Specific Identification Research Articles

High throughput Luminex beads based multiplex assay for identification of six major bacterial pathogens of mastitis in dairy animals.

Bovine mastitis is caused by over 150 different microorganisms. Specific identification and quantification of multiple bacteria in a single milk sample becomes essential for rapid intervention. In the present study a Luminex beads based multiplex assay emphasizing on the precise identification of six major bacterial pathogens of mastitis was developed. Assay was developed in two triplex sets, triplex 1 comprised of Streptococcus agalactiae, Streptococcus dysgalactiae and Streptococcus uberis while triplex 2 consisted of Staphylococcus aureus, E. coli and Klebsiella pneumoniae. The analytical sensitivity was 10 6 copies per reaction mixture for all the six bacteria. A 100% analytical specificity was observed for simultaneous detection of these bacteria. Clinical milk samples from 100 bovine quarters were tested for validation. The analytical sensitivity was similar to the findings reported earlier in real time PCR multiplex assay targeting the DNA of the 11 most common bacterial species or groups in mastitis. The analytical specificity of the optimized assay was 100% similar to reported earlier for simultaneous detection of Mycoplasma spp. and for seven entric viruses of humans.The developed assay indicates a concept proof of a rapid, cost effective high throughput diagnostic tool for identification of major bacteria causing mastitis.

Smallest dual-color qPCR device

The global outbreak of the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), necessitated the development of point-of-care (POC) systems for rapid and widespread population screening. Current POC systems, based on reverse transcription–quantitative polymerase chain reaction (RT-qPCR) methods, are unable to perform RNA target multiplexing, which is crucial for specific viral strain identification. This study presents a compact qPCR system capable of dual fluorescence channel gene multiplexing by simultaneously detecting two targets of the SARS-CoV-2 virus using 6-carboxyfluorescein (FAM)- and TxRed-labeled probes. The device measures (110 × 83 × 41) mm3 and weighs 210 gm. It features four virtual reaction chambers, a thermal cycle system, a real-time optical signal detection module, a USB interface, and a Bluetooth wireless data transmission module. Moreover, it can perform single fluorescent channel detection of PCR using intercalating dye, followed by a melting curve analysis and loop-mediated isothermal amplification. The proposed compact dual-color qPCR device significantly broadens its potential for POC applications, such as diagnostics or field testing, molecular biology, microfluidics, and virus detection.

VirhostlncR: A comprehensive database to explore lncRNAs and their targets in viral infections

Long non-coding-RNAs (lncRNAs) are an expanding set of cis-/trans-regulatory RNA genes that outnumber the protein-coding genes. Although being increasingly discovered, the functional role of the majority of lncRNAs in diverse biological conditions is undefined. Increasing evidence supports the critical role of lncRNAs in the emergence, regulation, and progression of various viral infections including influenza, hepatitis, coronavirus, and human immunodeficiency virus. Hence, the identification of signature lncRNAs would facilitate focused analysis of their functional roles accounting for their targets and regulatory mechanisms associated with infections. Towards this, we compiled 2803 lncRNAs identified to be modulated by 33 viral strains in various mammalian cell types and are provided through the resource named VirhostlncR (http://ciods.in/VirhostlncR/). The information on each of the viral strains, their multiplicity of infection, duration of infection, host cell name and cell types, fold change of lncRNA expression, and their specific identification methods are integrated into VirhostlncR. Based on the current datasets, we report 150 lncRNAs including differentiation antagonizing non-protein coding RNA (DANCR), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed gene 3 (MEG3), nuclear paraspeckle assembly transcript 1 (NEAT1), and plasmacytoma variant translocation 1 (PVT1) to be perturbed by two or more viruses. Analysis of viral protein interactions with human transcription factors (TFs) or TF-containing protein complexes identified that distinct viruses can transcriptionally regulate many of these lncRNAs through multiple protein complexes. Together, we believe that the current dataset will enable priority selection of lncRNAs for identification of their targets and serve as an effective platform for the analysis of noncoding RNA-mediated regulations in viral infections.

