Unveiling the micronutrient-immunity puzzle in inactivated COVID-19 vaccination: A comprehensive analysis of circulating micronutrient levels and humoral responses in healthy adults.

While micronutrients are crucial for immune function, their impact on humoral responses to inactivated COVID-19 vaccination remains unclear. We investigated the associations between seven key micronutrients and antibody responses in 44 healthy adults with two doses of an inactivated COVID-19 vaccine. Blood samples were collected pre-vaccination and 28 days post-booster. We measured circulating minerals (iron, zinc, copper, and selenium) and vitamins (A, D, and E) concentrations alongside antibody responses and assessed their associations using linear regression analyses. Our analysis revealed inverse associations between blood iron and zinc concentrations and anti-SARS-CoV-2 IgM antibody binding affinity (AUC for iron: ß = -258.21, p < 0.0001; zinc: ß = -17.25, p = 0.0004). Notably, antibody quality presented complex relationships. Blood selenium was positively associated (ß = 18.61, p = 0.0030), while copper/selenium ratio was inversely associated (ß = -1.36, p = 0.0055) with the neutralizing ability against SARS-CoV-2 virus at a 1:10 plasma dilution. There was no significant association between circulating micronutrient concentrations and anti-SARS-CoV-2 IgG binding affinity. These findings suggest that circulating iron, zinc, and selenium concentrations and copper/selenium ratio, may serve as potential biomarkers for both quantity (binding affinity) and quality (neutralization) of humoral responses after inactivated COVID-19 vaccination. Furthermore, they hint at the potential of pre-vaccination dietary interventions, such as selenium supplementation, to improve vaccine efficacy. However, larger, diverse studies are needed to validate these findings. This research advances the understanding of the impact of micronutrients on vaccine response, offering the potential for personalized vaccination strategies.

Association between Single Nucleotide Polymorphisms of PRKD1 and KCNQ3 Gene and Milk Quality Traits in Gannan Yak (Bos grunniens).

Protein kinase D1 (PRKD1) functions primarily in normal mammary cells, and the potassium voltage-gated channel subfamily Q member 3 (KCNQ3) gene plays an important role in controlling membrane potential and neuronal excitability, it has been found that this particular gene is linked to the percentage of milk fat in dairy cows. The purpose of this study was to investigate the relationship between nucleotide polymorphisms (SNPs) of PRKD1 and KCNQ3 genes and the milk quality of Gannan yak and to find molecular marker sites that may be used for milk quality breeding of Gannan yak. Three new SNPs were detected in the PRKD1 (g.283,619T>C, g.283,659C>A) and KCNQ3 gene (g.133,741T>C) of 172 Gannan lactating female yaks by Illumina yak cGPS 7K liquid-phase microarray technology. Milk composition was analyzed using a MilkoScanTM milk composition analyzer. We found that the mutations of these three loci significantly improved the lactose, milk fat, casein, protein, non-fat milk solid (SNF) content and acidity of Gannan yaks. The lactose content of the TC heterozygous genotype population at g.283,619T>C locus was significantly higher than that of the TT wild-type population (p < 0.05); the milk fat content of the CA heterozygous genotype population at g.283,659C>A locus was significantly higher than that of the CC wild-type and AA mutant populations (p < 0.05); the casein, protein and acidity of the CC mutant and TC heterozygous groups at the g.133,741T>C locus were significantly higher than those of the wild type (p < 0.05), and the SNF of the TC heterozygous group was significantly higher than that of the mutant group (p < 0.05). The results showed that PRKD1 and KCNQ3 genes could be used as candidate genes affecting the milk traits of Gannan yak.

Proteomic Analysis Reveals the Effects of Different Dietary Protein Levels on Growth and Development of Jersey-Yak.

