Search

Dendrobium officinale (D. officinale), often used as a dual-use plant with herbal medicine and food applications, has attracted considerable attention for health-benefiting components and wide economic value. The antioxidant ability of D. officinale is of great significance to ensure its health care value and safeguard consumers’ interests. However, the common analytical methods for evaluating the antioxidant ability of D. officinale are time-consuming, laborious, and costly. In this study, near-infrared (NIR) spectroscopy and chemometrics were employed to establish a rapid and accurate method for the determination of 2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity, 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity, and ferric reducing antioxidant power (FRAP) in D. officinale. The quantitative models were developed based on the partial least squares (PLS) algorithm. Two wavelength selection methods, namely the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) method, were used for model optimization. The CARS-PLS models exhibited superior predictive performance compared to other PLS models. The root mean square errors of cross-validation (RMSECVs) for ABTS, FRAP, and DPPH were 0.44%, 2.64 μmol/L, and 2.06%, respectively. The results demonstrated the potential application of NIR spectroscopy combined with the CARS-PLS model for the rapid prediction of antioxidant activity in D. officinale. This method can serve as an alternative to conventional analytical methods for efficiently quantifying the antioxidant properties in D. officinale.

The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.

Kentucky bluegrass (Poa pratensis L.) is an important cool season turfgrass species with a high cold tolerance, but it is sensitive to drought. It is valuable for the applications of Kentucky bluegrass to improve its drought tolerance. However, little is known about the underlying drought mechanism. In the present study, transcriptomic profiling in the roots and leaves of the Kentucky bluegrass cultivar ‘Qinghai’, in response to osmotic stress in the form of treatment with 2 h and 50 h of 25% (v/v) PEG-6000, was analyzed. The results showed that a large number of genes were significantly up-regulated or down-regulated under osmotic stress. The majority of genes were up-regulated in leaves but down-regulated in roots after 2 h and 50 h of osmotic stress, among them were 350 up-regulated DEGs and 20 down-regulated DEGs shared in both leaves and roots. GO and KEGG analysis showed that carbohydrate metabolism, polyamine and amino acid metabolism and the plant hormone signaling pathway were enriched in the leaves and roots of ‘Qinghai’ after osmotic stress. The genes involving in carbohydrate metabolism were up-regulated, and sucrose, trehalose and raffinose levels were consistently increased. The genes involved in polyamine and amino acid metabolism were up-regulated in leaves in response to osmotic stress and several amino acids, such as Glu, Met and Val levels were increased, while the genes involved in photosynthesis, carbon fixation and citrate cycle in leaves were down-regulated. In addition, the genes involved in plant hormone biosynthesis and signal transduction were altered in leaves after osmotic stress. This study provided promising candidate genes for studying drought mechanisms in ‘Qinghai’ and improving the drought tolerance of Kentucky bluegrass and drought-sensitive crops.