Differential Detection Research Articles

Robust real-time single-pixel imaging based on a spinning mask via differential detection

Single-pixel imaging (SPI) has gained considerable attention over the past two decades but is still inadequate for imaging moving objects in practical scenarios. Recently, spinning masks have been employed for real-time SPI due to their fast modulation speeds. However, the intensity fluctuations caused by ambient light can overlap with the desired modulation from the mask, leading to a significant degradation in image quality, making it unable to function properly in environments with unstable lighting. Here we propose a rotating disc encoded with cyclic S-matrices, leveraging both reflective and transmissive signals for differential detection. Simulations and experiments demonstrate interference-resistant real-time SPI, achieving 31 fps at about 60 kHz modulation rates. This approach effectively suppresses external noise and is extendable to infrared and terahertz bands, advancing SPI’s practical applications.

TREMSUCS-TCGA - an integrated workflow for the identification of biomarkers for treatment success.

Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.

Mass Spectrometry Imaging for Spatial Ingredient Classification in Plant-Based Food.

Mass spectrometry imaging (MSI) techniques enable the generation of molecular maps from complex and heterogeneous matrices. A burger patty, whether plant-based or meat-based, represents one such complex matrix where studying the spatial distribution of components can unveil crucial features relevant to the consumer experience or production process. Furthermore, the MSI data can aid in the classification of ingredients and composition. Thin sections of different burger samples and vegetable constituents (carrot, pea, pepper, onion, and corn) were prepared for matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI analysis. MSI measurements were performed on all samples, and the data sets were processed to build three machine learning models aimed at detecting meat adulteration in vegetable burger samples, identifying individual ingredients within the vegetable burger matrix, and discriminating between burgers from different manufacturers. Ultimately, the successful detection of adulteration and differentiation of various burger recipes and their constituent ingredients were achieved. This study demonstrates the potential of MSI coupled with building machine learning models to enable the comprehensive characterization of burgers, addressing critical concerns for both the food industry and consumers.

Diagnostic potential of rhinovirus C using reverse-transcription loop-mediated isothermal amplification (RT-LAMP).

Rhinoviruses (RVs), particularly RV-C, frequently cause acute respiratory infections and asthma exacerbations. However, there is a lack of routine detection methods. Thus, this study aims to develop a rapid molecular and differential diagnostic detection method for RV-C using the reverse transcription (RT) loop-mediated isothermal amplification (LAMP) approach. The RT-LAMP assay targeting the 5'UTR region of RV-C genome was optimized by varying the reaction temperature, magnesium sulphate, betaine concentrations, and reaction time. Compared with conventional RT-PCR with a sensitivity of 106 copies of RNA, RT-LAMP demonstrated a significant increase in efficiency and sensitivity with a quantifiable viral load of at least 101 copies of RNA by gel electrophoresis and colour change, and 104 copies of RNA for end-point detection with a turbidimeter for 40min. The assay is also specific without amplifying RV-A16 and RV-B72 genomic RNA. In the proof-of-concept assay using 30 clinical respiratory samples with known etiological agents, it detected all RV-C isolates, of which its accuracy was confirmed by sequencing. The newly developed RT-LAMP assay demonstrated good analytical sensitivity and specificity toward RV-C. The assay provides an alternative for improved RV-C diagnosis.

Innovative dual-mode miniaturized nitrite sensor: Incorporating colorimetric and electrochemical detectors for comprehensive monitoring on a single platform

This study introduces a novel, miniaturized analytical platform for a comprehensive range of founded nitrite concentrations on a single, portable device. Nitrites, essential for extending shelf life and enhancing color, are commonly found in both natural and industrial environments. In environmental settings, it is important to detect low nitrite concentrations, industrial applications, such as in the food industry, require handling higher concentrations effectively. This groundbreaking approach combines colorimetry and electrochemical detection for nitrite monitoring in a single portable paper-based device (PAD). Utilizing potassium permanganate (KMnO4) as a chromogenic agent, the device enables rapid, visual detection and quantification of high nitrite concentrations via smartphone-based color intensity analysis. For sensitive detection of low-level nitrite, it incorporates a cellulose acetate-modified screen-printed electrode (CA/SPCE) with differential pulse voltammetric detection. Under optimal conditions, the proposed dual-detection platform effectively detects nitrite concentrations ranging from 0.05 to 12.5 mM. Specifically, it covers (0.05 to 0.6 mM using electrochemical detection and 0.5 to 12.5 mM through colorimetric analysis. High nitrite levels produce a faded color in the test zone, while low or undetectable color changes trigger electrochemical analysis for enhanced precision. This PAD platform is portable, cost-effective, and minimizes reagent usage. Importantly, the method’s detection limit aligns with the World Health Organization’s acceptable daily nitrite intake in drinking water (3 mg L−1, approximately 65 µM), making it a practical tool for nitrite screening.

