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Summary: Dust, or particulate matter (PM2.5), is among the most harmful pollutants negatively affecting human health. Predicting indoor PM2.5 concentrations is essential to achieve acceptable indoor air quality. This study aims to investigate data‐driven models to accurately predict PM 2.5 pollution. Notably, a comparative study has been conducted between twenty‐one machine learning and deep learning models to predict PM2.5 levels. Specifically, we investigate the performance of machine learning and deep learning models to predict ambient PM2.5 concentrations based on other ambient pollutants, including SO2$$ {}_2 $$, NO2$$ {}_2 $$, O3$$ {}_3 $$, CO, and PM10. Here, we applied Bayesian optimization to optimally tune hyperparameters of the Gaussian process regression with different kernels and ensemble learning models (i.e., boosted trees and bagged trees) and investigated their prediction performance. Furthermore, to further enhance the forecasting performance of the investigated models, dynamic information has been incorporated by introducing lagged measurements in the construction of the considered models. Results show a significant improvement in the prediction performance when considering dynamic information from past data. Moreover, three methods, namely, random forest (RF), decision tree, and extreme gradient boosting, are applied to assess variables contribution and revealed that lagged PM2.5 data contribute significantly to the prediction performance and enables the construction of parsimonious models. Hourly concentration levels of ambient air pollution from the air quality monitoring network located in Seoul are employed to verify the prediction effectiveness of the studied models. Six measurements of effectiveness are used for assessing the prediction quality. Results showed that deep learning models are more efficient than the other investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost). Also, the results showed that the bidirectional long short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU) networks produce higher performance than the investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost) and deep learning models (i.e., LSTM, GRU, and convolutional neural network). [ABSTRACT FROM AUTHOR]

Renal tubular epithelial-to-mesenchymal transition (EMT) and renal tubular interstitial fibrosis are the main pathological changes of diabetic nephropathy (DN), which is a common cause of end-stage renal disease. Previous studies have suggested that berberine (BBR) has antifibrotic effects in the kidney and can reduce apoptosis and inhibit the EMT of podocytes in DN. However, the effect of BBR on the renal tubular EMT in DN and its mechanisms of action are unknown. This study was performed to explore the effects of BBR on the renal tubular EMT and the molecular mechanisms of BBR in DN model KKAy mice and on the high glucose (HG)-induced EMT in mouse renal tubular epithelial cells. Our results showed that, relative to the model mice, the mice in the treatment group had an improved general state and reduced blood glucose and 24-h urinary protein levels. Degradation of renal function was ameliorated by BBR. We also observed the protective effects of BBR on renal structural changes, including normalization of an index of renal interstitial fibrosis and kidney weight/body weight. Moreover, BBR suppressed the activation of the Notch/snail pathway and upregulated the α-SMA and E-cadherin levels in DN model KKAy mice. BBR was further found to prevent HG-induced EMT events and to inhibit the HG-induced expression of Notch pathway members and snail1 in mouse renal tubular epithelial cells. Our findings indicate that BBR has a therapeutic effect on DN, including its inhibition of the renal tubular EMT and renal interstitial fibrosis. Furthermore, the BBR-mediated EMT inhibition occurs through Notch/snail pathway regulation., Competing Interests: Disclosure The authors report no conflicts of interest in this work.