Simple Identification Method Research Articles

Impact of direct identification of bacteria in blood culture–positive specimens by matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry on physician selection of antimicrobial therapy

BackgroundRapid identification of the causative organism in blood stream infections is essential for early initiation of appropriate antimicrobial therapy. Direct identification of bacteria in positive blood culture bottles using matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry is a promising application. A variety of direct identification methods have been reported; however, few studies have evaluated the impact of these methods on physician decision making regarding antimicrobial therapy. MethodsWe developed a simple method for direct bacterial identification and applied it to daily clinical practice to investigate the impact of direct identification of bacteria in positive blood culture bottles on physicians’ choice of antimicrobial agents for treatment. ResultsFrom January 2016 to December 2022, we attempted direct identification in 98 cases and successfully acquired identification results in 88 cases. In three cases, no empiric antimicrobial agents were initiated at the time of venipuncture for blood culture but later initiated based on the direct identification results. In the remaining 85 cases, empiric antimicrobial therapy was initiated at the time blood cultures were performed, and in 29 cases, empiric antimicrobial therapy was changed after direct identification. In 17 of these 29 cases, the antimicrobial therapy was changed based on the direct identification of bacterial genus/species, resulting in a change to an effective antimicrobial therapy before the antimicrobial susceptibility testing results were available. ConclusionsDirect identification of bacteria from positive blood culture bottles could contribute to earlier selection of or changes to antimicrobial therapies by attending physicians.

Multispectral pathogens detection in food using multiplex hyperbranched saltatory rolling circle amplification

Foodborne illnesses caused by Salmonella and Staphylococcus aureus are a significant public health concern, leading to societal and economic repercussions. It is important to develop a simple and straightforward bacteria detection and identification method. A triple-probe multiplex rolling circle amplification technique has been developed to simultaneously detect Salmonella Typhimurium and S. aureus. This method utilizes fluorophore-labeled long padlock probes targeting S. Typhimurium invA and S. aureus glnA specific genes, along with a pH-based detection approach for direct visual identification. The multiplex hyperbranched saltatory rolling circle amplification assay at 30 °C has showed promising results with synthetic targets within 30 min and real bacteria within 2 h after establishing the detection settings. The assay is specific for S. aureus and S. Typhimurium, with a limit of detection of 39 μM for fluorescence and 78 μM for colorimetric. In the simulative test of this method for the detection of S. Typhimurium and S. aureus in milk, the limit of detection for the fluorescence signal after 2 h of amplification was 10 CFU/mL and 5 CFU/mL, respectively. The detection method was evaluated to be stable enough to detect pathogen for 3.29 months. Consequently, this triple-probe-multiplex rolling circle amplification method displays notable specificity, sensitivity, as well as ease of interpretation when testing food samples for harmful pathogens.

Calixarene-Based Supramolecular Sensor Array for Pesticide Discrimination.

The identification and detection of pesticides is crucial to protecting both the environment and human health. However, it can be challenging to conveniently and rapidly differentiate between different types of pesticides. We developed a supramolecular fluorescent sensor array, in which calixarenes with broad-spectrum encapsulation capacity served as recognition receptors. The sensor array exhibits distinct fluorescence change patterns for seven tested pesticides, encompassing herbicides, insecticides, and fungicides. With a reaction time of just three minutes, the sensor array proves to be a rapid and efficient tool for the discrimination of pesticides. Furthermore, this supramolecular sensing approach can be easily extended to enable real-time and on-site visual detection of varying concentrations of imazalil using a smartphone with a color scanning application. This work not only provides a simple and effective method for pesticide identification and quantification, but also offers a versatile and advantageous platform for the recognition of other analytes in relevant fields.

Ferulic acid detection in honey using carbon paste electrode modified by C-C3N4/Li2CoMn3O8 nanocomposite

