Desired Precision Research Articles

Switchable deep eutectic solvent as green and efficient media for liquid-phase microextraction of phenoxyacetic acid herbicides in water and food matrices

In this work, a switchable deep eutectic solvent (SDES) based on fatty acid and polyetheramine ion pair was prepared for liquid-phase microextraction (LPME) of phenoxyacetic acid herbicides in drinking water, beverage and honey matrices. The as-synthesized SDES equipped with an interesting characteristic of fast and reversible polarity switching, achieving homogeneous extraction and rapid bi-phase separation simultaneously. Several key parameters affecting the extraction performance were investigated comprehensively by Box-Behnken design. Under the optimal conditions, the method exhibited excellent linearity (15–4000 μg L−1), low detection limits (3–5 μg L−1), desirable precision (RSD < 8.1 %), and satisfactory recovery (72.6–98.7 %). More importantly, the introduction of SDES can simplify the pre-treatment procedure, shorten extraction time (4 min), and avoid the usage of traditional organic solvent during the whole extraction process. In addition, the switching mechanism of SDES was characterized by FT-IR and 1H NMR, and the forming mechanism of SDES was investigated using density-functional theory. The green of the method was estimated using the analytical ecological scale, the analytical green calculator, and the green analytical procedure index. The cytotoxicity of SDES was investigated and the result displayed that toxicity of the SDES was very low with the EC50 > 500 mg/L. Therefore, the proposed method was green and efficient and revealed considerable application prospects for the extraction of trace analytes from complex materials.

Calibration assessment of the PSI proton therapy Gantry 2 scanning system after 10 years of operation

Sub-millimetre precision is a crucial criterion for beam delivery in Pencil Beam Scanning (PBS) proton therapy. Nowadays, most of the therapy systems use PBS technique where single beams with a regulated number of protons are delivered sequentially to different locations within the target. Beam energy defines the depth of the beam in the target and scanner magnets deflect the beam to the desired lateral position.The PSI Gantry 2 was among the first gantries to use the PBS technique. Available clinical proton beam energies ranging from 70 MeV to 230 MeV are achieved by de-grading a 250 MeV beam, initially provided by a super-conducting cyclotron. Beam scanning is performed just before the last bending magnet producing a nearly parallel beam at iso-centre with a well-focused beam over the full scan area of 20×12 cm2. We developed a calibration algorithm for the scanner magnets in order to achieve the desirable precision of the beam in the target. Measuring the beam position with a strip ion chamber in the gantry nozzle, we parametrized the propagation of the beam to the target calculating the beam angle at different scan positions as well as all gantry angles. During 10 years of operation we performed regular Quality Assurance (QA) tests to validate parameters obtained during the system commissioning. The QA data proved a stable system operation and no parametrization update was required so far. In this contribution, we describe the calibration pro-cess of scanning magnets with respect to beam position at the isocentre and show the stability of this implementation over the time.

Trainable Spiking-YOLO for low-latency and high-performance object detection

Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving good detection accuracy and high detection speed. To overcome the aforementioned challenges, we propose an end-to-end Trainable Spiking-YOLO (Tr-Spiking-YOLO) for low-latency and high-performance object detection. We evaluate our model on not only frame-based PASCAL VOC dataset but also event-based GEN1 Automotive Detection dataset, and investigate the impacts of different decoding methods on detection performance. The experimental results show that our model achieves competitive/better performance in terms of accuracy, latency and energy consumption compared to similar artificial neural network (ANN) and conversion-based SNN object detection model. Furthermore, when deployed on an edge device, our model achieves a processing speed of approximately from 14 to 39 FPS while maintaining a desirable mean Average Precision (mAP), which is capable of real-time detection on resource-constrained platforms.

