False Positive Detection Research Articles

Improved display and detection of small renal stones using photon-counting detector CT compared to conventional energy-integrating detector CT.

To compare same-day photon-counting detector CT (PCD-CT) to conventional energy-integrating detector CT (EID-CT) for detection of small renal stones (≤ 3mm). Patients undergoing clinical dual-energy EID-CT for known or suspected stone disease underwent same-day research PCD-CT. Patients with greater than 10 stones and no visible stones under 3mm were excluded. Three radiologists selected the optimal reconstruction configuration for each CTmodality and created the reference standard for renal stone presence. Two other radiologists, blinded to imaging modality, independently reviewed anonymized images to detect renal stones, rating confidence in potential stones using a Likert scale (1 = Definitely present, 2 = Probably present, 3 = Questionably present, 4 = Not seen). Sensitivity and false positive detections for PCD and EID-CT were calculated. Twenty-one patients underwent clinical EID-CT followed by same-day PCD-CT, with the reference standard identifying 121 renal stones (mean size 2.8 ± 2.6mm). 0.4-mm PCD-CT images were more likely to display a stone as definitely present compared to 1- or 2-mm EID-CT images (p < 0.0001). Overall sensitivity for detection of all stones was greater at PCD-CT (0.75 vs. 0.55, p < 0.05). Pooled sensitivity of stones 3mm was also significantly higher at PCD-CT (0.67 vs. 0.41, p < 0.05), with false positive detections differing between readers and modalities (PCD-CT vs. EID-CT: R1-7 v. 5; R2 - 7 v. 1). Sensitivity for renal stones was significantly higher using high spatial resolution PCD-CT vs. EID-CT, especially for stones 3mm or less in size, which may be important for at-risk patient populations. Prospective evaluation in larger patient populations that will benefit from detection of small stones is warranted.

Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low false positive rate. Therefore, the proposed method has the potential to effectively classify medical images. The proposed model was verified using a public COVID-19 radiology dataset and a public COVID-19 lung CT scan dataset. The results show that the model has made significant progress, with the false positive rate reduced to 11.3% and 7.5%, and the area under the ROC curve increased to 92.8% and 97.01%.

Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP.

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.

Optimized molecule detection in localization microscopy with selected false positive probability

Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline. Furthermore, current methods lack standardization, which hinders reproducibility. Here, we present an optimized molecule detection method which combines probabilistic thresholding with theoretically optimal filtering. The probabilistic thresholding enables control over false positive detections while optimal filtering minimizes false negatives. A theoretically optimal Poisson matched filter is used as a performance benchmark to evaluate existing filtering methods. Overall, our approach allows the detection of molecules in a robust, single-parameter and user-unbiased manner. This will minimize artifacts and enable data reproducibility in SMLM.

In vivo Study of Nano Chitosan Beads-Based ELISA versus Traditional Sandwich ELISA for the Early Diagnosis of Trichinosis

Human trichinosis is a serious foodborne parasitic zoonosis. Diagnosing human trichinosis is usually difficult due to the nonspecific clinical picture and the limited effectiveness of serological tests in acute infections. While ELISA can detect circulating Trichinella antigens, aiding in early diagnosis, its sensitivity may be low. The application of nanoparticles can improve the sensitivity of ELISA and allow a specific early diagnosis of the disease. This work compares the nano chitosan beads-based ELISA (NCSB-ELISA) and traditional sandwich ELISA for the detection of circulating Trichinella spiralis (T. spiralis) crude extract-antigen (CEA) in serum samples of experimentally infected mice.Fifty-seven mice included in this study were classified into 3 groups: T. spiralis infected group (Group I) (36 mice), which was equally subdivided into six subgroups according to the time of sacrifice (6, 8, 10, 12, 14 and 16) day post-infection (dpi), cross-reactivity group (Group II) (9 mice) and negative control group (Group III) (12 mice). T. spiralis AW-CEA prepared from the adult worms were used to produce anti- T. spiralis IgG-polyclonal antibodies in rabbits; these antibodies were utilized to detect AW-CEA in serum samples by traditional sandwich ELISA and NCSB-ELISA. Using NCSB-ELISA, T. spiralis AW-CEA was detected in sera collected at 8 dpi, with a sensitivity of 50% and a specificity of 100%. Meanwhile, traditional sandwich ELISA could not detect the antigen at the same time interval. Both ELISA techniques were able to detect the antigen in samples collected at 10, 12, 14 and 16 dpi with a sensitivity of 16.67%, 50%, 67.67% and 83.67%, respectively, for traditional sandwich-ELISA and a specificity of 100% at 10, 12 and 14 dpi while at 16 dpi specificity was decreased to 90.91%. In contrast, the sensitivity of NCSB-sandwich ELISA on the same days was 66.67%, 83.34%, 100% and 100%, respectively, with a specificity of 100% at all days. False positive detection of T. spiralis AW-CEA in the serum of mice in GII was recorded on day 16 pi by only traditional sandwich ELISA.This study concluded that NCSB-ELISA is a promising and sensitive technique for the early and specific diagnosis of acute trichinosis in an animal model.

