Rapid Detection Method Research Articles

Highly Specific Aptamer-Antibody Birecognized Sandwich Module for Ultrasensitive Detection of a Low Molecular Weight Compound.

Herein, the aptamer-antibody sandwich module was first introduced to accurately recognize a low molecular weight compound (mycotoxin). Impressively, compared with the large steric hindrance of a traditional dual-antibody module, the aptamer-antibody sandwich with low Gibbs free energy and a low dissociation constant has high recognition efficiency; thus, it could reduce false positives and false negatives caused by a dual-antibody module. As a proof of concept, a sensitive electrochemiluminescence (ECL) biosensor was constructed for detecting mycotoxin zearalenone (ZEN) based on an aptamer-antibody sandwich as a biological recognition element and porous ZnO nanosheets (Zn NSs) supported Cu nanoclusters (Cu NCs) as the signal transduction element, in which the antibody was modified on the vertex of a tetrahedral DNA nanostructure (TDN) with a rigid structure to increase the kinetics of target recognition for promoting the detection sensitivity. Moreover, the Cu NCs/Zn NSs exhibited an excellent ECL response that was attributed to the aggregation-induced ECL enhancement through electrostatic interactions. The sensing platform achieved trace detection of ZEN with a low detection limit of 0.31 fg/mL, far beyond that of the enzyme-linked immunosorbent assay (ELISA, the current rapid detection method) and high-performance liquid chromatography (HPLC, the national standard detection method). The strategy has great application potential in food analysis, environmental monitoring, and clinical diagnosis.

Development of a novel Cas13a/Cas12a-mediated 'one-pot' dual detection assay for genetically modified crops

IntroductionGenetically modified (GM) crops have been widely cultivated across the world and the development of rapid, ultrasensitive, visual multiplex detection platforms that are suitable for field deployment is critical for GM organism regulation. ObjectiveIn this study, we developed a novel one-pot system, termed MR-DCA (Multiplex RPA and Dual CRISPR assay), for the simultaneous detection of CaMV35S and NOS genetic targets in GM crops. This innovative approach combined Multiplex RPA (recombinase polymerase amplification) with the Dual CRISPR (clustered regularly interspaced short palindromic repeat) assay technique, to provide a streamlined and efficient method for GM crop detection. MethodsThe RPA reaction used for amplification CaMV35S and NOS targets was contained in the tube base, while the dual CRISPR enzymes were placed in the tube cap. Following centrifugation, the dual CRISPR (Cas13a/Cas12a) detection system was initiated. Fluorescence visualization was used to measure CaMV35S through the FAM channel and NOS through the HEX channel. When using lateral flow strips, CaMV35S was detected using rabbit anti-digoxin (blue line), whilst NOS was identified using anti-mouse FITC (red line). Line intensity was quantified using Image J and depicted graphically. ResultsDetection of the targets was completed in 35 min, with a limit of detection as low as 20 copies. In addition, two analysis systems were developed and they performed well in the MR-DCA assay. In an analysis of 24 blind samples from GM crops with a wide genomic range, MR-DCA gave consistent results with the quantitative PCR method, which indicated high accuracy, applicability and semi-quantitative ability. ConclusionThe development of MR-DCA represents a significant advancement in the field of GM detection, offering a rapid, sensitive and portable method for multiple target detection that can be used in resource-limited environments.

An aptamer magnetic capture based colorimetric method for rapid and sensitive detection of Listeria monocytogenes

An aptamer magnetic capture based colorimetric method for rapid and sensitive detection of Listeria monocytogenes

An aptamer-based fluorometric method for the rapid berberine detection in Kampo medicines

