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Quantitative Real-Time Polymerase Chain Reaction, better known as qPCR, is the most sensitive and specific technique we have for the detection of nucleic acids. Even though it has been around for more than 30 years and is preferred in research applications, it has yet to win broad acceptance in routine practice. This requires a means to unambiguously assess the performance of specific qPCR analyses. Here we present methods to determine the limit of detection (LoD) and the limit of quantification (LoQ) as applicable to qPCR. These are based on standard statistical methods as recommended by regulatory bodies adapted to qPCR and complemented with a novel approach to estimate the precision of LoD.
Resolving the COVID-19 pandemic requires diagnostic testing to determine which individuals are infected and which are not. The current gold standard is to perform RT-PCR on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of ~100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, resulting in more false negatives. However, the false-negative rate for a given LoD remains unknown. Here we address this question using over 27,500 test results for patients from across our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 13%, missing an additional one in eight infected patients. The highest LoDs on the market will miss a majority of infected patients, with false negative rates as high as 70%. These results suggest that choice of assay has meaningful clinical and epidemiological consequences. The limit of detection matters.
Waveguide-based photonic sensors provide a unique combination of high sensitivity, compact size and label-free, multiplexed operation. Interferometric configurations furthermore enable a simple, fixed-wavelength read-out making them particularly suitable for low-cost diagnostic and monitoring devices. Their limit of detection, i.e., the lowest analyte concentration that can be reliably observed, mainly depends on the sensors response to small refractive index changes, and the noise in the read-out system. While enhancements in the sensors response have been extensively studied, noise optimization has received much less attention. Here we show that order-of-magnitude enhancements in the limit of detection can be achieved through systematic noise reduction, and demonstrate a limit of detection of ∼ 10 - 8 RIU with a silicon nitride sensor operating at telecom wavelengths.
The development of a highly sensitive electrochemical sensor (E-sensor) is described based on stand-alone plastic electrodes (PE) for phosphate detection, being an essential nutrient in the marine environment. The detection mechanism is based on the chemical affinity between polyoxomolybdate anions (POM) and orthophosphate to form an electroactive phosphomolybdate complex. The custom-made E-sensor was formulated with an organic octamolybdate derivative (TBA4Mo8O26) incorporated with periodic mesoporous organosilica (PMO) to obtain a significant improvement in the analytical performances of phosphate determination. This POM@PMO combination was found to be advantageous in the determination of low concentrations of phosphate in standard solutions ranging from 1 to 500 nM, using square wave voltammetry as the detection technique. This sensitivity enhancement can be attributed to the effect of hydrophobic PMO in loading more POM moieties, owing to its highly porous structure and charged shell. Consequently, the POM@PMO-PE sensor achieved a competitive sensitivity of 4.43 ± 0.14 μA.nM-1.cm-2 and a limit of detection of 0.16 nM with good selectivity against silicates. Finally, seawater and treated wastewater samples have been tested to validate the sensor response in comparison to the official method of phosphate determination.
Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.
The AlGaN/GaN-based sensor is a promising POCT (point-of-care-testing) device featuring miniaturization, low cost, and high sensitivity. BNP is an effective protein biomarker for the early diagnosis of HF (heart failure). In this work, a novel AlGaN/GaN device with the Kelvin connection structure and the corresponding detection technique was proposed. This technique can effectively suppress the background noise and improve the SNR (signal-to-noise ratio). A BNP detection experiment was carried out to verify the effectiveness of this technique. It is shown that compared with that of the traditional detection method, the LOD (limit of detection) was improved from 0.47 ng/mL to 1.29 pg/mL. The BNP detection experiment was also carried out with a traditional electrochemical Au-electrode sensor with the same surface functionalization steps. The AlGaN/GaN sensor showed a better LOD than the Au-electrode sensor. Moreover, the influence of AlGaN/GaN sensor package on background noise was investigated with the mechanism of the noise source revealed. Finally, based on the optimized package, the optimal SNR quiescent operating point of the AlGaN/GaN sensor was determined. By biasing the sensor at the optimal quiescent operating point and immobilizing the magnetic beads with anti-BNP on the gate of the AlGaN/GaN sensor, the LOD for BNP detection was further improved to 0.097 pg/mL.