OP63 Incorporating Machine Learning Methods In Health Economic Evaluations: A Case Study On Depression Prevention

IntroductionNew methodologies such as machine learning are becoming widely available and are increasingly used. However, more guidance on their role in the context of economic evaluations would be beneficial.MethodsWe developed a machine learning model to predict recurrent depressive episodes and incorporated the model outcomes in a health economic model to assess the cost effectiveness of offering targeted prevention of recurrent depression. We considered the impact on cost effectiveness (defined as cost per quality-adjusted life-year) for machine learning models with different thresholds for classifying a patient as being at risk, resulting in different precision-recall pairs.ResultsTargeted prevention of recurrent depression could enhance the cost effectiveness of depression treatment by preventing a small number of recurrent depressive episodes in patients where the estimated risk of recurrence is relatively high. More depressive episodes could be prevented with the trade-off of less cost effectiveness for the healthcare system.ConclusionsHealth economic modeling approaches can be augmented with machine learning methods, which broadens the areas in which evidence can be generated for policy makers to base their budget allocation. The precision of such predictive machine learning models must be high enough to be able to improve a care-as-usual healthcare system. Machine learning models generally let you set the level of precision acquired, at the cost of a possibly low recall, thereby limiting the impact on the healthcare system as a whole. More and better data for training these machine learning models will allow developed models to better distinguish patients who will and won’t develop a recurrent depressive episode, and for higher recall given a desired precision threshold. This will translate into a more substantial improvement in the treatment of depressive disorders in the healthcare system.

Optofluidic-based cell multi-axis controllable rotation and 3D surface imaging

The controlled rotation of individual cells plays a crucial role in enabling three-dimensional multi-angle observation of cellular structure, function, and dynamic processes. Reported cell rotation techniques often struggle to strike a balance between high precision and simple control, and they exhibit limited control flexibility, typically achieving only uniaxial cell rotation. In this study, we propose a cell rotation technique in three dimensions based on optofluidics, which utilizes optical tweezers to immobilize the cell and exploits the asymmetry of the surrounding flow to drive cell rotation. By adjusting the focal position of the optical tweezers, cells can be positioned within various flow profiles, enabling control of the rotation speed, rotation direction, and rotation axis of cells. This approach simplifies the manipulation procedure, achieving desirable control precision and greater rotation flexibility. Using our approach, multi-angle surface imaging projections of target cells can be rapidly obtained, followed by capturing the cell contour data from the images. By combining the cell contour data with corresponding angular position information, we have reconstructed the 3D surface of the target cell. We have employed this technique in experiments for the analysis of red blood cell morphology. Based on the constructed 3D surface images of diverse-shaped red blood cells, we quantified structural parameters including cell surface area, volume, sphericity, and surface roughness, which demonstrates the potential application of this cell rotation technique for cellular morphological analysis.

Box-supervised dynamical instance segmentation for in-field cotton

Crop growing information is important for precision agriculture. Instance segmentation can be employed to extract not only the location and quantity information of objects from images, but also the individual phenotype information of each target. However, when it is applied on real in-field images, complex environment increases the cost of annotating. Besides, it is hard for instance segmentation network with complicated structure to be deployed in real application scenarios. To cost-efficiently extract data used for precision management from field images, this paper proposes a novel model based on dense prediction and conditionally parameterized convolution termed Box-supervised dynamical regression instance segmentation net(BDRISNet) for segmenting individuals from image in a simple way, using box-level supervision. By formulating segmentation into dense regression from each pixel to the location of its instance indicators, mask features can be generated without redundant convolution branch. The compact construction of proposed network achieves efficient inference for segmentation. Robustness and accuracy of proposed method are evaluated on an in-field cotton boll image dataset we provide. Comparison experiments with other state-of-the-arts demonstrate that our method has an average improvement of 3.13%, 5.55%, 7.08% in AP, AP50 and AP75. Furthermore, predictions of proposed method have desirable and balanced recall and precision.

Matrix effect in bioanalytical assay development using supercritical fluid chromatography-mass spectrometry.