BackgroundBerberine (BBR), a key component in Kampo medicine, is a cationic benzylisoquinoline alkaloid whose detection plays a critical role in the quality control of these traditional remedies. Traditional methods for detecting BBR often involve complex procedures, which can be time-consuming and costly. To address this challenge, our study focuses on developing a simpler, faster, and more efficient detection method for BBR in Kampo medicine formulations. ResultsWe successfully developed a rapid fluorometric detection method for BBR using colloidal gold nanoparticle-based systematic evolution of ligands by exponential enrichment (GOLD-SELEX). Initially, specific single-stranded DNA (ssDNA) sequences were selected for their ability to enhance BBR's fluorescence intensity. The optimal ssDNA sequence, identified as BBR38, was further truncated to produce BBR38S, a stem-loop ssDNA that improved fluorescence upon interaction with BBR. To further enhance the fluorescence, the BBR38S aptamer underwent additional modifications, including stem truncation and nucleotide mutations, resulting in the higher fluorescence variant BBR38S-3 A10C. The final product, TetBBR38S, a tetramer version of BBR38S-3 A10C, exhibited a linear detection range of 0.780–50.0 μg mL–1 and a limit of detection of 0.369 μg mL−1. The assay demonstrated sufficient selectivity and was successfully applied to analyze 128 different Kampo medicine formulations, accurately detecting BBR content with high precision. SignificanceThis study represents an advancement in Kampo medicine research, marking the first successful application of an aptamer-based approach for BBR detection in complex matrices. The developed method is not only simple and rapid (with a detection time of 5 min) but also cost-effective, which is crucial for widespread application.

An ultrasensitive strip sensor for rapid detection of African swine fever virus

African swine fever (ASF) is a highly contagious disease caused by the African swine fever virus (ASFV) infecting pigs, which has caused huge economic losses in countries around the world. Currently, there is no effective vaccine, and the prevention and control of ASF is mainly through rapid detection, so it is particularly important to carry identify and develop rapid detection methods for ASFV. In this study, recombinant plasmid PET-28a(+)-p30 was constructed, and the recombinant protein was obtained by inducing expression and Ni+ resin affinity column purification. Mice were immunized with recombinant p30 protein, and after three immunizations, ten strains of hybridoma cells that stably secreted monoclonal antibodies (mAbs) against p30 protein were obtained by cell fusion and subcloning. A colloidal gold immunochromatography assay (GICA) based on double antibody sandwich technology was established to screen the paired antibodies, and the trapping and detecting antibodies were mAb-11F11 and mAb-7A8, respectively, with a detection limit of 1 ng/mL, which laid an important material foundation for the early detection of ASF in the future.

A novel polydiacetylene-functionalized fibrinogen paper-based biosensor for on-spot and rapid detection of Staphylococcus aureus

Staphylococcus aureus contamination continues to be a harmful foodborne pathogen threatening of human health, and there is a growing need for rapid detection technologies. This study proposed a novel paper biosensor based on a polydiacetylene (PDA) polymer functionalized fibrinogen (Fg) for the detection of S. aureus in food sources. The fluorophore was developed based on the high binding ability of fibrinogen-binding proteins on the surface of S. aureus. This binding caused twisting in the PDA backbone, leading to changes in chromatic and fluorescent. The detection limit of this method was 50.1 CFU/mL for S. aureus-contaminated foodstuffs and 65.0 CFU/mL for the pure S. aureus culture, and the novelty came from its rapidity and selectivity for S. aureus compared to other foodborne bacteria. In summary, the present work provides a rapid detection method for S. aureus detection, which will help in addressing food safety-related issues.

Identification and Removal of Pollen Spectral Interference in the Classification of Hazardous Substances Based on Excitation Emission Matrix Fluorescence Spectroscopy.