Jersey-yak is a hybrid offspring of Jersey cattle and yak (Bos grunniens). Changing the feeding system of Jersey-yak can significantly improve its growth performance. In this study, tandem mass tag (TMT) proteomics technology was used to determine the differentially expressed proteins (DEPs) of the longissimus lumborum (LL) muscle of Jersey-yak fed different protein levels of diet. The results showed that compared with the traditional grazing feeding, the growth performance of Jersey-yaks was significantly improved by crude protein supplementation after grazing. A total of 3368 proteins were detected in these muscle samples, of which 3365 were quantified. A total of 434 DEPs were identified. Through analyses, it was found that some pathways related to muscle growth and development were significantly enriched, such as Rap1 signaling pathway, mTOR signaling pathway, and TGF-beta signaling pathway. A number of DEPs enriched in these pathways are related to muscle cell development, differentiation, and muscle development, including integrin subunit alpha 7 (ITGA7), myosin heavy chain 8 (MYH8), and collagen type XII alpha 1 chain (COL12A1). In conclusion, the results of this study provide insights into the proteomics of different feeding patterns of Jersey-yak, providing a stronger basis for further understanding the biological mechanism of hybrid varieties.

Economical synthesis of γ-cyclodextrin catalyzed by oriented cyclodextrin glycosyltransferase displayed on bacterial polyhydroxyalkanoate nanogranules.

BACKGROUND: The advantages of γ-cyclodextrin (γ-CD) include its high solubility, ability to form inclusion complexes with various poorly water-soluble molecules, and favorable toxicological profile; thus, γ-CD is an attractive functional excipient widely used in many industrial settings. Unfortunately, the high cost of γ-CD caused by the low activity and stability of γ-cyclodextrin glycosyltransferase (γ-CGTase) has hampered large-scale production and application. RESULTS: This study reports the in vivo one-step production of immobilized γ-CGTase decorated on the surface of polyhydroxyalkanoate (PHA) nanogranules by the N-terminal fusion of γ-CGTase to PHA synthase via a designed linker. The immobilized γ-CGTase-PHA nanogranules showed outstanding cyclization activity of 61.25 ± 3.94 U/mg (γ-CGTase protein) and hydrolysis activity of 36,273.99 ± 1892.49 U/mg, 44.74% and 18.83% higher than that of free γ-CGTase, respectively. The nanogranules also exhibited wider optimal pH (cyclization activity 7.0-9.0, hydrolysis activity 10.0-11.0) and temperature (55-60 °C) ranges and remarkable thermo- and pH-stability, expanding its utility to adapt to wider and more severe reaction conditions than the free enzyme. A high yield of CDs (22.73%) converted from starch and a high ratio (90.86%) of γ-CD in the catalysate were achieved at pH 9.0 and 50 °C for 10 h with 1 mmol/L K+, Ca2+, and Mg2+ added to the reaction system. Moreover, γ-CGTase-PHA beads can be used at least eight times, retaining 82.04% of its initial hydrolysis activity and 75.73% of its initial cyclization activity. CONCLUSIONS: This study provides a promising nanobiocatalyst for the cost-efficient production of γ-CD, which could greatly facilitate process control and economize the production cost.

Antimicrobial agent chloroxylenol targets ß­catenin­mediated Wnt signaling and exerts anticancer activity in colorectal cancer.

Chloroxylenol is the active ingredient of the antibacterial agent Dettol. The anticancer effect and underlying mechanisms of this compound and other common antimicrobial agents have not been clearly elucidated. In the present study, the effects of chloroxylenol, benzalkonium chloride, benzethonium chloride, triclosan and triclocarban on ß­catenin­mediated Wnt signaling in colorectal cancer were evaluated using the SuperTOPFlash reporter assay. It was demonstrated that chloroxylenol, but not the other antimicrobial agents tested, inhibited the Wnt/ß­catenin signaling pathway by decreasing the nuclear translocation of ß­catenin and disrupting ß­catenin/T­cell factor 4 complex, which resulted in the downregulation of the Wnt target genes Axin2, Survivin and Leucine­rich G protein­coupled receptor­5. Chloroxylenol effectively inhibited the viability, proliferation, migration and invasion, and sphere formation, and induced apoptosis in HCT116 and SW480 cells. Notably, chloroxylenol attenuated the growth of colorectal cancer in the MC38 cell xenograft model and inhibited organoid formation by the patient­derived cells. Chloroxylenol also demonstrated inhibitory effects on the stemness of colorectal cancer cells. The results of the present study demonstrated that chloroxylenol could exert anti­tumor activities in colorectal cancer by targeting the Wnt/ß­catenin signaling pathway, which provided an insight into its therapeutic potential as an anticancer agent.

