Rapid Detection Method Research Articles

Establishment and methodological evaluation of a rapid detection method for Cryptococcus neoformans and Cryptococcus gattii species complexes based on CRISPR-Cas12a technology

PurposeThe opportunistic pathogens causing Cryptococcal meningitis are Cryptococcus neoformans and Cryptococcus gattii species complexes. At present, clinical detection methods for this condition include culture, ink staining, and cryptococcal antigen detection. In addition, enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and real-time quantitative PCR (qPCR) can be applied for the detection of Cryptococcus. Nevertheless, these methods cannot achieve point-of-care detection (POCT); thus, there is a pressing need to establish a fast, sensitive, and effective detection method. MethodsRecombinase polymerase amplification (RPA) and clustered regularly spaced short palindromic repeat (CRISPR) techniques are effective tools for achieving rapid POCT. In this study, RPA was combined with CRISPR-Cas12a to establish a fast, sensitive, and specific detection method for cryptococcal meningitis. ResultsThis study included RPA-Cas12a fluorescence detection and RPA-Cas12a immunochromatographic detection, which can be performed within 50 min. Moreover, the detection limit was as low as 102 copies/μL. Interestingly, the developed method demonstrated satisfactory specificity and no cross-reactivity with other fungi and bacteria. 36 clinical samples were tested, and the consistency between the test results and those obtained using the commonly used clinical culture method was 100 %. ConclusionIn this study, a rapid detection method for Cryptococcus neoformans and Cryptococcus gattii species complexes was developed based on CRISPR-Cas12a technology, characterized by its high sensitivity and specificity, ease of use, and cost-effectiveness, making it suitable for on-site detection.

Establishment of a Rapid Detection Method for Cadmium Ions via a Specific Cadmium Chelator N-(2-Acetamido)-Iminodiacetic Acid Screened by a Novel Biological Method.

Heavy metal ions such as cadmium, mercury, lead, and arsenic in the soil cannot be degraded naturally and are absorbed by crops, leading to accumulation in agricultural products, which poses a serious threat to human health. Therefore, establishing a rapid and efficient method for detecting heavy metal ions in agricultural products is of great significance to ensuring the health and safety. In this study, a novel optimized spectrometric method was developed for the rapid and specific colorimetric detection of cadmium ions based on N-(2-Acetamido)-iminodiacetic acid (ADA) and Victoria blue B (VBB) as the chromogenic unit. The safety evaluation of ADA showed extremely low biological toxicity in cultured cells and live animals. The standard curve is y = 0.0212x + 0.1723, R2 = 0.9978, and LOD = 0.08 μM (0.018 mg/kg). The liner concentrations detection range of cadmium is 0.1-10 μM. An inexpensive paper strip detection method was developed with a detection limit of 0.2 μM to the naked eye and a detection time of less than 1 min. The method was successfully used to assess the cadmium content of rice, soybean, milk, grape, peach, and cabbage, and the results correlated well with those determined by inductively coupled plasma-mass spectrometry (ICP-MS). Thus, our study demonstrated a novel rapid, safe, and economical method for onsite, real-time detection of cadmium ions in agricultural products.

Establishment of LAMP-CRISPR/Cas12a for rapid detection of Escherichia coli O157:H7 and one-pot detection

Escherichia coli O157:H7 is a pathogenic serotype of Escherichia coli. Consumption of food contaminated with E. coli O157:H7 could cause a range of diseases. Therefore, it is of great importance to establish rapid and accurate detection methods for E. coli O157:H7 in food. In this study, based on LAMP and combined with the CRISPR/cas12a system, a sensitive and specific rapid detection method for E. coli O157:H7 was established, and One-Pot detection method was also constructed. The sensitivity of this method could stably reach 9.2 × 10° CFU/mL in pure culture, and the whole reaction can be completed within 1 h. In milk, E. coli O157:H7 with an initial contamination of 7.4 × 10° CFU/mL only needed to be cultured for 3 h to be detected. The test results can be judged by the fluorescence curve or by visual observation under a UV lamp, eliminating instrument limitations and One-Pot detection can effectively prevent the problem of false positives. In a word, the LAMP-CRISPR/cas12a system is a highly sensitive and convenient method for detecting E. coli O157:H7.