The World Health Organization (WHO) emphasizes that tuberculosis (TB) in children and adolescents is often overlooked by healthcare providers and difficult to diagnose. As childhood TB cases rise, finding a diagnostic high in sensitivity and specificity is critical. In this study 91 urine samples from children aged 1-10 years were analyzed for tuberculostearic acid (TBSA) by gas chromatography/mass spectrometry (GC/MS) and capture ELISA (C-ELISA). In C-ELISA the CS35/A194-01 antibody performed very poorly with both curve-based and model-based cutoffs. The area under the ROC curve (AUC) of the CS35 OD450 values was only 0.60. Replacing the capture antibody with BJ76 gave a better performance in both sensitivity and specificity (AUC = 0.95). When these samples were analyzed by GC/MS, 41 classified as 'probable/possible' for TB were distinctly TBSA positive with ten samples having <3 ng/mL LAM. However, from the 50 samples with 'unlikely' TB classification, 36 were negative but 7 had >3 ng/mL and were designated as LAM positive. This experimental assay assessment study signifies that i) the antibody pair CS35/A194-01 that has been successful for adult active TB diagnosis is not adequate when LAM level is low as in pediatric TB; ii) no one mAb appears to recognize all TB-specific LAM epitopes.
The Electronic tongue (ET) has been used as a diagnostic technique in the medical sector. It is composed of a multisensor array set with high cross-sensitivity and low selectivity characteristics. The research investigated using Astree II Alpha MOS ET to determine the limit of early detection and diagnosis of food-borne human pathogenic bacteria and to recognize unknown bacterial samples relying on pre-stored models. Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC25922) were proliferated in nutrient broth (NB) medium with original inoculum (approximately 107*105 CFU/mL). They were diluted up to 10-14 and the dilutions ranging from 10-14 to 10-4 were measured using ET. The partial least square (PLS) regression model detected the limit of detection (LOD) of the concentration that was monitored to grow the bacteria with different incubation periods (from 4 to 24 h). The measured data were analysed by principal component analysis (PCA) and followed by projecting unknown bacterial samples (at specific concentrations and time of incubation) to examine the recognition ability of the ET. Astree II ET was able to track bacterial proliferation and metabolic changes in the media at very low concentrations (between the dilutions 10-11 and 10-10 for both bacteria). S.aureus was detected after 6 h incubation period and between 6 and 8 h for E.coli. After creating the strains' models, ET was also able to classify unknown samples according to their foot-printing characteristics in the media (S.aureus, E.coli or neither of them). The results considered ET a powerful potentiometric tool for the early identification of food-borne microorganisms in their native state within a complex system to save patients' lives.
Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for β-cell (GRSβ) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRSβ, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRSβ but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants.
Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or "communities", that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules (Nicolini and Bifone, 2016), in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymptotical Surprise with leading community detection methods currently in use and show its superior sensitivity in the detection of small modules even in the presence of noise and intersubject variability such as those observed in fMRI data. We apply our novel approach to functional connectivity networks from resting state fMRI experiments, and demonstrate a heterogeneous modular organization, with a wide distribution of clusters spanning multiple scales. Finally, we discuss the implications of these findings for the identification of connector hubs, the brain regions responsible for the integration of the different network elements, showing that the improved resolution afforded by Asymptotical Surprise leads to a different classification compared to current methods.
Fluorescence correlation spectroscopy (FCS) is an extremely versatile tool that has been widely used to measure chemical reaction rates, protein binding, nanoparticle-protein interactions, and biomolecular dynamics in vitro and in vivo. As an inherently micro-sized approach, FCS is compatible with high-throughput screening applications, as demanded for drug design, but typically limited to nanomolar concentrations, which restricts possible applications. Here, we show how massively parallel camera-based detection with side illumination can extend the usable concentration range of FCS more than 100-fold to measure low affinity processes. Our line illumination (LIM) approach is robust, fast (1 s acquisition times), and does not require any reference measurements to characterize the observation volume size.