GPU-Free Specific Emitter Identification Using Signal Feature Embedded Broad Learning

Emerging wireless networks may suffer severe security threats due to the ubiquitous access of massive wireless devices. Specific emitter identification (SEI) is considered as one of the important techniques to protect wireless networks, which aims to identifying legal or illegal devices through the radio frequency (RF) fingerprints contained in RF signals. Existing SEI methods are implemented with either traditional machine learning or deep learning. The former relies on manual feature extraction which is usually inefficient, while the latter relies on the powerful GPU computing power but with limited applications and high cost. To solve these problems, in this paper, we propose a GPU-free SEI method using a signal feature embedded broad learning network (SFEBLN), for efficient emitter identification based on a single-layer forward propagation network on the CPU platform. With this method, the original RF data is first pre-processed through external signal processing nodes, and then processed to generate mapped feature nodes and enhancement nodes by nonlinear transformation. Next, we design the internal signal processing nodes to extract effective features from the processed RF signals. The final input layer consists of mapped feature nodes, enhancement nodes and internal signal processing nodes. Then, the network weight parameters are obtained by solving the pseudo inverse problem. Experiments are conducted over the CPU platform and the results show that our proposed SEI method using SFEBLN achieves a superior identification performance and robustness under various scenarios.

Quantitative multiphoton imaging of cell metabolism, stromal fibers, and keratinization enables label-free discrimination of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) features atypical clinical manifestations and a low 5-year survival rate (< 5% in many developing countries where most of the disease occurs). Precise ESCC detection and grading toward timely and effective intervention are therefore crucial. In this study, we propose a multidimensional, slicing-free, and label-free histopathological evaluation method based on multispectral multiphoton fluorescence lifetime imaging microscopy (MM-FLIM) for precise ESCC identification. To assess the feasibility of this method, comparative imaging on fresh human biopsy specimens of different ESCC grades is performed. By constructing fluorescence spectrum- and lifetime-coded images, ESCC-induced morphological variations are unveiled. Further quantification of cell metabolism and stromal fibers reveals potential indicators for ESCC detection and grading. The specific identification of keratin pearls provides additional support for the early detection of ESCC. These findings demonstrate the viability of using MM-FLIM and the series of derived indicators for histopathological evaluation of ESCC. As there is an increasing interest in developing multiphoton endoscopes and multiphoton FLIM systems for clinical use, the proposed method would probably allow noninvasive, label-free, and multidimensional histological detection and grading of ESCC in the future.

Educational & income disparities among ethnic minorities of China

While disparities between Han and non-Han groups are well established in China, little is known about which ethnic minorities experience the greatest disparities or how these have changed over time. This study examines disparities in income and educational attainment across six of China’s largest ethnic minority populations and how they have changed over the past two decades. Analyses included 66,077 observations over ten waves (1989–2015) of the China Health and Nutrition Survey (CHNS) which also includes information on specific ethnic identification. We found substantial disparities between ethnic minorities in both their income and educational attainment, and these disparities either persisted or grew over time. These disparities also remained when controlling for rural residence. These results suggest that despite years of explicit government efforts to create equality among ethnic groups, such disparities persist and have in some cases grown.

Additions and corrections to taxonomy of Apobaetis fiuzai Salles &amp; Lugo-Ortiz, 2002 and Apobaetis kallawaya Nieto, 2006 (Ephemeroptera, Baetidae)

Recently, it has been hypothesized that the wide distribution of A. fiuzai Salles &amp; Lugo-Ortiz is due to a wide diagnosis and conflicting keys. The same arguments can be applied to A. kallawaya Nieto. In order to test this hypothesis, we revised the type material of A. fiuzai, literature of A. kallawaya, and part of the recorded specimens in Brazil. As a result, three new species from previous records are recognized: Apobaetis pasternakae sp. nov., former A. fiuzai in Amazonas, Roraima and Maranhão states; Apobaetis jaquelinae sp. nov., former A. fiuzai in Rondônia State; Apobaetis luanae sp. nov., former A. kallawaya in Rondônia State. The new data showed that A. fiuzai does not occur in the Amazon Biome and A. kallawaya does not occur in Brazil. Records of A. fiuzai that could not be reviewed in this study are treated as putative and should be evaluated in the light of the new evidence. We also studied the intraspecific variation in A. fiuzai and A. pasternakae sp. nov. The pigment pattern of the nymphs was not considered a robust characteristic for the species differentiation due to the wide variation in and between populations, and interspecific similarity of many features. On the other hand, classical morphological characters such as mouthparts, legs, spines of terga, paraproct and caudal filaments have minimal variation, stable and reliable for specific identification.