Semantic Consistent Embedding for Domain Adaptive Zero-Shot Learning.

Unsupervised domain adaptation has limitations when encountering label discrepancy between the source and target domains. While open-set domain adaptation approaches can address situations when the target domain has additional categories, these methods can only detect them but not further classify them. In this paper, we focus on a more challenging setting dubbed Domain Adaptive Zero-Shot Learning (DAZSL), which uses semantic embeddings of class tags as the bridge between seen and unseen classes to learn the classifier for recognizing all categories in the target domain when only the supervision of seen categories in the source domain is available. The main challenge of DAZSL is to perform knowledge transfer across categories and domain styles simultaneously. To this end, we propose a novel end-to-end learning mechanism dubbed Three-way Semantic Consistent Embedding (TSCE) to embed the source domain, target domain, and semantic space into a shared space. Specifically, TSCE learns domain-irrelevant categorical prototypes from the semantic embedding of class tags and uses them as the pivots of the shared space. The source domain features are aligned with the prototypes via their supervised information. On the other hand, the mutual information maximization mechanism is introduced to push the target domain features and prototypes towards each other. By this way, our approach can align domain differences between source and target images, as well as promote knowledge transfer towards unseen classes. Moreover, as there is no supervision in the target domain, the shared space may suffer from the catastrophic forgetting problem. Hence, we further propose a ranking-based embedding alignment mechanism to maintain the consistency between the semantic space and the shared space. Experimental results on both I2AwA and I2WebV clearly validate the effectiveness of our method. Code is available at https://github.com/tiggers23/TSCE-Domain-Adaptive-Zero-Shot-Learning.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses.

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.

Comparative Study on Nutritional Characteristics and Volatile Flavor Substances of Yak Milk in Different Regions of Gannan.

This study aimed to investigate the nutritional properties of yak milk in various areas of Gannan. The milk composition analyzer, automatic amino acid analyzer, and flavor analyzer were used to detect the conventional nutrients, amino acids, and volatile flavor substances of 249 yak milks in Meiren grassland, Xiahe grassland, and Maqu grassland (hereinafter referred to as Meiren yak, Xiahe yak, and Maqu yak) in the Gannan area. The results showed that the fat content of Meiren yak milk was significantly higher than that of Maqu yak and Xiahe yak (p < 0.05). The protein content of Meiren yak milk was significantly higher than that of Xiahe yak (p < 0.05), but not significantly different from that of Maqu yak (p > 0.05). The casein content in the milk of Maqu yak was significantly higher than that of Meiren yak and Xiahe yak (p < 0.05). There was no significant difference in the lactose content of yak milk in the three regions (p > 0.05). The content of glutamic acid in the milk of Meiren yak, Xiahe yak, and Maqu yak was noticeably high, which was 1.03 g/100 g, 1.07 g/100 g, and 1.10 g/100 g, respectively. The total amino acid (TAA) content was 4.78 g/100 g, 4.87 g/100 g, and 5.0 g/100 g, respectively. The ratios of essential amino acids (EAA) and total amino acids (TAA) in the milk of Meiren yak, Xiahe yak, and Maqu yak were 42.26%, 41.27%, and 41.39%, respectively, and the ratios of essential amino acids (EAA) and nonessential amino acids (NEAA) were 73.19%, 70.28%, and 70.61%, respectively. In the yak milk samples collected from three different regions, a total of 34 volatile flavor compounds were detected, including 10 aldehydes, five esters, six ketones, four alcohols, two acids, and seven others. The main flavor substances qualitatively obtained from Meiren yak milk were ethyl acetate, n-valeraldehyde, acetic acid, heptanal, and n-hexanal. Xiahe yak milk mainly contains ethyl acetate, isoamyl alcohol, n-valeraldehyde, heptanal, and ethyl butyrate. Maqu yak milk mainly contains ethyl acetate, n-valeraldehyde, isoamyl alcohol, heptanal, ethyl butyrate, and n-hexanal. Principal component analysis showed that the flavor difference between Xiahe yak and Maqu yak was small, while the flavor difference between Xiahe yak, Maqu yak, and Meiren yak was large. The findings of this research can serve as a foundation for the future advancement and application of yak milk.