Matrix effect (ME) is commonly caused by coelution of compounds with target analytes, resulting in either suppression or enhancement of analyte ionization. Thus, to achieve the desired accuracy, precision, and sensitivity, ME needs to be evaluated and controlled during bioanalytical method development. As the application of supercritical fluid chromatography-mass spectrometry (SFC-MS) for analysis of biological samples has increased, ME using SFC-MS has also been investigated with a focus on the difference in ME in SFC-MS compared to other chromatographic techniques used for achiral separation in biological samples. Here, we provide a summary of the status of ME evaluation and mitigation in SFC-MS methods. This review presents an overview of the phenomenon of ME and methods for evaluating ME in bioanalysis. Next, the factors that can impact ME in SFC-MS-based bioanalytical methods are discussed in detail with an emphasis on SFC. A literature review of the evaluation of ME in targeted bioanalytical methods using SFC-MS is included at the end. Robust instrumentation, effective sample preparation, and superb separation selectivity are the foundations of reliable analytical methods as well as the ability to mitigate detrimental ME in SFC-MS methods.

Determination of tobacco-specific nitrosamines in tobacco and mainstream cigarette smoke using one-step clean-up coupled with liquid chromatography- tandem mass spectrometry

A method for determining tobacco-specific nitrosamines (TSNAs) in tobacco and cigarette smoke using liquid chromatography-tandem mass spectrometry was established. The established method amended the deficiencies that exist in current mainstream methods. In this method, TSNAs in tobacco and cigarette smoke were extracted by water. The aqueous extract was then extracted by dichloromethane, and the extract could be analyzed by liquid chromatography-tandem mass spectrometry after a solvent replacement. This method was used to analyze flue-cured tobacco samples, and the response of the target compounds was about 10 times higher than that of the ammonium acetate extraction method. When analyzing cigarette smoke samples, the response strength and chromatographic peak purity of the target compounds were also significantly improved. The proposed method exhibited good linearities for both tobacco and cigarette smoke samples (r2 > 0.99). The limits of detection (LODs) for tobacco and cigarette smoke samples were 0.2–1.0 ng/g and 0.1–0.3 ng/cigarette, respectively. Additionally, this method exhibited desirable accuracy and precision. The TSNAs recovery values from tobacco and cigarette smoke samples ranged from 95.7 % to 107.7 % with inter- and intra-day relative standard deviations (RSDs) of less than 7.4 %. This method is simple, effective, and has wide adaptability. It is a useful upgrade to the existing methods for analyzing TSNAs in tobacco and cigarette smoke.

Facile fabrication of naphthalene-functionalized magnetic nanoparticles for efficient extraction of polycyclic aromatic hydrocarbons from environmental water and fish samples

In this study, naphthalene-modified magnetic nanoparticles (Fe3O4@Nap) were simply prepared based on specific chelation interaction between phosphate groups and metal ions on Fe3O4 surface. The resultant Fe3O4@Nap were characterized by FTIR, BET, SEM, TEM, NAM, TGA, and VSM techniques. With Fe3O4@Nap as adsorbent, the polycyclic aromatic hydrocarbons (PAHs) were efficiently extracted by magnetic solid-phase extraction (MSPE) from environmental water and fish samples through the π-π interaction between modified naphthalene groups and PAHs, followed by their determination by GC–MS/MS. The key parameters influencing the extraction efficiency were investigated. Under the optimized conditions, the Fe3O4@Nap-based MSPE/GC-MS/MS method proposed in this paper was evaluated and applied for analyzing PAHs in environmental water and fish samples. And the proposed MSPE/GC-MS/MS method exhibited good linearities for water samples (in the range of 0.1–10 ng/mL, R2 >0.9945) and for fish samples (in the range of 1–100 ng/g, R2 > 0.9905). The limits of detection (LODs) for water and fish samples were 0.004–0.031 ng/mL and 0.07–0.28 ng/g, respectively. Additionally, this method exhibited desirable accuracy and precision. The PAH recovery values from water and fish samples ranged from 81.5% to 109.6% with inter- and intra-day relative standard deviations (RSDs) of less than 12.8%. The MSPE/GC-MS/MS method was successfully applied to the analysis of real environmental water and fish samples. Overall, the newly synthesized Fe3O4@Nap exhibited high sensitivity, specificity, reusability, repeatability, and it could efficiently extract PAHs from environmental water and fish samples by MSPE.

