Multiplexed detection and quantification of human antibody response to COVID-19 infection using a plasmon enhanced biosensor platform

  1. Nathaniel C. Cady
  2. Natalya Tokranova
  3. Armond Minor
  4. Nima Nikvand
  5. Klemen Strle
  6. William T. Lee
  7. William Page
  8. Ernest Guignon
  9. Arturo Pilar
  10. George N. Gibson

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Abstract

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  1. ScreenIT

    SciScore for 10.1101/2020.09.02.20187070: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Blood sampling and testing was approved by the SUNY Polytechnic Institute Institutional Review Board (protocol #IRB-2020–10).
    Randomizationnot detected.
    BlindingSamples received from the DIL for GC-FP analysis were tested blind (no sample information provided).
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Ciencia image analysis LabView software was then used to define a region of interest (ROI) for each individual spot on the GC-FP biosensor chip and the fluorescence intensity of each spot was measured.
    LabView
    suggested: (LabView , RRID:SCR_014325)
    Normalized spot intensity data was exported from the software and further analyzed using Microsoft Excel and/or GraphPad Prism 8.0 software.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    To further account for variation between chips and experiments, normalized intensity data for positive control and COVID-19 antigen spots (mean intensity, ) were divided by the average negative control spot intensity, plus three times the standard deviation (σ) of the negative control spot intensity to produce a detection metric as follows: To determine the threshold detection ratio values for diagnosis, serum or dried blood spots samples with confirmed COVID-19 history (confirmed positive or confirmed negative/no known exposure) were used to perform receiver operator characteristic (ROC) analysis using GraphPad Prism 8.0.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Detection ratios for each COVID-19 antigen, for both positive and negative samples, were entered into Prism 8.0 and analyzed with the ROC analysis package, with area under the curve (AUC) analysis performed for each ROC curve.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad Prism 8.0 was used to perform an unpaired T-test on the calculated detection ratios for all COVID-19 antigens (for both serum and dried blood spots) to determine whether there was a significant difference in the mean GC-FP detection ratio for positive vs. negative samples.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The nu-SVC package within LibSVM was utilized with sigmoid kernel, and a grid search for cost and gamma parameters was conducted to maximize the prediction accuracy of the SVM model.
    LibSVM
    suggested: (LIBSVM, RRID:SCR_010243)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1016/j.bios.2020.112679
  3. Version published to 10.1101/2020.09.02.20187070 on medRxiv