Conventional Detection Methods Research Articles

An Allysine-Conjugatable Probe for Fluorogenically Imaging Fibrosis.

Allysine, a pivotal biomarker in fibrogenesis, has prompted the development of various radioactive imaging probes. However, fluorogenic probes targeting allysine remain largely unexplored. Herein, by leveraging the equilibrium between the nonfluorescent spirocyclic and the fluorescent zwitterionic forms of rhodamine-cyanine hybrid fluorophores, we systematically fine-tuned the environmental sensitivity of this equilibrium toward the development of fluorogenic probes for fibrosis. The trick lies in modulating the nucleophilicity of the ortho-carboxyl group, which is terminated with a hydrazide group for allysine conjugation. Probe B2 was developed with this strategy, which featured an N-sulfonyl amide group and exhibited superior fibrosis-to-control imaging contrast. Initially presenting as nonfluorescent spirocyclic aggregates in aqueous solutions, B2 displayed a notable fluorogenic response upon conjugation with protein allysine through its hydrazide group, inducing deaggregation and switching to the fluorescent zwitterionic form. Probe B2 outperformed the traditional Masson stain in imaging contrast, achieving an about 260-2600-fold ratio for fibrosis-to-control detection depending on fibrosis severity. Furthermore, it demonstrated efficacy in evaluating antifibrosis drugs. Our results emphasize the potential of this fluorogenic probe as an alternative to conventional fibrosis detection methods. It emerges as a valuable tool for antifibrosis drug evaluation.

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Label-free white blood cells classification using a deep feature fusion neural network

White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.

Mapping and predicting cassava mosaic disease outbreaks using earth observation and meteorological data-driven approaches

Cassava Mosaic Disease (CMD) poses a significant threat to cassava (Manihot esculenta Crantz) production, both globally and in Thailand. Caused by the Cassava Mosaic Begomovirus (CMB), CMD conventional detection methods involve field-based visual assessment and mapping, which can be time-consuming and labour-intensive.In this study, field-based surveys have been conducted during the early wet season cassava planting period in three different productive regions of Thailand. Five classification models (namely ANN, RF, XGBoost, SVM, and Ensemble learning) were employed to predict CMD occurrence with various sets of optimized predictive meteorological and remote sensing features. Field based CMD observations have been used for modelling validation and accuracy assessment.Utilizing time series of Sentinel-2 MSI Level-2A images, we estimate cassava start of season (SOS) across both before and after planting periods on a gridded basis. Establishing meteorological and remote sensing time frames, we aggregate predictive features through different temporal windows. Employing interactive and multistage correlation analysis, along with feature importance ranking, we optimize the selection of predictive features, effectively reducing multicollinearity. The Ensemble model and the usage of combined meteorological and remote sensing optimized predictive features showed best prediction results achieving an overall accuracy of 90%, F1-score of 0.90, Matthews Correlation Coefficient of 0.71, unit-normalized MCC* of 0.85, and an average central actual accuracy of approximately 0.85.Observed CMD patterns demonstrate that soil-crop suitability and farming practices (including the use of most CMD susceptible cultivars) significantly influence the CMD spread in selected farming areas. However, modelling analysis revealed that, among several meteorological features, CMD occurrence is associated with predominant winds blowing across the entire before and after planting periods, mainly from the first quadrant, and to a lesser extent, humidity. Notably, higher disease incidence areas corresponded with SOS events in January and February. In predicting CMD, individual S2-MSI bands were not crucial, but NDVI and EVI emerged as the most explanatory remote sensing features, followed by GNDVI and PSRI indices.The study's findings highlight that coupling CMD occurrence modelling, combined remote sensing and meteorological data with conventional field-based observations may significantly enhance targeted disease management protocols and assessing potential impacts on cassava yields.

The Development of a Specific Nanofiber Bioreceptor for Detection of Escherichia coli and Staphylococcus aureus from Air.

