Heavy-metal pollution occurs in various environments, including water, air and soil, and has serious effects on human health. Since heavy-metal pollution in drinking water causes various diseases including skin cancer, it has become a global problem worldwide. However, there is limited information on the mechanism of development of heavy-metal-mediated disease. We performed both fieldwork and experimental studies to elucidate the levels of heavy-metal pollution and mechanisms of development of heavy-metal-related disease and to develop a novel remediation system. Our fieldwork in Bangladesh, Vietnam and Malaysia demonstrated that drinking well water in these countries was polluted with high concentrations of several heavy metals including arsenic, barium, iron and manganese. Our experimental studies based on the data from our fieldwork demonstrated that these heavy metals caused skin cancer and hearing loss. Further experimental studies resulted in the development of a novel remediation system with which toxic heavy metals were absorbed from polluted drinking water. Implementation of both fieldwork and experimental studies is important for prediction, prevention and therapy of heavy-metal-mediated diseases.
The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
Utilizing visible and near-infrared (Vis-NIR) spectroscopy in conjunction with chemometrics methods has been widespread for identifying plant diseases. However, a key obstacle involves the extraction of relevant spectral characteristics. This study aimed to enhance sugarcane disease recognition by combining convolutional neural network (CNN) with continuous wavelet transform (CWT) spectrograms for spectral features extraction within the Vis-NIR spectra (380-1400 nm) to improve the accuracy of sugarcane diseases recognition. Using 130 sugarcane leaf samples, the obtained one-dimensional CWT coefficients from Vis-NIR spectra were transformed into two-dimensional spectrograms. Employing CNN, spectrogram features were extracted and incorporated into decision tree, K-nearest neighbour, partial least squares discriminant analysis, and random forest (RF) calibration models. The RF model, integrating spectrogram-derived features, demonstrated the best performance with an average precision of 0.9111, sensitivity of 0.9733, specificity of 0.9791, and accuracy of 0.9487. This study may offer a non-destructive, rapid, and accurate means to detect sugarcane diseases, enabling farmers to receive timely and actionable insights on the crops' health, thus minimizing crop loss and optimizing yields.
Hypertension is highly prevalent worldwide and is the major risk factor for heart failure (HF). More than half of the patients with HF in Asia suffer from hypertension. According to the 2022 American Heart Association/American College of Cardiology/Heart Failure Society of America HF guideline, there are four stages of HF, including at risk for HF (stage A), pre-HF (stage B), symptomatic HF (stage C), and advanced HF (stage D). Given the high prevalence of hypertension as well as HF and the stronger association between hypertension and cardiovascular diseases in Asians compared to the west, measures to prevent and alleviate the progression to clinical HF, especially controlling the blood pressure (BP), are of priority for Asian populations. After reviewing evidence-based studies, we propose a BP target of less than 130/80 mmHg for patients at stages A, B, and C. However, relatively higher BP may represent an opportunity to maximize guideline-directed medical therapy (GDMT), which could potentially result in a better prognosis for patients at stage D. Traditional antihypertensive drugs are the cornerstones for the management of hypertension at stages A and B. Notably, calcium channel blockers (CCBs) are inferior to other drug classes for the preventing of HF, whereas diuretics are superior to others. For patients at stage C, GDMT is essential which also helps the control of BP. In particular, sodium-glucose cotransporter-2 (SGLT2) inhibitors are newer therapies recommended for the treatment of HF and presumably even in hypertension to prevent HF. Regarding patients at stage D, GDMT is also recommended if tolerable and measures should be taken to improve hemodynamics.