Rapid and visual identification of HIV-1 using reverse transcription loop-mediated isothermal amplification integrated with a gold nanoparticle-based lateral flow assay platform.

Human immunodeficiency virus type one (HIV-1) infection remains a major public health problem worldwide. Early diagnosis of HIV-1 is crucial to treat and control this infection effectively. Here, for the first time, we reported a novel molecular diagnostic assay called reverse transcription loop-mediated isothermal amplification combined with a visual gold nanoparticle-based lateral flow assay (RT-LAMP-AuNPs-LFA), which we devised for rapid, specific, sensitive, and visual identification of HIV-1. The unique LAMP primers were successfully designed based on the pol gene from the major HIV-1 genotypes CRF01_AE, CRF07_BC, CRF08_BC, and subtype B, which are prevalent in China. The optimal HIV-1-RT-LAMP-AuNPs-LFA reaction conditions were determined to be 68°C for 35 min. The detection procedure, including crude genomic RNA isolation (approximately 5 min), RT-LAMP amplification (35 min), and visual result readout (<2 min), can be completed within 45 min. Our assay has a detection limit of 20 copies per test, and we did not observe any cross-reactivity with any other pathogen in our testing. Hence, our preliminary results indicated that the HIV-1-RT-LAMP-AuNPs-LFA assay can potentially serve as a useful point-of-care diagnostic tool for HIV-1 detection in a clinical setting.

Development and validation of real‐time PCR tests for specific identification of Xanthomonas species causing bacterial spot disease on tomato (Solanum lycopersicum) and pepper (Capsicum annuum)

AbstractBacterial spot is a significant disease of tomato and pepper crops caused by members of four Xanthomonas pathogens from three species, namely Xanthomonas euvesicatoria pv. euvesicatoria, Xanthomonas euvesicatoria pv. perforans, Xanthomonas vesicatoria and Xanthomonas hortorum pv. gardneri. These Gram‐negative bacterial plant pathogens are recommended for regulation as quarantine pests by the European and Mediterranean Plant Protection Organization and are currently regulated on tomato and pepper seeds in many regions of the world. Tomato and pepper seeds are a transmission pathway for all three species. Real‐time PCR TaqMan tests were developed and validated for specific identification of isolates from tomato and pepper seeds of these pathogens. These tests are designed to be used in seed health tests after isolation on semiselective media to reduce the number of isolates that need testing in follow‐up pathogenicity tests. A second set of real‐time TaqMan PCR tests was subsequently developed targeting two markers for each taxonomic group of strains. The analytical specificity of all developed tests was evaluated with in vitro and in silico analyses. Diagnostic specificity and sensitivity were also validated on collections of isolates in several laboratories.

A targeted phototheranostic agent with strong AIE effect and boosted type I ROS generation

Hypoxia and nonspecific killing always hinder the photodynamic therapy of malignant tumors and infectious diseases that both severely threatening human health. Herein, we report a type I photosensitizer (MTOTPy) with aggregation-induced near-infrared (NIR) emission feature and specific identification ability by rational molecular design. Enhanced electron-donating effect and electron-rich anion-π+ structure endow MTOTPy with strong NIR fluorescence and intensive type I reactive oxygen species generation in aggregation. It is demonstrated that MTOTPy can achieve fast identification of tumor cells by targeting mitochondria and specific recognition of Staphylococcus aureus (S. aureus) among other kinds of microbes. Moreover, MTOTPy can kill more than 90% tumor cells and S. aureus upon visible light irradiation at low concentration of 5.0 μM and 0.5 μM, respectively. The work provides a feasible idea for designing photosensitizers to overcome the hypoxic limitation of photodynamic therapy and advance the development of image-guided therapy with high efficiency.

