scholarly journals Application of Immunosignatures for Diagnosis of Valley Fever

2014 ◽  
Vol 21 (8) ◽  
pp. 1169-1177 ◽  
Author(s):  
Krupa Arun Navalkar ◽  
Stephen Albert Johnston ◽  
Neal Woodbury ◽  
John N. Galgiani ◽  
D. Mitchell Magee ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
Random Sequence ◽  
False Negative ◽  
False Negative Rate ◽  
Peptide Array ◽  
Igg Antibodies ◽  
False Negatives ◽  
Peptide Microarray ◽  
Valley Fever ◽  
Antibody Levels

ABSTRACTValley fever (VF) is difficult to diagnose, partly because the symptoms of VF are confounded with those of other community-acquired pneumonias. Confirmatory diagnostics detect IgM and IgG antibodies against coccidioidal antigens via immunodiffusion (ID). The false-negative rate can be as high as 50% to 70%, with 5% of symptomatic patients never showing detectable antibody levels. In this study, we tested whether the immunosignature diagnostic can resolve VF false negatives. An immunosignature is the pattern of antibody binding to random-sequence peptides on a peptide microarray. A 10,000-peptide microarray was first used to determine whether valley fever patients can be distinguished from 3 other cohorts with similar infections. After determining the VF-specific peptides, a small 96-peptide diagnostic array was created and tested. The performances of the 10,000-peptide array and the 96-peptide diagnostic array were compared to that of the ID diagnostic standard. The 10,000-peptide microarray classified the VF samples from the other 3 infections with 98% accuracy. It also classified VF false-negative patients with 100% sensitivity in a blinded test set versus 28% sensitivity for ID. The immunosignature microarray has potential for simultaneously distinguishing valley fever patients from those with other fungal or bacterial infections. The same 10,000-peptide array can diagnose VF false-negative patients with 100% sensitivity. The smaller 96-peptide diagnostic array was less specific for diagnosing false negatives. We conclude that the performance of the immunosignature diagnostic exceeds that of the existing standard, and the immunosignature can distinguish related infections and might be used in lieu of existing diagnostics.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

Measurement differences between three versus five photocells during collection of ground reaction forces in dogs

2007 ◽  
Vol 02 (02) ◽  
pp. 98-101 ◽  
Author(s):  
J. P. Punke ◽  
A. L. Speas ◽  
L. R. Reynolds ◽  
C. M. Andrews ◽  
S. C. Budsberg
Keyword(s):  
Ground Reaction Force ◽  
Reaction Force ◽  
False Negative ◽  
False Negative Rate ◽  
Second Phase ◽  
False Negatives ◽  
Two Systems ◽  
Reaction Forces ◽  
Force Data ◽  
Gathering Data

SummaryThe differences between velocities and accelerations obtained from three and five photocells were examined when obtaining ground reaction force (GRF) data in dogs. Ground reaction force data was collected 259 times from 16 different dogs in two experimental phases. The first phase compared velocities and accelerations reported by the two systems based on trials accepted by the three photocell system. The second phase accepted trials based on data from five photocells. Three photocell data were calculated mathematically in the second phase in order to compare the values of both systems. The velocity and acceleration values obtained from each system were significantly different (at the hundredth of a meter per second). Differences in measured values did not result in acceptance of data by the three photocell system that would not have been acceptable with the five photocell system (false positives), but did result in rejection of acceptable data by the three photocell system (11% false negative rate). Given the small differences between the two systems, GRF data collected should not be significantly different, though the three photocell system is less efficient in gathering data due to the number of trials rejected as false negatives.

scholarly journals An Effective Method for Controlling False Discovery and False Nondiscovery Rates in Genome-Scale RNAi Screens

2010 ◽  
Vol 15 (9) ◽  
pp. 1116-1122 ◽  
Author(s):  
Xiaohua Douglas Zhang
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
False Positives ◽  
P Value ◽  
Statistical Control ◽  
Small Interfering Rnas ◽  
False Negatives ◽  
Activation Effect ◽  
False Discovery ◽  
Genome Scale