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL). Yet, the lack of open sharing of pre-trained models hampers the practical application and evaluation of these methods beyond specific data sources used in each study. Here, we present the SHIVA-CMB detector, a 3D Unet-based tool trained on 450 scans taken from seven acquisitions in six different cohort studies that included both T2*- and susceptibility-weighted MRI. In a held-out test set of 96 scans, it achieved the sensitivity, precision, and F1 (or Dice similarity coefficient) score of 0.67, 0.82, and 0.74, with less than one false positive detection per image (FPavg = 0.6) and per CMB (FPcmb = 0.15). It achieved a similar level of performance in a separate, evaluation-only dataset with acquisitions never seen during the training (0.67, 0.91, 0.77, 0.5, 0.07 for the sensitivity, precision, F1 score, FPavg, and FPcmb). Further demonstrating its generalizability, it showed a high correlation (Pearson’s R = 0.89, p < 0.0001) with a visual count by expert raters in another independent set of 1992 T2*-weighted scans from a large, multi-center cohort study. Importantly, we publicly share both the pipeline (https://github.com/pboutinaud/SHiVAi/) and pre-trained models (https://github.com/pboutinaud/SHIVA-CMB/) to the research community to promote the active application and evaluation of our tool. We believe this effort will help accelerate research on the pathophysiology and functional consequences of CMB by enabling rapid characterization of CMB in large-scale studies.

An exploratory study of shielding strategies for boron neutron capture discrimination in 10B Neutron Capture Enhanced Particle Therapy.

To evaluate the impact of a range of shielding strategies on the rate of false positive detections by a simulated detector for application in Neutron Capture Enhanced Particle Therapy (NCEPT). In this work, we extend a previously published method for neutron capture detection and discrimination. A Geant4 Monte Carlo model was designed, with the simulated irradiation of a poly(methyl methacrylate) phantom and cubic 10B insert with carbon and helium ion beams and various shielding configurations. In the free-space configuration, shielding the crystal actually decreases the ratio of true/false positive detections (RTF) by more than 50% and increases the activation of the detector. In a closed-space configuration with a model of the beamline neutron fluence, RTF also decreases with shielding, although activation decreases in this case. However, for a detector with boron present in the printed circuit boards (PCBs), shielding with a thin layer of Gd2O3 improves RTF by up to 21%. Shielding of the detector crystal itself is unnecessary as shielding actually degrades discrimination accuracy relative to the unshielded detector. However, if the detector PCBs contain boron, then shielding the electronics provides a valuable increase in overall detector selectivity.

