High-throughput Monitoring Research Articles

A Monitoring Method for Agricultural Soil Moisture Using Wireless Sensors and the Biswas Model

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R2) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture.

Early Detection of Water Stress in Kauri Seedlings Using Multitemporal Hyperspectral Indices and Inverted Plant Traits

Global climate variability is projected to result in more frequent and severe droughts, which can have adverse effects on New Zealand’s endemic tree species such as the iconic kauri (Agathis australis). Several studies have investigated the physiological response of kauri to medium- and long-term water stress; however, no research has used hyperspectral technology for the early detection and characterization of water stress in this species. In this study, physiological (stomatal conductance (gs), assimilation rate (A), equivalent water thickness (EWT)) and leaf-level hyperspectral measurements were recorded over a ten-week period on 100 potted kauri seedlings subjected to control (well-watered) and drought treatments. In addition, plant functional traits (PTs) were retrieved from spectral reflectance data via inversion of the PROSPECT-D radiative transfer model. These data were used to (i) identify key PTs and narrow-band hyperspectral indices (NBHIs) associated with the expression of water stress and (ii) develop classification models based on single-date and multitemporal datasets for the early detection of water stress. A significant decline in soil water content and physiological responses (gs and A) occurred among the trees in the drought treatment in weeks 2 and 4, respectively. Although no significant treatment differences (p > 0.05) were observed in EWT across the whole duration of the experiment, lower mean values in the drought treatment were apparent from week 4 onwards. In contrast, several spectral bands and NBHIs exhibited significant differences the week after water was withheld. The number and category of significant NBHIs varied up to week 4, after which a substantial increase in the number of significant indices was observed until week 10. However, despite this increase, the single-date models did not show good model performance (F1 score > 0.70) until weeks 9 and 10. In contrast, when multitemporal datasets were used, the classification performance ranged from good to outstanding from weeks 2 to 10. This improvement was largely due to the enhanced temporal and feature representation in the multitemporal models. Among the input NBHIs, water indices emerged as the most important predictors, followed by photochemical indices. Furthermore, a comparison of inverted and measured EWT showed good correspondence (mean absolute percentage error (MAPE) = 8.49%, root mean squared error (RMSE) = 0.0026 g/cm2), highlighting the potential use of radiative transfer modelling for high-throughput drought monitoring. Future research is recommended to scale these measurements to the canopy level, which could prove valuable in detecting and characterizing drought stress at a larger scale.

Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm

Plant genomics have progressed significantly due to advances in information technology, but phenotypic measurement technology has not kept pace, hindering plant breeding. As maize is one of China’s three main grain crops, accurately measuring plant height is crucial for assessing crop growth and productivity. This study addresses the challenges of plant segmentation and inaccurate plant height extraction in maize populations under field conditions. A three-dimensional dense point cloud was reconstructed using the structure from motion–multi-view stereo (SFM-MVS) method, based on multi-view image sequences captured by an unmanned aerial vehicle (UAV). To improve plant segmentation, we propose a column space approximate segmentation algorithm, which combines the column space method with the enclosing box technique. The proposed method achieved a segmentation accuracy exceeding 90% in dense canopy conditions, significantly outperforming traditional algorithms, such as region growing (80%) and Euclidean clustering (75%). Furthermore, the extracted plant heights demonstrated a high correlation with manual measurements, with R2 values ranging from 0.8884 to 0.9989 and RMSE values as low as 0.0148 m. However, the scalability of the method for larger agricultural operations may face challenges due to computational demands when processing large-scale datasets and potential performance variability under different environmental conditions. Addressing these issues through algorithm optimization, parallel processing, and the integration of additional data sources such as multispectral or LiDAR data could enhance its scalability and robustness. The results demonstrate that the method can accurately reflect the heights of maize plants, providing a reliable solution for large-scale, field-based maize phenotyping. The method has potential applications in high-throughput monitoring of crop phenotypes and precision agriculture.

ZIncubascope: Long-term quantitative imaging of multi-cellular assemblies inside an incubator.

