ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy.

Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples. In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model's performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R2 of 0.933, an RMSE of 0.02, and an RPD of 3.856. These results support the accuracy and applicability of this method for rapidly predicting antioxidant activity in gentian root samples.

Rapid Identification of Wild Gentiana Genus in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net.

Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.

Glucocorticoids sensitize rat placental inflammatory responses via inhibiting lipoxin A4 biosynthesis.

Inflammation dysregulation in placenta is implicated in the pathogenesis of numerous pregnancy complications. Glucocorticoids (GCs), universally considered anti-inflammatory, can also exert proinflammatory actions under some conditions, whereas whether and how GCs promote placental inflammation have not been intensively investigated. In this paper we report the opposing regulation of rat placental inflammation by synthetic GC dexamethasone (Dex). When Dex was subcutaneously injected 1 h after we administered an intraperitoneal lipopolysaccharide (LPS) challenge, neutrophil infiltration and proinflammatory Il1b, Il6, and Tnfa expression in rat placenta were significantly reduced. In contrast, Dex pretreatment for 24 h potentiated rat placental proinflammatory response to LPS and delayed inflammation resolution, which involved MAPKs and NF-kappaB activation. Mechanically, Dex pretreatment promoted 5-lipoxygenase (ALOX5) activation and increased leukotriene B4 production, whereas it inhibited the anti-inflammatory and proresolving lipid mediator lipoxin A4 (LXA4) biosynthesis in rat placenta via downregulating ALOX15 and ALOX15B expression. Moreover, LXA4 supplementation dampened Dex-potentiated placental inflammation and suppressed Dex-mediated ALOX5 activation in vivo and in vitro. Taken together, these findings suggest that GCs exposure could promote placental inflammation initiation and delay resolution via disrupting LXA4 biosynthesis.