Rapid determination of nosiheptide in feed based on dispersive SPE coupled with HPLC.

A novel, simple, and reliable method based on high-performance liquid chromatography coupled with fluorescence detection has been developed for the determination of nosiheptide in feed. The feed samples were extracted with acetonitrile 0.1% formic acid aqueous solution and then purified via a dispersive solid-phase extraction procedure using silica gel powder as the sorbent. Using a mixture of acetonitrile and 5 mM ammonium acetate solution (containing 0.1% formic acid) as the mobile phase, good separation and peak shape were obtained for nosiheptide on a Poroshell C8 column (250 × 4.6 mm id, 4 µm) via the isocratic elution program. The resulting calibration curve shows high levels of linearity (r2  > 0.999) for nosiheptide concentrations of 50-1000 µg/L. At three spiked levels, i.e., 0.500, 2.50 and 5.00 mg/kg, the intra- and interday recoveries of nosiheptide in five types of feed ranged from 78.5-96.8 and 84.9-94.2%, respectively. The intra- and interday relative standard deviations were less than 10.8%. The limits of quantification for nosiheptide in complete feed and premixes were measured as 50 and 100 µg/kg, respectively. Compared with other common adsorbents, silica gel presents stronger recovery and purification results for feed samples during the dispersive solid-phase extraction process.

Freeze-thaw approach: A practical sample preparation strategy for residue analysis of multi-class veterinary drugs in chicken muscle.

Seven drugs from different classes, namely, fluoroquinolones (enrofloxacin, ciprofloxacin, sarafloxacin), sulfonamides (sulfadimidine, sulfamonomethoxine), and macrolides (tilmicosin, tylosin), were used as test compounds in chickens by oral administration, a simple extraction step after cryogenic freezing might allow the effective extraction of multi-class veterinary drug residues from minced chicken muscles by mix vortexing. On basis of the optimized freeze-thaw approach, a convenient, selective, and reproducible liquid chromatography with tandem mass spectrometry method was developed. At three spiking levels in blank chicken and medicated chicken muscles, average recoveries of the analytes were in the range of 71-106 and 63-119%, respectively. All the relative standard deviations were <20%. The limits of quantification of analytes were 0.2-5.0 ng/g. Regardless of the chicken levels, there were no significant differences (P > 0.05) in the average contents of almost any of the analytes in medicated chickens between this method and specific methods in the literature for the determination of specific analytes. Finally, the developed method was successfully extended to the monitoring of residues of 55 common veterinary drugs in food animal muscles.

Determination of macrolide antibiotics residues in pork using molecularly imprinted dispersive solid-phase extraction coupled with LC-MS/MS.

A class-specific macrolide molecularly imprinted polymer was synthesized by precipitation polymerization using tulathromycin as the template and methacrylic acid as the functional monomer. The polymers revealed different specific adsorption and imprinting factor for macrolides with different spatial arrangement of side chains as well as lactonic ring size. And the molecularly imprinted polymer possessed maximum adsorption capacity (54.1 mg/g) and highest imprinting factor (2.4) toward 15-membered ring azithromycin. On the basis of molecularly imprinted polymer dispersive solid-phase extraction, a rapid, selective, and reproducible method for simultaneous determination of seven macrolide antibiotics residues in pork was established by using liquid chromatography with tandem mass spectrometry. At spiking levels of 5, 10, 25, and 100 µg/kg, average recoveries of seven macrolides ranged from 68.6 to 95.5% with intraday and interday relative standard deviations below 8%. The limits of detection and limits of quantification were 0.2-0.5 and 0.5-2.0 µg/kg, respectively.

Quick Multi-Class Determination of Residues of Antimicrobial Veterinary Drugs in Animal Muscle by LC-MS/MS.

On the basis of the highly sensitive and selective liquid chromatography-tandem mass spectrometry technique, a generic extraction solvent and a sample dilution method was developed for the residue analysis of different polar veterinary drugs known as fluoroquinolones, sulfonamides, macrolides, and tiamulin in chicken muscle. The results showed that the matrix-matched calibration curves of all 10 compounds were in an effective linear relationship (r² ≥ 0.997) in the range of 0.2⁻100 µg L-1. At three spiking levels of 2 (5), 50, and 100 µg kg-1, average recoveries of analytes were between 67.1% and 96.6% with relative standard deviations of intra-day and inter-day below 20%. The limits of detection and limits of quantification of the method were in the range of 0.3⁻2.0 µg kg-1 and 2.0⁻5.0 µg kg-1, respectively, which were significantly lower than their maximum residue limits. In addition, the intensity of the target analytes and its corresponding matrix effects were obviously related to the sample dilution times (matrix concentration). There were no significant differences (p > 0.05) in the average content of almost any of the analytes in medicated chickens between this method and the method in the literature for determining analytes. Lastly, the proposed method was successfully applied for the simultaneous analysis of 10 common veterinary drugs in food animal muscle tissues.

Determination of Ten Macrolide Drugs in Environmental Water Using Molecularly Imprinted Solid-Phase Extraction Coupled with Liquid Chromatography-Tandem Mass Spectrometry.