Regional-Scale Detection of Palms Using VHR Satellite Imagery and Deep Learning in the Guyanese Rainforest

Arecaceae (palms) play a crucial role for native communities and wildlife in the Amazon region. This study presents a first-of-its-kind regional-scale spatial cataloging of palms using remotely sensed data for the country of Guyana. Using very high-resolution satellite images from the GeoEye-1 and WorldView-2 sensor platforms, which collectively cover an area of 985 km2, a total of 472,753 individual palm crowns are detected with F1 scores of 0.76 and 0.79, respectively, using a convolutional neural network (CNN) instance segmentation model. An example of CNN model transference between images is presented, emphasizing the limitation and practical application of this approach. A method is presented to optimize precision and recall using the confidence of the detection features; this results in a decrease of 45% and 31% in false positive detections, with a moderate increase in false negative detections. The sensitivity of the CNN model to the size of the training set is evaluated, showing that comparable metrics could be achieved with approximately 50% of the samples used in this study. Finally, the diameter of the palm crown is calculated based on the polygon identified by mask detection, resulting in an average of 7.83 m, a standard deviation of 1.05 m, and a range of {4.62, 13.90} m for the GeoEye-1 image. Similarly, for the WorldView-2 image, the average diameter is 8.08 m, with a standard deviation of 0.70 m and a range of {4.82, 15.80} m.

Score matching for differential abundance testing of compositional high-throughput sequencing data.

The class of a-b power interaction models, proposed by Yu et al. (2024), provides a general framework for modeling sparse compositional count data with pairwise feature interactions. This class includes many distributions as special cases and enables modeling of zero counts through power transformations, making it particularly suitable for modern high-throughput sequencing data with excess zeros, including single-cell RNA-Seq and microbial amplicon data. Here, we present an extension of this class of models that allows inclusion of covariate information, thus enabling accurate characterization of covariate dependencies in heterogeneous populations. Combining this model with a tailored differential abundance (DA) test leads to a novel DA testing scheme, cosmoDA, that can reduce false positive detection rate caused by correlated features. cosmoDA uses penalized generalized score matching for parsimonious model fitting. We show on simulated benchmarks that cosmoDA can accurately estimate feature interactions in the presence of population heterogeneity and significantly reduces the false discovery rate when testing for differential abundance of correlated features. Using single-cell and amplicon data, we illustrate cosmoDA's ability to estimate data-adaptive Box-Cox-type data transformations and assess the impact of zero replacement and power transformations on downstream differential abundance results. cosmoDA is available at https://github.com/bio-datascience/cosmoDA.

Achieving quality assurance in accepting automated pavement condition data: threshold development and error pattern analysis

ABSTRACT Automated pavement condition survey systems are widely used for network-wide pavement distress detection, yet concerns about data quality remain. To address this, it is crucial to develop effective data quality assurance (QA) procedures. This study proposes two indexes for QA procedures: the accuracy index based on the differences between automated measurements and manual audit measurements, and the precision index based on the differences between automated measurements of two consecutive years. Using automated distress data (2017–2021) and manual audit data from Texas, the Difference of Two-Sigma (D2S) method was applied to determine the thresholds of various distress types for the accuracy and precision indexes. Then, a validation of the proposed thresholds was conducted by visually checking the 2022 image data of a selected pavement network. Through this process, the reliability of the thresholds was evaluated, and the error patterns present in the automated data were analysed. The study results demonstrated that the developed thresholds were able to effectively identify data quality issues. The primary source of errors in the automated condition survey system was false-positive detections of pavement distresses, frequently caused by features in the pavement surface background, primarily due to the textures of the pavements.

Detecting and sizing the Earth with PLATO: A feasibility study based on solar data

Context. The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. Aims. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO’s ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. Methods. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by PlatoSim assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. Results. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values (b &gt; 0.8). For bright targets (8.5–10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag ≤ 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3% or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood.

Serum Albumin Alters [18F]FDG Activity in the Liver and Blood Pool

Abstract Objective This study aims to investigate the correlation between the 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) activity of the liver and blood pool, and the serum albumin. Methods A retrospective analysis was conducted on adult patients who underwent [18F]FDG positron emission tomography/computed tomography at the Nuclear Medicine Unit of a hospital in Hong Kong between January 1, 2023, and March 31, 2023. The mean standardized uptake value normalized to lean body mass (SULmean) was measured in the liver and blood pool. Pearson's correlation analyses between the SULmean of reference regions and serum albumin were performed. Multiple linear regression was used to analyze the effects of serum albumin and other parameters as the independent predictors on SULmean of the reference regions. Results A total of 146 patients were included, with their SULmean of the liver and blood pool showing significantly positive correlations with serum albumin (r = 0.393, p &lt; 0.001 and r = 0.207, p = 0.012, respectively). Multiple linear regression analyses confirmed serum albumin as an independent variable on SULmean of the liver and blood pool (p &lt; 0.001 and p = 0.014, respectively). Conclusion Serum albumin alters [18F]FDG biodistribution in the liver and blood pool. The decrease in liver background activity in patients with low serum albumin may produce a higher false-positive rate of lesion detection, particularly when there is a drop of serum albumin in serial scans. Nuclear medicine physicians should be cautious of image interpretation.