Polluted air and the presence of numerous airborne pathogens affect our daily lives. The sensitive and fast detection of pollutants and pathogens is crucial for environmental monitoring and effective medical diagnostics. Compared to conventional detection methods (PCR, ELISA, metabolic tests, etc.), biosensors bring a very attractive possibility to detect chemicals and organic particles with the mentioned reliability and sensitivity in real time. Moreover, by integrating nanomaterials into the biosensor structure, it is possible to increase the sensitivity and specificity of the device significantly. However, air quality monitoring could be more problematic even with such devices. The greatest challenge with conservative and sensing methods for detecting organic matter such as bacteria is the need to use liquid samples, which slows down the detection procedure and makes it more difficult. In this work, we present the development of a polyacrylonitrile nanofiber bioreceptor functionalized with antibodies against bacterial antigens for the specific interception of bacterial cells directly from the air. We tested the presented novel nanofiber bioreceptor using a unique air filtration system we had previously created. The prepared antibody-functionalized nanofiber membranes for air filtration and pathogen detection (with model organisms E. coli and S. aureus) show a statistically significant increase in bacterial interception compared to unmodified nanofibers. Creating such a bioreceptor could lead to the development of an inexpensive, fast, sensitive, and incredibly selective bionanosensor for detecting bacterial polluted air in commercial premises or medical facilities.

A facile nanozyme-based colorimetric method to realize the quantitative and specific detection of casein phosphopeptides in food samples

As a popular nutritional enhancer, casein phosphopeptides (CPPs) have attracted growing attention in food industry. However, conventional methods for CPPs detection are usually less precise or requires expensive instruments. Herein, a nanozyme-based colorimetric method was developed to achieve the quantitative detection of CPPs in food samples. This method is based on a facilely fabricated peroxidase-like nanozyme (Fe@UiO-66), which combines the specific binding of CPPs, as well as the nanozyme-catalyzed colorimetric sensing that can be easily detected by spectrometer. The method displayed good quantitative ability toward CPPs with the linear range of 2–30 μg/mL, the low limit of detection of 0.267 μg/mL and limit of quantification of 1.335 μg/mL. We highlighted the specificity, anti-interference and practicability of this method, by investigating the performances toward food samples. Besides, a smartphone-based colorimetric sensing platform was also established, which is conducive to the portable detection. The developed nanozyme-based colorimetric sensing method provides a promising strategy for CPPs detection in food samples.

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

Advancements in information and communications technology (ICT) have fundamentally transformed computing, notably through the internet of things (IoT) and its healthcare-focused branch, the internet of medical things (IoMT). These technologies, while enhancing daily life, face significant security risks, including ransomware. To counter this, the authors present a scalable, hybrid machine learning framework that effectively identifies IoMT ransomware attacks, conserving the limited resources of IoMT devices. To assess the effectiveness of their proposed solution, the authors undertook an experiment using a state-of-the-art dataset. Their framework demonstrated superiority over conventional detection methods, achieving an impressive 87% accuracy rate. Building on this foundation, the framework integrates a multi-faceted feature extraction process that discerns between benign and malign actions, with a subsequent in-depth analysis via a neural network. This advanced analysis is pivotal in precisely detecting and terminating ransomware threats, offering a robust solution to secure the IoMT ecosystem.

Is it there?—Estimating the invasion of armored sailfin catfish (Pterygoplichthys sp.) in the water bodies of Eastern Ghats, India using the eDNA approach

AbstractEarly detection of invasive species is crucial for effectively controlling the potential damage they can inflict on the ecosystems. In contrast to the many limitations that conventional detection methods like visual surveys and netting hold, environmental DNA assay provides a powerful alternative. This non‐invasive, highly sensitive, and user‐friendly technique offers the advantage of detecting invasive species even in areas where direct observation is challenging, thus addressing the shortcomings of traditional techniques and enhancing overall accuracy in estimating distribution. The spread of invasive Pterygoplichthys sp. has become a cause for concern in biodiversity‐rich countries like India. Despite this, comprehensive studies on the prevalence of this invasive species are limited. The Eastern Ghats of India remain under‐explored with a high potential for supporting diverse lifeforms. Studying the extent of biological invasions in the Eastern Ghats is essential for effective management and to mitigate the ecological and socioeconomic impacts of invasive species. In this study, we have designed and optimized an eDNA‐based quantitative PCR assay to map the presence and spread of invasive Pterygoplichthys sp. in selected freshwater ecosystems of the Eastern Ghats. This assay detected invasive Pterygoplichthys sp. in almost 65% of the total locations sampled. This study can be further extended to larger geographical areas, which can contribute to formulating necessary measures by the authorities to manage invasion and conserve the diversity of the freshwater ecosystem.