Sensitively detecting hazardous and suspected bioaerosols is crucial for safeguarding public health. The potential impact of pollen on identifying bacterial species through fluorescence spectra should not be overlooked. Before the analysis, the spectrum underwent preprocessing steps, including normalization, multivariate scattering correction, and Savitzky-Golay smoothing. Additionally, the spectrum was transformed using difference, standard normal variable, and fast Fourier transform techniques. A random forest algorithm was employed for the classification and identification of 31 different types of samples. The fast Fourier transform improved the classification accuracy of the sample excitation-emission matrix fluorescence spectrum data by 9.2%, resulting in an accuracy of 89.24%. The harmful substances, including Staphylococcus aureus, ricin, beta-bungarotoxin, and Staphylococcal enterotoxin B, were clearly distinguished. The spectral data transformation and classification algorithm effectively eliminated the interference of pollen on other components. Furthermore, a classification and recognition model based on spectral feature transformation was established, demonstrating excellent application potential in detecting hazardous substances and protecting public health. This study provided a solid foundation for the application of rapid detection methods for harmful bioaerosols.

A lightweight ground crack rapid detection method based on semantic enhancement

To address the issue of detecting complex-shaped cracks that rely on manual, which may result in high costs and low efficiency, this paper proposed a lightweight ground crack rapid detection method based on semantic enhancement. Firstly, the introduction of the Context Guided Block module enhanced the YOLOv8 backbone network, improving its feature extraction capability. Next, the incorporation of GSConv and VoV-GSCSP was introduced to construct a lightweight yet efficient neck network, facilitating the effective fusion of information from multiple feature maps. Finally, the detection head achieved more precise target localization by optimizing the probability around the labels. The proposed method was validated through experiments on the public dataset RDD-2022. The experimental results demonstrate that our method effectively detects cracks. Compared to YOLOv8, the model parameters have been reduced by 73.5 %, while accuracy, F1 score, and FPS have improved by 6.6 %, 4.3 %, and 116, respectively. Therefore, our proposed method is more lightweight and holds significant application value.

Detection of Carbapenemase Production in Klebsiella pneumoniae Isolates by Rapid Carbapenemase Detection Method in an OXA-48 Endemic Region

Background: Carbapenem resistance is one of the major global public health threats caused by the overuse or misuse of carbapenems, which are often the last resort in treating multidrug-resistant infections. Today, timely detection of carbapenemase-producing organisms is critical for infection control. Therefore, rapid and accurate detection of carbapenemases using a cost-effective method with high sensitivity and specificity is essential to guide appropriate treatment and prevent further spread. Objectives: This study aimed to evaluate the sensitivity of the newly developed rapid carbapenemase detection method (rCDM) with isolates whose resistance genes were determined by molecular methods and to compare it with the modified carbapenem inactivation method (mCIM), whose diagnostic performance characteristics are well-defined. Methods: A total of 130 Klebsiella pneumoniae isolates (65 carbapenem-susceptible, 65 carbapenem-resistant) from various clinical specimens were included in the study. Identification and antibiotic susceptibility testing of the isolates was performed using the BD Phoenix™ automated system. Phenotypic carbapenemase detection methods, mCIM and rCDM, were studied for all carbapenem-sensitive and carbapenem-resistant isolates with known resistance genes. Results: Among carbapenem-resistant K. pneumoniae isolates, blaOXA-48 was the most frequently detected resistance gene (78%). Using PCR results as the reference method, the sensitivity of rCDM was 100%. When rCDM and mCIM results were compared, both phenotypic methods were 100% compatible, with no statistically significant difference observed. Conclusions: Rapid carbapenemase detection method is a rapid and reliable phenotypic carbapenemase detection method with high sensitivity. Its ease of use and interpretation make it suitable for use in laboratories with limited resources. Additionally, its excellent performance in detecting common carbapenemase types and high consistency with mCIM support its utility in various laboratory settings without requiring additional materials or technical infrastructure.