Honey is regarded as a valuable food commodity which has many and varied components, including phenolic compounds. Ferulic acid (FA) is one of these compounds, which possesses activities such as antioxidant, anti-inflammatory, anti-diabetic, haemolytic, antiviral, anti-cancer, etc. In view of these unique characteristics, a quick, sensitive, simple and inexpensive method for FA identification is required. This research developed a novel electrochemical sensor for measuring FA in honey samples. The sensor utilizes a C-C3N4/Li2CoMn3O8 nanocomposite modified carbon paste electrode (C-C3N4/LCMO/CPE) to enhance the sensitivity and detection. The synthesized nanocomposite exhibits remarkable properties, featuring an abundance of electrochemically active sites and enhanced electron transfer rates, making it ideal for developing advanced electrochemical sensors. The XRD, SEM and EDX analysis were used to examine and look like the synthesised nanocomposite structure. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) methods were used to evaluate the electrochemical behavior of C-C3N4/LCMO/CPE sensor for FA detection and optimized by adjusting pH, and potential scan rate. FA was measured in a phosphate buffered saline (PBS; 0.1 M, pH 4.0) by DPV with the detection limit of 3.6 nM and quantitative limit of 0.009 µM in the linear response ranges of 0.009–3.000 and 5.00–100 µM. Chronoamperometry was utilized to calculate the diffusion coefficient of FA (D = 3.45 (±0.11) × 10−6 cm2.s−1) on the surface of the C-C3N4/LCMO/CPE, as well as the electron transfer coefficient (α = 0.68). The sensor’s high sensitivity for accurately assessing FA in real samples was demonstrated by recovery values ranging from 96.96 to 102.36 %.

Precise Crystal Orientation Identification and Twist-Induced Giant Modulation of Optical Anisotropy in 1T'-ReS2.

The ability to precisely identify crystal orientation as well as to nondestructively modulate optical anisotropy in atomically thin rhenium dichalcogenides is critical for the future development of polarization programmable optoelectronic devices, which remains challenging. Here, we report a modified polarized optical imaging (POI) method capable of simultaneously identifying in-plane (Re chain) and out-of-plane (c-axis) crystal orientations of the monolayer to few-layer ReS2, meanwhile, propose a nondestructive approach to modulate the optical anisotropy in ReS2 via twist stacking. The results show that parallel and near-cross POI are effective to independently identify the in-plane and out-of-plane crystal orientations, respectively, while regulating the twist angle allows for giant modulation of in-plane optical anisotropy from highly intrinsic anisotropy to complete optical isotropy in the stacked ReS2 bilayer (with either the same or opposite c-axes), as well modeled by linear electromagnetic theory. Overall, this study not only develops a simple optical method for precise crystal orientation identification but also offers an efficient light polarization control strategy, which is a big step toward the practical application of anisotropic van der Waals materials in the design of nanophotonic and optoelectronic devices.

Effect of Probiotic Supplements on the Oral Microbiota-A Narrative Review.

Data from systematic reviews and meta-analyses show that probiotics positively impact clinical parameters of oral diseases such as gingivitis, dental caries, and periodontitis. However, the working mechanism of probiotics is not fully understood, but is hypothesized to be mediated by direct and indirect interactions with the oral microbiota and the human host. In the present narrative review, we focused on the microbiological effect of probiotic supplements based on data retrieved from randomized clinical trials (RCTs). In addition, we assessed to what extent contemporary molecular methods have been employed in clinical trials in the field of oral probiotics. Multiple RCTs have been performed studying the potential effect of probiotics on gingivitis, dental caries, and periodontitis, as evaluated by microbial endpoints. In general, results are conflicting, with some studies reporting a positive effect, whereas others are not able to record any effect. Major differences in terms of study designs and sample size, as well as delivery route, frequency, and duration of probiotic consumption, hamper comparison across studies. In addition, most RCTs have been performed with a limited sample size using relatively simple methods for microbial identification, such as culturing, qPCR, and DNA-DNA checkerboard, while high-throughput methods such as 16S sequencing have only been employed in a few studies. Currently, state-of-the-art molecular methods such as metagenomics, metatranscriptomics, and metaproteomics have not yet been used in RCTs in the field of probiotics. The present narrative review revealed that the effect of probiotic supplements on the oral microbiota remains largely uncovered. One important reason is that most RCTs are performed without studying the microbiological effect. To facilitate future systematic reviews and meta-analyses, an internationally agreed core outcome set for the reporting of microbial endpoints in clinical trials would be desirable. Such a standardized collection of outcomes would most likely improve the quality of probiotic research in the oral context.

Rapid analysis of the chemical constituents of traditional Tibetan medicine Sbyor-bzo-ghi-wang using ballpoint electrospray ionization technique.