Spermine induced in-situ synthesis of polychromatic carbon nanodots towards smartphone-readable ratiometric fluorescence sensing of multiple catecholamines

Abnormal levels of catecholamines can indicate underlying health issues, such as Parkinson's disease, hypertension, and pheochromocytoma. However, it is difficult to accurately differentiate multiple catecholamines using traditional detection methods due to their highly similar structures. Herein, we proposed a carbon-based fluorescent probe with adaptability named spermine that can react with multiple catecholamines to generate polymer carbon dots with distinct fluorescence colors, enabling visual differentiation and quantitative detection of epinephrine, norepinephrine and levodopa. Spermine does not requires synthesis and exhibits excellent biocompatibility, as it naturally exists in bacteria and most animal cells. Additionally, ratiometric fluorescent probes with high sensitivity was constructed to eliminate nonspecific factors in the sample detection process. Furthermore, due to the simplicity of the designed fluorescence detection steps, a convenient paper-based biosensor for visual detection of multiple catecholamines was formed by pre-dropping the probe on the detection layer, which can be integrated into the portable platform and the detection value can directly display on the smartphone. Therefore, the integrated portable platform of the biosensor can differentiate catecholamines types through fluorescent color and intelligently detect the content of catecholamine in the sample.

Severity Classification of Parkinson’s Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments

Background/Objectives: Parkinson’s Disease is a prevalent neurodegenerative disorder affecting millions worldwide, primarily marked by motor and non-motor symptoms due to the degeneration of dopamine-producing neurons. Despite the absence of a cure, current treatments focus on symptom management, often relying on pharmacotherapy and surgical interventions. Early diagnosis remains a critical challenge, particularly in underserved areas, as existing diagnostic protocols lack standardization and accessibility. This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. Methods: Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models—a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)—to analyze these images. Our methodology allows for the accurate detection of PD and differentiation of its severity without requiring specialized equipment or professional oversight. The dataset used consists of labeled videos capturing the early stages of the disease, facilitating the potential for timely intervention. Results: The four models performed very accurately during the training phase. In fact, an accuracy higher than 99% was achieved by the ViT and ResNet models. Moreover, a lesser accuracy of 90% was achieved by the CNN five-layer model. During the test phase, only the best-performing models from the training experiments were tested. The ResNet-18 model has achieved a 100% accuracy. However, the ViT and the CNN five-layer models have achieved, respectively, 99.96% and 96.40% test accuracy. Conclusions: The results demonstrate high accuracy, highlighting the framework’s capabilities, and in particular the effectiveness of the workflow used for generating the SEI images. Given the nature of the dataset used, the proposed framework stands to function as a cost-effective and accessible tool for early PD detection in various healthcare settings. This study contributes to the advancement of mobile health technologies, aiming to enhance early diagnosis and monitoring of Parkinson’s Disease.

Fast moving table tennis ball tracking algorithm based on graph neural network

The key object tracking in sports video scenarios poses a pivotal challenge in the analysis of sports techniques and tactics. In table tennis, due to the small size and rapid motion of the ball, identifying and tracking the table tennis ball through video is a particularly arduous task, where the majority of existing detection and tracking algorithms struggle to meet the practical application requirements in real-world scenarios. To address this issue, this paper proposes a combined technical approach integrating detection and discrimination, tailored to the unique motion characteristics of table tennis. For the detector, we utilize and refine a common video differential detector. As for the discriminator, we introduce GMP (a Graph Max-message Pass Neural Network), which is designed specifically for tracking table tennis balls or similar objects. Furthermore, we enhance an existing dataset for table tennis tracking problems by enriching its scenarios. The results demonstrate that our proposed technical solution performs impressively on both the dataset and the intended real-world environments, showcasing the good scalability of our algorithms and models as well as their potential for application in other scenarios.

A Systematic Identification of RNA-Binding Proteins (RBPs) Driving Aberrant Splicing in Cancer.