Semantics of Verbs of Rotation in the Khanty language

The article is devoted to the identification of the national specifics of the representation of rotational movement in the Khanty linguoculture, the analysis of the semantics of verbs of rotation, the possibility of their lexical compatibility on the material of the Western dialects of the Khanty language (Kazym, Shuryshkar, Uralic) and one of the eastern (Surgut) by the types of situations described by them. The purpose of our study is to identify the parameters relevant to the lexical rotation zone. The article uses the method of corpus research: a sample of examples with verbs of rotation is analyzed and the boundaries of their compatibility are determined. The method of field research is used. There is a difference between movement along a trajectory close to an ideal circle (rotation in a circle) and movement along a chaotic trajectory, i.e. rotation of a set of homogeneous objects / gaseous particles (deer, snow vortex, smoke). In this case, the second type of rotation is marked. The subject performs multiple or single rotations around its axis or causates a rotational movement. The key parameter for distinguishing verbs of rotation and skating is animateness. Verbs of rotation around a landmark reflect the way of movement. Verbs of rolling and rotation do not reflect the environment of movement, but may reflect the direction of movement, which is regulated by the special compatibility of the subject and the predicate, or the word-formation capabilities of the predicate. The rolling zone reflects rotation in contact with the plane during translational motion and multidirectional rotation (about natural phenomena and about the movement of deer). The selected parameters and values are not lexicalized into separate verbs, but are combined inside lexemes. One lexeme describes situations of rotation in the same plane with and above the landmark, however, skating on the surface and rotation around the landmark is marked in dialects by separate lexemes.

Dual-mode sensing platform based on aptamer-tunable catalytic activity of mesoporous polydopamine/MnO2 nanozymes for detecting S. aureus

Staphylococcus aureus (S. aureus) infections pose a serious threat to human health worldwide. Therefore, selective and accurate detection of S. aureus is necessary for preventing infections. Herein, we design the colorimetric-electrochemical sensing platform based on specific oligonucleotide (SA31 aptamer)-tunable oxidase-like activity of mesoporous polydopamine/MnO2 (MPDA/MnO2) nanozymes for the S. aureus detection. The MPDA/MnO2 nanozymes possess excellent oxidase-like activity and electrochemical property. MPDA/MnO2 nanozymes can catalyze the oxidation of TMB to generate TMB+ (blue color) and TMB2+ (yellow color) products. The oxidase-like activity of MPDA/MnO2 can be inhibited when the SA31 is adsorbed on the surface of the nanozymes. In the presence of S. aureus, SA31 aptamer desorbs from MPDA/MnO2 surface based on the specific identification between S. aureus and aptamer, resulting in the recovery of their oxidase-like activity. Based on the different oxidative degree of TMB, the color of sensing solution changes from blue to yellow, the electrochemical signals also have changed simultaneously. The colorimetric-electrochemical sensing platform can accurately detect S. aureus, showing a low detection limit in 3 CFU/mL (S/N = 3). Furthermore, the dual-mode sensing platform can be utilized for the accurate and specific detection of S. aureus in real sample with the recovery of 95.15%− 115.28%. This work based on SA31 aptamer-tunable oxidase-like activity of MPDA/MnO2 nanozymes provides a dual-mode assay for specific and accurate detection of S. aureus.

A Knowledge Graph-Driven CNN for Radar Emitter Identification

In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method.

Flight-Test System Identification Techniques and Applications for Small, Low-Cost, Fixed-Wing Aircraft

This paper provides an overview of flight-test system identification methods applied in the Virginia Tech Nonlinear Systems Laboratory that focus on modeling small, inexpensive, fixed-wing aircraft controlled by a ground-based pilot. The general aircraft system identification approach is outlined with details provided on the flight-test facilities, experiment design methods, instrumentation systems, flight-test operations, data processing techniques, and model identification methods enabling small aircraft flight dynamics model development. Specific small aircraft system identification challenges are overcome, including low-cost control surface servo-actuators and instrumentation systems, as well as a greater sensitivity to atmospheric disturbances and limited piloting cues. Four recent system identification research advancements using the general system identification process are featured, including application of uncorrelated pilot inputs for remotely piloted aircraft, aero-propulsive model development for propeller aircraft, spin aerodynamic model development, and nonlinear dynamic modeling without mass properties information. Although this paper provides a summary of several research efforts, the core system identification approach is presented with sufficient detail to allow the methods to be readily adapted to other research efforts leveraging small, low-cost aircraft.