We modified the Hybritech Tandem-E prostate-specific antigen (PSA) assay by increasing the sample volume, increasing enzyme-substrate incubation time, and using diethanolamine buffer. Our modified method has a detection limit of 0.009 microgram/L (P < 0.01). The assay curve is linear from 0.01 to 1.0 micrograms/L and has an overall assay time of about 4 h. Linear plots are obtained when the 1.0 micrograms/L standard is diluted with either matrix buffer or serum from men containing PSA < 0.01 microgram/L. Recovery of PSA (0.10 microgram/L) added to serum from men averaged 94%. Interassay CVs were 13%, 7%, and 4% at PSA concentrations of 0.04, 0.07, and 0.30 micrograms/L, respectively (n = 33). This assay should be useful in the detection of early recurrence of prostate cancer after radical prostatectomy.
There is an increasing demand for convenient and accurate point-of-care tools that can detect and diagnose different stages of a disease in remote or impoverished settings. In recent years, lateral flow immunoassays (LFIA) have been indicated as a suitable medical diagnostic tool for these environments because they require little or no sample preparation, provide rapid and reliable results with no electronic components and thus can be manufactured at low costs and operated by unskilled personnel. However, even though they have been successfully applied to acute and chronic disease detection, LFIA based on gold nanoparticles, the standard marker, show serious limitations when high sensitivity is needed, such as early stage disease detection. Moreover, based on the lack of comparative information for label performance, significant optimization of the systems that are currently in use might be possible. To this end, in the presented work, we compare the detection limit between the four most used labels: colloidal-gold, silver enhanced gold, blue latex bead and carbon black nanoparticles. Preliminary results were obtained by using the biotin-streptavidin coupling as a model system and showed that carbon black had a remarkably low detection limit of 0.01 μg/mL in comparison to 0.1 μg/mL, 1 μg/mL and 1mg/mL for silver-coated gold nanoparticles, gold nanoparticles and polystyrene beads, respectively. Therefore, as a proof of concept, carbon black was used in a detection system for Dengue fever. This was achieved by immobilizing monoclonal antibodies for the nonstructural glycoprotein (NS1) of the Dengue virus to carbon black. We found that the colorimetric detection limit of 57 ng/mL for carbon black was ten times lower than the 575 ng/mL observed for standard gold nanoparticles; which makes it sensitive enough to diagnose a patient on the first days of infection. We therefore conclude that, careful screening of detection labels should be performed as a necessary step during LFIA development in order to enhance the detection limit in a final test system.
Circulating tumor DNA is a promising noninvasive tool for cancer monitoring. One of the challenges in applying this tool is the detection of low-frequency mutations. The detection limit of these mutations varies between different molecular methods. The aim of this study is to characterize the factors affecting the limit of detection for epidermal growth factor receptor p.T790M mutation in circulating tumor DNA of patients with lung adenocarcinoma.
Recently, shot noise has been shown to be an inherent part of all charge-transfer processes, leading to a practical limit of quantification of 2100 electrons (≈0.34 fC) [ Curr. Opin. Electrochem. 2020, 22, 170-177]. Attainable limits of quantification are made much larger by greater background currents and insufficient instrumentation, which restricts progress in sensing and single-entity applications. This limitation can be overcome by converting electrochemical charges into photons, which can be detected with much greater sensitivity, even down to a single-photon level. In this work, we demonstrate the use of fluorescence, induced through a closed bipolar setup, to monitor charge-transfer processes below the detection limit of electrochemical workstations. During this process, the oxidation of ferrocenemethanol (FcMeOH) in one cell is used to concurrently drive the oxidation of Amplex Red (AR), a fluorogenic redox molecule, in another cell. The spectroelectrochemistry of AR is investigated and new insights on the commonplace practice of using deprotonated glucose to limit AR photooxidation are presented. The closed bipolar setup is used to produce fluorescence signals corresponding to the steady-state voltammetry of FcMeOH on a microelectrode. Chronopotentiometry is then used to show a linear relationship between the charge passed through FcMeOH oxidation and the integrated AR fluorescence signal. The sensitivity of the measurements obtained at different timescales varies between 2200 and 500 electrons per detected photon. The electrochemical detection limit is approached using a diluted FcMeOH solution in which no faradaic current signal is observed. Nevertheless, a fluorescence signal corresponding to FcMeOH oxidation is still seen, and the detection of charges down to 300 fC is demonstrated.