Green hydrophobic maltodextrin nanosponges for magnetic solid-phase extraction of hypothalamic peptides from plasma samples

In this work, for the first time, magnetic-phthalated maltodextrin nanosponges (M-PAMDNSs) were synthetized and introduced as efficient and green sorbents. The integration of phthaloyl groups as hydrophobic moieties into networks of maltodextrin nanosponges provided good enrichment for hypothalamic-related peptides (HRPs). The synthesized materials were characterized by 1H nuclear magnetic resonance spectroscopy, water contact angle, attenuated total reflection-Fourier transform infrared spectroscopy, dynamic light scattering, zeta potential, pH point of zero charge, acid-base titration, field-emission scanning electron microscopy, Brunauer-Emmett-Teller, and vibrating sample magnetometer. Under the optimized conditions (sorbent amount: 5.0 mg, desorption solvent volume and type: 300 µL of methanol: H2O: trifluoroacetic acid, extraction time: 15 min, and desorption time: 10 min), the developed magnetic solid-phase extraction (MSPE) method in combination with HPLC-UV was used as a novel and sensitive analytical method for the determination of HRPs in plasma samples. The proposed MSPE-HPLC-UV method provided good linearity (1.5–500 ng mL−1 R2 ≥ 0.9988), low limits of detection (0.1–0.2 ng mL−1) and quantification (0.4–0.8 ng mL−1), desirable precision (RSD ≤ 8.8%, n ₌ 5), satisfactory enrichment factor (EFs ≥ 66.0), and well relative recoveries (92.8–108.8%). Overall, the established method effectively expanded the analytical potential of MSPE approach for the quantification of HRPs in biological samples.

An effervescence-assisted switchable deep eutectic solvent based liquid-phase microextraction of triazole fungicides in drinking water and beverage

A new effervescence-assisted switchable deep eutectic solvent-based liquid phase microextraction (EA-SDES-LPME) combined with HPLC-UV was developed for determination of common triazole fungicides in drinking water and beverages, including myclobutanil, flusilazole, hexaconazole and bitertanol. The alternative extraction solvent was prepared with hexafluoroisopropanol and dipropylamine with the merits of time-saving, easy to collect and cost-effectiveness. The SDES can be reversibly switched between hydrophilic and hydrophobic states by pH adjustment. The homogeneous extraction was achieved under the hydrophilic form of SDES, and the bi-phase separation was obtained easily by adjusting pH value to restore the original hydrophobicity. Moreover, the characterization of SDES was investigated by FTIR and 1H NMR. The main variables affecting extraction efficiency were optimized in detail. Under the optimal conditions, the proposed method shows desirable precision (RSDs less than 18.5%) and acceptable recovery (72.6–95.4%). The lower limits of detection and limits of quantitation were found to be in the range of 1–2 μg L-1 and 5–10 μg L-1, respectively. The formation mechanism of SDES and the extraction mechanism for target analytes were investigated by density functional theory. The proposed methodology was simplicity, sensitive, time-saving and successfully applied to determine triazole fungicides in drinking water and beverages, making it an alternative technique for the analysis of trace analytes with satisfactory sensitivity.

