Search

Maca (Lepidium meyenii Walp.), an edible medicinal plant and popular dietary supplement, is native to South America in the Andes mountains of Peru. However, maca is frequently adulterated due to its high nutritional and medicinal value, which compromises its safety and effectiveness. In the present study, maca and its two adulterants, Brassica rapa (turnip) and Raphanus sativus (radish), were identified by one nuclear barcode (ITS) and four chloroplast barcodes (matK, psbA-trnH, rbcL, and trnL-trnF). All five DNA barcodes were perfectly identified in maca, turnip, and radish. Out of the five tested barcodes, trnL-trnF could be regarded as the ideal barcode for detecting turnip/radish adulteration in maca as it generated different sequence length among these three species, it was further validated by mixing turnip/radish into maca at various proportions, and trnL-trnF efficiently and sensitively detected adulteration even at a very low level (0.5% of adulteration).

Phellodendron chinense Schneid is an important Chinese herb with berberine and phellodendrine in stems and leaves, but with little information available on in vitro culture of this species. Disinfection of explants in 75% alcohol for 45 s, sterilization in 0.1% HgCl2 for 20 min, and submersion in 1.0 mol L−1 gibberellin3 (GA3) solution for 24 h was the optimal condition for seed germination. Murashige and Skoog’s (MS) medium supplemented with 2.0 mg L−1 6-benzylaminopurine (6-BA) in combination with 1.5 mg L−1 1-naphthylacetic acid (NAA) was optimal for callus induction. MS medium supplemented with 2.0 mg L−1 6-BA was the appropriate medium for induction of adventitious shoots, and 1/2MS medium supplemented with 2.0 mg L−1 indole-3-butytric acid (IBA) and 0.5% active carbon was the optimal medium for root induction. The 15-d survival rate of regenerated plantlets after transplanting to basins containing perlite and peat moss (1:4) was greater than 80%, and the berberine and phellodendrine accumulation was lower in callus compared with regenerated plantlets. The establishment of highly efficient regeneration system provides technical support for genetic breeding of Phellodendron chinense Schneid.

Timely diagnosis of type 2 diabetes and early intervention and treatment of it are important for controlling metabolic disorders, delaying and reducing complications, reducing mortality, and improving quality of life. Type 2 diabetes was diagnosed by Fourier transform mid-infrared (FTIR) attenuated total reflection (ATR) spectroscopy in combination with extreme gradient boosting (XGBoost). Whole blood FTIR-ATR spectra of 51 clinically diagnosed type 2 diabetes and 55 healthy volunteers were collected. For the complex composition of whole blood and much spectral noise, Savitzky–Golay smoothing was first applied to the FTIR-ATR spectrum. Then PCA was used to eliminate redundant data and got the best number of principle components. Finally, the XGBoost algorithm was used to discriminate the type 2 diabetes from healthy volunteers and the grid search algorithm was used to optimize the relevant parameters of the XGBoost model to improve the robustness and generalization ability of the model. The sensitivity of the optimal XGBoost model was 95.23% (20/21), the specificity was 96.00% (24/25), and the accuracy was 95.65% (44/46). The experimental results show that FTIR-ATR spectroscopy combined with XGBoost algorithm can diagnose type 2 diabetes quickly and accurately without reagents.

The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey.

Consumption of fermented food increases the risk of dissemination of microbial antibiotic resistance genes (ARGs) in the human intestinal tract. However, the diversity and taxonomic origin of ARGs in fermented foods are not well understood. In this study, genome-centered metagenomics was used to identify distribution patterns of ARGs in major bacterial species in sufu. In total, 13 high-quality metagenome-assembled genomes (MAGs) (with average completeness of 88.24% and contamination of 2.08%) were recruited from a combined metagenome of 25 sufu samples, which were assigned to eight genera. The number of ARGs in MAGs differed in various phylogenetic positions; MAGs associated with the genera Enterococcus and Kurthia, and family Flavobacteriaceae, harbored more ARGs than those associated with the genera Lactobacillus and Weissella. Among the twelve detected ARG types, glycopeptide resistance genes were present in all MAGs, and genes conferring resistance to oxazolidinone, beta-lactam, macrolide-lincosamide-streptogramin, and tetracycline were identified in 84.6%, 76.9%, 76.9%, and 76.9% of the MAGs, respectively. The number and diversity of ARGs in MAGs differed in taxonomic lineages, reflecting antibiotic resistance in different sufu bacterial species. Thus, owing to an abundance of species with diverse ARGs, stringent quantity control involving microbial species, especially those carrying abundant ARGs, is critical to reduce the risk of ARG uptake through the consumption of fermented foods.

The attenuated total reflectance-Fourier transform mid-infrared (ATR-FTMIR) and near-infrared (NIR) spectra of royal jelly samples were collected. Low- and mid-level data fusion strategies in combination with the partial least squares (PLS) regression algorithm were used for quantitative modeling analysis of 10-hydroxy-2-decenoic acid (10-HDA) content in royal jelly samples. In low-level data fusion, each raw spectrum was pre-processed before splicing into a new data matrix for PLS model construction and analysis. In the mid-level data fusion, synergy interval (SI)-PLS was used for variable selection and principal component analysis/independent component analysis was used for feature extraction to obtain variables. The extracted variables were spliced before inputting into the PLS model for modeling and analysis. The results showed that the PLS analysis model constructed by mid-level data fusion is better than the PLS models constructed by independent data and low-level data fusion. Among these models, the PLS model that was constructed by the mid-level data fusion after SI-PLS variable selection had the best 10-HDA content prediction accuracy, with RMSEP = 0.1118(%) and RP = 0.9585. Therefore, a mid-level data fusion strategy based on the ATR-FTMIR and NIR spectra can be used as a reliable tool for 10-HDA quantitation.

This study aimed to explore the application of surface-enhanced Raman scattering (SERS) in the rapid diagnosis of gastric cancer. The SERS spectra of 68 serum samples from gastric cancer patients and healthy volunteers were acquired. The characteristic ratio method (CRM) and principal component analysis (PCA) were used to differentiate gastric cancer serum from normal serum. Compared with healthy volunteers, the serum SERS intensity of gastric cancer patients was relatively high at 722[Formula: see text]cm[Formula: see text], while it was relatively low at 588, 644, 861, 1008, 1235, 1397, 1445 and 1586[Formula: see text]cm[Formula: see text]. These results indicated that the relative content of nucleic acids in the serum of gastric cancer patients rises while the relative content of amino acids and carbohydrates decreases. In PCA, the sensitivity and specificity of discriminating gastric cancer were 94.1% and 94.1%, respectively, with the accuracy of 94.1%. Based on the intensity ratios of four characteristic peaks at 722, 861, 1008 and 1397[Formula: see text]cm[Formula: see text], CRM presented the diagnostic sensitivity and specificity of 100% and 97.4%, respectively, and the accuracy of 98.5%. Therefore, the three peak intensity ratios of I[Formula: see text]/I[Formula: see text], I[Formula: see text]/I[Formula: see text] and I[Formula: see text]/I[Formula: see text] can be considered as biological fingerprint information for gastric cancer diagnosis and can rapidly and directly reflect the physiological and pathological changes associated with gastric cancer development. This study provides an important basis and standards for the early diagnosis of gastric cancer.