Critical assessment of nanopore sequencing for the detection of multiple forms of DNA modifications.

While nanopore sequencing is increasingly used for mapping DNA modifications, it is important to recognize false positive calls as they can mislead biological interpretations. To assist biologists and methods developers, we describe a framework for rigorous evaluation that highlights the use of false discovery rate with rationally designed negative controls capturing both general background and confounding modifications. Our critical assessment across multiple forms of DNA modifications highlights that while nanopore sequencing performs reliably for high-abundance modifications, including 5-methylcytosine (5mC) at CpG sites in mammalian cells and 5-hydroxymethylcytosine (5hmC) in mammalian brain cells, it makes a significant proportion of false positive detections for low-abundance modifications, such as 5mC at CpH sites, 5hmC and N6-methyldeoxyadenine (6mA) in most mammal cell types. This study highlights the urgent need to incorporate this framework in future methods development and biological studies, and advocates prioritizing nanopore sequencing for mapping abundant over rare modifications in biomedical applications.

Repeat-Rich Regions Cause False-Positive Detection of NUMTs: A Case Study in Amphibians Using an Improved Cane Toad Reference Genome.

Mitochondrial DNA (mtDNA) has been widely used in genetics research for decades. Contamination from nuclear DNA of mitochondrial origin (NUMTs) can confound studies of phylogenetic relationships and mtDNA heteroplasmy. Homology searches with mtDNA are widely used to detect NUMTs in the nuclear genome. Nevertheless, false-positive detection of NUMTs is common when handling repeat-rich sequences, while fragmented genomes might result in missing true NUMTs. In this study, we investigated different NUMT detection methods and how the quality of the genome assembly affects them. We presented an improved nuclear genome assembly (aRhiMar1.3) of the invasive cane toad (Rhinella marina) with additional long-read Nanopore and 10× linked-read sequencing. The final assembly was 3.47 Gb in length with 91.3% of tetrapod universal single-copy orthologs (n = 5,310), indicating the gene-containing regions were well assembled. We used 3 complementary methods (NUMTFinder, dinumt, and PALMER) to study the NUMT landscape of the cane toad genome. All 3 methods yielded consistent results, showing very few NUMTs in the cane toad genome. Furthermore, we expanded NUMT detection analyses to other amphibians and confirmed a weak relationship between genome size and the number of NUMTs present in the nuclear genome. Amphibians are repeat-rich, and we show that the number of NUMTs found in highly repetitive genomes is prone to inflation when using homology-based detection without filters. Together, this study provides an exemplar of how to robustly identify NUMTs in complex genomes when confounding effects on mtDNA analyses are a concern.

AI-Based Malware Detection and Classification for Wireless Networks

Abstract: This study highlights the shortcomings of conventional detection strategies like signature-based and anomaly-based approaches by examining the application of artificial intelligence (AI) techniques for malware detection and classification in wireless networks. Using machine learning and deep learning algorithms, the study shows notable gains in false positive rates, detection accuracy, and adaptation to changing threats. The acquisition of data from many sources, the extraction of features from malware samples, and the thorough training and assessment of models are important techniques. The results demonstrate how successful AI-based systems are in detecting and reducing malware threats, even in the face of issues like dataset variety and interpretability. The potential of AI technology to improve cybersecurity measures in wireless environments is highlighted by this research.

Detection and Quantification of Soil-Borne Wheat Mosaic Virus, Soil-Borne Cereal Mosaic Virus and Japanese Soil-Borne Wheat Mosaic Virus by ELISA and One-Step SYBR Green Real-Time Quantitative RT-PCR.