Combination of Capture-SELEX and Post-SELEX for procymidone-specific aptamer selection and broad-specificity aptamer discovery, and development of aptamer-based lateral flow assay

BackgroundDue to its wide application, procymidone has become one of the pesticides with high detection rates in supervision and sampling. Therefore, it is necessary to establish a rapid and efficient method for the detection of procymidone. However, an important bottleneck restricting the development of rapid detection methods of procymidone is that its specific recognition elements are rarely reported. In this work, Capture-SELEX and post-SELEX were used in aptamer screening, and the obtained aptamers were used to construct an aptamer-based lateral flow assay (LFA). ResultsFirstly, a specific aptamer Seq15 was obtained for procymidone by Capture-SELEX, and its dissociation constant (Kd) was 24.22 nM. Secondly, post-SELEX was used to analyze and modify Seq15 to improve its performance, and the Kd of the truncated sequence Seq15-2 was 21.28 nM. In addition to this, the broad-specificity aptamer Seq17-1 was obtained via post-SELEX. Seq17-1 could broadly recognize dicarboximide fungicides (procymidone, iprodione, chlozolinate, dimethachlon and vinclozolin) and their metabolic derivative (3,5-dichloroaniline). Finally, the specific aptamer-based LFA of procymidone was constructed, and the limit of detection (LOD) was 0.79 ng/mL. Meanwhile, the LODs of dicarboximide fungicides and their metabolic derivative were 0.62, 0.64, 0.71, 0.69, 0.64 and 0.66 ng/mL, respectively. The above LFAs were highly specific and stable, and had been successfully used for the detection of vegetable samples. SignificanceUnder the combination of Capture-SELEX and Post-SELEX, this study not only provides specific recognition elements for rapid detection of procymidone, but also provides new ideas for the discovery of broad-specificity aptamers. Combining broad-specificity primary detection and single-specificity quantification, a composite aptamer-based LFA detection platform has been developed, which significantly improves detection efficiency.

A New Dual Fluorescence Method for Rapid Detection of Infectious Bronchitis Virus at Constant Temperature.

Infectious bronchitis virus (IBV) causes infectious bronchitis in chicken, an acute, highly contagious respiratory infection. Because of genetic mutations and recombination, IBV forms many subtypes, which makes it difficult to treat the disease and apply commercial vaccines. Therefore, to detect IBV in time and stop the virus from spreading, a novel and convenient IBV detection technology based on reverse transcription recombinase-aided amplification (RT-RAA) was established in this study. According to the S1 gene of IBV CH I-V and Mass genotypes and S1 gene of IBV CH VI genotype, a set of optimal primers were designed and selected to establish a real-time dual fluorescence RT-RAA method. The lowest detection line was 10 copies/μL of RNA molecules and the method exhibited no cross-reactivity with avian reticuloendotheliosis virus (REV), infectious bursal disease virus (IBDV), avian leukosis virus (ALV), Newcastle disease virus (NDV), chicken infectious anemia virus (CIAV), infectious laryngotracheitis virus (ILTV), Marek's disease virus (MDV), and H9N2 avian influenza virus (H9N2), demonstrating high specificity. When compared to qPCR detection results, our method achieved a sensitivity of 96.67%, a specificity of 90%, and a Kappa value of 0.87 for the IBV CH I-V and Mass genotypes, and achieved a sensitivity of 100%, a specificity of 97.73%, and a Kappa value of 0.91 for the IBV CH VI genotype.

Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.

Establishment and Validation of a Multiplex PCR Detection System for the Identification of Six Common Edible Meat Components.

To establish a rapid, accurate, and sensitive multiplex PCR detection method for the simultaneous identification of the six common edible meats (beef, lamp, chicken, pork, goose, duck), and to evaluate its application value in meat adulteration identification. Based on complete mitochondrial genomic sequences of six species in the GenBank database, DNA sequences (cattle：16S rRNA; sheep：COX-1; chickens：Cytb; pig：COX-1; goose：NADH2; duck：16S rRNA) with intra-species conservation and inter-species specificity were screened, and species-specific primers were designed to construct a multiplex PCR detection system that can simultaneously detect the meat of six common species. The species specificity, sensitivity and reproducibility of the system were studied, and the simulated mixture sample detection was performed. This study successfully constructed a multiplex PCR detection system that can detect the meats of six common species simultaneously. The system was not effective in DNA amplification of non-target species. When the DNA template sizes were 0.062 5-2 ng/μL, the amplified products of all six species could be detected. The duck component was still detected when the mixing ratio of duck and beef was as low as 0.5%. This study constructs and establishes a multiplex PCR detection system with strong specificity, high sensitivity, and good reproducibility. It can accurately identify the components of animal origin in common edible meats and provide a simple and practical method for identifying adulteration of common edible meats and meat products in China.