Heterogeneous Feature Fusion for Improving Performance of Action Detection

We present a novel framework aimed at improving video action detection through the integration of heterogeneous features. Conventional action detection methods which focus on modeling the relationships between person/object instances rely exclusively on video features and do not exploit valuable intra-instance heterogeneous features, such as person pose, positional information or object category, that can support action recognition. Our proposed framework, termed Heterogeneous Feature Fusion (HFF) framework, addresses this limitation by integrating such intra-instance heterogeneous features for person/object instances, and can improve existing action detection methods. To efficiently exploit each heterogeneous feature, which vary in importance depending on actions and/or scenes, we introduce an attention mechanism to dynamically enhance important heterogeneous features within an instance. Experiments on JHMDB and AVA v2.2 datasets show that our HFF significantly enhances the action detection performance of two existing methods.

An adaptive frequency-hopping detection for slowly-varying fading dispersive channels.

Frequency hopping (FH) signals have been widely used to improve performance against frequency selective fading phenomenon of underwater channels. However, the channel is slowly varying in regard to changes in weather conditions, and thus the conventional FH detection transmitting signals with fixed frequency cannot guarantee good detection performance in the dynamic underwater environment. To overcome the performance degradation in slowly-varying fading dispersive channels, this paper proposes an adaptive frequency-hopping (AFH) target detection method. Compared with conventional FH detection methods, the AFH can adaptively select the optimal detection frequency based on premeasured background noise and channel frequency response measured from previous experiments. Numerical simulations and lake trials are conducted to verify the effectiveness of the AFH. The simulation results show that the AFH has better detection performance than the conventional FH. The lake trial results have also verified the validity and feasibility of AFH. Importantly, AFH also achieves a better output signal-to-noise ratio under actual noise interference.

Exploring new frontiers: cell surface vimentin as an emerging marker for circulating tumor cells and a promising therapeutic target in advanced gastric Cancer

BackgroundCirculating tumor cells (CTCs) hold immense promise in guiding treatment strategies for advanced gastric cancer (GC). However, their clinical impact has been limited due to challenges in identifying epithelial-mesenchymal transition (EMT)-CTCs using conventional methods.MethodsTo bridge this knowledge gap, we established a detection platform for CTCs based on the distinctive biomarker cell surface vimentin (CSV). A prospective study involving 127 GC patients was conducted, comparing CTCs enumeration using both EpCAM and CSV. This approach enabled the detection of both regular and EMT-CTCs, providing a comprehensive analysis. Spiking assays and WES were employed to verify the reliability of this marker and technique. To explore the potential inducer of CSV+CTCs formation, a combination of Tandem Mass Tag (TMT) quantitative proteomics, m6A RNA immunoprecipitation–qPCR (MeRIP–qPCR), single-base elongation- and ligation-based qPCR amplification method (SELECT) and RNA sequencing (RNA-seq) were utilized to screen and confirm the potential target gene. Both in vitro and in vivo experiments were performed to explore the molecular mechanism of CSV expression regulation and its role in GC metastasis.ResultsOur findings revealed the potential of CSV in predicting therapeutic responses and long-term prognosis for advanced GC patients. Additionally, compared to the conventional EpCAM-based CTCs detection method, the CSV-specific positive selection CTCs assay was significantly better for evaluating the therapeutic response and prognosis in advanced GC patients and successfully predicted disease progression 14.25 months earlier than radiology evaluation. Apart from its excellent role as a detection marker, CSV emerges as a promising therapeutic target for attenuating GC metastasis. It was found that fat mass and obesity associated protein (FTO) could act as a potential catalyst for CSV+CTCs formation, and its impact on the insulin-like growth factor-I receptor (IGF-IR) mRNA decay through m6A modification. The activation of IGF-I/IGF-IR signaling enhanced the translocation of vimentin from the cytoplasm to the cell surface through phosphorylation of vimentin at serine 39 (S39). In a GC mouse model, the simultaneous inhibition of CSV and blockade of the IGF-IR pathway yielded promising outcomes.ConclusionIn summary, leveraging CSV as a universal CTCs marker represents a significant breakthrough in advancing personalized medicine for patients with advanced GC. This research not only paves the way for tailored therapeutic strategies but also underscores the pivotal role of CSV in enhancing GC management, opening new frontiers for precision medicine.

