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Rhododendron, with its high ornamental value and ecological benefits, is severely impacted by the azalea lace bug (Stephanitis pyrioides), one of its primary pests. This study utilized three Rhododendron cultivars, 'Zihe', 'Yanzhimi', and 'Taile', to conduct a non-targeted metabolomic analysis of leaf samples before and after azalea lace bug stress using headspace solid-phase microextraction combined with gas chromatography–mass spectrometry (HS-SPME/GCMS) and liquid chromatography–mass spectrometry (LCMS). A total of 81 volatile metabolites across 11 categories and 448 nonvolatile metabolites across 55 categories were detected. Significant differences in metabolic profiles were observed among the different cultivars after pest stress. A total of 47 volatile compounds and 49 nonvolatile metabolites were upregulated in the most susceptible cultivar 'Zihe', including terpenes, alcohols, nucleotides, amino acids, and carbohydrates, which are involved in energy production and secondary metabolism. Conversely, 'Yanzhimi' showed a downtrend in both the differential volatiles and metabolites related to purine metabolism and zeatin biosynthesis under pest stress. The resistant cultivar 'Taile' exhibited moderate changes, with 17 volatile compounds and 17 nonvolatile compounds being upregulated and enriched in the biosynthesis of amino acids, pentose, glucuronate interconversions, carbon metabolism, etc. The phenylalanine metabolic pathway played an important role in the pest resistance of different susceptible cultivars, and relevant metabolites such as phenylethyl alcohol, methyl salicylate, and apigenin may be involved in the plant's resistance response. The results of this study provide a new perspective on the metabolomics of Rhododendron–insect interactions and offer references for the development of pest control strategies. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

Mercury is a highly toxic element that is widely present in all types of environmental media and can accumulate in living organisms. Prolonged exposure to high levels of mercury can lead to brain damage and death, so the detection of mercury is of great importance. In this study, a cost-effective and easy-to-operate electrochemical sensing method was successfully developed based on an amino-functionalized titanium-based MXene (NH 2 -Ti 3 C 2 T x ) for the rapid and selective detection of Hg 2+ that could have a coordination effect with the -NH 2 group of NH 2 -Ti 3 C 2 T x to promote the efficient accumulation of Hg 2+ . In this strategy, the NH 2 -Ti 3 C 2 T x was first modified on glassy carbon electrodes (GCE) to fabricate the electrochemical sensor. Benefiting from the excellent electrical conductivity, abundant active sites, and strong adsorption capacity performance of the NH 2 -Ti 3 C 2 T x , the NH 2 -Ti 3 C 2 T x modified GCE (NH 2 -Ti 3 C 2 T x /GCE) exhibited satisfactory selectivity and enhanced square wave anodic stripping voltammetry (SWASV) measurement for the rapid detection of trace amounts of Hg 2+ in aqueous solutions. The electrochemical sensor was found to be capable of detecting Hg 2+ with a low detection limit of 8.27 nmol L -1 and a linear range of 0.5 μmol L -1 to 50 μmol L -1 . The response time of the electrochemical sensing method was 308 s. In addition, the electrochemical sensing method has good selectivity, repeatability and stability, and multiple heavy metal ions have no effect on its detection, with repeatability and stability RSDs of 1.68% and 1.43%, respectively. Furthermore, the analysis of practical water samples demonstrated that the developed method was highly practical for the actual determination of Hg 2+ with recoveries in the range of 99.22-101.90%.

A simple dual-signal assay that combined colorimetric and fluorometric strategy for uric acid (UA) rapid detection was designed based on the versatility of facile synthesized MnO 2 nanosheet. The oxidization of 3,3',5,5'-tetramethylbenzidine (TMB) and the fluorescence quenching of quantum dots (QDs) occurred simultaneously in the presence of MnO 2 nanosheet. UA could decompose MnO 2 nanosheet into Mn 2+ , resulting in the fluorescence recovery of QDs, along with the fading of the blue color of ox TMB. Based on the principles above, the detection of UA could be realized by the change of the dual signals (colorimetric and fluorometric). The linear range of the colorimetric mode was 5-60 μmol L -1 , and the limit of detection (LOD) was 2.65 μmol L -1 ; the linear range of the fluorescence mode was wide at 5-120 μmol L -1 , and the LOD could be as low as 1.33 μmol L -1 . The method was successfully used for analyzing UA levels in human serum samples, indicating that this new dual-signal method could be applied in clinical diagnosis.

Hexokinase 1 (HK1) and pyruvate kinase M2 (PKM2) are two key regulators in glycosis and oncogenic markers in cancers. In the present study, we investigated the expression profile by Western blotting and immunohistochemistry and determined their prognostic values in the gastric cancer. Expression of HK1 and PKM2 was remarkably increased in gastric cancer tissues and was significantly associated lymphatic metastasis and advanced TNM staging. In the COX regression model, HK1 and TNM stage were analyzed as adverse prognostic indicators in gastric cancer. Furthermore, patients with HK1 expression showed remarkable shorter survival duration in both lymphatic metastasis cohort and advanced staging cohort. Our results suggest that overexpression of PKM2 and HK1, especially the latter, significantly associates with lymphatic metastasis, advanced clinical staging and unfavorable prognosis in gastric cancer.