Recent advances in bioengineering have made it possible to develop increasingly complex biological systems to recapitulate organ functions as closely as possible in vitro. Monitoring the assembly and growth of multi-cellular aggregates, micro-tissues or organoids and extracting quantitative information is a crucial but challenging task required to decipher the underlying morphogenetic mechanisms. We present here an imaging platform designed to be accommodated inside an incubator which provides high-throughput monitoring of cell assemblies over days and weeks. We exemplify the capabilities of our system by investigating human induced pluripotent stem cells (hiPSCs) enclosed in spherical capsules, hiPSCs in tubular capsules and yeast cells in spherical capsules. Combined with a customized pipeline of image analysis, our solution provides insight into the impact of confinement on the morphogenesis of these self-organized systems.

Real-time microbial growth curve (RMGC) system: an improved microplate reader with a graphical interface for automatic and high-throughput monitoring of microbial growth curves.

The study of microbial growth curves is essential for comprehending microbial behavior and enhancing related processes. Current monitoring methods face limitations, including low automation, inefficient detection, and insufficient throughput. To address these challenges, we developed the Real-Time Microbial Growth Curve (RMGC) system, which offers fully automated and high-throughput monitoring of microbial growth through an Improved Microplate Reader (IMR) with a user-friendly graphical interface. By optimizing and calibrating the optical pathways, we achieve high-precision and consistent absorbance detection using LED light sources, surpassing traditional xenon lamp microplate readers, which lack continuous operation capabilities. We validated the RMGC system by cultivating 96 samples of Escherichia coli (E. coli) at a concentration of 105 CFU mL-1. After approximately 12 hours of continuous monitoring, the system exhibited a relative standard deviation (RSD) of less than 3.25% for optical density (OD) measurements and an RSD of 2.52% for the point of inflection (POI). These results indicate a similar level of precision but a longer monitoring time compared to conventional microplate readers, reflecting the effectiveness of the RMGC system in accurately monitoring microbial growth. The RMGC system showcases its versatility through various applications, such as microorganism gradient cultures, anaerobic microbial cultures, and antimicrobial susceptibility testing (AST). Its capabilities have important implications for multiple industries, including pharmaceuticals for antibiotic development, food safety for microbial contamination testing, and microbiological research.

Direct injection UPLC-MS/MS method for analysing 77 compounds including human biomarkers, illicit drugs, new psychoactive substances and metabolites in wastewater.

Wastewater analysis technology has emerged as a promising tool for monitoring illicit drug consumption. However, the current reliance on the solid-phase extraction (SPE) pre-treatment method presents significant challenges for widespread adoption and high-throughput monitoring, as it consumes a large amount of time and labor as well as requires specialized instruments. This study has developed a direct injection (DI) technique for UPLC-MS/MS, enabling the detection of 77 compounds encompassing metabolites of human biomarkers, illicit drugs, and new psychoactive substances. The DI method underwent rigorous optimization and validation, demonstrating a lower limit of quantitation (LLOQ) ranging from 1 ng L-1 to 100 ng L-1 and a limit of detection (LOD) ranging from 0.5 ng L-1 to 80 ng L-1. The SPE method comprising two common SPE cartridges and the DI method were compared in terms of matrix effects, recoveries, and accuracies through analyzing spiked wastewater samples. The DI method exhibited superior capability in detecting a wider range of compounds while being more time-efficient, and it also significantly demonstrated a better recovery, lower matrix effect, and lower relative error in spiked samples. Real wastewater samples from 25 wastewater treatment plants (WWTPs) were analyzed using this method. This study expanded the targets species of wastewater analysis by DI method and provided practical strategies for conducting large-scale drug monitoring.

A high-throughput method for monitoring growth of lettuce seedlings in greenhouses based on enhanced Mask2Former