Pb-based metal organic framework as substrate: Chemical vapor generation-visual/smartphone colorimetric analytical system for sensitive and selective detection of sulfide ion in water and beers

A visual/smartphone colorimetric system was developed for the sensitive and selective detection of sulfide ion (S2−) using chemical vapor generation (CVG) as a gaseous sampling technique. S2− in samples were converted into H2S after the addition of H2SO4, which separated from the solution during CVG process, ensuring high efficiency of vapor generation (sensitivity) and eliminated interferences (selectivity). The H2S was subsequently reacted with Pb-BTC and PbS was thus formed, causing the test paper turned to black. It was utilized for the detection of S2− by visual/smartphone colorimetric system. Detectable limits of 0.05 μg/mL and 0.2 μg/mL were obtained under smartphone mode and visual mode, respectively. Furthermore, this colorimetric system was successfully used for the analysis of S2− in several beer samples and water samples, with recoveries ranging 97 %–111 %. This system represents a potential miniaturized, easy used and high-effective method for rapid and on-site detection of S2−.

Rapid and accurate quantification of trypsin activity using integrated infrared and ultraviolet spectroscopy with data fusion techniques

Proteases play a crucial role in industrial enzyme formulations, with activity fluctuations significantly impacting product quality and yield. Therefore, developing a method for precise and rapid detection of protease activity is paramount. This study aimed to develop a rapid and accurate method for quantifying trypsin activity using integrated infrared (IR) and ultraviolet (UV) spectroscopy combined with data fusion techniques. The developed method evaluates the enzymatic activity of trypsin under varying conditions, including temperature, pH, and ionic strength. By comparing different data fusion methods, the study identifies the optimal model for accurate enzyme activity prediction. The results demonstrated significant improvements in predictive performance using the feature-level data fusion approach. Additionally, substituting the spectral data of the samples in the validation sets into the best prediction model resulted in a minimal residual difference between predicted and true values, further verifying the model's accuracy and reliability. This innovative approach offers a practical solution for the efficient and precise quantification of enzyme activity, with broad applications in industrial processes.

Development of a novel sandwich immunoassay based on targeting recombinant Francisella outer membrane protein A for the diagnosis of tularemia.

Tularemia, caused by the bacterium Francisella tularensis, poses health risks to humans and can spread through a variety of routes. It has also been classified as a Tier 1 Select agent by the CDC, highlighting its potential as a bioterrorism agent. Moreover, it is difficult to diagnose in a timely fashion, owing to the non-specific nature of tularemia infections. Rapid, sensitive, and accurate detection methods are required to reduce mortality rates. We aimed to develop antibodies directed against the outer membrane protein A of F. tularensis (FopA) for rapid and accurate diagnosis of tularemia. We used a baculovirus insect cell expression vector system to produce the FopA antigen and generate anti-FopA antibodies through immunization of BALB/c mice. We then employed hybridoma and phage display technologies to screen for antibodies that could recognize unique epitopes on FopA. Two monoclonal antibodies, 6B12 and 3C1, identified through phage display screening specifically bound to recombinant FopA in a dose-dependent manner. The binding affinity of the anti-FopA 6B12 and 3C1 antibodies was observed to have an equilibrium dissociation constant of 1.76 × 10-10 M and 1.32 × 10-9 M, respectively. These antibodies were used to develop a sandwich ELISA system for the diagnosis of tularemia. This assay was found to be highly specific and sensitive, with detection limits ranging from 0.062 ng/mL in PBS to 0.064 ng/mL in skim milk matrices. Our findings demonstrate the feasibility of a novel diagnostic approach for detecting F. tularensis based on targeting FopA, as opposed to existing tests that target the bacterial lipopolysaccharide.