Polymorphisms of CCSER1 Gene and Their Correlation with Milk Quality Traits in Gannan Yak (Bos grunniens).

Coiled-coil serine-rich protein 1 (CCSER 1) gene is a regulatory protein gene. This gene has been reported to be associated with various economic traits in large mammals in recent years. The aim of this study was to investigate the association between CCSER1 gene single nucleotide polymorphisms (SNPs) and Gannan yaks and to identify potential molecular marker loci for breeding milk quality in Gannan yaks. We genotyped 172 Gannan yaks using Illumina Yak cGPS 7K liquid microarrays and analyzed the correlation between the three SNPs loci of the CCSER1 gene and the milk qualities of Gannan yaks, including milk fat, protein and casein. It was found that mutations at the g.183,843A>G, g.222,717C>G and g.388,723G>T loci all affected the fat, protein, casein and lactose traits of Gannan yak milk to varying extents, and that the milk quality of individuals with mutant phenotypes was significantly improved. Among them, the milk fat content of AG heterozygous genotype population at g.183,843A>G locus was significantly higher than that of AA and GG genotype populations (p < 0.05); the casein and protein content of mutant GG and CG genotype populations at g.222,717C>G locus was significantly higher than that of wild-type CC genotype population (p < 0.05); and the g.388,723G>T locus of the casein and protein contents of the mutant TT genotype population were significantly higher (p < 0.05) than those of the wild-type GG genotype population. These results provide potential molecular marker sites for Gannan yak breeding.

Multi-omics revealed the effects of dietary energy levels on the rumen microbiota and metabolites in yaks under house-feeding conditions.

Yak (Bos grunniens) is a unique large ruminant species in the Qinghai-Tibetan Plateau (QTP). Changing the energy levels of their rations can significantly improve their growth performance. Therefore, studying the effects of dietary energy levels on the rumen microflora and metabolites of yak is crucial for enhancing the development of the yak industry. Currently, there is a lack of understanding regarding the impact of feeding energy diets on rumen fermentation parameters, microbial functions, and metabolites. This study was designed to determine the appropriate energy level for feeding yak. Three test diets with metabolizable energy levels of 7.57 MJ/kg, 9.44 MJ/kg, and 11.9 MJ/kg were used and the concentration of volatile fatty acids (VFA) in rumen fluid was measured. The microbial communities, functions, and metabolites in yaks were studied by 16S rRNA sequencing, metagenome, and LC-MS non-targeted metabolomics to investigate the relationships among rumen fermentation parameters, microbial diversity, and metabolites. Ration energy levels significantly affect total VFA, acetate, propionate, butyrate, iso-valerate, valerate, and acetate/propionate (p < 0.05). At the phylum level, the dominant phyla in all three treatment groups were Bacteroidota, Firmicutes, and Actinobacteriota. At the genus level, the abundance of the unclassified_o__Bacteroidales, norank_f_Muribaculaceae, Lachnospiraceae_NK4A136_group, and Family _XIII_AD3011_group showed significant differences (p < 0.05) and were significantly correlated with differential metabolites screened for phosphatidylcholine [PC(16:0/0:0), PC(18:3/0:0)], uridine 3'-monophosphate, and adenosine monophosphate, etc. CAZymes family analysis showed that GHs and CEs differed significantly among the three groups. In addition, differential metabolites were mainly enriched in the pathways of lipid metabolism, nucleotide metabolism, and biosynthesis of other secondary metabolites, and the concentrations of differential metabolites were correlated with microbial abundance. In summary, this study analyzed the effects of ration energy levels on rumen microorganisms and metabolites of yaks and their relationships. The results provided a scientific basis for the selection of dietary energy for yaks in the house feeding period in the future.