In most genome-scale RNA interference (RNAi) screens, the ultimate goal is to select siRNAs with a large inhibition or activation effect. The selection of hits typically requires statistical control of 2 errors: false positives and false negatives. Traditional methods of controlling false positives and false negatives do not take into account the important feature in RNAi screens: many small-interfering RNAs (siRNAs) may have very small but real nonzero average effects on the measured response and thus cannot allow us to effectively control false positives and false negatives. To address for deficiencies in the application of traditional approaches in RNAi screening, the author proposes a new method for controlling false positives and false negatives in RNAi high-throughput screens. The false negatives are statistically controlled through a false-negative rate (FNR) or false nondiscovery rate (FNDR). FNR is the proportion of false negatives among all siRNAs examined, whereas FNDR is the proportion of false negatives among declared nonhits. The author also proposes new concepts, q*-value and p*-value, to control FNR and FNDR, respectively. The proposed method should have broad utility for hit selection in which one needs to control both false discovery and false nondiscovery rates in genome-scale RNAi screens in a robust manner.

scholarly journals Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

2020 ◽  
Author(s):  
Merve Dede ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
False Negative ◽  
False Negative Rate ◽  
Blind Spot ◽  
Integrated Analysis ◽  
False Negatives ◽  
Genome Wide ◽  
Cancer Subtype ◽  
Crispr Screen ◽  
Single Knockout ◽  
Blind Spots

AbstractIt is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ∼20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation. In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches.

Correct Classification, False Positives, and False Negatives in Predicting Completion of the Ph.d. from GRE Scores

1996 ◽  
Vol 79 (3) ◽  
pp. 939-945 ◽  
Author(s):  
Cooper B. Holmes ◽  
Megan J. Beishline
Keyword(s):  
Success Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Doctoral Program ◽  
False Positives ◽  
Correct Classification ◽  
False Negatives ◽  
Program Success ◽  
High False Negative Rate ◽  
Psychology Doctoral

Combined Verbal and Quantitative GRE scores were obtained from the records of 24 former students of a master's degree program (from a total of 128 students) who had successfully completed a doctorate in psychology or who had withdrawn from a psychology doctoral program. Success rate by classification with the GRE was calculated using both a cut-off of 1000 and a cut-off of 1100. The results indicated a high false negative rate, that is, students whose GRE scores would not predict success but who obtained a Ph.D.

scholarly journals SARS-CoV2 Testing: The Limit of Detection Matters

2020 ◽  
Author(s):  
Ramy Arnaout ◽  
Rose A. Lee ◽  
Ghee Rye Lee ◽  
Cody Callahan ◽  
Christina F. Yen ◽  
...  
Keyword(s):  
Limit Of Detection ◽  
Diagnostic Testing ◽  
False Negative ◽  
False Negative Rate ◽  
Fold Increase ◽  
Test Results ◽  
Rt Pcr ◽  
False Negatives ◽  
Healthcare Network ◽  
Negative Rate

AbstractResolving 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.

Modeling probability of additional cases of natalizumab-associated JCV sero-negative progressive multifocal leukoencephalopathy

2013 ◽  
Vol 20 (6) ◽  
pp. 757-760 ◽  
Author(s):  
Robert L Carruthers ◽  
Tanuja Chitnis ◽  
Brian C Healy
Keyword(s):  
Progressive Multifocal Leukoencephalopathy ◽  
Gold Standard ◽  
False Negative ◽  
False Negative Rate ◽  
Serologic Test ◽  
Expected Number ◽  
False Negatives ◽  
Test Characteristics ◽  
No Gold Standard ◽  
Binomial Function

JCV serologic status is used to determine PML risk in natalizumab-treated patients. Given two cases of natalizumab-associated PML in JCV sero-negative patients and two publications that question the false negative rate of the JCV serologic test, clinicians may question whether our understanding of PML risk is adequate. Given that there is no gold standard for diagnosing previous JCV exposure, the test characteristics of the JCV serologic test are unknowable. We propose a model of PML risk in JCV sero-negative natalizumab patients. Using the numbers of JCV sero-positive and -negative patients from a study of PML risk by JCV serologic status (sero-positive: 13,950 and sero-negative: 11,414), we apply a range of sensitivities and specificities in order calculate the number of JCV-exposed but JCV sero-negative patients (false negatives). We then apply a range of rates of developing PML in sero-negative patients to calculate the expected number of PML cases. By using the binomial function, we calculate the probability of a given number of JCV sero-negative PML cases. With this model, one has a means to establish a threshold number of JCV sero-negative natalizumab-associated PML cases at which it is improbable that our understanding of PML risk in JCV sero-negative patients is adequate.