Monitoring plant growth is crucial for cultivation management. Agronomists can assess the health status of lettuce seedlings based on monitoring results to implement relevant management measures for improving the quality and yield of lettuce seedlings. This study developed a non-destructive, high-throughput growth monitoring method suitable for large-scale assessment of lettuce seedling quality in nurseries. The method utilizes a plant high-throughput phenotyping platform to acquire 10-day time-series imagery data. An Mask2Former network model enhanced by multidimensional collaborative attention mechanism, combined with sliding window and morphological operations, achieves precise recognition and localization of seedling trays, varieties, and individual seedling plants in a progressive manner. Based on individual seedling localization and segmentation results, the method estimates emergence numbers and rates for each variety, and further achieves instance segmentation and counting of individual seedling leaves, innovatively constructing leaf segmentation results of different varieties across the entire seedling tray. Applied to time-series images, the method automatically monitored seedling emergence changes and growth trends for 1,086 lettuce varieties. In monitoring these varieties, the method achieved a coefficient of determination (R2) of 0.96 for emergence number estimation. The extraction of all six key phenotypic parameters demonstrated exceptionally high correlations: projected area, projected perimeter, convex hull area, and convex hull perimeter all showed R2 above 0.99, while leaf compactness R2 was 0.9698, and leaf count R2 was 0.91. Results demonstrate that this high-throughput, reliable method can effectively monitor the growth status of large-scale lettuce seedlings and provide technical support for lettuce nursery quality assessment.

Maleylpyruvic Acid-Inducible Gene Expression System and Its Application for the Development of Gentisic Acid Biosensors.

Gentisic acid is a secondary plant metabolite, known for its health benefits, not only widely used as a supplement but also implicated as a potential biomarker for cancer-associated metabolism alterations. To advance bioproduction and detection of this compound or its derivatives, cell-based approaches have become of interest in recent years. However, the lack of tools for high-throughput gentisic acid monitoring and compound-metabolizing organism screening limits the progress in this area. Here, we analyzed the gene cluster responsible for gentisic acid metabolism in Cupriavidus necator H16. The transcriptional regulator GtdR-based inducible gene expression system CnGtdR/PgtdA was elucidated, showing that it was activated when C. necator cells were subjected to gentisic acid. Subsequently, a 3-maleylpyruvic acid was identified as a primary inducer for this inducible system. Furthermore, genes gtdA and gtdT, encoding for gentisate 1,2-dioxygenase and MFS transporter, were shown to be essential for inducible system activation in the presence of gentisic acid with GtdA enabling conversion of this phenolic acid into the inducer. The CnGtdRAT/PgtdA-based inducible system was employed to develop a whole-cell biosensor for the intracellular and extracellular detection of gentisic acid. The potential of the 3-maleylpyruvic acid-inducible system was demonstrated by its application in metabolic pathway research, detection of highly unstable 3-maleylpyruvic acid, and development of biosensors for the intracellular or extracellular determination of gentisic acid. In addition, the utility of the biosensor was emphasized by its application for detection of gentisic acid as a potential biomarker for cancer in urine samples.

SAM-ResNet50: A Deep Learning Model for the Identification and Classification of Drought Stress in the Seedling Stage of Betula luminifera

Betula luminifera, an indigenous hardwood tree in South China, possesses significant economic and ecological value. In view of the current severe drought situation, it is urgent to enhance this tree’s drought tolerance. However, traditional artificial methods fall short of meeting the demands of breeding efforts due to their inefficiency. To monitor drought situations in a high-throughput and automatic approach, a deep learning model based on phenotype characteristics was proposed to identify and classify drought stress in B. luminifera seedlings. Firstly, visible-light images were obtained from a drought stress experiment conducted on B. luminifera shoots. Considering the images’ characteristics, we proposed an SAM-CNN architecture by incorporating spatial attention modules into classical CNN models. Among the four classical CNNs compared, ResNet50 exhibited superior performance and was, thus, selected for the construction of the SAM-CNN. Subsequently, we analyzed the classification performance of the SAM-ResNet50 model in terms of transfer learning, training from scratch, model robustness, and visualization. The results revealed that SAM-ResNet50 achieved an accuracy of 1.48% higher than that of ResNet50, at 99.6%. Furthermore, there was a remarkable improvement of 18.98% in accuracy, reaching 82.31% for the spatial transform images generated from the test set images by applying movement and rotation for robustness testing. In conclusion, the SAM-ResNet50 model achieved outstanding performance, with 99.6% accuracy and realized high-throughput automatic monitoring based on phenotype, providing a new perspective for drought stress classification and technical support for B. luminifera-related breeding work.

