology analysis. Then, molecular docking and simulations results suggest diosgenin, luteolin and quercetin against diabetes via regulation of the genes ESR1, CAV1, VEGFA, TP53, CAT, AKT1, IL6 and IL1. These compounds and genes may be key factors of fenugreek in treating diabetes. Cells results demonstrate that fenugreek has good biological safety and can effectively improve the glucose consumption of IR‐HepG2 cells. Pathway enrichment analysis revealed that the anti‐diabetic effect of fenugreek was regulated by the AGE‐RAGE and NF‐κB signalling pathways. It is mainly associated with anti‐oxidative stress, anti‐inflammatory response and β‐cell protection. Our study identified the active constituents and potential signalling pathways involved in the anti‐diabetic effect of fenugreek. These findings provide a theoretical basis for understanding the mechanism of the anti‐diabetic effect of fenugreek. Finally, this study may help for developing anti‐diabetic dietary supplements or drugs based on fenugreek. [ABSTRACT FROM AUTHOR]

The presence of blood–brain barrier (BBB) that limits effective penetration of therapeutics is the main reason for poor outcomes of glioblastoma (GBM) treatment. Ultrasound (US) combined with microbubbles (MBs) can precisely disrupt the tight junctions of brain endothelial cells, thus creating "acoustic pores" and non‐invasive opening the BBB. Here, chitosan oligosaccharide (COS) is conjugated with a sonosensitizer protoporphyrin IX (PpIX) and an immune‐enhancing adjuvant Poly(I:C) via electrostatic adsorption, and cross‐linked with a tumor‐targeting molecule hyaluronic acid (HA) affording nanosonosensitizers HA‐Poly(I:C)/COS‐PpIX (abbreviated as "HP/CP" NSs). HP/CP NSs can target and penetrate GBMs, and trigger reactive oxygen species production upon US, simultaneously causing mitochondrial dysfunction and DNA damage. Tumor‐associated antigens released by sonodynamic therapy‐induced immunogenic cell death and loaded Poly(I:C) form an in situ vaccine together to potentiate antitumor immune responses. In orthotopic GBM mice models, the HP/CP+US treatments prolong mice survival, enhance cytotoxic T‐lymphocytes infiltration, and activate peripheral immune circulation. Besides, HP/CP NSs possess favorable biosafety profiles. Collectively, this study sheds light on the application of HP/CP NSs for synergistic sono‐immunotherapy of GBMs after non‐invasive opening of the BBB. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers in the world with tumor heterogeneity. Currently, cancer treatment mainly relies on surgical intervention, chemotherapy, and radiotherapy, for which the side effects, drug resistance and cost need to be resolved. In this study, we develop a natural medicine targeted therapy system. Phosphatidylcholine (PC), doxorubicin (DOX), procyanidin (PA), and epigallocatechin gallate (EGCG) are assembled and PC@DOX-PA/EGCG nanoparticles (NPs) are obtained. In addition, the HER2, ER and PR ligands were grafted on the surface of the NPs to acquire the targeted nanoparticles NP-ER, NP-ER-HER2, and NP-ER-HER2-PR. The physicochemical properties of the nanoparticles were detected and it was found that the nanoparticles are spherical and less than 200 nm in diameter. Furthermore, in vitro and in vivo results indicate that the nanoparticles can target BT-474, MCF-7, EMT-6, and MDA-MB-231 breast cancer cells, effectively inhibiting the growth of the breast cancer cells. In short, this research will provide some strategies for the treatment of heterogeneous breast cancer. [ABSTRACT FROM AUTHOR]

Bone diseases such as osteomalacia, osteoporosis, and osteomyelitis are major illnesses that threaten the health of human. This study aimed to provide an idea at the molecular level of material properties determined with UV specific surface approaches. The tert-butyl hydroperoxide (t-BHP) exposure aging model bone mesenchymal stem cells (BMSCs) were reverted by using a poly-hybrid scaffold (PS), which is a carbon nanotube (CNT) coated polycaprolactone (PCL) and polylactic acid (PLA) scaffold, combined with insulin-like growth factor-1 (IGF). Then, the region-specific PS photo-immobilized with different growth factors (GFs) was obtained by interference and diffraction of ultraviolet (UV) light. Additionally, the reverted BMSCs were regionally pattern differentiated into three kinds of cells on the GF immobilized PS (GFs/PS). In vivo, the GFs/PS accelerate bone healing in injured Sprague-Dawley (SD) rats. The data showed that GFs/PS effectively promoted the differentiation of reverted BMSCs in the designated area on 21st day. These results suggest region-specific interface immobilization of GFs concurrently differentiating reverted BMSCs into three different cells in the same scaffold. This method might be considered as a short-time, low cost, and simple operational approach to scaffold modification for tissue regeneration in the future. [ABSTRACT FROM AUTHOR]

Distant supervision has a good effect in relation extraction tasks. Meanwhile, most methods use multi-instance learning to reduce the impact of training data been wrong labelled in distant supervision. However, the effect of multi-instance learning depends on the sentence feature vector extracted by the neural network. At present, most methods for extracting sentence features only pay attention to the structural features of sentences, while ignoring semantic features. As a result, structural feature sentences and semantic feature sentences cannot occupy the same proportion in multi-instance learning, which further influences the precision of the model. To alleviate this issue, we propose a BiLSTM-CNN-Attention model (BLCANN) based on semantic dependency graph to extract sentence features. In this model, we extract the shortest dependency path between the two entities from the semantic dependency graph as the input to the model. The shortest path combines the structural and semantic features of the sentence, which contributes to distinguishing between positive and negative examples in multi-instance learning. Experimental results show that our model is adept in extracting structural features and semantic features. Our model has increased the precision of the relationship extraction on Top100 by 10 percent compared to the baseline [9]. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers in the world with tumor heterogeneity. Currently, cancer treatment mainly relies on surgical intervention, chemotherapy, and radiotherapy, for which the side effects, drug resistance and cost need to be resolved. In this study, we develop a natural medicine targeted therapy system. Phosphatidylcholine (PC), doxorubicin (DOX), procyanidin (PA), and epigallocatechin gallate (EGCG) are assembled and PC@DOX-PA/EGCG nanoparticles (NPs) are obtained. In addition, the HER2, ER and PR ligands were grafted on the surface of the NPs to acquire the targeted nanoparticles NP-ER, NP-ER-HER2, and NP-ER-HER2-PR. The physicochemical properties of the nanoparticles were detected and it was found that the nanoparticles are spherical and less than 200 nm in diameter. Furthermore, in vitro and in vivo results indicate that the nanoparticles can target BT-474, MCF-7, EMT-6, and MDA-MB-231 breast cancer cells, effectively inhibiting the growth of the breast cancer cells. In short, this research will provide some strategies for the treatment of heterogeneous breast cancer.