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

The fast development in precision manufacturing and product quality inspection of ferromagnetic material desperately demands that the defect determining the performance, service life and reliability of the mechanical part should be accurately sensed, detected and evaluated. However, conventional defect detection methods and related magnetic induction sensors are usually characterized with low accuracy in small defect detection, which makes the non-destructive testing (NDT) for ferromagnetic material full of challenges. Thus, a new magnetic focusing sensor is proposed and designed for the precision defect detection of ferromagnetic steel plate here. First, the theoretical principle analysis of the magnetic focusing sensor is introduced, and the preliminary finite element modeling (FEM) is conducted to demonstrate the feasibility of the magnetic focusing sensor in tiny defect inspection. Then, both the static and coupling dynamic characterizations of the magnetic focusing sensor are investigated using the FEM method. The optimal structural parameters of the magnetic focusing (MF) sensor in static conditions are summarized. Furthermore, the effects of the dynamic movement of the MF sensor on the multi-physical fields of the magnetization field, electric field, electromagnetic attraction force field and thermal field are all revealed. Finally, experiments regarding the performance evaluation and comparison between the MF sensor and other two magnetic flux leakage (MFL) sensors are conducted, and the prominent capabilities of accurate and precise defect detection for the MF sensor is demonstrated through the defect detection with different sizes. The effectiveness and feasibility of the MF sensor in detecting tiny defects on ferromagnetic object, as well as its great potential for practical application is therefore validated. Additionally, the main conclusions, advantages of the MF sensor are summarized and the future work is discussed.

Design and fabrication of a molecularly imprinted electrochemical sensor with high sensitivity for zearalenone assessment in maize

A highly sensitive and selective molecularly imprinted polymer-based electrochemical sensor was developed for rapid detection of zearalenone (ZEA) in maize samples. The sensor exhibited a wide linear dynamic range from 1 × 10−4 to 1 × 102 ng/mL with an impressive limit of detection of 3.4 × 10−5 ng/mL. It demonstrated excellent selectivity towards ZEA over structurally similar mycotoxins and provided accurate quantification in spiked maize extracts with recoveries ranging from 92 % to 108 % and relative standard deviations under 10 %. Comparative analysis with a conventional HPLC-fluorescence detection method revealed strong correlation (R2 = 0.982) between the two datasets. The proposed imprinted electrochemical sensing platform offers distinct advantages of simplicity, portability, cost-effectiveness, and high analytical performance, making it a promising tool for rapid on-site screening of ZEA contamination in food commodities.

N/S co-doped carbon dots-based solution-gated graphene transistors (SGGT) for ultra-sensitive and selective determination of Hg (II) ions in real water and serum samples

The conventional methods for detecting Hg2+ ions are generally impaired by low sensitivity, a high limit of detection (LOD), and the long detection time required. In this work, we develop a novel solution gated graphene transistor (SGGT) based on carbon dots doped with N and S elements (N/S CDs) as gate electrode probes. The prepared N/S CDs, which are 3–5 nm in diameter, are first modified at the gate of the SGGT through covalent bonding that specifically recognizes Hg2+ ions. The SGGT is then used as a Hg2+ sensor, exhibiting good selectivity, ultrahigh sensitivity, good reproducibility, and a short detection time. The SGGT sensor has an excellent linear detection range, from 0.1 fM to 1 nM. The LOD can reach 28 aM, which is far lower than that of conventional detection methods. Moreover, the SGGT sensor also has a rapid response time of ∼ 40 s. Finally, the ability to analyze real water samples and serum samples is demonstrated.