Layer-By-Layer Designed Spark-Type AuCuPt Alloy with Robust Broadband Absorption to Enhance Sensitivity in Flexible Detection of Estriol by a Lateral Flow Immunoassay.

Excessive intake of estrogen poses significant health risks to the human body; hence, there is a necessity to develop rapid detection methods to monitor its levels of addition. Gold nanoparticles (AuNPs), commonly utilized as colorimetric signal labels, find extensive application in lateral flow immunoassay (LFIA). However, the detection sensitivity of traditional AuNPs-LFIA is typically constrained by low molar extinction coefficients and reliance on a single signal. Herein, in this work, unique spark-type AuCuPt nanoflowers modified with tannic acid (AuCuPt@TA) were precisely designed by reasonable layer-by-layer element composition and green modification. The obtained AuCuPt displays robust broadband absorption spanning the visible to near-infrared spectrum, showcasing a notable molar extinction coefficient of 2.38 × 1012 M-1 cm-1 and a photothermal conversion efficiency of 48.5%. Based on this, selecting estriol (E3) as a model analyte, colorimetric/photothermal dual-signal LFIA (CLFIA and PLFIA) was developed. Limits of detection (LOD) of the CLFIA and PLFIA were achieved at 0.033 ng mL-1 and 0.021 ng mL-1, respectively, which represent a 9.3- and 14.6-fold improvement compared to the visual LOD of AuNPs-LFIA. Moreover, the application feasibility of the immunoassay was further evaluated in the milk and pork with satisfactory recoveries ranging from 86.21% to 117.91%. Thus, this work has enhanced the performance of LFIA for E3 detection and exhibited enormous potential for other sensing platform construction.

A pretreatment-free and eco-friendly rapid detection for mycotoxins in edible oils based on magnetic separation technique

Pretreatment steps of current rapid detection methods for mycotoxins in edible oils not only restrict detection efficiency, but also produce organic waste liquid to pollute environment. In this work, a pretreatment-free and eco-friendly rapid detection method for edible oil is established. This proposed method does not require pretreatment operation, and automated quantitative detection could be achieved by directly adding oil samples. According to polarity of target molecules, the content of surfactant in reaction solutions could be adjusted to achieve the quantitative detection of AFB1 in peanut oil and ZEN in corn oil. The recoveries are between 96.5%–110.7% with standard deviation <10.4%, and the limit of detection is 0.17 μg/kg for AFB1 and 4.91 μg/kg for ZEN. This method realizes full automation of the whole chain detection, i.e. sample in-result out, and is suitable for the on-site detection of batches of edible oils samples.

A facile fabricated electrochemical sensor based on dual-shell Sn4CoO9.3 microspheres for simultaneous detection of Pb2+ and Hg2+

The presence of heavy metal ions poses significant health hazards to humans, necessitating an urgent demand for a rapid and efficient detection method. Electrochemical sensing offers a promising approach that enables high sensitivity and selectivity in detecting heavy metal ions. In this study, a novel bimetallic oxide, Sn4CoO9.3, with dual shells and multiple pores was developed by a simple template-free method. The unique dual-shell structure and multiple pores greatly enhance its adsorption capacity for heavy metal ions, while the mesoporous pores facilitate electrolyte and ion diffusion. A sensor based on Sn4CoO9.3/CS-ERGO with the excellent electroactive properties and outstanding absorption capacity was fabricated for the simultaneous detection of different heavy metal ions (Pb2+ and Hg2+). By harnessing the benefits of Sn4CoO9.3 and graphene sheets, the sensors that were created exhibited exceptional sensitivity, stability, and minimal interference when detecting Pb2+ and Hg2+ in real samples. These results suggest promising prospects for utilizing the sensor in environmental water quality monitoring applications.