High-Throughput and High-Sensitivity Biomarker Monitoring in Body Fluid by Fast LC SureQuant IS-Targeted Quantitation

Targeted proteomics methods have been greatly improved and refined over the last decade and are becoming increasingly the method of choice in protein and peptide quantitative assays. Despite the tremendous progress, targeted proteomics assays still suffer from inadequate sensitivity for lower abundant proteins and throughput, especially in complex biological samples. These attributes are essential for establishing targeted proteomics methods at the forefront of clinical use. Here, we report an assay utilizing the SureQuant internal standard-triggered targeted method on a latest generation mass spectrometer coupled with an EvoSep One liquid chromatography platform, which displays high sensitivity and a high throughput of 100 samples per day. We demonstrate the robustness of this method by quantifying proteins spanning six orders of magnitude in human wound fluid exudates, a biological fluid that exhibits sample complexity and composition similar to plasma. Among the targets quantified were low-abundance proteins such at tumor necrosis factor A and interleukin 1-β, highlighting the value of this method in the quantification of trace amounts of invaluable biomarkers that were until recently hardly accessible by targeted proteomics methods. Taken together, this method extends the toolkit of targeted proteomics assays and will help to drive forward mass spectrometry-based proteomics biomarker quantification.

Self-powered flexible sensor network for continuous monitoring of crop micro-environment and growth states

Monitoring crops’ micro-environment and growth states is significant for developing smart agriculture. However, the traditional rigid and large-sized sensor systems cannot meet the demands of precision agriculture for multi-parameter and high-throughput monitoring. Here, we integrate a flexible sensor system with low-power communication techniques, designing a two-layer star topology sensor network based on BLE and NB-IoT protocol. The sensors are arranged on flexible substrates, allowing them to closely adhere to various parts of crops, such as stems and leaves. To ensure the accuracy of the sensors, they are evaluated and calibrated in the laboratory. A solar energy harvesting system is designed for the sensor network to make it self-powered and successfully measure temperature, humidity, illumination, and leaf angles stably, demonstrating the system’s feasibility. The deployment cost of the sensor network is significantly lower than that of traditional automatic weather stations, showcasing its potential for widespread application in farmlands.

Dual-Purpose Aptamer-Based Sensors for Real-Time, Multiplexable Monitoring of Metabolites in Cell Culture Media.

The availability of high-frequency, real-time measurements of the concentrations of specific metabolites in cell culture systems will enable a deeper understanding of cellular metabolism and facilitate the application of good laboratory practice standards in cell culture protocols. However, currently available approaches to this end either are constrained to single-time-point and single-parameter measurements or are limited in the range of detectable analytes. Electrochemical aptamer-based (EAB) biosensors have demonstrated utility in real-time monitoring of analytes in vivo in blood and tissues. Here, we characterize a pH-sensing capability of EAB sensors that is independent of the specific target analyte of the aptamer sequence. We applied this dual-purpose EAB to the continuous measurement of pH and phenylalanine in several in vitro cell culture settings. The miniature EAB sensor that we developed exhibits rapid response times, good stability, high repeatability, and biologically relevant sensitivity. We also developed and characterized a leak-free reference electrode that mitigates the potential cytotoxic effects of silver ions released from conventional reference electrodes. Using the resulting dual-purpose sensor, we performed hourly measurements of pH and phenylalanine concentrations in the medium superfusing cultured epithelial tumor cell lines (A549, MDA-MB-23) and a human fibroblast cell line (MRC-5) for periods of up to 72 h. Our scalable technology may be multiplexed for high-throughput monitoring of pH and multiple analytes in support of the broad metabolic qualification of microphysiological systems.

Pipette-tip solid-phase extraction coupled with matrix-assisted laser desorption/ionization mass spectrometry enables rapid and high-throughput analysis of antidepressants in rat serum.