A Convergence Path to Deep Learning on Noisy Labels.

In many real-world machine learning classification applications, the model performance based on deep neural networks (DNNs) oftentimes suffers from label noise. Various methods have been proposed in the literature to address this issue, primarily by focusing on designing noise-tolerant loss functions, cleaning label noise, and correcting the objective loss. However, the noise-tolerant loss functions face challenges when the noise level increases. This article aims to reveal a convergence path of a trained model in the presence of label noise, and here, the convergence path depicts the evolution of a trained model over epochs. We first propose a theorem to demonstrate that any surrogate loss function can be used to learn DNNs from noisy labels. Next, theories on the general convergence path for the deep models under label noise are presented and verified through a series of experiments. In addition, we design an algorithm based on the proposed theorems that make efficient corrections on the noisy labels and achieve strong robustness in the DNN models. We designed several experiments using benchmark datasets to assess noise tolerance and verify the theorems presented in this article. The comprehensive experimental results firmly confirm our theoretical results and also clearly validate the effectiveness of our method under various levels of label noise.

Robust Semi-Supervised Traffic Sign Recognition via Self-Training and Weakly-Supervised Learning.

Traffic sign recognition is a classification problem that poses challenges for computer vision and machine learning algorithms. Although both computer vision and machine learning techniques have constantly been improved to solve this problem, the sudden rise in the number of unlabeled traffic signs has become even more challenging. Large data collation and labeling are tedious and expensive tasks that demand much time, expert knowledge, and fiscal resources to satisfy the hunger of deep neural networks. Aside from that, the problem of having unbalanced data also poses a greater challenge to computer vision and machine learning algorithms to achieve better performance. These problems raise the need to develop algorithms that can fully exploit a large amount of unlabeled data, use a small amount of labeled samples, and be robust to data imbalance to build an efficient and high-quality classifier. In this work, we propose a novel semi-supervised classification technique that is robust to small and unbalanced data. The framework integrates weakly-supervised learning and self-training with self-paced learning to generate attention maps to augment the training set and utilizes a novel pseudo-label generation and selection algorithm to generate and select pseudo-labeled samples. The method improves the performance by: (1) normalizing the class-wise confidence levels to prevent the model from ignoring hard-to-learn samples, thereby solving the imbalanced data problem; (2) jointly learning a model and optimizing pseudo-labels generated on unlabeled data; and (3) enlarging the training set to satisfy the hunger of deep learning models. Extensive evaluations on two public traffic sign recognition datasets demonstrate the effectiveness of the proposed technique and provide a potential solution for practical applications.

Breast tumor classification through learning from noisy labeled ultrasound images.

PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, most clinical samples generally do not have biopsy results. Previous works have proposed generating benign/malignant labels according to Breast Imaging, Reporting and Data System (BI-RADS) ratings. However, this approach will cause noisy labels, which means that the benign/malignant labels produced from BI-RADS diagnoses may be inconsistent with the ground truths. Consequently, deep models will overfit the noisy labels and hence obtain poor generalization performance. In this work, we mainly focus on how to reduce the negative effect of noisy labels when they are used to train breast tumor classification models. METHODS: We propose an effective approach called noise filter network (NF-Net) to address the problem of noisy labels when training breast tumor classification models. Specifically, to prevent deep models from overfitting the noisy labels, we propose incorporating two softmax layers for classification. Additionally, to strengthen the effect of clean labels, we design a teacher-student module for distilling the knowledge of clean labels. RESULTS: We conduct extensive comparisons with the existing works on addressing noisy labels. Our method achieves a classification accuracy of 73%, with a precision of 69%, recall of 80%, and F1-score of 0.74. This result is significantly better than those of the existing state-of-the-art works on addressing noisy labels. CONCLUSIONS: This work provides a means to overcome the label shortage problem in training breast tumor classification models. Specifically, we can generate benign/malignant labels according to the BI-RADS ratings. Although this approach will cause noisy labels, the design of NF-Net can effectively reduce the negative effect of such labels.

