Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
Polycystic ovarian syndrome (PCOS) causes swollen ovaries in women at reproductive age due to hormonal disorder with small cysts on the outer edges. The cause of the disorder is still yet to be found. Multiple factors have increased PCOS prevalence, hyperandrogenism, oxidative stress, inflammation, and insulin resistance. Various animal PCOS models have been developed to imitate the pathophysiology of PCOS in humans. Zebrafish is one of the most versatile animal experimental models because of the transparency of the embryos, small size, and rapid growth. The zebrafish similarity to higher vertebrates made it a useful non-mammalian model for PCOS drug testing and screening. This review provides an insight into the usage of zebrafish, a non-mammalian model for PCOS, as an opportunity for evaluating future initiatives in such a research domain.
Small molecules as well as peptide-based therapeutic approaches have attracted global interest due to their lower or no toxicity in nature, and their potential in addressing several health complications including immune diseases, cardiovascular diseases, metabolic disorders, osteoporosis and cancer. This study proposed a peptide, GE18 of subtilisin-like peptidase from the virulence factor of aquatic pathogenic fungus Aphanomyces invadans, which elicits anti-cancer and anti-microbial activities. To understand the potential GE18 peptide-induced biological effects, an in silico analysis, in vitro (L6 cells) and in vivo toxicity assays (using zebrafish embryo), in vitro anti-cancer assays and anti-microbial assays were performed. The outcomes of the in silico analyses demonstrated that the GE18 peptide has potent anti-cancer and anti-microbial activities. GE18 is non-toxic to in vitro non-cancerous cells and in vivo zebrafish larvae. However, the peptide showed significant anti-cancer properties against MCF-7 cells with an IC50 value of 35.34 µM, at 24 h. Besides the anti-proliferative effect on cancer cells, the peptide exposure does promote the ROS concentration, mitochondrial membrane potential and the subsequent upregulation of anti-cancer genes. On the other hand, GE18 elicits significant anti-microbial activity against P. aeruginosa, wherein GE18 significantly inhibits bacterial biofilm formation. Since the peptide has positively charged amino acid residues, it targets the cell membrane, as is evident in the FESEM analysis. Based on these outcomes, it is possible that the GE18 peptide is a significant anti-cancer and anti-microbial molecule.