Green synthesis of N,S dual heteroatom-doped fluorescent carbon dots (N,S-CDs) and their applications in gentamicin sensing and dual-switch anti-counterfeiting encryption

Herein, we report a facile one-step solvothermal synthesis of nitrogen and sulfur co-doped fluorescent carbon dots (N,S-CDs) using benzoic acid as the carbon source and sulfonamide as the doping agent. The synthesized N,S-CDs exhibit stable photoluminescence with an impressive fluorescence quantum yield of 16.8 %. These N,S-CDs maintain their luminescent properties under various challenging conditions, including high salt concentrations, elevated temperatures, UV radiation exposure, acidic or alkaline environments, long-term storage, and different solvents. Notably, the photoluminescence of N,S-CDs is excitation-wavelength dependent, with emission wavelengths tunable from 470 nm to 585 nm. Furthermore, the N,S-CDs display distinct fluorescent hues in aqueous and ethanolic media, presenting a novel dual-switch anti-counterfeiting ink system. Leveraging the sensitivity of N,S-CDs to gentamicin, we have developed a facile and rapid detection method for this antibiotic, which has been successfully applied to the analysis of authentic milk samples. This research contributes a novel and efficient protocol for the fabrication of high-performance carbon dots, thereby expanding their potential applications in food analysis and anti-counterfeiting technologies.

In Situ growth of high-crystallinity MOF@CTF core-shell hybrid heterostructures and their application in photoelectrochemical sensing of vancomycin

Vancomycin is a first-line drug for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant bacterial infections. It is of great significance to develop rapid detection methods for vancomycin in different matrices. In this work, a novel method for the in-situ preparation of MOF@CTF core-shell hybrid heterogeneous structures was proposed and applied to the photoelectrochemical detection of vancomycin. The insolubility of the imine precursor structure in homogeneous solvents and the Lewis acidic centers in the MOF structure induced the initial arrangement of CTF on the surface of PCN-224. By encapsulating a layer of highly crystalline CTF structure onto the initial morphology of PCN-224, a MOF@CTF heterogeneous structure was formed. Importantly, the introduction of anthracene-based electron-donating groups into the CTF structure and the formation of a donor-acceptor system with triazine rings enhanced the photoconversion efficiency of the heterogeneous structure by establishing a continuous separated built-in electric field within the core-shell structure. The successful in-situ synthesis of the core-shell hybrid heterogeneous structure was confirmed through microscopic morphology, PXRD, XPS, and FT-IR. Furthermore, investigations into the semiconductor properties of the heterogeneous structure, including semiconductor type, bandgap, and photo-voltage, were conducted. Results showed that the heterogeneous structure exhibited a doubled photocurrent density compared to the original PCN-224. Additionally, a molecularly imprinted photoelectrochemical sensor (MIPECS) for vancomycin was constructed. Under optimized conditions, the sensor demonstrated a superior concentration response range (0.1 nM - 1 μM) and detection limit (0.031 nM, 3σ/S, n = 11). Simultaneously, the potential applicability of the sensor in animal-derived foods, serum, and environmental water samples was evaluated, yielding satisfactory results.

Rapid detection of fertilizer information based on Raman spectroscopy and machine learning

The rapid detection of fertilizer nutrient information is a crucial element in enabling intelligent and precise variable fertilizer application. However, traditional detection methods possess limitations, such as the difficulty in quantifying multiple components and cross-contamination. In this study, a rapid detection method was proposed, leveraging Raman spectroscopy combined with machine learning, to identify five types of fertilizers: K2SO4, (CO(NH2)2, KH2PO4, KNO3, and N:P:K (15-15-15), along with their concentrations. Qualitative and quantitative models of fertilizers were constructed using three machine learning algorithms combined with five spectral preprocessing methods. Two variable selection methods were used to optimize the quantitative model. The results showed that the classification accuracy of the five fertilizer solutions obtained by random forest (RF) was 100 %. Moreover, in terms of regression, partial least squares regression (PLSR) outperformed extreme learning machine (ELM) and least squares support vector machine (LSSVM), yielding prediction Rp2 within the range of 0.9843–0.9990 and a root mean square error in the range of 0.0486–0.1691. In addition, this study evaluated the impact of different water types (deionized water, well water, and industrial transition water) on the detection of fertilizer information via Raman spectroscopy. The results showed that while different water types did not notably affect the identification of fertilizer nutrients, they did exert a pronounced effect on the quantification of concentrations. This study highlights the efficacy of combining Raman spectroscopy with machine learning in detecting fertilizer nutrients and their concentration information effectively.

Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit

External defects in paprika fruit (Capsicum annuum, L. var. angulosum) directly impact their storage and marketability, potentially leading to internal decay and altering fruit appearance. However, automatically detecting various paprika defects, including cracks, scars, diseases, and bruises, poses a significant challenge due to variations in defect size and visual similarities among different types of defects. This study proposed a comprehensive, accurate, and rapid defect detection method utilizing hyperspectral imaging (HSI) alongside multivariate analysis and imaging technologies. The reflective surface of fruit poses a primary limitation to efficient defect detection using imaging technologies. Therefore, a polarized HSI visible–near-infrared (VIS-NIR, 397–1000 nm) system was developed, and over 500 paprika fruit with various types of defects were imaged using the developed system. Additionally, fruit were manually bruised in different sizes to ensure spectral data variability and measured every 12 hours for two days. Spectral data (over 4580 spectral data) from the regions of interest (ROIs) of the paprika fruit samples, including manually bruised fruit, were extracted. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was developed to distinguish between normal and defective fruit. Remarkably, the developed model achieved an accuracy of over 99 % in predicting normal and defective fruit. Moreover, the chemical images generated enhance the inspection process. The successive projection algorithm (SPA) and variable importance in projection (VIP) were further utilized for critical band selection, yielding an accuracy of 93 % and 98 % in the prediction set, respectively. Furthermore, a classification algorithm based on analysis of variance (ANOVA) was employed to determine the optimal wavelength band ratios (F-values). This approach yielded an impressive accuracy rate of over 88.8 % and further enhanced the performance of the band ratio by an improved watershed segmentation algorithm (IWSA) to reach an accuracy of 91.3 % in multi-defect detection. These findings offer a practical and efficient solution for the external quality inspection of paprika fruit, particularly in cases with naturally occurring defects. By integrating hyperspectral imaging, PLS-DA, band selection methods, ANOVA-based classification, and IWSA, this comprehensive analysis provides a reliable and practical means for identifying and categorizing defects in paprika fruit, ultimately enhancing product quality and consumer satisfaction.

A portable multifunctional biosensor based on a newly screened endogenous reference gene for pork adulteration detection

The adulteration of animal-derived food is a widespread problem with meat adulteration emerging as a global concern. To address the need for on-site rapid detection and visual analysis, a portable multifunction biosensor (PMB) was developed. This device integrates Recombinase Polymerase Amplification (RPA), CRISPR/Cas12a assay, and a custom-designed 3D-printed multifunction device, specifically for the visual and rapid detection of pork in food. Initially, a screening model was constructed using Perl bioinformatics based on chromosomal gene information, and the LOC110262058 gene was selected as a reliable endogenous reference gene for pigs. Subsequently, a one-tube RPA-CRISPR/Cas12a system was established to simplify the procedures, converting pork-specific signals into fluorescence while preventing cross-contamination. In addition, an independently designed 3D-printed multifunction device was developed to support the RPA-CRISPR/Cas12a system for one-tube detection, incorporating functions such as heating, centrifugation, and imaging. The reaction conditions of the PMB were optimized to reduce the reaction time to 20–30 min with a sensitivity of 0.01 g/100 g. The analysis of actual samples indicated that the PMB has high selectivity and practical effectiveness for pork detection. The proposed biosensor offers several detection advantages, including output, ease of use, stability, and independence from complex equipment, making it a powerful method for rapid pork detection in food.

Preparation of a multiple interacting magnetic fluid for rapid and sensitive simultaneous extraction of β-nicotinamide mononucleotide metabolites in biological samples