Therapeutic drug monitoring is essential for ensuring the efficacy and safety of medications. This study introduces a streamlined approach that combines pipette-tip solid-phase extraction (PT-SPE) with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), facilitating rapid and high-throughput monitoring of drug concentrations. As a demonstration, this method was applied to the extraction and quantification of antidepressants in serum. Utilizing Zip-Tip C18, the method enabled the extraction of antidepressants from complex biological matrices in less than 2min, with the subsequent MALDI-MS analysis yielding results in just 1min. Optimal extraction recoveries were achieved using a sampling solution at pH 9.0 and a 10 μL ethanol desorption solution containing 0.1% phosphoric acid. For MALDI analysis, 2,5-dihydroxybenzoic acid was identified as the most effective matrix for producing the highest signal intensity. The quantification strategy exhibited robust linearities (R2 ≥ 0.997) and satisfactory limits of quantification, ranging from 0.05 to 0.5 μg/mL for a suite of antidepressants. The application for monitoring dynamic concentration changes of antidepressants in rat serum emphasized the method's efficacy. This strategy offers the advantages of high throughput, minimal sample usage, environmental sustainability, and simplicity, providing ideas and a reference basis for the subsequent development of methods for therapeutic drug monitoring.

IMSIS: An instrumented microphysiological system with integrated sensors for monitoring cellular metabolic activities

Well plates are widely used in biological experiments, particularly in pharmaceutical sciences and cell biology. Its popularity stems from its versatility to support a variety of fluorescent markers for high throughput monitoring of cellular activities. However, using fluorescent markers in traditional well plates has its own challenges, namely, they can be potentially toxic to cells, and thus, may perturb their biological functions; and it is difficult to monitor multiple analytes concurrently and in real-time inside each well. This paper presents a fully instrumented microphysiological system with integrated sensors (IMSIS) with a similar well format. Each well in the microphysiological system has a set of sensors for monitoring multiple metabolic analytes in real-time. The IMSIS platform is supported by integrated bioelectronic circuits and a graphical user interface for easy user configuration and monitoring. The system has integrated microfluidics to maintain its microphysiological environment within each well. The IMSIS platform currently incorporates O2, H2O2, and pH sensors inside each well, allowing up to six wells to perform concurrent measurements in real-time. Furthermore, the architecture is scalable to achieve an even higher level of throughput. The miniaturized design ensures portability, suitable for small offices and field applications.The IMSIS platform was successfully used to monitor in real-time the mitochondrial functions of live bovine embryos in O2 consumption, H2O2 release as an indication of ROS production, and extracellular acidity changes before and after the introduction of external substrates.

A rapid method to monitor structural perturbations of high-concentrated therapeutic antibody solutions using Intrinsic Tryptophan Fluorescence Emission spectroscopy

Drug product development of therapeutic antibody formulations is still dictated by the risk of protein particle formation during processing or storage, which can lead to loss of potency and potential immunogenic reactions. Since structural perturbations are the main driver for irreversible protein aggregation, the conformational integrity of antibodies should be closely monitored. The present study evaluated the applicability of a plate reader-based high throughput method for Intrinsic Tryptophan Fluorescence Emission (ITFE) spectroscopy to detect protein aggregation due to protein unfolding in high-concentrated therapeutic antibody samples. The impact of fluorophore concentration on the ITFE signal in microplate readers was investigated by analysis of dilution series of two therapeutic antibodies and pure tryptophan. At low antibody concentrations (< 5 mg/mL, equivalent to 0.8 mM tryptophan), the low inner filter effect suggests a quasi-linear relationship between antibody concentration and ITFE intensity. In contrast, the constant ITFE intensity at high protein concentrations (> 40 mg/mL, equivalent to 6.1 mM tryptophan) indicate that ITFE spectroscopy measurements of IgG1 antibodies are feasible in therapeutically relevant concentrations (up to 223 mg/mL). Furthermore, the capability of the method to detect low levels of unfolding (around 1 %) was confirmed by limit of detection (LOD) determination with temperature-stressed antibody samples as degradation standards. Change of fluorescence intensity at the maximum (ΔIaM) was identified as sensitive descriptor for protein degradation, providing the lowest LOD values. The results demonstrate that ITFE spectroscopy performed in a microplate reader is a valuable tool for high-throughput monitoring of protein degradation in therapeutic antibody formulations.