With the extensive application of antibiotics in livestock, their contamination of the aquatic environment has received more attention. Molecularly imprinted polymer (MIP), as an eco-friendly and durable solid-phase extraction material, has shown great potential for the separation and enrichment of antibiotics in water. This study aims at developing a practical and economical method based on molecularly imprinted solid phase extraction (MISPE) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) for simultaneously detecting ten macrolide drugs in different sources of water samples. The MIP was synthesized by bulk polymerization using tylosin as the template and methacrylic acid as the functional monomer. The MIP exhibited a favorable load-bearing capacity for water (>90 mL), which is more than triple that of non-molecularly imprinted polymers (NIP). The mean recoveries of macrolides at four spiked concentration levels (limit of quantification, 40, 100, and 400 ng/L) were 62.6⁻100.9%, with intra-day and inter-day relative standard deviations below 12.6%. The limit of detection and limit of quantification were 1.0⁻15.0 ng/L and 3.0⁻40.0 ng/L, respectively. Finally, the proposed method was successfully applied to the analysis of real water samples.

Aggressive breast cancer in western Kenya has early onset, high proliferation, and immune cell infiltration.

BACKGROUND: Breast cancer incidence and mortality vary significantly among different nations and racial groups. African nations have the highest breast cancer mortality rates in the world, even though the incidence rates are below those of many nations. Differences in disease progression suggest that aggressive breast tumors may harbor a unique molecular signature to promote disease progression. However, few studies have investigated the pathology and clinical markers expressed in breast tissue from regional African patient populations. METHODS: We collected 68 malignant and 89 non-cancerous samples from Kenyan breast tissue. To characterize the tumors from these patients, we constructed tissue microarrays (TMAs) from these tissues. Sections from these TMAs were stained and analyzed using immunohistochemistry to detect clinical breast cancer markers, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 receptor (HER2) status, Ki67, and immune cell markers. RESULTS: Thirty-three percent of the tumors were triple negative (ER-, PR-, HER2-), 59% were ER+, and almost all tumors analyzed were HER2-. Seven percent of the breast cancer patients were male, and 30% were <40 years old at diagnosis. Cancer tissue had increased immune cell infiltration with recruitment of CD163+ (M2 macrophage), CD25+ (regulatory T lymphocyte), and CD4+ (T helper) cells compared to non-cancer tissue. CONCLUSIONS: We identified clinical biomarkers that may assist in identifying therapy strategies for breast cancer patients in western Kenya. Estrogen receptor status in particular should lead initial treatment strategies in these breast cancer patients. Increased CD25 expression suggests a need for additional treatment strategies designed to overcome immune suppression by CD25+ cells in order to promote the antitumor activity of CD8+ cytotoxic T cells.

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.

MI-DABAN: A dual-attention-based adversarial network for motor imagery classification.

The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) is widely used because of its convenience and safety. However, due to the distributional disparity between EEG signals, data from other subjects cannot be used directly to train a subject-specific classifier. For efficient use of the labeled data, domain transfer learning and adversarial learning are gradually applied to BCI classification tasks. While these methods improve classification performance, they only align globally and ignore task-specific class boundaries, which may lead to the blurring of features near the classification boundaries. Simultaneously, they employ fully shared generators to extract features, resulting in the loss of domain-specific information and the destruction of performance. To address these issues, we propose a novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN). Our framework leverages multiple subjects' knowledge to improve a single subject's motor imagery classification performance by cleverly using a novel adversarial learning method and two unshared attention blocks. Specifically, without introducing additional domain discriminators, we iteratively maximize and minimize the output difference between the two classifiers to implement adversarial learning to ensure accurate domain alignment. Among them, maximization is used to identify easily confused samples near the decision boundary, and minimization is used to align the source and target domain distributions. Moreover, for the shallow features from source and target domains, we use two non-shared attention blocks to preserve domain-specific information, which can prevent the negative transfer of domain information and further improve the classification performance on test data. We conduct extensive experiments on two publicly available EEG datasets, namely BCI Competition IV Datasets 2a and 2b. The experiment results demonstrate our method's effectiveness and superiority.

MI-CAT: A transformer-based domain adaptation network for motor imagery classification.

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain-computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer's self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain-computer interfaces (BCIs).

MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals.

Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain-computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under https://github.com/zhangdx21/MI-DAGSC.

Rapid multiresidue analysis of authorized/banned cyclopolypeptide antibiotics in feed by liquid chromatography-tandem mass spectrometry based on dispersive solid-phase extraction.