Backgroundβ-nicotinamide mononucleotide stands out as an essential breakthrough in “anti-aging” and consistently leads the list of top-selling nutritional supplements in terms of quantity. As the metabolites of β-nicotinamide mononucleotide, the detection of nicotinamide and N1-methylnicotinamide is of great significance for evaluating the nutritional effect of dietary supplements of β-nicotinamide mononucleotide. However, due to the extremely low concentration of nicotinamide and N1-methylnicotinamide in vivo and the serious matrix interference in biological samples, there is an increasing demand for materials and methods of pre-treatment. ResultsIn this study, Fe3O4@hydroxypropyl methyl cellulose@dodecylbenzenesulfonic acid magnetic fluid was synthesized for the first time, and it was used as pretreatment material to detect nicotinamide and N1- methylnicotinamide in urine samples by high performance liquid chromatography. Compared with other adsorption materials, Fe3O4@hydroxypropyl methyl cellulose@dodecylbenzenesulfonic acid nanoparticles are a kind of uniform magnetic fluid. Furthermore, they have more types and quantities of interaction sites (electrostatic interactions, hydrophobic interactions, hydrogen bonding interactions, and π-π interactions), so they own greater adsorption capacity. When the pH of the solution is 4, they can be adsorbed quickly within 10 s. The method successfully detected trace nicotinamide and N1-methylnicotinamide in urine samples in the linear range of 0.1–80 μg mL−1, the low detection limits were 0.30 ng mL−1 and 1.5 ng mL−1 respectively, and the quantification limits were 1.0 ng mL−1 and 5.0 ng mL−1, respectively. At the same time, the standard urine samples of nicotinamide and N1-methylnicotinamide showed satisfactory recovery rate 94.50–109.1 %. The results indicated that the established method can accurately and quantitatively determine trace nicotinamide and N1-methylnicotinamide in urine samples. SignificanceConsequently, low concentration of β-nicotinamide mononucleotide metabolites can be detected simultaneously, and the interference can be eliminated during the detection process, which provides an efficient and convenient pretreatment method and a rapid and sensitive detection method for the analysis of β-nicotinamide mononucleotide metabolites. What's more, it has a wide application prospect in the analysis of other similar metabolites in biological samples.

Pillar[n]arene-Based Fluorescence Turn-On Chemosensors for the Detection of Spermine, Spermidine, and Cadaverine in Saline Media and Biofluids.

Polyamines are essential analytes due to their critical role in various biological processes and human health in general. Due to their role as regulators for cell growth and proliferation (putrescine and spermine), as neuroprotectors, gero-, and cardiovascular protectors (spermidine), and as bacterial growth indicators (cadaverine), rapid, simple, and cost-effective methods for polyamine detection in biofluids are in demand. The present study focuses on the development and investigation of self-assembled and fluorescent host⋅dye chemo-sensors based on sulfonated pillar[5]arene for the specific detection of polyamines. Binding studies, as well as stability and functionality assessments of the turn-on chemosensors for selective polyamine detection in saline and biologically relevant media, are shown. Furthermore, the practical applicability of the developed chemo-sensors is demonstrated in biofluids such as human urine and saliva.

Rapid detection technology of sewing thread based on DeblurGAN-BSV3 defuzzification algorithm and ST-FPN detection algorithm

Stitch defect detection is an important means to ensure the quality of stitched fabrics. In order to realize the intellectualization of the sewing process, a rapid defect detection method based on machine vision was proposed. In order to solve the problem of motion blur caused by sewing speed, an image DeblurGAN-BSV3 algorithm is proposed. Firstly, different attention fusion mechanisms are introduced to achieve accurate extraction of image features. Secondly, the depth-separable convolution of the trunk network is replaced by blueprint convolution to ensure the processing speed of the detection algorithm and the clarity of the output image. In order to solve the difficulty of rapid detection of pin defects, a student–teacher feature pyramid matching method was proposed. Firstly, the abnormal region interpretation technology was used to explicitly visualize the defect area of the identified abnormal instances, and then the abnormal detection score was performed on the detected images to make a quality judgment. The experimental results show that the combination of the above two methods has a good ability for rapid defect detection. Rapid detection of common pin defects is realized.

Electrochemical Detection of Pseudomonas aeruginosa Quorum Sensing Molecule (S)-N-Butyryl Homoserine Lactone Using Molecularly Imprinted Polymers.