Alternative Splicing (AS) is a post-transcriptional process that allows a single RNA to produce different mRNA variants and, in some cases, multiple proteins. Various processes, many yet to be discovered, regulate AS. This study focuses on regulation by RNA-binding proteins (RBPs), which are not only crucial for splicing regulation but also linked to cancer prognosis and are emerging as therapeutic targets for cancer treatment. CLIP-seq experiments help identify where RBPs bind on nascent transcripts, potentially revealing changes in splicing status that suggest causal relationships. Selecting specific RBPs for CLIP-seq experiments is often driven by a priori hypotheses. We developed an algorithm to detect RBPs likely related to splicing changes between conditions by integrating several CLIP-seq databases and a differential splicing detection algorithm. This work refines a previous study by improving splicing event prediction, testing different enrichment statistics, and performing additional validation experiments. The new method provides more accurate predictions and is included in the Bioconductor package EventPointer 3.14. We tested the algorithm in four experiments involving knockdowns of seven different RBPs. The algorithm accurately assessed the statistical significance of these RBPs using only splicing alterations. Additionally, we applied the algorithm to study sixteen cancer types from The Cancer Genome Atlas (TCGA) and three from TARGET. We identified relationships between RBPs and various cancer types, including alterations in CREBBP and MBNL2 in adenocarcinomas of the lung, liver, prostate, rectum, stomach, and colon. Some of these findings are validated in the literature, while others are novel. The developed algorithm enhances the ability to predict and understand RBP-related splicing changes, offering more accurate predictions and novel insights into cancer-related splicing alterations. This work highlights the potential of RBPs as therapeutic targets and contributes to the broader understanding of their roles in cancer biology.

Localized Amino Acid Enrichment Analysis as a Tool for Understanding Protein Extremophilicity.

Sequence conservation analyses offer us a powerful glimpse of natural selection at work. Standard tools for measuring sequence conservation report conservation as a function of a specific location in a multiple sequence alignment and have proven indispensable in identifying highly constrained features such as active site residues. The advent of large-scale genomic sequencing efforts allows researchers to expand this paradigm and investigate more nuanced relationships between sequence and function. Here, we present a simple tool (SWiLoDD: Sliding Window Localized Differentiation Detection) that allows researchers to analyze local, rather than site-specific, conservation using a sliding window approach. Our tool accepts multiple sequence alignments partitioned based on a biological differentiator and returns alignment position-based, localized differential enrichment metrics for amino acids of choice. We present two case studies of this analysis in action: local-but-diffuse glycine enrichments in the ATPase subunits of thermophilic and psychrophilic bacterial gyrase homologs, and ligand- and interface-specific amino acid enrichments in halophilic bacterial crotonyl-CoA carboxylases/reductases. Though we have described examples of extremophilic bacterial proteins in this study, our tool may be used to investigate any set of homologous sequences from which sub-groups can be meaningfully partitioned. Our results suggest that investigating differential localized conservation in partitioned MSAs will expand our understanding of how sequence conservation and protein function are connected.

Differential Detection of Clonal Bone Marrow Plasma Cells in IgA Vs. IgG Monoclonal Gammopathy of Undetermined Significance and Its Association with Disease Progression

Differential Detection of Clonal Bone Marrow Plasma Cells in IgA Vs. IgG Monoclonal Gammopathy of Undetermined Significance and Its Association with Disease Progression

Fluorometric detection and analog discrimination of melatonin by amide naphthotube-based indicator displacement assays

Melatonin, a hormone synthesized by the pineal gland, is renowned for its pivotal role in governing circadian rhythms and its promising therapeutic attributes encompassing anti-inflammatory, anti-aging, and cancer-preventative properties. Nevertheless, the improper utilization of melatonin may lead to health hazards, highlighting the critical necessity for accurate detection of melatonin in pharmaceutical or biological samples. Furthermore, melatonin metabolites exhibit analogous chemical structures yet divergent pathophysiological functions, emphasizing the significance of distinguishing between these analogs. In this study, we report a supramolecular sensor that combines amide naphthotube and toluidine blue O in an indicator displacement assay for the quantitative detection of melatonin and differentiation of its analogs. The sensor demonstrated remarkable sensitivity, with a low detection limit of approximately 0.70 μM, and a broad dynamic range of 0.70–98.8 μM, along with excellent selectivity and stability. Notably, the sensor enables the determination of melatonin levels in various sample matrices, including milk, artificial urine, and pharmaceutical preparations. Through optimization of the host–guest complex ratio, our newly designed sensor effectively distinguishes melatonin from its six analogs. This approach addresses a current research gap, as existing macrocycles have limited capability to differentiate between melatonin analogs, with only a few achieving precise melatonin quantification.

