We evaluated the anti-inflammatory effects of SNAH in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages by performing nitric oxide (NO) assays, cytokine enzyme-linked immunosorbent assays, Western blotting, and real-time reverse transcription-polymerase chain reaction analysis. SNAH inhibited the production of NO (nitric oxide), reactive oxygen species (ROS), tumor necrosis factor (TNF)-α, and interleukin (IL)-6. Additionally, 100 μM SNAH significantly inhibited total NO and ROS inhibitory activity by 93% (p < 0.001) and 34% (p < 0.05), respectively. Protein expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) stimulated by LPS were also decreased by SNAH. Moreover, SNAH significantly (p < 0.001) downregulated the TNF-α, IL-6, and iNOS mRNA expression upon LPS stimulation. In addition, 3–100 µM SNAH was not cytotoxic. Docking simulations and enzyme inhibitory assays with COX-2 revealed binding scores of −6.4 kcal/mol (IC₅₀ = 47.8 μM) with SNAH compared to −11.1 kcal/mol (IC₅₀ = 0.45 μM) with celecoxib, a known selective COX-2 inhibitor. Our results demonstrate that SNAH exerts anti-inflammatory effects via suppression of ROS and NO by COX-2 inhibition. Thus, SNAH may be useful as a pharmacological agent for treating inflammation-related diseases.

Eight compounds were isolated from the roots of Glycyrrhiza uralensis and tested for cholinesterase (ChE) and monoamine oxidase (MAO) inhibitory activities. The coumarin glycyrol (GC) effectively inhibited butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) with IC₅₀ values of 7.22 and 14.77 µM, respectively, and also moderately inhibited MAO-B (29.48 µM). Six of the other seven compounds only weakly inhibited AChE and BChE, whereas liquiritin apioside moderately inhibited AChE (IC₅₀ = 36.68 µM). Liquiritigenin (LG) potently inhibited MAO-B (IC₅₀ = 0.098 µM) and MAO-A (IC₅₀ = 0.27 µM), and liquiritin, a glycoside of LG, weakly inhibited MAO-B (>40 µM). GC was a reversible, noncompetitive inhibitor of BChE with a K_i value of 4.47 µM, and LG was a reversible competitive inhibitor of MAO-B with a K_i value of 0.024 µM. Docking simulations showed that the binding affinity of GC for BChE (−7.8 kcal/mol) was greater than its affinity for AChE (−7.1 kcal/mol), and suggested that GC interacted with BChE at Thr284 and Val288 by hydrogen bonds (distances: 2.42 and 1.92 Å, respectively) beyond the ligand binding site of BChE, but that GC did not form hydrogen bond with AChE. The binding affinity of LG for MAO-B (−8.8 kcal/mol) was greater than its affinity for MAO-A (−7.9 kcal/mol). These findings suggest GC and LG should be considered promising compounds for the treatment of Alzheimer’s disease with multi-targeting activities.

Background: Dog-associated infections are related to more than 70 human diseases. Given that the health diagnosis of a dog requires expertise of the veterinarian, an artificial intelligence model for detecting dog diseases could significantly reduce time and cost required for a diagnosis and efficiently maintain animal health. Objective: We collected normal and multispectral images to develop classification model of each three dog skin diseases (bacterial dermatosis, fungal infection, and hypersensitivity allergic dermatosis). The single models (normal image- and multispectral image-based) and consensus models were developed used to four CNN model architecture (InceptionNet, ResNet, DenseNet, MobileNet) and select well-performed model. Results: For single models, such as normal image- or multispectral image-based model, the best accuracies and Matthew’s correlation coefficients (MCCs) for validation data set were 0.80 and 0.64 for bacterial dermatosis, 0.70 and 0.36 for fungal infection, and 0.82 and 0.47 for hypersensitivity allergic dermatosis. For the consensus models, the best accuracies and MCCs for the validation set were 0.89 and 0.76 for the bacterial dermatosis data set, 0.87 and 0.63 for the fungal infection data set, and 0.87 and 0.63 for the hypersensitivity allergic dermatosis data set, respectively, which supported that the consensus models of each disease were more balanced and well-performed. Conclusions: We developed consensus models for each skin disease for dogs by combining each best model developed with the normal and multispectral images, respectively. Since the normal images could be used to determine areas suspected of lesion of skin disease and additionally the multispectral images could help confirming skin redness of the area, the models achieved higher prediction accuracy with balanced performance between sensitivity and specificity.

Thirteen compounds were isolated from the Canavalia lineata pods and their inhibitory activities against human monoamine oxidase-A (hMAO-A) and -B (hMAO-B) were evaluated. Among them, compounds 8 (medicarpin) and 13 (homopterocarpin) showed potent inhibitory activity against hMAO-B (IC₅₀ = 0.45 and 0.72 µM, respectively) with selectivity index (SI) values of 44.2 and 2.07, respectively. Most of the compounds weakly inhibited MAO-A, except 9 (prunetin) and 13. Compounds 8 and 13 were reversible competitive inhibitors against hMAO-B (K_i = 0.27 and 0.21 µM, respectively). Structurally, the 3-OH group at A-ring of 8 showed higher hMAO-B inhibitory activity than 3-OCH3 group at the A-ring of 13. However, the 9-OCH3 group at B-ring of 13 showed higher hMAO-B inhibitory activity than 8,9-methylenedioxygroup at the B-ring of 12 (pterocarpin). In cytotoxicity study, 8 and 13 showed non-toxicity to the normal (MDCK) and cancer (HL-60) cells and moderate toxicity to neuroblastoma (SH-SY5Y) cell. Molecular docking simulation revealed that the binding affinities of 8 and 13 for hMAO-B (−8.7 and −7.7 kcal/mol, respectively) were higher than those for hMAO-A (−3.4 and −7.1 kcal/mol, respectively). These findings suggest that compounds 8 and 13 be considered potent reversible hMAO-B inhibitors to be used for the treatment of neurological disorders.