The presence of microplastics (MPs) in agricultural soils substantially affects the growth, reproduction, feeding, survival, and immunity levels of soil biota. Therefore, it is crucial to investigate fast, effective, and accurate techniques for the detection of soil MPs. This work explores the integration of terahertz time-domain spectroscopy (THz-TDS) techniques with machine learning algorithms to develop a method for the classification and detection of MPs. First, THz spectral image data were preprocessed using moving average (MA). Subsequently, three classification models were developed, including random forest (RF), linear discriminant analysis, and support vector machine (SVM). Notably, the SVM model had an F1 score of 0.9817, demonstrating its ability to rapidly classify MPs in soil samples. Three regression models, namely, principal component regression (PCR), RF, and least squares support vector machine (LSSVM), were developed for the detection of three MPs polymers in agricultural soils. Six feature extraction methods were used to extract the relevant parts of the data containing key information. The results of the study showed that the regression accuracies of PCR, RF, and LSSVM were greater than 83%. Among them, the RF had the highest overall regression accuracy. Notably, PE-UVE-RF had the best performance with Rc2, Rp2, root mean square error of calibration, and root mean square error of prediction values of 0.9974, 0.9916, 0.1595, and 0.2680, respectively. Furthermore, this model gets a better performance by hypothesis testing and predicting real samples.
Bacterial infections have long been a formidable challenge in global public health, further compounded by the emergence of drug-resistant bacteria resulting from the overuse and misuse of antibiotics. Intelligent antibacterial strategies are garnering escalating attention and concern due to their ability to accurately recognize bacterial infections, efficiently eliminate pathogens, and timely monitor infection end points in order to mitigate the adverse effects of excessive treatment on normal tissues. Hence, in this study, we developed a multifunctional antibacterial nanohydrogel that exhibited bacteria-triggered fluorescence activity, serving as a fluorescent indicator for bacterial infections. Moreover, the bacteria can induce the release of Fe3+, photosensitizers, and antibiotics within the nanohydrogel, thereby exerting synergistic antibacterial effects through chemodynamic and photodynamic treatment, glutathione depletion, and antibiotics. Consequently, the nanohydrogel demonstrated remarkable efficacy in eradicating bacteria within wounds while significantly enhancing wound healing. The construction strategy and design principles of the antibacterial nanohydrogel broaden the horizons of clinical photodynamic antibacterial therapy, offering a novel perspective for the advancement of integrated theranostic approaches against bacterial infections.
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