Nondestructive differential eddy current testing for corrosion detection on coated aluminium alloys

PurposeThe objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal.Design/methodology/approachPitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters.FindingsThe size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating.Originality/valueThe objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

MiR-16a-5p antagonizes FGF-2 in ligamentogenic differentiation of MSC: a new therapeutic perspective for tendon regeneration

With the increasing demand for exercise, the population of patients with ankle sprain to anterior talofibular ligament injury has the characteristics of a large base and high requirements for returning to sports, and how to promote the repair of damaged ligaments from a microscopic perspective is an urgent problem to be solved. In many studies, human amniotic mesenchymal stem cells have strong differentiation ability, and can be induced to continuously differentiate into ligament cells to achieve the purpose of repairing damaged ligaments. Human amniotic stem cells were extracted and cultured from human amniotic tissues, evaluated by cell identification and other techniques, and evaluated into ligament differentiation by toluidine blue, alizarin red, oil red O staining and detection of ligament cell differentiation, protein detection by Western blot, mRNA level by qPCR, and finally, the targeted binding relationship between miR-16a-5p and mRNA FGF2 was verified by double luciferase reporter assay. The expression of collagen type 1 (COL 1), collagen type 3 (COL3), SCX and MKX was increased by overexpression of mRNA FGF2, respectively, and miR-16a-5p had a targeted effect on FGF2 and regulated the ligamentous differentiation of human amniotic mesenchymal stem cells. We found that the regulatory effect of overexpressed mRNA FGF2 on mesenchymal stem cells could be inhibited by up-regulation of miR-16a-5p, while the knockdown of FGF2 could reverse the regulatory effect of miR-16a-5p inhibition on ligament-forming differentiation of human amniotic mesenchymal stem cells. In this study, we discovered the existence of the miR-16a-5p–FGF2 axis in human amniotic mesenchymal stem cells, and the differentiation of human amniotic mesenchymal stem cells into ligamentous cells can be regulated by regulating various links in this axis.

An enzymatic reaction-based SERS saliva analysis microporous array chip for chiral differentiation and high-throughput detection of D-amino acids.

ARaman-active boronate modified surface-enhanced Raman scattering (SERS) microporous array chip based on the enzymatic reaction was constructed for reliable, sensitive, and quantitative monitoring of D-Proline (D-Pro) and D-Alanine (D-Ala) in saliva. Initially, 3-mercaptophenylboronic acid (3-MPBA) was bonded to Au-coated Si nanocrown arrays (Au/SiNCA) via Au-S bonding. Following this, H2O2 obtained from D-amino acid oxidase (DAAO)-specific catalyzed D-amino acids (D-AAs) further reduced 3-MPBA to 3-hydroxythiophenol (3-HTP) with a new Raman peak at 882cm-1. Meanwhile, the original characteristic peak at 998cm-1 remained unchanged. Therefore, the I882/I998 ratio increased with increasing content ofD-AAs in the sample to be tested, allowing D-AAs to be quantitatively detected. TheAu/SiNCA with large-area periodic crown structure prepared provided numerous, uniform "hot spots," and the microporous array chip with 16 detection units was employed as the platform for SERS analysis, realizing high-throughput, high sensitivity, high specificity and high-reliability quantitative detection of D-AAs (D-Pro and D-Ala). The limits of detection (LOD) were down to 10.1µM and 13.7µM throughout the linear range of 20-500µM. The good results of the saliva detection suggested that this SERS sensor could rapidly differentiate between early-stage gastric cancer patients and healthy individuals.

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

Mucosa-like differentiation of head and neck cancer cells is inducible and drives the epigenetic loss of cell malignancy