Inclusion of Anti-COVID-19 Related Research Highlights into the Classroom: Why, What and How?

Bringing recent research highlights into the classroom has drawn an intense amount of interest because it can show students what are being discovered, especially those anti-COVID-19 works. Therefore, we aim to study the feasibility and share the thoughts on the relevant innovation in education during the pandemic of COVID-19. To do so, we first examine participants’ (consisting of bachelor, master and PhD students at different universities) attitudes of towards the inclusion of anti-COVID-19 related research highlights into the classroom via the survey questionnaires. It is found that students mostly preferred such an innovative move. We then feature multiple anti-COVID-19 research highlights, ranging from the anti-fog materials, new inhalation treatment approach, antiviral 2D materials, efficiency enhancement of mask materials, rapid detection methods to vaccine development. Next, we elaborate the class design such as contents, objectives, difficulty level and learning outcomes with two specific examples. Lastly, suggestions for better implementation of including anti-COVID-19 related research highlights into the classroom are provided. The findings of this study can help universities and teachers to further improve the class design, thereby creating strong anti-COVID-19 related theory-practice linkages for students.

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology.

(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

Surface-Imprinted Polysiloxane with Recognition Ability Based on an ITO Layer for Rapid Detection of Fusarium oxysporum f. sp. cubense by the Naked Eye.

Direct observation by the naked eye of fluorescence-stained microbes adsorbed on surface imprinted polymers (SIPs) is highly challenging and limited by speed, accuracy and the semiquantitative nature of the method. In this study, we tested for the presence of spores of Fusarium oxysporum f. sp. cubense race 4 (Foc4), which cause severe banana Fusarium wilt disease and reduces the area of banana plants. This kind of spore can become dormant in soil, which means that the detection of secreted molecules (molecular imprinting) in soil may be inaccurate; detection methods such as polymerase chain reaction (PCR) and Raman spectroscopy are more accurate but time-consuming and inconvenient. Therefore, a semiquantitative and rapid SIP detection method for Foc4 was proposed. Based on the ITO conductive layer, a reusable and naked-eye-detectable Foc4-PDMS SIP film was prepared with a site density of approximately 9000 mm-2. Adsorption experiments showed that when the Foc4 spore concentration was between 104 to 107 CFU/mL, the number of Foc4 spores adsorbed and the fluorescence intensity were strongly correlated with the concentration and could be fully distinguished by the naked eye after fluorescence staining. Adsorption tests on other microbes showed that the SIP film completely recognized only the Foc series. All the results were highly consistent with the naked-eye observations after fluorescence staining, and the results of the Foc4-infected soil experiment were also close to the ideal situation. Taken together, these results showed that Foc4-PDMS SIPs have the ability to rapidly and semiquantitatively detect the concentration of Foc in soil, which can provide good support for banana cultivation. This method also has potential applications in the detection of other fungal diseases.

Establishment and evaluation of a qPCR method for the detection of pfmdr1 mutations in Plasmodium falciparum, the causal agent of fatal malaria

Drug resistance surveillance is a major integral part of malaria control programs. Molecular methods play a pivotal role in drug resistance detection and related molecular research. This study aimed to develop a rapid and accurate detection method for drug resistance of Plasmodium falciparum (P. falciparum). A quantitative real-time PCR (qPCR) assay has been developed that identifies the mutation at locus A256T in the P.falciparum multi-drug resistance(pfmdr1) gene producing amino acid change at position 86. The results of 198 samples detected by qPCR were consistent with nested PCR and sequencing, giving an accuracy of 94.3%. The sensitivity, specificity, positive and negative predictive value of qPCR were 85.7%, 97.6%, 90.0% and 96.4%, respectively. The results of qPCR are basically consistent with the nested PCR, which is expected to replace the nested PCR as a new molecular biological method for drug resistance detection, providing reliable technical support for global malaria prevention and control.