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Objective: To explore the potential targets for melatonin in the treatment of periodontitis through network pharmacologic analysis and experimental validation via in vivo animal models and in vitro cellular experiments. Materials and methods: In this study, we first screened melatonin targets from Pharm Mapper for putative targets, Drug Bank, and TCMSP databases for known targets. Then, disease database was searched and screened for differential expressed genes associated with periodontitis. The intersection of disease and melatonin-related genes yielded potential target genes of melatonin treatment for periodontitis. These target genes were further investigated by protein-protein interaction network and GO/KEGG enrichment analysis. In addition, the interactions between melatonin and key target genes were interrogated by molecular docking simulations. Then, we performed animal studies to validate the therapeutic effect of melatonin by injecting melatonin into the peritoneal cavity of ligation-induced periodontitis (LIP) mice. The effects of melatonin on the predicted target proteins were also analyzed using Western blot and immunofluorescence techniques. Finally, we constructed an in vitro cellular model and validated the direct effect of melatonin on the predicted targets by using qPCR. Results: We identified 8 potential target genes by network pharmacology analysis. Enrichment analysis suggests that melatonin may treat periodontitis by inhibiting the expression of three potential targets (MPO, MMP8, and MMP9). Molecular docking results showed that melatonin could effectively bind to MMP8 and MMP9. Subsequently, melatonin was further validated in a mouse LIP model to inhibit the expression of MPO, MMP8, and MMP9 in the periodontal tissue. Finally, we verified the direct effect of melatonin on the mRNA expression of MPO, MMP8, and MMP9 in an in vitro cellular model. Conclusions: Through a combination of network pharmacology and experimental validation, this study provides a more comprehensive understanding of the mechanism of melatonin to treat periodontitis. Our study suggests that MPO, MMP8, and MMP9 as key target genes of melatonin to treat periodontitis. These findings present a more comprehensive basis for further investigation into the mechanisms of pharmacological treatment of periodontitis by melatonin.

Smoking disrupts bone homeostasis and serves as an independent risk factor for the development and progression of osteoporosis. Tobacco toxins inhibit the proliferation and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), promote BMSCs aging and exhaustion, but the specific mechanisms are not yet fully understood. Herein, we successfully established a smoking-related osteoporosis (SROP) model in rats and mice through intraperitoneal injection of cigarette smoke extract (CSE), which significantly reduced bone density and induced aging and inhibited osteogenic differentiation of BMSCs both in vivo and in vitro. Bioinformatics analysis and in vitro experiments confirmed that CSE disrupts mitochondrial homeostasis through oxidative stress and inhibition of mitophagy. Furthermore, we discovered that CSE induced BMSCs aging by upregulating phosphorylated AKT, which in turn inhibited the expression of FOXO3a and the Pink1/Parkin pathway, leading to the suppression of mitophagy and the accumulation of damaged mitochondria. MitoQ, a mitochondrial-targeted antioxidant and mitophagy agonist, was effective in reducing CSE-induced mitochondrial oxidative stress, promoting mitophagy, significantly downregulating the expression of aging markers in BMSCs, restoring osteogenic differentiation, and alleviating bone loss and autophagy levels in CSE-exposed mice. In summary, our results suggest that BMSCs aging caused by the inhibition of mitophagy through the AKT/FOXO3a/Pink1/Parkin axis is a key mechanism in smoking-related osteoporosis.

Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km2/a); sharp decrease until 2014 (−19.44 km2/a); and steady increase (22.93 km2/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km2/a), then decreased (−35.89 km2/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.

Strong tropical cyclones have made a drastic effect on human life and natural environment. As large amounts of meteorological data and monitoring data continue to accumulate, traditional methods for predicting tropical cyclone tracks face numerous challenges regarding their prediction efficiency and accuracy. Deep learning methods recently have been proven to be able to learn both spatial and temporal features from a large amount of dataset and be extremely efficient and accurate for forecasting data in complex structures. In this paper, we propose a novel data-driven deep learning model to predict tropical cyclone tracks using the spatial locations and multiple meteorological factors. This model comprises a multi-dimensional feature selection layer, a CNN layer and a GRU layer. The proposed model was trained using a dataset of real-world tropical cyclones from the years 1945 to 2017. Through the comparison experiments, the results verify that the proposed model outperforms the traditional forecasting methods, including a climatologically aware forecasting technique, the Sanders Barotropic technique and a numerical weather prediction (NWP) model. In addition, the proposed model has better accuracy than some deep learning methods, including RNN, GRU, CNN, AE-RNN, CNN-RNN, and CNN-GRU without the proposed feature selection layer.

Early, high-throughput, and accurate recognition of osteogenic differentiation of stem cells is urgently required in stem cell therapy, tissue engineering, and regenerative medicine. In this study, we established an automatic deep learning algorithm, i.e., osteogenic convolutional neural network (OCNN), to quantitatively measure the osteogenic differentiation of rat bone marrow mesenchymal stem cells (rBMSCs). rBMSCs stained with F-actin and DAPI during early differentiation (day 0, 1, 4, and 7) were captured using laser confocal scanning microscopy to train OCNN. As a result, OCNN successfully distinguished differentiated cells at a very early stage (24 h) with a high area under the curve (AUC) (0.94 ± 0.04) and correlated with conventional biochemical markers. Meanwhile, OCNN exhibited better prediction performance compared with the single morphological parameters and support vector machine. Furthermore, OCNN successfully predicted the dose-dependent effects of small-molecule osteogenic drugs and a cytokine. OCNN-based online learning models can further recognize the osteogenic differentiation of rBMSCs cultured on several material surfaces. Hence, this study initially demonstrated the foreground of OCNN in osteogenic drug and biomaterial screening for next-generation tissue engineering and stem cell research.