An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures.

BACKGROUND: Computer-aided diagnosis (CAD) in the medical field has received more and more attention in recent years. One important CAD application is to detect and classify breast lesions in ultrasound images. Traditionally, the process of CAD for breast lesions classification is mainly composed of two separated steps: i) locate the lesion region of interests (ROI); ii) classify the located region of interests (ROI) to see if they are benign or not. However, due to the complex structure of breast and the existence of noise in the ultrasound images, traditional handcrafted feature based methods usually can not achieve satisfactory result. METHODS: With the recent advance of deep learning, the performance of object detection and classification has been boosted to a great extent. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection and classification methods for breast lesions CAD. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. We evaluate different deep learning architectures and conduct comprehensive experiments on our newly collected dataset. RESULTS: For the lesion regions detecting task, Single Shot MultiBox Detector with the input size as 300×300 (SSD300) achieves the best performance in terms of average precision rate (APR), average recall rate (ARR) and F1 score. For the classification task, DenseNet is more suitable for our problems. CONCLUSIONS: Our experiments reveal that better and more efficient detection and convolutional neural network (CNN) frameworks is one important factor for better performance of detecting and classification task of the breast lesion. Another significant factor for improving the performance of detecting and classification task, which is transfer learning from the large-scale annotated ImageNet to classify breast lesion.

Influence of design probe and sequence mismatches on the efficiency of fluorescent RPA.

Recombinase polymerase amplification (RPA) is an isothermal amplification technique. Because of its short detection cycle and high specificity, it has been applied in various fields. However, the design of probe on the efficiency of RPA is not well understood and the effect of sequence mismatches of oligonucleotides on the performance of RPA is rarely discussed. In this study, we found that different primers with the same probe have a slight effect on the efficiency of fluorescent RPA, and different probes with the same amplified region have a great influence on the efficiency of fluorescent RPA. We summarized the design rules of probes suitable for fluorescent RPA by analyzing the experimental data. The rule is that the best distance between fluorescent groups in the probe is 1-2 bases, and the G content should be reduced as far as possible. In addition, we verified this rule by designing a series of probes. Furthermore, we found the base mismatches of the probe had a significant effect on RPA, which can lead to false positives and can change the amplification efficiency. However, 1-3 mismatches covering the center of the primer sequence only affect the amplification efficiency of RPA, not its specificity. And with an increase in the number of primer mismatches, the efficiency of RPA will decrease accordingly. This study suggests that the efficiency of fluorescent RPA is closely related to the probe. We recommend that when designing a fluorescent probe, one must consider the presence of closely related non-targets and specific bases.

Upconversion luminescence nanoparticles-based immunochromatographic assay for quantitative detection of triamcinolone acetonide in cosmetics.

Triamcinolone acetonide (TCA) abuse in cosmetics is a common phenomenon. A rapid lateral flow immunochromatographic assay (ICA) was developed for the quantitative detection of TCA using a probe based on upconversion luminescence nanoparticles. Lanthanide-doped upconversion nanoparticles (UCNPs) were synthesized in a system comprising water and ethylene glycol, and a silicon dioxide layer was covered at the carboxyl site. A binding site protection strategy was used to decrease the background signal of UCNPs-ICA. Using dexamethasone derivative as a coating antigen, the optimal UCNPs-ICA exhibits good dynamic linear detection for TCA in the range 1.0-100 ng mL-1 with a median inhibitory concentration of 9.8 ng mL-1. The detection limits for TCA in a cosmetic sample are 20 µg kg-1. The pretreatment of samples only needs dilution with water, suggesting the assay can quantitate TCA on-site using a portable upconversion luminescence reader with a cumulative analysis time of only 10 min.

Development of propidium monoazide-recombinase polymerase amplification (PMA-RPA) assay for rapid detection of Streptococcus pyogenes and Streptococcus agalactiae.