The chemical constituents of traditional Tibetan medicines (TTM) can be identified using high-performance liquid chromatography and high-resolution mass spectrometry (HPLC-MS/MS) technique. However, the HPLC-MS/MS technique requires the sample to be pretreated and then separated using the specific liquid chromatography method, which is time consuming. This study developed a ballpoint electrospray ionization (BPESI) technique for analyzing the chemical constituents of Sbyor-bzo-ghi-wang. This technique is a simple and inexpensive method for the rapid identification of the chemical constituents of TTMs. After the important parameters of the homemade BPESI device were optimized, the chemical constituents of Sbyor-bzo-ghi-wang were quickly identified without sample pretreatment. The raw data were converted to mzML file using MSConvert and then identified using SIRIUS 5 software. The results showed that 30 compounds were identified from Sbyor-bzo-ghi-wang, namely eight bile acids, six flavonoids, four alkaloids, three amino acids, and nine others. Compared to the ultra-high-performance liquid chromatography-Q/Orbitrap and high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS) technique, the BPESI technique identified almost similar types of compounds and also a comparable number of compounds. Compared with the traditional HPLC-MS/MS methods, the BPESI technique does not require complex sample pretreatment and subsequent chromatographic separation steps; also it consumes a small quantity of samples. Therefore, BPESI can be used for the qualitative analysis of the chemical constituents of Sbyor-bzo-ghi-wang.

Development and application of a TaqMan-probe-based multiplex real-time PCR assay for simultaneous detection of porcine circovirus 2, 3, and 4 in Guangdong province of China.

Porcine circoviruses disease (PCVD), caused by porcine circovirus (PCVs), is an important swine disease characterized by porcine dermatitis, nephrotic syndrome and reproductive disorders in sows. However, diseases caused by PCV2, PCV3, or PCV4 are difficult to distinguish, so a simple, rapid, accurate and high-throughput diagnostic and identification method is urgently needed to differentiate these three types. In this study, specific primers and probes were designed based on the conserved region sequences of the Rep gene of PCV2, and the Cap gene of PCV3 and PCV4. A multiplex qPCR assay was developed and optimized that the limit of detection concentration could reach as low as 3.8 copies/μL, with all correlation coefficients (R2) exceeding 0.999. Furthermore, the method showed no cross-reaction with other crucial porcine viral pathogens, and both intra-repeatability and inter-reproducibility coefficients of variation were below 2%. The assay was applied to the detection of 738 pig samples collected from 2020 to 2021 in Guangdong Province, China. This revealed positive infection rates of 65.18% for PCV2, 29.27% for PCV3, and 0% for PCV4, with a PCV2/PCV3 co-infection rate of 23.17%. Subsequently, complete genome sequences of 17 PCV2 and 4 PCV3 strains were obtained from the above positive samples and pre-preserved positive circovirus samples. Nucleotide sequence analysis revealed that the 17 PCV2 strains shared 96.7-100% complete nucleotide identity, with 6 strains being PCV2b and 11 strains being PCV2d; the 4 PCV3 strains shared 98.9-99.4% complete nucleotide identity, with 2 strains being PCV3a-1 and 2 strains being PCV3b. This research provides a reliable tool for rapid PCVs identification and detection. Molecular epidemiological investigation of PCVs in pigs in Guangdong Province will help us to understand PCV2 and PCV3 epidemiological characteristics and evolutionary trends.

Development and validation of a simple and affordable LC-UV method for identification and assay of selected antimicrobial medicines

Antimicrobials, particularly antibiotics, are among the most common classes of drugs reported as substandard and falsified (SF) in developing countries. Therefore, it is important to develop simple and affordable analytical methods for the quality control of antimicrobial medicines. In this study, a liquid chromatographic method with ultraviolet detection (LC-UV) was developed and validated for the screening and quantification of 13 antimicrobial medicines and one beta-lactamase inhibitor in pharmaceutical formulations. LC separation was carried out on a Kinetex C18 column (150 mm × 4.6 mm, 2.6 µm) with gradient elution. The mobile phase consisted of mixtures of acetonitrile-water-10 mM phosphate buffer pH 3.5 at ratios of 3:92:5, v/v/v for mobile phase A and 50:45:5, v/v/v for mobile phase B with a flow rate of 0.5 mL/min. The screening method was intended for confirmation of the identity of the actives and validated for specificity and robustness, whereas the quantification method (using only a different detection wavelength) was further validated in terms of linearity, accuracy, sensitivity and precision (repeatability, intermediate precision). For all compounds, the method was found to be linear (r2 > 0.999), precise (%RSD < 1%), accurate (% recovery of 98–102%), sensitive, specific and robust. The developed LC method was successfully applied for the identification and assay of 12 antimicrobial samples from Ethiopia. Among the 12 samples analyzed, one (8.3%) product was confirmed to be falsified.