In the context of the coronavirus disease 2019 (COVID-19) pandemic there has been an increase of the use of antigen-detection rapid diagnostic tests (Ag-RDT). The performance of Ag-RDT vary greatly between manufacturers and evaluating their analytical limit of detection (LOD) has become high priority. Here we describe a manufacturer-independent evaluation of the LOD of 19 marketed Ag-RDT using live SARS-CoV-2 spiked in different matrices: direct culture supernatant, a dry swab, and a swab in Amies. Additionally, the LOD using dry swab was investigated after 7 days' storage at - 80 °C of the SARS-CoV-2 serial dilutions. An LOD of ≈ 5.0 × 102 pfu/ml (1.0 × 106 genome copies/ml) in culture media is defined as acceptable by the World Health Organization. Fourteen of 19 Ag-RDTs (ActiveXpress, Espline, Excalibur, Innova, Joysbio, Mologic, NowCheck, Orient, PanBio, RespiStrip, Roche, Standard-F, Standard-Q and Sure-Status) exceeded this performance criteria using direct culture supernatant applied to the Ag-RDT. Six Ag-RDT were not compatible with Amies media and a decreased sensitivity of 2 to 20-fold was observed for eleven tests on the stored dilutions at - 80 °C for 7 days. Here, we provide analytical sensitivity data to guide appropriate test and sample type selection for use and for future Ag-RDT evaluations.
Breath analysis is considered to be an effective method for point-of-care diagnosis due to its noninvasiveness, quickness and simplicity. Gas sensors for breath analysis require detection of low-concentration substances. In this paper, we propose that reduction of the background current improves the limit of detection of enzymatic biogas sensors utilizing chromatography paper. After clarifying the cause of the background current, we reduced the background current by improving the fabrication process of the sensors utilizing paper. Finally, we evaluated the limit of detection of the sensor with the sample vapor of ethanol gas. The experiment showed about a 50% reduction of the limit of detection compared to previously-reported sensor. This result presents the possibility of the sensor being applied in diagnosis, such as for diabetes, by further lowering the limit of detection.
The need in cancer research or evolutionary biology to detect rare mutations or variants present at very low frequencies (<10-5) poses an increasing demand on lowering the detection limits of available methods. Here we demonstrated that amplifiable DNA lesions introduce important error sources in ultrasensitive technologies such as single molecule PCR (smPCR) applications (e.g. droplet-digital PCR), or next-generation sequencing (NGS) based methods. Using templates with known amplifiable lesions (8-oxoguanine, deaminated 5-methylcytosine, uracil, and DNA heteroduplexes), we assessed with smPCR and duplex sequencing that templates with these lesions were amplified very efficiently by proofreading polymerases (except uracil), leading to G->T, and to a lesser extent, to unreported G->C substitutions at 8-oxoguanine lesions, and C->T transitions in amplified uracil containing templates. Long heat incubations common in many DNA extraction protocols significantly increased the number of G->T substitutions. Moreover, in ∼50-80% smPCR reactions we observed the random amplification preference of only one of both DNA strands explaining the known 'PCR jackpot effect', with the result that a lesion became indistinguishable from a true mutation or variant. Finally, we showed that artifactual mutations derived from uracil and 8-oxoguanine could be significantly reduced by DNA repair enzymes.
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