Residue and illegal addition of Dexamethasone (DEX) in food has received widespread attention over the past few decades. Long-term intake of DEX will have a strong endocrine-disrupting effect, and there is an urgent need to develop highly sensitive and rapid on-site detection methods. In this work, a colorimetric sensor based on an unmodified aptamer and gold nanoparticles (Au NPs) was designed to detect DEX in milk and glucosamine. Under optimized conditions, the absorbance ratio of Au NPs increased linearly with DEX concentration over the range of 10–350 nmol/mL (r2 = 0.997), with a limit of detection (LOD) of 0.5 nmol/mL, and the recoveries ranged from 93.6 to 117%. To explore the interaction mechanism between aptamer and DEX, molecular docking and molecular dynamics simulations were applied to probe intermolecular interactions and structures of the complex. The establishment of aptamer-based sensors effectively avoids the antibody screening response, with a cost-efficient, excellent selective and great potential in DEX determination.

Various biosensors based on aptamers are currently the most popular rapid detection approaches, but the performance of these sensors is closely related to the affinity of aptamers. In this work, a strategy for constructed high-affinity aptamer was proposed. By truncating the bases flanking the 59 nt dexamethasones (DEX) original aptamer sequence to improve the sensitivity of the aptamer to DEX, and then base mutation was introduced to further improve the sensitivity and selectivity of aptamers. Finally, the 33 nt aptamer Apt-M13 with G-quadruplex structures was obtained. The dissociation constant (Kd) was determined to be 200 nM by Graphene oxide (GO)-based fluorometry. As-prepared Apt-M13 was used for a label-free colorimetric aptamer sensor based on gold nanoparticles, the LOD was 3.2-fold lower than the original aptamer described in previous works. The anti-interference ability of DEX analogs is also further improved. It indicates that truncation technology effectively improves the specificity of the aptamer to DEX in this work, and the introduction of mutation further improves the affinity and selectivity of the aptamer to DEX. Therefore, the proposed aptamer optimization method is also expected to become a general strategy for various aptamer sequences.

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Background Cistanche tubulosa, as a homology of medicine and food, not only has a unique medicinal value but also is widely used in health care products. Polysaccharide is one of its important quality indicators. Objective In this study, an analytical model based on near infrared (NIR) spectroscopy combined with machine learning was established to predict the polysaccharide content of Cistanche tubulosa. Methods The content of polysaccharide in the samples determined by the phenol-sulfuric acid method was used as a reference value, and a machine learning was applied to relate the spectral information to the reference value. Dividing the samples into a calibration set and a prediction set using the Kennard-Stone algorithm. The model was optimized by various pre-processing methods, including Savitzky-Golay (SG), standard normal variate (SNV), multiple scattering correction (MSC), first-order derivative (FD), second-order derivative (SD), and combinations of them. Variable selection was performed through the successive projections algorithm (SPA) and the stability competitive adaptive reweighted sampling (sCARS). Four machine learning were used to build quantitative models, including the random forest (RF), partial least squares (PLS), principal component regression (PCR) and support vector machine (SVM). The evaluation indexes of the model were the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD). Results RF performs best among the four machine learning models. R2c and RMSEC% were 0.9763. and 0.3527 for calibration, respectively. R2p, RMSEP%, and RPD were 0.9230, 0.5130, and 3.33 for prediction, respectively. Conclusions The results indicate that NIR combined with the RF was an effective method applied to the quality evaluation of polysaccharides of Cistanche tubulosa. Highlights Four quantitative models were developed to predict the polysaccharide content in Cistanche tubulosa, and good results were obtained. The characteristic variables were basically determined by the sCARS algorithm, and the corresponding characteristic moieties were analyzed.

It is well-known that many vegetables and fruits have abundant polyphenols, such as anthocyanins, which benefit many cardiovascular diseases due to their anti-oxidative and anti-inflammatory effects. To explore the protective effect of anthocyanin on atherosclerosis from a metabolic perspective, alterations in plasma metabolic profiling of apoE-deficient (apoE–/–) mice in response to treatment with anthocyanin extracts derived from Xinjiang wild cherry plum (Prunus divaricata Ledeb) peel was investigated through UHPLC-Q-TOF/MS. The mice were fed with a normal diet or high-fat diet supplementation with or without anthocyanin extracts (ACNE, 75, 150, 250 mg/kg body weight) for 18 weeks, corresponding to control (Con), model (Mod), and treatment group (LD, low dose; MD, medium dose; HD, high dose), respectively, along with a positive control group (posCon, treatment with Atorvastatin, 0.003 mg/kg body weight). The results showed that ACNE could significantly enhance the antioxidant capacity and lower the plasma lipid, but have no evident influence on the body weight of apoE–/– mice. A series of differential metabolites, predominantly related to lipid metabolism, were identified, including docosahexaenoic acid, palmitoyl ethanolamide, stearoylcarnitine, L-palmitoylcarnitine, indoxyl sulfate (IS), 1-palmitoyl-lysophosphatidylcholine, phenylacetylglycine (PAGly), and so on. Among these, both IS and PAGly were host-microbial metabolites. These differential metabolites were mainly enriched in the pathway of glycerophospholipid metabolism and linoleic acid metabolism. Several important enzymes related to glycerophospholipid metabolisms such as LCAT, LPCAT, GPCPD1, PLA2G1B, PPARG, LIPE, PNPLA2, AGPAT1, and ENPP2 were recognized as underlying targets for anti-atherogenic effects of ACNE. These findings suggest that ACNE derived from Xinjiang wild cherry plum exhibits protective effects against atherosclerosis via modulating glycerophospholipid metabolism.