Pseudomonas aeruginosa is a multidrug-resistant Gram-negative bacterium that poses a significant threat to public health, necessitating rapid and on-site detection methods for rapid recognition. The goal of the project is therefore to indirectly detect the presence of P. aeruginosa in environmental water samples targeting one of its quorum-sensing molecules, namely, (S)-N-butyryl homoserine lactone (BHL). To this aim, molecularly imprinted polymers (MIPs) were synthesized via bulk free-radical polymerization using BHL as a template molecule. The obtained MIP particles were immobilized onto screen-printed electrodes (MIP-SPEs), and the BHL rebinding was analyzed via electrochemical impedance spectroscopy (EIS). To study the specificity of the synthesized MIPs, isotherm curves were built after on-point rebinding analysis performed via LC-MS measurements for both MIPs and NIPs (nonimprinted polymers, used as a negative control), obtaining an imprinting factor (IF) of 2.8 (at C f = 0.4 mM). The MIP-SPEs were integrated into an electrochemical biosensor with a linear range of 1 × 101-1 × 103 nM and a limit of detection (LoD) of 31.78 ± 4.08 nM. Selectivity measurements were also performed after choosing specific interferent molecules, such as structural analogs and potential interferents, followed by on-point analysis performed in spiked tap water to prove the sensor's potential to detect the presence of the quorum-sensing molecule in environmentally related real-life samples.

Development of a reusable polymeric fluorescence sensor based on acryloyl β-cyclodextrin for the determination of aflatoxin B1 in grain products

AFB1 is a harmful substance that can be found in agricultural products and can seriously affect human health, even in trace amounts. Therefore, monitoring AFB1 levels to ensure food safety and protect public health is crucial. New, highly reliable, selective, and rapid detection methods are needed to achieve this goal. Our work involves the development of a polymeric membrane sensor using radical polymerization that can accurately detect AFB1. Various spectroscopic techniques (Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM)) were used to obtain information about the structural and morphological properties of the prepared sensor. The sensor displayed fluorescence selectively responsive to AFB1 at the excitation wavelength of 376 nm and emission wavelength of 423 nm. The polymeric fluorescence sensor showed good sensitivity and a wide linear range from 9.61 × 10−10 and 9.61 × 10−9 mol/L for AFB1quantification. The limit of detection (LOD) is as low as 3.84 × 10−10 mol/L for AFB1. Other mycotoxins, such as aflatoxin B2 and aflatoxin G1, did not interfere with the sensor’s high selectivity towards AFB1. To test the sensor’s effectiveness in detecting AFB1 in real samples, three different grain samples – peanuts, hazelnut butter, and peanuts with a sauce known to contain AFB1 – were utilized. The results were satisfactory and demonstrated that the sensor can be successfully employed in real samples, with an error range of 0.43 % to 12.10 %.

Rapid SARS-CoV-2 sensing through oxygen reduction reaction catalysed by Au@Pt/Au core@shell nanoparticles

The development of rapid, accurate, sensitive, and low-cost diagnostic methods for COVID-19 detection in real-time is the unique way to control infection sources and monitor illness progression. In this work, we propose an electrochemical biosensor for the rapid and accuracy diagnosis of COVID-19, through the determination of ORF1ab specific sequence. The biosensor is based on the immobilization of a thiolated sequence partially complementary (domain 1) to ORF1ab on gold screen-printed electrodes and the use of bifunctional Au@Pt/Au core@shell nanoparticles modified with a second thiolated sequence partially complementary to ORF1ab (domain 2) as electrochemical indicator of the hybridization of DNA sequences. The synthesized Au@Pt/Au nanoparticles consist of an Au core, a shell of Pt (Au@Pt NPs), that provides an excellent electrocatalytic activity toward the oxygen reduction reaction (ORR) even after formation of hybrid biomaterials by modification, through the Au protuberances growth on the NPs surface, with an oligonucleotide with recognition ability. The ORR electrochemical activity, enhanced by the label element (Au@Pt/Au NPs), has been employed, for the first time, as indicator of the hybridization event. Based on this strategy, target sequences of the SARS-CoV-2 virus have been detected with a detection limit of 32 pM. The selectivity of the biosensor was confirmed by analysing ORF1ab sequence in the presence of DNA sequences from other viruses. The biosensor has been successfully applied to the direct detection of the virus in non-amplified samples of nasopharyngeal swabs from infected and non-infected patients. Results compare well with those obtained through RT-qPCR but our method is more rapid since does not need any amplification process.

Mix-VIO: A Visual Inertial Odometry Based on a Hybrid Tracking Strategy.