An effective analytical strategy was developed for simultaneous determination of seven cyclopolypeptide antibiotics (vancomycin, polymyxin B, polymyxin E, teicoplanin A2, bacitracin A, daptomycin and virginiamycin M1) in feed using liquid chromatography-tandem mass spectrometry. The extraction of sample was based on acidified methanol-aqueous solution, followed by a simply dispersive solid-phase extraction for further purification with primary secondary amine as an adsorbent. The method showed good linearity for target analytes in the experimental concentration ranges. At three spiked concentration levels of 50, 100 and 200 µg/kg, the average recoveries for target compounds were in the range of 63.1%-107.5% with the relative standard deviations less than 14.7%. Matrix effects in different feeds were evaluated and the strong signal suppression (>50%) was observed. Compared with the corresponding methods in literature, the developed method is more economical, more sensitive and involves the detection of authorized/banned cyclopolypeptides in animal feed. The proposed method was successfully applied to the routine supervision and rapid screening of cyclopolypeptide antibiotics in complete and premix feed.

Analysis of Nosiheptide in Food Animal Tissues via Its Unique Degradation Product by Liquid Chromatography-Tandem Mass Spectrometry after Alkaline Hydrolysis.

Very weak signals of fragment ions of nosiheptide could be observed using liquid chromatography-tandem mass spectrometry. The preparation of 4-hydroxymethyl-3-methyl-1H-indole-2-carboxylic acid (HMIA), a specific fragment of nosiheptide, by alkaline hydrolysis is described. HMIA showed a good mass spectrometric signal in negative electrospray ionization mode. In the new method, the nosiheptide residue in muscle tissue was hydrolyzed with sodium hydroxide aqueous solution; this was followed by cleanup using mixed mode cartridges. Identification and quantification of nosiheptide were carried out by analyzing HMIA in hydrolysate of muscles. Nosiheptide showed a good linear relationship (r > 0.996) in the calibration range of 2-500 µg/kg, and a low limit of quantification of 2 µg/kg was obtained in swine, chicken, and fish muscles. Recoveries of nosiheptide from spiked muscle samples were 85-108% with relative standard deviations less than 10%. The proposed method was successfully applied for the detection of the nosiheptide residue in medicated animal tissues samples.

Selective Depletion of Chiral 4-Hydroxypraziquantel Metabolites in Three Types of Aquaculture Fish by LC-MS/MS.

The rise of aquaculture has necessitated the use of antibiotics and other agents in these densely populated and often closed environments. An enantioselective depletion study of four chiral 4-hydroxypraziquantel metabolites (4-OH-PZQ) in perch, tilapia, and ricefield-eel muscles was done using a simple, sensitive, and reliable liquid-chromatography-tandem-mass-spectrometry (LC-MS/MS) method. These metabolites result from the uptake of the drug praziquantel (PZQ), which is used in aquaculture. A novel strategy of using a C18 short column in tandem with a chiral hydroxypropyl-ß-cyclodextrin superficially porous particle (CDShell-RSP) column produced the optimal separation for both the enantiomers and diastereoisomers of 4-OH-PZQ. The method was linear over the concentration range of 1-250 µg L-1 ( r2 ≥ 0.99) for R- trans-4-OH-PZQ, S- trans-4-OH-PZQ, R- cis-4-OH-PZQ, and S- cis-4-OH-PZQ. The average recoveries of four analytes at three spiked levels of 1, 10, and 100 µg kg-1 ranged from 84.2 to 93.1%, and the intraday and interday relative standard deviations were less than 7.9%. The limits of quantification of the four 4-OH-PZQ metabolites in perch-, tilapia-, and ricefield-eel-muscle matrices were 1.0 µg kg-1. The method was utilized to monitor the depletion of trans- and cis-4-OH-PZQ enantiomers in perch, tilapia, and ricefield-eel muscle following oral administration (medicine bath for ricefield eel). Species-specific differences in the PZQ metabolism of isomers were observed. In addition, new metabolites of PZQ were observed: 3-hydroxypraziquantel diastereomers.

Simultaneous determination of eight cyclopolypeptide antibiotics in feed by high performance liquid chromatography coupled with evaporation light scattering detection.

A high throughput, reliable and reproducible analysis strategy based on high performance liquid chromatography combined to evaporative light scattering detector (HPLC-ELSD) was developed for simultaneous determination of eight cyclopolypeptide antibiotics including vancomycin, polymyxin B (polymyxin B1 and polymyxin B2), polymyxin E (colistin A and colistin B), teicoplanin, bacitracin A, daptomycin and virginiamycin M1 in animal Feed. Feed samples were extracted with methanol-2% formic acid aqueous solution, followed by a solid-phase extraction step using a HLB cartridge. Under the optimum chromatographic conditions and ELSD parameters, target compounds were separated well on a short column filled with biphenyl stationary phase. The method was developed in accordance with pig complete feed and then extended to detect polypeptide antibiotics in piglet premix, pig feed additive, poultry complete feed and fattening pig premix. The results showed that logarithmic calibration curves of eight analytes were linear (r2 > 0.99) within the concentration range of 5-200 mg mL-1. The developed method provided good accuracy and precision for quantification of eight polypeptides in five kinds of feeds with recoveries ranging from 72.0% to 105.4% with relative standard deviations <9.5%. The limits of detection ranged from 2 to 5 mg kg-1. Finally, the method was successfully applied to analyze polypeptide antibiotics in commercial feed.