Head and neck squamous cell carcinoma (HNSCC) is a highly malignant disease with high death rates that have remained substantially unaltered for decades. Therefore, new treatment approaches are urgently needed. Human papillomavirus-negative tumors harbor areas of terminally differentiated tissue that are characterized by cornification. Dissecting this intrinsic ability of HNSCC cells to irreversibly differentiate into non-malignant cells may have tumor-targeting potential. We modeled the cornification of HNSCC cells in a primary spheroid model and analyzed the mechanisms underlying differentiation by ATAC-seq and RNA-seq. Results were verified by immunofluorescence using human HNSCC tissue of distinct anatomical locations. HNSCC cell differentiation was accompanied by cell adhesion, proliferation stop, diminished tumor-initiating potential in immunodeficient mice, and activation of a wound-healing-associated signaling program. Small promoter accessibility increased despite overall chromatin closure. Differentiating cells upregulated KRT17 and cornification markers. Although KRT17 represents a basal stem cell marker in normal mucosa, we confirm KRT17 to represent an early differentiation marker in HNSCC tissue. Cornification was frequently found surrounding necrotic areas in human tumors, indicating an involvement of pro-inflammatory stimuli. Indeed, inflammatory mediators activated the differentiation program in primary HNSCC cells. In HNSCC tissue, distinct cell differentiation states were found to create a common tissue architecture in normal mucosa and HNSCCs. Our data demonstrate a loss of cell malignancy upon faithful HNSCC cell differentiation, indicating that targeted differentiation approaches may be therapeutically valuable. Moreover, we describe KRT17 to be a candidate biomarker for HNSCC cell differentiation and early tumor detection.

A-240 Validation of the OSOM Flu SARS-CoV-2 Combo Home Test

Abstract Background COVID-19 and Influenza respiratory infections often cause similar symptoms, however, treatment is different for each infection. Delay in diagnosis can be fatal in some cases, therefore rapid detection and differentiation are critical for appropriate medical treatment. The OSOM Flu SARS-CoV-2 Combo Home Test is a lateral flow immunoassay intended for the qualitative and differential detection of nucleocapsid protein from SARS-CoV-2 and influenza A/B proteins using self-collected anterior nasal swab samples (self-collection is only recommended for those 14 years of age and older). An interpretation card, included with the test, is used to reliably distinguish among possible testing results. Methods A prospective study was conducted at multiple geographically distinct sites in the U.S during the 2023-2024 season for the validation of the OSOM Flu SARS-CoV-2 Combo Home Test to detect SARS-CoV-2/Flu A/Flu B in self-collected anterior nasal swab samples. The study evaluated the performance in symptomatic individuals only. A total of 703 prospective samples were evaluable for SARS. For Flu A/B, four additional subject samples were unevaluable due to comparator results not being available and a total of 699 samples were included in the data analysis. The comparator tests were FDA 510(k)-cleared molecular assays. Results Influenza A was detected with a sensitivity of 93.1% (95% CI: 84.8%-97.0%) and a specificity of 99.5% (95% CI: 98.6% - 99.8%). Influenza B was detected with a sensitivity of 89.1% (95% CI: 77.0% - 95.3%) and a specificity of 99.7% (95% CI: 98.9% - 99.9%). SARS CoV-2 was detected with a sensitivity of 87.0% (95% CI 78.6% - 92.4%) and a specificity of 99.1% (95% CI: 97.9% - 99.6%) when Ct values are standardized and 10% of samples are considered low positives, having a Ct value of ≥ 30. Conclusions With respiratory infections often showing indistinguishable symptoms and the possibility of co infection, lowering the testing hurdles for individuals to be able to perform a rapid differential test at home without the aid from a healthcare worker is the most effective way to mitigate spread and patient management from a public health perspective. Comparing the OSOM Flu SARS-CoV-2 Combo Home test to the FDA cleared comparators in a clinical setting, the OSOM Flu SARS-CoV-2 Combo Home test retains acceptable sensitivity and specificity. Using self-collected anterior nasal swabs which are minimally invasive alongside the low cost of a lateral flow immunochromatographic assay remains the most effective way to perform the serial screening testing recommended by the CDC in situations where transmission and severe outcome risk is high.

A differential low frequency eddy current test method for detecting moisture content of high voltage cable water-blocking tape

ABSTRACT The excessive moisture content in the water-blocking tape has been one of the main causes of high voltage cable erosion failures in recent years. However, the 2–4 mm thick aluminium sheath wrapped around the water-blocking tape makes it challenging to detect the moisture content. This study introduces a method for detecting the moisture content of high voltage cable water-blocking tapes based on low-frequency eddy currents. Initially, longitudinal and transverse impedance measurement experiments of the water-blocking tape were conducted to establish the relationship between impedance and moisture content. Since the impedance of the water-blocking tape is significantly higher compared to the aluminium sheath, the eddy current signal was obtained using a differential detection method. An excitation frequency of 10 Hz was chosen to achieve a deeper skin depth. Additionally, an eddy current finite element analysis model was developed, showing that moisture content has an almost linear relationship with the amplitude and phase of the eddy current signal. Finally, an experiment was conducted on an equivalent high voltage cable structure, and the experimental results aligned well with the simulation results.