Detection of Campylobacter jejuni based on CRISPR/Cas12a with novel target yclQ obtained by pan-genome analysis

Campylobacter jejuni (C. jejuni) is one of the most common causes of bacterial gastrointestinal foodborne infections worldwide. Conventional detection methods based on bacterial culture are time-consuming, therefore, rapid detection methods are urgently needed to be developed. The objective of this study was to screen a novel species-specific target and to develop a rapid and accurate method for the detection of C. jejuni in food. Through pan-genome analysis of 298 Campylobacter whole-genome sequence data, a new species-specific target yclQ was screened. The yclQ gene was verified through PCR to be unique to C. jejuni. Subsequently, based on the target yclQ, we developed of a CRISPR/Cas12a-based platform for fluorescence visualization and nucleic acid test strips. The RAA-CRISPR/Cas12a system demonstrates the ability to detect C. jejuni at 1.8 CFU/mL within 50 min. Compared to PCR (1.8 × 101 CFU/mL), the RAA and RAA-CRISPR/Cas12a assay showed higher sensitivity. The detection results of the actual samples were consistent with those of the isolation and culture procedures. In conclusion, the RAA-CRISPR/Cas12a system, based on the target yclQ, demonstrates significant potential for the rapid and accurate detection of C. jejuni in food.

Design of liquid level detection circuit based on sampling probe structure capacitance.

Liquid level detection system is an essential core functional component of automatic clinical medical testing instrument. The conventional liquid level detection method has low detection accuracy and sensitivity, and may have the problem of false detection, which may lead to the inaccurate test results. This paper presents a high sensitivity liquid level detection system based on the principle of variable capacitance. When the sampling probe contacts the liquid level, the probe capacitance will change. The liquid level detection circuit board judges whether the probe contacts the liquid level by sensing the change of probe capacitance. When judging the liquid level signal, the combination of slope detection and amplitude detection is used. The liquid level detection circuit board takes the phase-locked loop(PLL) circuit as the center to detect the change of the capacitance. The reference signal of the PLL is set as a square wave of 375kHz. The double tube probe is used as a part of the tuning capacitor of the voltage controlled oscillator to control the frequency of the output signal, which can realize the rapid phase locking. The experimental results show that the system has accurate detection results, high sensitivity, stable and reliable operation, good dynamic response performance in the case of large and small liquid volume. Compared with other liquid level detection methods based on machine vision, ultrasonic, optics and so on, the proposed liquid level detection system has simpler structure and lower cost, it can avoid the problems of collision, carryover contamination and empty suction by controlling the depth of sampling needle inserted into liquid.

Detection of Serrated Adenoma in NBI Based on Multi-Scale Sub-Pixel Convolution

Colorectal cancer ranks third in global malignancy incidence, and serrated adenoma is a precursor to colon cancer. However, current studies primarily focus on polyp detection, neglecting the crucial discrimination of polyp nature, hindering effective cancer prevention. This study established a static image dataset for serrated adenoma (SA) and developed a deep learning SA detection model. The proposed MSSDet (Multi-Scale Sub-pixel Detection) innovatively modifies each layer of the original feature pyramid’s structure to retain high-resolution polyp features. Additionally, feature fusion and optimization modules were incorporated to enhance multi-scale information utilization, leveraging the narrow-band imaging endoscope’s ability to provide clearer colonoscopy capillary and texture images. This paper utilized 639 cases of colonic NBI endoscopic images to construct the model, achieving a mean average precision (mAP) of 86.3% for SA in the test set. The SA detection rate via this approach has significantly surpassed conventional object detection methods.