New methods for the identification of malachite pigments with varying particle sizes used in ancient Chinese murals by spectroscopic techniques

This study attempts to propose a simple, low-interventive and higher accuracy identification method for malachite pigments with varying particle sizes based on attenuated total reflection-Fourier transform infrared (ATR-FTIR), and evaluate the accuracy of ATR-FTIR and ultraviolet–visible near-infrared (UV-VIS-NIR) spectroscopy for identifying particle sizes. The micro-morphology, particle size, colour difference, and spectral characteristics of the malachite samples were determined using scanning electron microscopy (SEM), laser particle size analysis, colourimetry, UV-VIS-NIR and ATR-FTIR spectrometry. The principal component analysis (PCA) and mathematical models based on nonlinear curve fitting using UV-VIS-NIR and FTIR spectral data were developed and evaluated for the particle size identification of various malachite samples (pure malachite samples, fresh and aged malachite-rabbit glue samples). SEM morphological results showed that the malachite pigments with smaller particle sizes appear to have irregular flakes with a few loose agglomerated granules attached to the surface. The weight-specific surface area also increased gradually as the pigment particle size decreased. The colour difference results revealed that smaller malachite particle size gradually shifted toward the red–blue regions in CIE L*a*b* colour space. In addition, more intense reflectance and IR spectral absorbance peaks were observed in the case of the smaller particle size. The above proposed methods of particle size identification proved to be highly feasible and effective in predicting pigment particle size, which were confirmed by the PCA identification results and the mathematical model. Also, ATR-FTIR spectroscopy could provide more accurate predictions than UV-VIS-NIR spectroscopy, by keeping prediction errors below 5.5 μm.

Molecular approach for identification and validation of Eurycoma longifolia, Labisia pumila, and Orthosiphon stamineus in herbal products

BackgroundEurycoma longifolia (E. longifolia), Labisia pumila (L. pumila), and Orthosiphon stamineus (O. stamineus) are popular species known for their therapeutic properties. An increase in local demand for herbal products makes them susceptible to adulteration, which poses a risk to their safety and efficacy. Current identification methods, such as organoleptic, microscopic, and macroscopic analysis, need to be revised to identify plant species in highly processed herbal products due to their limited ability to detect morphological features and provide comprehensive plant taxonomy information.MethodsThis research objective was to develop a simple, reliable, and accurate DNA molecular identification method based on polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP) for E. longifolia, L. pumila, and O. stamineus, used to validate the species identification for herbal products. PCR–RFLP was developed for rapid identification using restriction enzymes TaqI, BamH I, HinfI, EcoRI, EcoRV, Mbol, and Mspl.ResultsThe nuclear DNA internal transcribed spacer 2 (ITS2) sequences were identified and compared between plant specimens of E. longifolia, L. pumila, and O. stamineus and 101 samples of commercial herbal products. Plant specimens of E. longifolia, L. pumila, and O. stamineus were successfully identified with high similarity of 100%, 100%, and 99.33%, respectively, based on National Center for Biotechnology Information (NCBI) GenBank. The recovery of DNA sequences from the herbal products was 60.4%, of which 81.97% were identified, and 18.03% showed no sequence through Basic Local Alignment Search Tool (BLAST) identification.ConclusionA reliable approach for identifying and validating plant species in herbal products has been created using restriction enzymes. This simple and accurate PCR–RFLP approach efficiently identifies E. longifolia, L. pumila, and O. stamineus by analysing ITS2 sequences, assuring consumer health and safety.

Using only the red-edge bands is sufficient to detect tree stress: A case study on the early detection of PWD using hyperspectral drone images

