Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

Lysine-specific demethylase 1 (LSD1/KDM1A) has emerged as a promising therapeutic target for treating various cancers (such as breast cancer, liver cancer, etc.) and other diseases (blood diseases, cardiovascular diseases, etc.), owing to its observed overexpression, thereby presenting significant opportunities in drug development. Since its discovery in 2004, extensive research has been conducted on LSD1 inhibitors, with notable contributions from computational approaches. This review systematically summarizes LSD1 inhibitors investigated through computer-aided drug design (CADD) technologies since 2010, showcasing a diverse range of chemical scaffolds, including phenelzine derivatives, tranylcypromine (abbreviated as TCP or 2-PCPA) derivatives, nitrogen-containing heterocyclic (pyridine, pyrimidine, azole, thieno[3,2-b]pyrrole, indole, quinoline and benzoxazole) derivatives, natural products (including sanguinarine, phenolic compounds and resveratrol derivatives, flavonoids and other natural products) and others (including thiourea compounds, Fenoldopam and Raloxifene, (4-cyanophenyl)glycine derivatives, propargylamine and benzohydrazide derivatives and inhibitors discovered through AI techniques). Computational techniques, such as virtual screening, molecular docking and 3D-QSAR models, have played a pivotal role in elucidating the interactions between these inhibitors and LSD1. Moreover, the integration of cutting-edge technologies such as artificial intelligence holds promise in facilitating the discovery of novel LSD1 inhibitors. The comprehensive insights presented in this review aim to provide valuable information for advancing further research on LSD1 inhibitors.

Overexpression of lysine specific demethylase 1 (LSD1) has been found in many cancers. New anticancer drugs targeting LSD1 have been designed. The research on irreversible LSD1 inhibitors has entered the clinical stage, while the research on reversible LSD1 inhibitors has progressed slowly so far. In this study, 41 stilbene derivatives were studied as reversible inhibitors by three-dimensional quantitative structure–activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA ${\mathrm{q}}^2$ = 0.623, ${\mathrm{r}}^2$ = 0.987, ${\mathrm{r}}_{\mathrm{pred}}^2$ = 0.857) and comparative molecular similarity indices analysis (CoMSIA ${\mathrm{q}}^2$ = 0.728, ${\mathrm{r}}^2$ = 0.960, ${\mathrm{r}}_{\mathrm{pred}}^2$ = 0.899) were used to establish the model, and the structure–activity relationship of the compounds was explained by the contour maps. The binding site was predicted by two different kinds of software, and the binding modes of the compounds were further explored. A series of key amino acids Val288, Ser289, Gly314, Thr624, Lys661 were found to play a key role in the activity of the compounds. Molecular dynamics (MD) simulations were carried out for compounds 04, 17, 21, and 35, which had different activities. The reasons for the activity differences were explained by the interaction between compounds and LSD1. The binding free energy was calculated by molecular mechanics generalized Born surface area (MM/GBSA). We hope that this research will provide valuable information for the design of new reversible LSD1 inhibitors in the future.

Lysine-specific demethylase 1 (LSD1) is a histone-modifying enzyme, which is a significant target for anticancer drug research. In this work, 40 reported tetrahydroquinoline-derivative inhibitors targeting LSD1 were studied to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR). The established models CoMFA (Comparative Molecular Field Analysis (q² = 0.778, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.709)) and CoMSIA (Comparative Molecular Similarity Index Analysis (q² = 0.764, ${\mathrm{R}}_{\mathrm{pred}}^2$ = 0.713)) yielded good statistical and predictive properties. Based on the corresponding contour maps, seven novel tetrahydroquinoline derivatives were designed. For more information, three of the compounds (D1, D4, and Z17) and the template molecule 18x were explored with molecular dynamics simulations, binding free energy calculations by MM/PBSA method as well as the ADME (absorption, distribution, metabolism, and excretion) prediction. The results suggested that D1, D4, and Z17 performed better than template molecule 18x due to the introduction of the amino and hydrophobic groups, especially for the D1 and D4, which will provide guidance for the design of LSD1 inhibitors.

Human adipose-derived mesenchymal stem cells (hAD-MSCs) are mesenchymal stem cells with the capability to modulate immune responses. Evidence showing that hAD-MSCs could mediate innate immune responses through pattern recognition receptors (PRRs) is increasing. However, the roles of PRRs in regulating the innate sensing of virus nucleic acids (RNA and DNA) in hAD-MSCs have not yet been investigated. This study focused on the abundant expression of PRRs, including Toll-like receptor 3 (TLR3) and retinoic acid-inducible gene I (RIG-I), which recognize viral RNA, and gamma-interferon inducible protein 16 (IFI16), which recognizes viral DNA in hAD-MSCs. Poly(I:C), a synthetic dsRNA analogy, activated TLR3 and RIG-I and induced the expression of type I interferons (IFN-α/β) and antivirus proteins, including IFN-stimulating gene 15, 2′5′-oligoadenylate synthetase, and Mx GTPase 1 in hAD-MSCs, which were attenuated by the knockdown of each TLR3 or RIG-I. Synthetic herpes simplex viral DNA (HSV60) activated IFI16 and induced the expression of IFN-α/β and antivirus proteins in hAD-MSCs, which were inhibited by the knockdown of IFI16. Both poly(I:C) and HSV60 induced the expression of IFN-α/β through the phosphorylation of IFN-regulatory factor 3. All these results indicated that PRRs recognizing virus nucleic acids were expressed and can mediate antivirus responses in hAD-MSCs.