On-site detection of methicillin-resistant Staphylococcus aureus (MRSA) utilizing G-quadruplex based isothermal exponential amplification reaction (GQ-EXPAR)

Methicillin-resistant Staphylococcus aureus (MRSA) has a high incidence in infectious hospitals and communities, highlighting the need for early on-site detection due to its resistance to methicillin antibiotics. The present study introduces a highly sensitive detection system for mecA, a crucial methicillin marker, utilizing an RCA-based isothermal exponential amplification reaction. The G-quadruplex-based isothermal exponential amplification reaction (GQ-EXPAR) method designs probes to establish G-quadruplex secondary structures incorporating thioflavin T for fluorescence. The system, unlike conventional genetic detection methods, works with portable isothermal PCR devices (isoQuark), facilitating on-site detection. A detection limit of 0.1 fmol was demonstrated using synthetic DNA, and effective detection was proven using thermal lysis. The study also validated the detection of targets swabbed from surfaces within bacterial 3D nanostructures using the GQ-EXPAR method. After applying complementary sequences to the padlock probe for the target, the GQ-EXPAR method can be used on various targets. The developed method could facilitate rapid and accurate diagnostics within MRSA strains.

CeO2 nanocages with tetra-enzyme mimetic activities for dual-channel ratiometric colorimetric detection of microcystins-LR

BackgroundMicrocystin-leucine-arginine (MC-LR) produced by various cyanobacteria during harmful algal bloom poses serious threats to drinking water safety and human health. Conventional chromatography-based detection methods require expensive instruments and complicated sample pretreatment, limiting their application for on-site detection. Colorimetric aptasensors are simple and rapid, and are amenable to fast detection. However, they provide only one output signal, resulting in poor sensitivity and accuracy. Dual-channel ratiometric colorimetric method based on the peroxidase-like activity of nanozyme can achieve self-calibration by recording two reverse signals, providing significantly enhanced sensitivity and accuracy. ResultsCeO2 nanocages (CeO2 NCs) with tetra-enzyme mimetic activities (oxidase-, peroxidase-, catalase- and superoxide dismutase-like activities) were facilely synthesized using zeolitic imidazolate framework-67 (ZIF-67) as sacrificial template. The peroxidase-like activity of CeO2 NCs can be regulated by DNA, and it showed opposite response to two chromogenic substrates (2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) and 3,3′,5,5′-tetramethylbenzidine (TMB)), which was mainly attributed to the changed affinity. On the basis of MC-LR aptamer-tunable peroxidase-like activity of CeO2 NCs in TMB and ABTS channel, a dual-channel ratiometric colorimetric aptasensor was constructed for detection of MC-LR. Compared with conventional single-signal colorimetric assays, the proposed method showed lower limit of detection (0.66 pg mL−1) and significantly enhanced sensitivity. Moreover, the practicability of the ratiometric colorimetric assay was demonstrated by detecting MC-LR in real water samples, and satisfactory recoveries (94.9–101.9 %) and low relative standard deviations (1.6–6.3 %) were obtained. SignificanceThis work presents a nanozyme-based ratiometric colorimetric aptasensor for MC-LR detection by recording the reverse responses of two chromogenic reactions. Benefiting from the self-calibration function, the method can achieve higher sensitivity and accuracy. The short detection time and practical application in real water samples show great potential for environmental monitoring.

Progress in the Genetic Detection of Thalassemia Based on Third-Generation Gene Sequencing --Review

Thalassemia is most widely distributed single gene autosome recessive genetic disease in the world, whose clinical manifestation was changed from asymptomatic anemia to severe anemia requiring continous blood transfusion to maintain life, thus resulting in a serious economic burden to society and families. Therefore, it is necessary to carry out the corresponding prentatal screening and diagnosis. Most of the conventional detection methods can only detect the common thalassemia genotype, it can easy to cause misdiagnosis or missed diagnosis for those rate genetic variantions. The third-generation sequecing (TGS) has been applied to the detection of thalassemia genes, which is more accurate, reliable and superior to the converntional detection methods. This article reviews the latest research progress of the TGS technology in genetic testing of thalassemia.