Research on predicting photosynthetic pigments in tomato seedling leaves based on near-infrared hyperspectral imaging and machine learning

Conventional chemical approaches could be limited in monitoring the concentration of pigments in plants in high volumes. To overcome these limitations, researchers often turn to non-invasive, high-throughput, and real-time monitoring techniques, such as spectroscopy and hyperspectral imaging, which allow for the assessment of pigment concentration in plants without the need for destructive sampling and offer the ability to monitor large volumes of plants efficiently. This research focused on the utilization of machine learning in conjunction with hyperspectral imaging to develop models for predicting the concentration of three pigments, namely chlorophyll-a, chlorophyll-b, and carotenoids, in tomato seedlings. The sample tomato seedlings were sourced from two distinct varieties: the wild type and the Long Hypocotyl-5-deficient (HY5) type. The spectral data were acquired using a near-infrared (NIR) camera with a spectral range spanning approximately 900–1700 nm. Machine learning algorithms such as partial least squares regression (PLSR) and extreme learning machine (ELM) were utilized to explore the latent relationship between hyperspectral information and chemical measurements. In addition, principal component analysis (PCA), independent component analysis (ICA), and competitive adaptive reweighted sampling (CARS) methods were used to extracted informative wavelengths from the reflectance spectrum. And a comprehensive analysis regarding to spectroscopy was conducted to investigate the validity and efficiency of the results of feature extraction. The ELM model demonstrated the highest effectiveness, achieving R2 values of 0.86, 0.83, and 0.83 for chlorophyll-a, chlorophyll-b, and carotenoids, respectively, on the test set. By integrating the predictive models with classifiers such as Logistic Regression, Support Vector Classifier (SVC), and K-nearest Neighbors (KNN), tomato seedlings were categorized into wild type and HY5 type. The findings showed that the proposed approach efficiently predicted the concentration of the pigments in tomato seedlings and explored the feasibility of using these results to identify tomato gene types.

Crop aboveground biomass monitoring model based on UAV spectral index reconstruction and Bayesian model averaging: A case study of film-mulched wheat and maize

Aboveground biomass (AGB) directly reflects crop carbon fixation capacity. Utilizing unmanned aerial vehicle (UAV) remote sensing for high-throughput AGB monitoring offers benefits to agricultural management and food production. This study aimed to develop a method for monitoring AGB in film-mulched crops using UAV multispectral data. A three-year field experiment was conducted using wheat (Exps. 1 and 2 in 2021–2022, and Exps. 5 and 6 in 2022–2023) and maize (Exps. 3 and 4) with varying cropping patterns. Spectral indices (SIs) of different feature reflectances (e.g., vegetation, soil, and mulch pixels) were reconstructed using least absolute shrinkage and selection operator methods. The Bayesian model averaging (BMA) method integrated seven independent machine learning algorithms to construct a Bayesian ensemble model (BEM). The results demonstrated that spectral index reconstruction (SIR) enhanced the correlation between SIs and AGB, while BMA reduced model uncertainty and improved AGB prediction accuracy. The BEM model, based on Exps. 1 and 2 datasets, achieved determination coefficients of 0.78–0.84 and root mean square error (RMSE) of 0.81–0.89 t ha−1. Model validation across years and crops demonstrated the generalization of the SIR + BEM method for AGB estimation. The RMSEs of the model inter-annual transport predictions were 0.83 and 0.93 t ha−1 in Exps. 5 and 6, respectively, and RMSE of the predictions between crops was 1.43 t ha−1 for the maize dataset (Exps. 3 + 4). The approach proposed in this study has great potential for monitoring growth status of film-mulched crops at the farm scale and providing guidance for precision agriculture management.

3D spheroid HepaRG and fluorescent biphasic tracer for CYP3A4-mediated antibiotic interaction monitoring in sepsis.