Optimization of the Hardware Layer for IoT Systems Using a Trust-Region Method With Adaptive Forward Finite Differences

Trust-region (TR) algorithms represent a popular class of local optimization methods. Owing to straightforward setup and low computational cost, TR routines based on linear models determined using forward finite differences (FD) are often utilized for performance tuning of microwave and antenna components incorporated within the Internet of Things systems. Despite usefulness for design of complex structures, performance of TR methods vastly depends on the quality of FD-based local models. The latter are normally identified from perturbations determined a priori using either rules-of-thumb, or as a result of manual tuning. In this work, a framework for automatic determination of FD steps and their adjustment between the TR algorithm iterations is proposed. The method involves numerical optimization of perturbations so as to equalize the objective function changes w.r.t. the center design to the desirable precision. To maintain acceptable cost, the FD-tuning procedure is executed using the same approximation model as the one exploited in the course of the structure optimization. The proposed framework has been tested on a total of twelve design problems. Furthermore, the presented method has been thoroughly validated against TR-algorithms with static, a priori selected perturbations. Numerical results indicate that the proposed framework provides up to 50% performance improvement (in terms of the optimized designs quality) compared to the state-of-the-art TR-based approaches. Usefulness of the proposed method for the real-world Internet of Things systems has been implicitly demonstrated through utilization of one of the optimized structures in a hardware layer of a real-time localization system.

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators.

Hexafluoroisopropanol-based supramolecular solvent for liquid phase microextraction of triazole fungicides in drinking water and beverages

A new supramolecular solvent (SUPRAS) was synthesized using hexafluoroisopropanol (HFIP) as hydrogen bond donor and undecanol as hydrogen bond acceptor. Then SUPRAS was used as extraction solvent to enrich triazole fungicides in drinking water and beverages by liquid phase microextraction (LPME). Additionally, several variables that influence extraction efficiency were optimized, and the significant factors were investigated by Box-Behnken design. At optimum condition, the method shows desirable precision (RSDs less than 19.4%) and acceptable recovery (63.4%-112.1%). The low limits of detection and limits of quantitation of this method were found to be in the range of 1.0–5.0 μg/L and 5.0–20 μg/L, respectively. Compared with other analytical techniques, the method demonstrated comparable sensitivity, consumed less extractant (750 μL) and a shorter pre-treatment time (10 min). The formation mechanism of SUPRAS was investigated by density functional theory. The proposed methodology was green, sensitive, time-saving and successfully applied to determine triazole fungicides in drinking water and beverages, making it an alternative technique for the analysis of trace analytes with satisfactory sensitivity.

Adaptive sliding mode control for robotic manipulators with backlash

This paper provides the consequences for trajectory tracking of uncertain n-DOF robotic manipulators in the presence of external disturbances and backlash. The control strategy consists of decoupling system based on inverse dynamics, sliding mode controller, and optimal controller. The tendency of robotic manipulators with gear backlash is to function in two unique phases: contact phase or backlash phase. The sliding mode controller works in the contact phase, after the decoupling of the manipulator, a adaptive second-order sliding mode controller is designed to guarantee system achieve desirable tracking precision and robust stability. The optimal controller works in the backlash phase, which makes the motor quickly traverse the backlash gap and re-contact the joints without collision. Finally, the proposal is verified in simulation using a model of a PUMA560 robot manipulator identified from real data.

A sensitive and drug tolerant assay for detecting anti-AAV9 antibodies using affinity capture elution

Adeno-associated virus (AAV) based gene therapies are gaining significant momentum as a novel therapeutic modality. However, a yet unsolved concern for using AAV as a vector is the high potential to elicit humoral and cellular responses, which are often exacerbated by pre-existing immunity due to exposure to wild type AAV. Therefore, characterization of pre-existing and treatment emergent anti-AAV antibodies is of great importance to the development of AAV based gene therapies. In this project, a sensitive and drug tolerant total antibody (TAb) assay was developed using recombinant AAV9-GFP (green fluorescent protein) as a surrogate AAV9. The assay format was affinity capture and elution (ACE) with ruthenium labeled AAV9-GFP as detection. Upon evaluation, three commercial anti-AAV9 monoclonal antibodies (clones HI17, HI35, and HL2374) were chosen and mixed at equal concentrations as positive control material. The assay sensitivity was estimated to be 11.2 ng/mL. Drug tolerance was estimated to be 5.4 × 10E10 DRP/mL AAV9-GFP at 100 ng/mL anti-AAV9 antibodies and to be at least 1 × 10E11 DRP/mL at 500 ng/mL and 250 ng/mL anti-AAV9 antibodies. The assay showed desirable specificity and precision. Using this TAb assay, significant pre-existing antibodies were detected from normal human sera.