Pine wilt disease (PWD) is a destructive disease of pine trees caused by the pine wood nematode and early identification is crucial for disease control. Hyperspectral drone imagery has the potential to achieve early detection, although specific methods have not been sufficiently explored, including the spectral characteristics of early infections and the most efficient identification methods. This study aimed to examine the spectral responses to early infection, quantify the separability of healthy and early infections, and compare the accuracy and efficiency of different methods including single bands, vegetation indices (VIs) and 1st derivative reflectance and indices. We collected hyperspectral drone data in southeast China and used linear discriminant analysis (LDA) to determine the separability of healthy trees and trees at an early stage of infection. We also used bands with the same wavelengths as Sentienl-2 images (denoted S2 bands) to propose a standard for band selection.We found that it was possible to separate healthy trees and those at an early stage of infection around 0.71–0.78 using different methods. Among individual bands, the red-edge bands had the highest separability of 0.74. Using standard vegetation indices resulted in separability between 0.67 and 0.71; in addition, we proposed three new indices that achieved separability between 0.73 and 0.75. The 1st derivative reflectance at 714 nm had the highest separability of 0.78 in this study, while using the 1st derivative reflectance indices was slightly less accurate. The classification accuracy was also slightly lower when using Random Forest (RF) with all bands, sensitive bands, and S2 bands.We conclude that, of the methods tested, the red-edge bands are most sensitive to early infection, and using the 1st derivative reflectance at 714 nm or using the 1st derivative reflectance at the red-edge and blue-edge inflection point (REIP and BEIP) was sufficient for early identification. Acquiring and using additional wavelengths hardly improved the classification. Using wavelengths similar to those in the Sentienl-2 images achieved similar results, and thus can be used as a standard for dimensionality reduction of hyperspectral data pertaining to forest disease. The newly proposed VIs and 1st derivative reflectances at the yellow-edge inflection point (YEIP) and BEIP delivered better performance than the other tested indices, and could be alternatives for early identification. This study proposes simple and practical methods for early identification of wilt and provides insights for efficient data acquisition and data reduction.

Hexagonal zoning deadbeat model predictive control of induction motor

AbstractDeadbeat model predictive control is known as a precise control method in motor control applications. Recently, finite set deadbeat control is applied to power electronics applications. A voltage‐based cost function is used to select the closest voltage vector to the reference vector by examining all possible states in the cost function. This method needs a high amount of computation when it is applied to multilevel inverters or virtual voltage vectors due to a considerable increase in the number of candidates. Some studies have been done to reduce the number of candidate voltage vectors and computation burden. The methodology in these methods is based on finding the adjacent area and applying the vertex vectors to the cost function. So, these strategies are multi‐step and complex. The hexagonal zoning deadbeat model predictive control is proposed and introduced. The basic idea of the proposed method is that each zone should contain only one vector. In this way, the area identification is equal to finding the optimum voltage selection. Hexagonal zoning is used, which simplifies the process of identifying the area. A simple area identification method is developed based on the reference voltage phase angle. The proposed method can be applied to different inverters with different spatial vector diagrams. Furthermore, the synthesis of the virtual vector is manipulated to reduce the torque and flux ripple. The proposed method is experimentally evaluated by applications to the virtual voltage switching of a two‐level inverter for the induction motor drive in this research.

Deep learning analysis for rapid detection and classification of household plastics based on Raman spectroscopy

The overuse of plastics releases large amounts of microplastics. These tiny and complex pollutants may cause immeasurable damage to human social life. Raman spectroscopy detection technology is widely used in the detection, identification and analysis of microplastics due to its advantages of fast speed, high sensitivity and non-destructive. In this work, we first recorded the Raman spectra of eight common plastics in daily life. By adjusting parameters such as laser wavelength, laser power, and acquisition time, the Raman data under different acquisition conditions were diversified, and the corresponding Raman spectra were obtained, and a database of eight household plastics was established. Combined with deep learning algorithms, an accurate, fast and simple classification and identification method for 8 types of plastics is established. Firstly, the acquired spectral data were preprocessed for baseline correction and noise reduction, Then, four machine learning algorithms, linear discriminant analysis (LDA), decision tree, support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN), are used to classify and identify the preprocessed data. The results showed that the classification accuracy of the three machine learning models for the Raman spectra of standard plastic samples were 84%, 93% and 93% respectively. The 1D-CNN model has an accuracy rate of up to 97% for Raman spectroscopy. Our study shows that the combination of Raman spectroscopy detection techniques and deep learning algorithms is a very valuable approach for microplastic classification and identification.

An accurate and convenient method for Mycoplasma pneumoniae via one-step LAMP-CRISPR/Cas12b detection platform.