Cytochrome P450 3A4 (CYP3A4) is a crucial enzyme in the metabolism of xenobiotics, particularly in drug metabolism interactions (DDIs), making it a significant factor in clinical drug use. However, current assay techniques are both laborious and costly, making it difficult to construct a high-throughput monitoring method that can be used in conjunction with the clinic. This poses certain safety hazards for drug combination. Therefore, it is crucial to develop a synchronized monitoring method for the inhibition and induction of CYP3A4. In this study, we utilized 3D culture technology to develop a HepaRG cells spheroid model. The CYP450 and transporter expression, the albumin secretion, and urea synthesis capacity characteristics were analyzed. The NEN probe was utilized as a tracer molecule for CYP3A4. The fluorescence intensity of metabolites was characterized by laser confocal technique to determine the inhibition and expression of CYP3A4 in the HepaRG cell spheroid model by the antibiotics for sepsis. The results indicate that the HepaRG sphere model successfully possessed the physiological phenotype of the liver, which could be used for drug interaction monitoring. Through positive drug testing, NEN probe was able to achieve bidirectional characterization of CYP3A4 induction and inhibition. The monitoring method described in this paper was successfully applied to drug interaction monitoring of commonly used antibiotics in sepsis patients, which is a convenient and rapid monitoring method. The proposed method offers a new strategy for monitoring CYP3A4-mediated drug-drug interactions with a high-throughput assay, which will help to improve the safety of clinical drug combination.

Improved analysis of 230 pesticide residues in three fermented soy products by using automated one-step accelerated solvent extraction coupled with GC–MS/MS

Consumer concerns over healthy diets are increasing as a result of the toxicity and persistence of pesticide residues in foodstuffs. Developing sensitive and high-throughput monitoring techniques for these trace residues is seen as an essential step in ensuring food safety. An automatic and sensitive multi-residue analytical method was developed and validated for the simultaneous determination of 230 compounds, including pesticides and their hazardous metabolites, in fermented soy products. The method included preparing the sample using on-line extraction and clean-up system based on accelerated solvent extraction (ASE), then determining the analytes using GC–MS/MS techniques. The homogenized samples (soy sauce, douchi, and sufu) were automatically extracted at 80 °C and 10.3 MPa and at the same time, in situ cleaned by 300 mg of primary secondary amine (PSA) combined with 20 mg of hydroxylated multi-walled carbon nanotubes in an extraction cell. The method obtained excellent calibration linearity (r > 0.9220) and a satisfactory analysis of the targeted compounds, which were evaluated with matrix-matched calibration standards over the range of 5–500 μg L−1. The limit of detections (LODs) of analytes were in the range of 0.01–1.29 μg kg−1, 0.01–1.39 μg kg−1, and 0.01–1.34 μg kg−1 in soy sauce, douchi, and sufu, respectively. The limit of quantifications (LOQs), which defined as the lowest spiking level, were set at 5.0 μg kg−1. The recoveries were within 70–120 % for over 95 % of the analytes, and the relative standard deviations (RSDs) were below 13.6 %. Moreover, a positive detection rate of 47 % were obtained when the proposed method was used on 15 real fermented soy products. These results suggested that the developed high-throughput method is highly feasible for monitoring of these target analytes in trace level.

Oxidized TMB mediated dual-optical sensing-based paper analytical device for high-throughput visual monitoring of 2,4-dichlorophenoxyacetic acid

High-throughput and accurate monitoring of pesticide residues is extremely crucial, but remains challenging. Herein, oxidized 3,3′,5,5′-tetramethylbenzidine (oxTMB) mediated dual-optical sensing-based paper analytical device (PAD) was unveiled for on-site visual detection of 2,4-dichlorophenoxyacetic acid (2,4-D) in real samples. Colorimetric blue oxTMB (652 nm) can be reduced to colorless TMB by ascorbic acid (AA). Meanwhile, oxTMB was demonstrated for the first time to switch oxidation and condensation reactions between o-phenylenediamine and AA to produce yellow-emission (564 nm) and blue-emission (429 nm) probes. Thus, colorimetric and ratiometric fluorescent dual-mode detection system was developed for 2,4-D detection by combining with alkaline phosphatase (ALP) initiating AA generation (ascorbic acid-2-phosphate as the substrate). Notably, the developed method can correct itself to improve the accuracy due to the dual-response signals. Using the principle, ALP-loaded PAD (array 3 × 10) was designed for efficient and high-performance detection of 2,4-D in turnip and cauliflower with good sensitivity (0.09 μg/mL for colorimetry, 0.06 μg/mL for fluorometry), satisfactory recovery (98.0 %–107.2 %), and high consistency (relative error ≤ 3.1 %). This work demonstrated a promising strategy for high-throughput visual detection of 2,4-D, which may provide a new pathway for the further development of dual-mode sensing platforms to efficiently detect other targets for food safety.