Furoviruses are bipartite viruses causing mosaic symptoms and stunting in cereals. Infection with these viruses can lead to severe crop losses. The virus species Furovirus tritici with soil-borne wheat mosaic virus (SBWMV), Furovirus cerealis with soil-borne cereal mosaic virus (SBCMV) and Furovirus japonicum with Japanese soil-borne wheat mosaic virus (JSBWMV) and French barley mosaic virus (FBMV) as members are biologically and genetically closely related. Here, we develop SYBR green-based real-time quantitative RT-PCR assays to detect and quantify the RNA1 and RNA2 of the three virus species. Using experimental data in combination with Tm-value prediction and analysis of primer and amplicon sequences, we determine the capacity of our method to discriminate between the different viruses and evaluate its genericity to detect different isolates within the same virus species. We demonstrate that our method is suitable for discriminating between the different virus species and allows for the detection of different virus isolates. However, JSBWMV RNA1 primers may amplify SBWMV samples, bearing a risk for false positive detection with this primer. We also test the efficiency of polyclonal antibodies to detect the different viruses by ELISA and suggest that ELISA may be applied as a first screening to identify the virus. The real-time qRT-PCR assays developed provide the possibility to screen for quantitative disease resistance against SBCMV, SBWMV and JSBWMV. Moreover, with our method, we hope to promote research to unravel yet unresolved questions with respect to furovirus-host interaction concerning host range and resistance as well as regarding the role of multipartite genomes.

B-232 One Year Post-Implementation Experience Using a Custom Designed DNA Contamination Detection Module for a Clinical Myeloid Neoplasm Next Generation Sequencing Assay

Abstract Background In complex automated clinical laboratory Next Generation Sequencing (NGS) workflows, numerous opportunities exist for contamination events to occur. For oncology testing applications, detection of these events is crucial for accurate results reporting. A contamination detection module (MICon) using microhaplotype (MH) regions and an in-house designed analysis model was developed for use in a NGS assay for myeloid neoplasms (NGSHM) [1]. Variant detection for NGSHM has a validated analytical sensitivity of 2% variant allele frequency (VAF). Gross contamination events can potentially cause erroneous false positive variant detection whereas true low VAF mutations may be masked and evade detection. The following study was performed to evaluate MICon module performance on samples processed over the course of a year. Methods MH regions contain highly conserved tandem single nucleotide polymorphisms (SNPs) with high global heterogeneity occurring within a 300-nucleotide span. From the work of Kidd et al [2], 27 MH regions covering 92 SNPs and spanning 16 chromosomes were selected for inclusion in the 47 gene NGSHM target-capture panel. Sequencing was performed on a NovaSeq 6000 and analyzed through an internal bioinformatics pipeline, which incorporates the MICon module. MICon uses a binary classification model incorporating VAFs from MH regions, number of MH genotypes, and the contamination estimation score from verifyBamID to compute a single value on a 0-100 scale. Scores &amp;lt;50 are classified as non-contaminated and scores ≥50 require further investigation. Results The dataset was comprised of 10,990 samples. A total of 10,232 patient cases (93.1%) had non-contaminated MICon scores &amp;lt;50. Control replicates accounted for 261 samples (2.4%). There were 497 patient cases (4.5%) with MICon scores ≥50. Of the 497 patient cases, 137 (27.6%) had undergone hematopoietic stem cell transplantation (HSCT) and 114 (22.9%) contained gross chromosomal abnormalities, representing iatrogenic or tumor biological factors. An additional 135 (27.2%) of cases were contaminated from laboratory processing errors, 45 (9.1%) occurred due to chemistry failures during run processing, and 66 (13.2%) had an undetermined cause for a high score, representing laboratory-related and unknown factors. Overall, the 135 laboratory processing errors detected accounted for 1.3% of patient cases analyzed by the laboratory. These cases were repeated, and the non-contaminated results were accurately reported. Conclusions Herein it was shown the implementation of MICon has improved patient safety over the course of a year. The 1.3% of patient cases identified as arising from laboratory processing errors were successfully reprocessed. While the overall rate of laboratory processing errors appears low, the absolute number can be significant in a high-volume test setting, despite reliance on highly automated processes. Value is added by MICon through identifying errors that could result in the release of inaccurate patient results. By detecting previously unrealized errors, MICon has become an invaluable asset to the NGSHM assay by enhancing patient laboratory testing safety.