Workspace Mapping Method Based on Edge Drifting for the Teleoperation System

The workspace mapping method for master-slave isomeric robots is a key research direction. In order to enhance the safety and stability of the operation of such robots, a position-joint hybrid mapping method based on edge drifting is proposed in this paper. First, teleoperation is divided into two phases, that is, task approach and task operation. During the task approach, the edge drifting algorithm is adopted to largely cover the workspace of a robot. Rate mapping is used to quickly control the movement of the slave robot to the target position. During task operation, a hybrid mapping algorithm integrating the Cartesian space and the joint space highly precisely maps both the position and pose of the slave robot to accurately follow the control command of the master end and achieve precise control. Lastly, the proposed method is validated on the experimental platform for master-slave isomeric teleoperation. The experimental results indicate that this mapping method allows the master-slave isomeric teleoperation system to cover a large workspace precisely with desirable precision in position and pose following.

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

Quality Evaluation of Crude and Salt-Processed Cuscutae Semen through Qualitative and Quantitative Analysis of Multiple Components Using HPLC Combined with Chemometrics

Cuscutae Semen (CS; Chinese common name “Tusizi”) is one of the most common traditional herbal medicines used to treat liver and kidney diseases in China. Although it is most commonly used as a processed product, little progress has been made on the quality control of CS and salt-processed Cuscutae Semen (PCS). The purpose of this study was to establish a comprehensive strategy integrating chromatographic analysis and chemometric methods for quality evaluation and discrimination of CS and PCS. An accurate and reliable HPLC method was established for the simultaneous quantification of 12 analyte compounds in CS and PCS. The results showed that this method exhibited desirable sensitivity, precision, stability, and repeatability. Multivariate chemometric methods were applied to analyze the obtained HPLC fingerprints, including hierarchical cluster analysis (HCA), principle component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results indicated that CS and PCS samples showed a clear classification of the two groups, and three chemical markers with great contributions to the differentiation were screened out. Collectively, the chemometrics combined with the quantitative analysis based on HPLC fingerprint results indicated that salt processing may change the contents and types of components in Cuscutae Semen.

A Methodology for Quantitation of Dictamnine and Fraxinellone and its Application to Study Pharmacokinetics and Bioavailability in Rats Via Oral and Intravenous Administration.

The pharmacological activities of dictamnine and fraxinellone have been well reported; however, only a few studies have focused on the pharmacokinetics and bioavailability of concomitant delivery of these drugs in vivo. To shed light on this neglected area, we developed a rapid and sensitive UPLC-MS/MS method that quantified the levels of dictamnine and fraxinellone simultaneously in rat plasma. This method was initiated by a one-step protein precipitation strategy to purify plasma samples collected from rats treated with either oral or intravenous administration of dictamnine and fraxinellone. The mobile phase contained acetonitrile and 0.1% formic acid at a steady flow rate of 0.6mL/min. As a result, an excellent analyte peak resolution was achieved, and the entire process took only 3min per sample. The results were indicative of the desired linearity (r2 ≥ 0.999), precision (RSD% was within 15%), accuracy (RE% was within 15%), recoveries (≥80.66 and 68.15% for dictamnine and fraxinellone, respectively) and matrix effects (≥94.66 and 91.37% for dictamnine and fraxinellone, respectively). Additionally, the detectable limits of these two compounds were both low even when they reached 5ng/mL. Taken together, these findings contribute to a better understanding of the pharmacokinetics and bioavailability properties of concomitant delivery of dictamnine and fraxinellone.