Geometric morphometric analysis of Neolithic wheat grains: insights into the early development of free-threshing forms

Current knowledge of the origins and routes of introduction of both tetraploid and hexaploid free-threshing wheats (FTWs) from western Asia into Europe remains imprecise. Archaeobotanical distinction of ploidy level is often dependent on sparsely recovered rachis segments, while more specific identification of cultivars within ploidy groups using morphological characteristics of either grain or chaff is generally considered unreliable. This study offers a complementary approach by using geometric morphometric (GMM) analysis of grain shape to assess taxonomic and variety level distinctions in archaeobotanical FTW remains. Two substantial and well-preserved assemblages of Neolithic FTW grains from 7th millennium bce Çatalhöyük (central Anatolia, Turkey) and 6th millennium Kouphovouno (Peloponnese, Greece) were analysed with the aim of shedding light on the naked wheats cultivated by these early agricultural communities. While chaff remains from Çatalhöyük indicate a hexaploid FTW crop, no such diagnostic remains were recovered from Kouphovouno. GMM analysis of the archaeobotanical grains has corroborated early cultivation of hexaploid naked wheat at Çatalhöyük, whilst revealing that a tetraploid wheat, most closely resembling ‘unimproved’ durum wheat, was likely grown at Kouphovouno. The Kouphovouno findings are contextualised within existing theories of a Mediterranean route for introduction of tetraploid FTWs from western Asia to south-western Europe. At both sites, an assessment of the role of FTWs over time suggests changes in the cultural value attributed to these crops and/or the sustainability of the agricultural regimes they were associated with.

Label-free electrochemical biosensor with magnetically induced self-assembly for the detection of cancer antigen 125

Magnetically induced self-assembly technology was used to construct a label-free electrochemical biosensor based on magnetic Mg0.5Cu0.5Fe2O4-Au nanocomposites for the sensitive detection of cancer antigen 125 (CA125). To aim for this, Mg0.5Cu0.5Fe2O4 nanoparticles were first prepared via the rapid combustion process. The average diameter of the nanoparticles calcined at 800 °C with the absolute ethanol volume of 20 mL was about 169.3 nm. Then Au was used for the surface modification by NaBH4 reduction reaction approach. The magnetic glassy carbon electrode (MGCE) was modified by Mg0.5Cu0.5Fe2O4-Au via a magnetic induction self-assembly process. The reduced thiol-modified single-stranded DNA was attached to the Mg0.5Cu0.5Fe2O4-Au nanocomposites by Au-S bonds without any coupling agent. CA125 antigen was grabbed directly by its aptamer DNA due to its specific identification with the aptamer. Finally, the modified electrodes were blocked with BSA and then characterized. Finally, DPV analysis was used for CA125 detection, the novel fabricated biosensor demonstrated good detection properties for CA125 with a linear range of 5–125 U/mL and a detection limit of 4.4 U/mL. The results showed that the aptamer sensor had good specificity, repeatability, and stability. The feasibility of our sensor for the determination of CA125 was also demonstrated by measuring CA125 levels in serum using the Roche gold-standard instrument of the People’s Hospital of Danyang as the reference value. The recoveries of real serum samples were 94.65–101.71%, and RSDs were 1.26–4.65%. Moreover, the surface of the electrode could be cleaned and reused by magnetic separation, greatly reducing the cost and providing the possibility for a point-of-care test (POCT). This work demonstrated a new strategy for integrating both nanostructures and biocompatibility to build advanced cancer biomarker sensors with wide applications.

Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Deep learning (DL) and deep neural networks (DNNs) can learn the hidden features of data and build the classifier automatically for decision making, which have been widely used in the SEI research. Considering the insufficiently labeled training samples and large unlabeled training samples, semi-supervised learning-based SEI (SS-SEI) methods have been proposed. However, there are few SS-SEI methods focusing on extracting the discriminative and generalized semantic features of radio signals. In this paper, we propose an SS-SEI method using metric-adversarial training (MAT). Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open-source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and WiFi dataset and is compared with state-of-the-art methods. The simulation results show that the proposed method achieves better identification performance than existing state-of-the-art methods. Specifically, when the ratio of the number of labeled training samples to the number of all training samples is 10%, the identification accuracy is 84.80% under the ADS-B dataset and 80.70% under the WiFi dataset. Our code can be downloaded from https://github.com/lovelymimola/MAT-based-SS-SEI.