In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking approach, combining traditional handcrafted tracking techniques with Deep Neural Network (DNN)-based feature extraction and matching pipelines. The system employs deep learning methods for rapid feature point detection, while integrating traditional optical flow methods and deep learning-based sparse feature matching methods to enhance front-end tracking performance under rapid camera motion and environmental illumination changes. In the back-end, we utilize sliding window and bundle adjustment (BA) techniques for local map optimization and pose estimation. We conduct extensive experimental validations of the hybrid feature extraction and matching methods, demonstrating the system's capability to maintain optimal tracking results under illumination changes and image blur.

A method to standardize the temperature for near infrared spectra of the indigo pigment in non-dairy cream based on symbolic regression

Near infrared (NIR) spectroscopy is sensitive to physical conditions such as sample temperature, meaning that rapid detection methods based on NIR spectroscopy are significantly influenced by temperature. To address this challenge, symbolic regression was employed to mitigate the effects of temperature. The Weighted Windowed Adaptive Optimization algorithm was proposed and combined with the Sequential Projection Algorithm to extract temperature-related feature points and remove redundant data. Subsequent 3D modeling of these feature points revealed that absorbance alterations due to temperature comprised two distinct segments. Consequently, based on symbolic regression, the temperature standardization algorithm was devised to generate piecewise equations. This algorithm surpassed genetic programming and non-segmented methods in performance metrics. The piecewise function equations generated by the algorithm were used to regress the absorbance at different temperatures to the standard temperature. Non-dairy cream, with different indigo pigment contents, was temperature standardized using a piecewise function to obtain spectra at two standard temperatures; 18°C and 28°C. The r2 for the quantitative regression model improved from 0.71 to 0.95 at 18°C and from 0.63 to 0.85 at 28°C. The temperature standardization method offers interpretable equations for spectra that model the complex changes with temperature, factoring out the temperature variation, thereby facilitating the practical use of NIR spectroscopy in rapid detection applications.

A novel artificial intelligence-enhanced electrochemical method for rapid methylation detection in cfDNA: Advancing multi-cancer early diagnosis.

181 Background: The detection of methylation in circulating free DNA (cfDNA) has become a pivotal approach for the early screening and diagnosis of cancer. Traditional methods, face challenges including high detection limits, inconvenience, and elevated costs. Our prior work developed a rapid, cost-effective, and sensitive electrochemical methylation detection method for multi-cancer early detection (MCED), yet it couldn't identify the tissue of origin (TOO). We aimed to enhance this method by integrating traditional tumor biomarkers with artificial intelligence (AI) algorithms for improved cfDNA methylation detection and rapid TOO determination. Methods: A training cohort of 626 individuals was analyzed, including 173 colorectal cancer (CRC) patients, 49 patients with hematological tumors, 273 patients with other types of tumors, and 131 healthy controls. This analysis utilized electrochemical detection, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9). We evaluated the diagnostic performance of these markers using ten types of AI algorithms. The optimal algorithm was selected to construct diagnostic models for CRC and hematological tumors. These models were then validated in an external cohort comprising 100 cancer patients. Results: In the training cohort, the logistic algorithm outperformed others, achieving AUC of 0.915 for CRC, 0.850 for hematological tumors, and 0.860 for other tumors. Based on these results, we opted to utilize the logistic algorithm for the development of diagnostic models for CRC and hematological tumors. The CRC model surpassed the performance of the electrochemical adsorption rate (AUC = 0.817) in CRC, though it showed limited efficacy for hematological tumors (AUC = 0.544) and other tumor types (AUC = 0.806). Conversely, the hematological tumors model achieved its highest performance in hematological tumors (AUC = 0.833), with commendable results for CRC (AUC = 0.831) and other cancers (AUC = 0.799) as well. In the validation cohort, the CRC model accurately identified 32 out of 35 CRC patients, and 17 of these were also detected by the hematological tumors model. Among the hematological tumors patients, 18 out of 20 positive with the hematological tumors model, and 15 were detected by the CRC model. For the remaining 45 patients with various other cancer, 22 tested positive with the CRC model and 28 with the hematological tumors model. Conclusions: Integrating machine learning with electrochemical cfDNA methylation detection and tumor markers provides a powerful approach for accurate cancer detection and TOO determination, promising to improve early diagnosis and personalized treatment strategies.