Mycoplasma pneumoniae (MP) is the major cause of respiratory infections that threaten the health of children and adolescents worldwide. Therefore, an early, simple, and accurate detection approach for MP is critical to prevent outbreaks of MP-induced community-acquired pneumonia. Here, we explored a simple and accurate method for MP identification that combines loop-mediated isothermal amplification (LAMP) with the CRISPR/Cas12b assay in a one-pot reaction. In the current study, the whole reaction was completed within 1 h at a constant temperature of 57°C. The limit of detection of this assay was 33.7 copies per reaction. The specificity of the LAMP-CRISPR/Cas12b method was 100%, without any cross-reactivity with other pathogens. Overall, 272 clinical samples were used to evaluate the clinical performance of LAMP-CRISPR/Cas12b. Compared with the gold standard results from real-time PCR, the present method provided a sensitivity of 88.11% (126/143), specificity of 100% (129/129), and consistency of 93.75% (255/272). Taken together, our preliminary results illustrate that the LAMP-CRISPR/Cas12b method is a simple and reliable tool for MP diagnosis that can be performed in resource-limited regions.

Understanding sensitivity and cross-reactivity of xylazine lateral flow immunoassay test strips for drug checking applications.

The continued prevalence of xylazine in the illicit drug market has necessitated development of quick and simple methods for identification, including lateral flow immunoassays (also known as "test strips"), like those frequently used to detect fentanyl. This study explored the drug checking applicability of the first publicly available xylazine test strips (XTS) using four sub-studies: reproducibility (i.e., consistency of positive results in a highly-concentrated xylazine solution); limit of detection on a calibration curve of xylazine concentrations; cross-reactivity against 77 commonly encountered drugs, cutting agents, and other structurally similar compounds; and applicability for analyzing community-acquired samples-where 100 drug residue samples were analyzed using XTS, direct analysis in real time mass spectrometry (DART-MS), and gas chromatography tandem mass spectrometry (GC-MS/MS). XTS consistently detected xylazine at concentrations ≥2.5μg/ml, and XTS results were reproducible. Sensitivity and specificity of XTS were calculated by comparing expected versus obtained results based on xylazine concentration of community-acquired samples measured by GC-MS/MS. XTS consistently detected xylazine in samples with concentration >2μg/ml and yielded a sensitivity of 0.974, specificity of 1.00, and overall accuracy of 0.986. Cross-reactivity with lidocaine, a common cutting agent, and lack of XTS reactivity with other α2 -agonists found in the illicit drug supply highlight the need to offer consumers comprehensive drug checking services that identify a range of substances and better inform them about drug contents.

Colorimetric sensing of uric acid based on sawdust-deposited silver nanoparticles via an eco-friendly and cost-effective approach

Uric acid is directly linked to gout, arthritis, neurological, cardiovascular, and kidney-related disorders. It is a byproduct obtained from the breakdown of purines and a significant indicator of hyperuricemia observed in both urine and blood. In the absence of any enzyme, it's quite difficult to develop a novel, cost-effective, and clinical method for uric acid detection. Herein, we report a very simple, low-cost, and non-enzymatic method for the selective identification and quantification of uric acid using green synthesized silver nanoparticles (Ag NPs). The desired Ag NPs were synthesized by the hydrothermal method using Erythrina suberosa sawdust as a deagglomeration agent and Psidium guajava extract as a reductant. The synthesis of the sensing platform, i.e., sawdust-deposited Ag NPs, was confirmed through different techniques such as UV-Vis spectrophotometer, FTIR, XRD, EDX, and scanning electron microscopy (SEM). Sawdust can offer a good, environmentally friendly, and cost-effective strategy to overcome the problem of agglomeration in nanoparticles. The enzyme mimic, with the help of H2O2, oxidizes the colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to oxidized TMB with a blue-green color. The addition of uric acid reduces the oxidized TMB to a colorless product, resulting in a colorimetric change. For quality improvement, different reaction parameters, including pH, time, TMB, and NPs concentration, were optimized. Our proposed sensor responds in linear ranges of 0.04–0.360 μM, with a limit of quantification of 0.01 μM and a limit of detection of 0.004 μM. The suggested enzyme mimic detected uric acid in blood samples, with particular specificity in the presence of competitive analytes.

Angiographic evaluation of the distance from the top of the jugular bulb to the inferior petrosal sinus-internal jugular vein junction: simple classification and identification method for the orifice of the non-visualized inferior petrosal sinus during neuroendovascular surgery.