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

Mosquitoes on a chip-environmental DNA-based detection of invasive mosquito species using high-throughput real-time PCR.

The monitoring of mosquitoes is of great importance due to their vector competence for a variety of pathogens, which have the potential to imperil human and animal health. Until now mosquito occurrence data is mainly obtained with conventional monitoring methods including active and passive approaches, which can be time- and cost-consuming. New monitoring methods based on environmental DNA (eDNA) could serve as a fast and robust complementary detection system for mosquitoes. In this pilot study already existing marker systems targeting the three invasive mosquito species Aedes (Ae.) albopictus, Ae. japonicus and Ae. koreicus were used to detect these species from water samples via microfluidic array technology. We compared the performance of the high-throughput real-time PCR (HT-qPCR) system Biomark HD with real-time PCR (qPCR) and also tested the effect of different filter media (Sterivex® 0.45 µm, Nylon 0.22 µm, PES 1.2 µm) on eDNA detectability. By using a universal qPCR protocol and only 6-FAM-MGB probes we successfully transferred these marker systems on the HT-qPCR platform. All tested marker systems detected the target species at most sites, where their presence was previously confirmed. Filter media properties, the final filtration volume and observed qPCR inhibition did not affect measured Ct values via qPCR or HT-qPCR. The Ct values obtained from HT-qPCR were significantly lower as Ct values measured by qPCR due to the previous preamplification step, still these values were highly correlated. Observed incongruities in eDNA detection probability, as manifested by non-reproducible results and false positive detections, could be the result of methodological aspects, such as sensitivity and specificity issues of the used assays, or ecological factors such as varying eDNA release patterns. In this study, we show the suitability of eDNA-based detection of mosquito species from water samples using a microfluidic HT-qPCR platform. HT-qPCR platforms such as Biomark HD allow for massive upscaling of tested species-specific assays and sampling sites with low time- and cost-effort, thus this methodology could serve as basis for large-scale mosquito monitoring attempts. The main goal in the future is to develop a robust (semi)-quantitative microfluidic-based eDNA mosquito chip targeting all haematophagous culicid species occurring in Western Europe. This chip would enable large-scale eDNA-based screenings to assess mosquito diversity, to monitor species with confirmed or suspected vector competence, to assess the invasion progress of invasive mosquito species and could be used in pathogen surveillance, when disease agents are incorporated.

Synthesis of trisaccharide antigens featuring colitose, abequose and fucose residues and assessment of antibody binding on antigen arrays

Deoxy-hexose sugars, such as rhamnose and quinovose, and the dideoxy-hexoses colitose, abequose, and tyvelose are highly antigenic given that they are absent from animal glycoconjugates. To investigate the specificity of antibodies towards structurally similar carbohydrate epitopes found in bacteria, we synthesized trisaccharides containing colitose, abequose, and fucose motifs. Each trisaccharide was designed with a spacer ending with a primary amino group. These trisaccharide constructs were immobilized on O-succinimide coated glass slides alongside bacterial lipopolysaccharides (LPS) containing colitose, abequose, and fucose residues. We compared the recognition of the synthetic trisaccharides and natural LPS including structurally related epitopes by monoclonal and polyclonal antibodies targeting bacterial LPS. Additionally, we used arrays displaying the synthetic trisaccharides and natural LPS to assess the variability of IgA reactivity from breast milk samples towards the carbohydrate antigens. The results obtained underlined the cross-reactivity of polyclonal antibodies towards structurally related carbohydrate antigens and revealed a broad reactivity of breast milk-derived IgA towards the carbohydrate antigens tested. The significant cross-reactivity of antibodies towards structurally related LPS antigens may lead to false-positive detection of bacterial serotypes when used for diagnostic purposes.