Streptococcus pyogenes (Group A Streptococcus, GAS) and Streptococcus agalactiae (Group B Streptococcus, GBS) are common pathogens that threaten public health. In this study, a double recombinase polymerase (RPA) amplification assay was developed to rapidly detect these pathogens. Specificity tests revealed that the GAS and GBS strains were positive for speB and SIP genes, respectively. In clinical samples, the double assay performed similarly to the traditional biochemical method. The limits of detection were both ≤100 copies per reaction. In tests for simulant-contaminated samples, bacterial-culture media containing 103 CFU/mL original concentrations of S. pyogenes and S. agalactiae were positive in RPA assays after incubating for 4 h. Results can be obtained at 37 °C in 20 min. To determine whether propidium monoazide (PMA) can eliminate the influence of DNA extracted from dead cells, a bacterial suspension was treated with PMA before DNA extraction. Findings of RPA assay showed that DNA extracted from dead cells had no fluorescence signal. Therefore, the PMA-RPA assay is a promising technology for field tests and rapid point-of-care diagnosis.

[Rapid determination of 15 nutrients in rice by ultra high performance liquid chromatography-linear ion trap/orbitrap high resolution mass spectrometry].

A rapid method was developed for the simultaneous determination of 15 nutrients containing eight vitamin E, six γ-oryzanols and ß-carotene in rice by ultra high performance liquid chromatography-linear ion trap/orbitrap high resolution mass spectrometry (UHPLC-LTQ/Orbitrap HRMS). The analytes were extracted by methanol containing 0.05% (v/v) 2,6-di-tert-butyl-4-methylphenol (BHT) under the ultrasonic condition, then separated by a Poroshell 120 PFP column (150 mm×3.0 mm, 2.7 µm) with a gradient elution program using 0.1% (v/v) formic acid aqueous solution and methanol containing 0.1% (v/v) formic acid as mobile phases. The detection was performed using an LTQ/Orbitrap HRMS detector with full scan in positive ion mode. Fifteen nutrients were simultaneously separated within 13 min. Furthermore, matrix effects of the white and brown rices were investigated. The correlation coefficients (r) were ≥ 0.9950 in the linear ranges of the 15 nutrients. The limits of detection (LODs, S/N=3) and limits of quantification (LOQs, S/N=10) of the 15 nutrients were 0.2-1.8 µg/L and 0.7-6.1 µg/L, respectively. At the three spiked levels, the recoveries were 73.2%-101.5% with the relative standard deviations (RSDs) ranging from 1.1% to 5.0% (n=3). The method is accurate, efficient and reliable. It is suitable for the simultaneous determination of various nutrients in rice.

Determination of Trace Rhodamine B in Chili Oil by Deep Eutectic Solvent Extraction and an Ultra High-Performance Liquid Chromatograph Equipped with a Fluorescence Detector.

Rhodamine B was forbidden in food by law because of its carcinogenic properties to humans. However, due to its low cost, it was often used to dope chili oil by some counterfeiters to improve its natural color. However, it was difficult to quantify rhodamine B in chili oil due to its complex substrates and high viscosity. In this study, deep eutectic solvents, comprised of choline chloride and ethylene glycol, were first used as an extraction medium to separate rhodamine B from chili oil.

Characterization of the Thermal Degradation of Vinegar and the Construction of an Identification Model for Chinese Geographical Indication Vinegars by the Py-GC-MS Technique.

The pyrolysis (Py)-GC-MS technique was first introduced for the identification of two kinds of Chinese geographical indication vinegars because its advantages are that it is a simple and convenient sample pretreatment and inlet method. Abundant Py information about vinegars was obtained using Py-GC-MS; 21 common peaks were selected. With the help of the classical partial least-squares (PLS) modeling method for data analysis, two identification models for Shanxi extra-aged (SX) and Zhenjiang (ZJ) vinegars were established, respectively. An N-reducing method was used to select the variables. The variables were reduced one at a time to build the PLS models with the lowest number of misjudgments. Both models had good recognition rates, identifying over 90% of samples correctly. Thus, combining Py-GC-MS and PLS could be regarded as an effective method for the identification of SX and ZJ vinegars.