The inferior petrosal sinus (IPS) is the transvenous access route for neurointerventional surgery that is occasionally undetectable on digital subtraction angiography (DSA) because of blockage by a clot or collapse. This study was aimed at analyzing the distance from the jugular bulb (JB) to the IPS-internal jugular vein (IJV) junction and proposing a new anatomical classification system for the IPS-IJV junction to identify the non-visualized IPS orifice. DSA of 708 IPSs of 375 consecutive patients were retrospectively investigated to calculate the distance from the top of the JB to the IPS-IJV junction, and a simple classification system based on this distance was proposed. The median distance from the top of the JB to the IPS-IJV junction was 20.8 ± 14.7mm. Based on the lower (10.9mm) and upper (31.1mm) quartiles, IPS-IJV junction variants were: type I, 0-10mm (22.3%); type II, 11-30mm (45.8%); type III, > 31mm (23.9%); and type IV, no connection to the IJV (8.0%). Bilateral distances showed a positive interrelationship, with a correlation coefficient of 0.86. The bilateral symmetry type (visualized IPSs bilaterally) according to our classification occurred in 267 of 300 (89.0%) patients. In this study, the IPS-IJV junction was located far from the JB (types II and III), with a higher probability (69.6%). This distance and the four-type classification demonstrated high degrees of homology with the contralateral side. These results would be useful for identifying the non-visualized IPS orifice.

Simple and selective screening method for the synthetic cathinone MDPT in forensic samples using carbon nanofiber screen-printed electrodes

3′,4′-Methylenedioxy-N-tert-butylcathinone (MDPT), also known as tBuONE or D-Tertylone, is a synthetic cathinone (SC) frequently abused for recreational purposes due to its potent stimulant effects and similarity to illegal substances like methamphetamine and ecstasy. The structural diversity and rapid introduction of new SC analogs to the market poses significant challenges for law enforcement and analytical methods for preliminary screening of illicit drugs. In this work, we present, for the first time, the electrochemical detection of MDPT using screen-printed electrodes modified with carbon nanofibers (SPE-CNF). MDPT exhibited three electrochemical processes (two oxidations and one reduction) on SPE-CNF. The proposed method for MDPT detection was optimized in 0.2 mol L−1 Britton-Robinson buffer solution at pH 10.0 using differential pulse voltammetry (DPV). The SPE-CNF showed a high stability for electrochemical responses of all redox processes of MDPT using the same or different electrodes, with relative standard deviations less than 4.7% and 1.5% (N = 3) for peak currents and peak potentials, respectively. Moreover, the proposed method provided a wide linear range for MDPT determination (0.90–112 μmol L−1) with low LOD (0.26 μmol L−1). Interference studies for two common adulterants, caffeine and paracetamol, and ten other illicit drugs, including amphetamine-like compounds and different SCs, showed that the proposed sensor is highly selective for the preliminarily identification of MDPT in seized forensic samples. Therefore, SPE-CNF with DPV can be successfully applied as a fast and simple screening method for MDPT identification in forensic analysis, addressing the significant challenges posed by the structural diversity of SCs.

Simple and field-adapted species identification of biological specimens combining multiplex multienzyme isothermal rapid amplification, lateral flow dipsticks, and universal primers for initial rapid screening without standard PCR laboratory.

Species identification of biological specimens can provide the valuable clues and accelerate the speed of prosecution material processing for forensic investigation, especially when the case scene is inaccessible and the physical evidence is cumbersome. Thus, establishing a rapid, simple, and field-adapted species identification method is crucial for forensic scientists, particularly as first-line technology at the crime scene for initial rapid screening. In this study, we established a new field-adapted species identification method by combining multiplex multienzyme isothermal rapid amplification (MIRA), lateral flow dipstick (LFD) system, and universal primers. Universal primers targeting COX I and COX II genes were used in multiplex MIRA-LFD system for seven species identification, and a dedicated MIRA-LFD system primer targeting CYT B gene was used to detect the human material. DNA extraction was performed by collecting DNA directly from the centrifuged supernatant. Our study found that the entire amplification process took only 15min at 37°C and the results of LFDs could be visually observed after 10min. The detection sensitivity of human material could reach 10pg, which is equivalent to the detection of single cell. Different common animal samples mixed at the ratio of 1ng:1ng, 10ng:1ng, and 1ng:10ng could be detected successfully. Furthermore, the damaged and degraded samples could also be detected. Therefore, the convenient, feasible, and rapid approach for species identification is suitable for popularization as first-line technology at the crime scene for initial rapid screening and provides a great convenient for forensic application.