Specific Proteomic Identification of Collagen-Binding Proteins in Escherichia coli O157:H7: Characterisation of OmpA as a Potent Vaccine Antigen.

Escherichia coli is a versatile commensal species of the animal gut that can also be a pathogen able to cause intestinal and extraintestinal infections. The plasticity of its genome has led to the evolution of pathogenic strains, which represent a threat to global health. Additionally, E. coli strains are major drivers of antibiotic resistance, highlighting the urgent need for new treatment and prevention measures. The antigenic and structural heterogeneity of enterohaemorrhagic E. coli colonisation factors has limited their use for the development of effective and cross-protective vaccines. However, the emergence of new strains that express virulence factors deriving from different E. coli diarrhoeagenic pathotypes suggests that a vaccine targeting conserved proteins could be a more effective approach. In this study, we conducted proteomics analysis and functional protein characterisation to identify a group of proteins potentially involved in the adhesion of E. coli O157:H7 to the extracellular matrix and intestinal epithelial cells. Among them, OmpA has been identified as a highly conserved and immunogenic antigen, playing a significant role in the adhesion phenotype of E. coli O157:H7 and in bacterial aggregation. Furthermore, antibodies raised against recombinant OmpA effectively reduced the adhesion of E. coli O157:H7 to intestinal epithelial cells. The present work highlights the role of OmpA as a potent antigen for the development of a vaccine against intestinal pathogenic E. coli.

CRISPR based biosensing: An ultrasensitive theranostic tool for the detection of early Breast Cancer biomarkers – A mini review

According to The International Agency for Research on Cancer (IARC) survey, the most commonly diagnosed breast cancer (BC) encumbrance will rise to ˃3 million with ˃1 million bereavements per year by 2040. This creates a high demand for effective early diagnosis of BC to reduce psychological and economic distress. Recently, the body fluid cancer biomarkers detection, a minimally invasive method has an imperative role to identify the cancer stages for developing new strategies for early diagnosis. Traditional complicated and sophisticated detection tool preparation and time consuming detection technologies are lately replaced with nanotechnology incorporated Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated protein (CRISPR/Cas) technology based biosensors. This technology is an in vitro, next generation ultrasensitive diagnostic device meant to achieve highly specific, ultra-sensitive, and rapid identification of BC nucleic acids, protein biomarkers, whole cells, and small molecules. Till date, many reviews focussing on the use of this technology in diagnosing contagious diseases, liver and colon cancers and later state BC biomarkers are published. Yet review article highlighting the CRISPR/Cas modified biosensors and point-of-care devices (POCDs), exclusively for early BC detection is rarely reported. Hence, this review involves critical thinking on CRISPS-Cas based biosensors research published between Jan 2017–Nov 2022 especially for early BC biomarkers based liquid biopsy diagnosis to develop ultrasensitive and rapid POC tool. The challenges faced by the researchers to bring out CRISPR based biosensing into real time and the future perspectives are also briefed at the end.

An improved DNA extraction method for detecting Bacillus subtilis spores in spiked foods and beverages

Bacillus and Paenibacillus spp. are essential aerobic spoilage bacteria in various food industry sectors. Spoilage from microorganisms occurs at many points throughout food production systems. Due to their complex wall structures, spores can resist heat, radiation, chemical agents, and enzymatic treatments. An alkaline lysis and mechanical disruption combination method was developed and evaluated to counter this. This combination method effectively improved DNA extraction from B. subtilis spore cells spiked into food (solid) and beverages (liquid milk and coffee) at concentrations down to 102 CFU/mL or g when spiked into food matrices and drinks. Released DNA recoveries were 27 % and 25 % for potato salad and 38 % and 36 % for whole corn spiked at 106 and 103 CFU/mL concentrations. Conversely, there was a low recovery for wheat flour (10 % and 8.8 %) and milk powders (12 % and 25 %) at 106 and 103 CFU/mL spiked concentrations. The combination method provides rapid, specific, reliable, and accurate signature sequences identification for the detection and presence confirmation of psychrophilic and psychrotolerant spoilage spore cells, improving food spoilage assessments and food control applications.