scholarly journals False Negatives Are a Significant Feature of Next Generation Sequencing Callsets

2016 ◽  
Author(s):  
Dean Bobo ◽  
Mikhail Lipatov ◽  
Juan L. Rodriguez-Flores ◽  
Adam Auton ◽  
Brenna M. Henn
Keyword(s):  
Next Generation Sequencing ◽  
False Negative ◽  
False Negative Rate ◽  
Significant Feature ◽  
False Negatives ◽  
High Coverage ◽  
1000 Genomes ◽  
Complete Genomics ◽  
Ngs Data ◽  
Generation Sequencing

AbstractShort-read, next-generation sequencing (NGS) is now broadly used to identify rare or de novo mutations in population samples and disease cohorts. However, NGS data is known to be error-prone and post-processing pipelines have primarily focused on the removal of spurious mutations or “false positives” for downstream genome datasets. Less attention has been paid to characterizing the fraction of missing mutations or “false negatives” (FN). Here we interrogate several publically available human NGS autosomal variant datasets using corresponding Sanger sequencing as a truth-set. We examine both low-coverage Illumina and high-coverage Complete Genomics genomes. We show that the FN rate varies between 3%-18% and that false-positive rates are considerably lower (<3%) for publically available human genome callsets like 1000 Genomes. The FN rate is strongly dependent on calling pipeline parameters, as well as read coverage. Our results demonstrate that missing mutations are a significant feature of genomic datasets and imply additional fine-tuning of bioinformatics pipelines is needed. To address this, we design a phylogeny-aware tool [PhyloFaN] which can be used to quantify the FN rate for haploid genomic experiments, without additional generation of validation data. Using PhyloFaN on ultra-high coverage NGS data from both Illumina HiSeq and Complete Genomics platforms derived from the 1000 Genomes Project, we characterize the false negative rate in human mtDNA genomes. The false negative rate for the publically available mtDNA callsets is 17-20%, even for extremely high coverage haploid data.

scholarly journals Proteome-wide analysis of differentially-expressed SARS-CoV-2 antibodies in early COVID-19 infection

2020 ◽  
Author(s):  
Xiaomei Zhang ◽  
Xian Wu ◽  
Dan Wang ◽  
Minya Lu ◽  
Xin Hou ◽  
...  
Keyword(s):  
Early Stage ◽  
False Negative ◽  
False Negative Rate ◽  
Antibody Detection ◽  
Igg Antibodies ◽  
Protein Subunit ◽  
Igg Antibody ◽  
S Protein ◽  
N Protein ◽  
Acute Myocardial Injury

AbstractRapid and accurate tests that detect IgM and IgG antibodies to SARS-CoV-2 proteins are essential in slowing the spread of COVID-19 by identifying patients who are infected with COVID-19. Using a SARS-CoV-2 proteome microarray developed in our lab, we comprehensively profiled both IgM and IgG antibodies in forty patients with early-stage COVID-19, influenza, or non-influenza who had similar symptoms. The results revealed that the SARS-CoV-2 N protein is not an ideal biomarker for COVID-19 diagnosis because of its low immunogenicity, thus tests that rely on this marker alone will have a high false negative rate. Our data further suggest that the S protein subunit 1 receptor binding domain (S1-RBD) might be the optimal antigen for IgM antibody detection, while the S protein extracellular domain (S1+S2ECD) would be the optimal antigen for both IgM and IgG antibody detection. Notably, the combination of all IgM and IgG biomarkers can identify 87% and 73.3% COVID-19 patients, respectively. Finally, the COVID-19-specific antibodies are significantly correlated with the clinical indices of viral infection and acute myocardial injury (p≤0.05). Our data may help understand the function of anti-SARS-CoV-2 antibodies and improve serology tests for rapid COVID-19 screening.

scholarly journals Analysis of Preoperative Detection for Apex Prostate Cancer by Transrectal Biopsy

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Tomokazu Sazuka ◽  
Takashi Imamoto ◽  
Takeshi Namekawa ◽  
Takanobu Utsumi ◽  
Mitsuru Yanagisawa ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Needle Biopsy ◽  
False Negative ◽  
False Negative Rate ◽  
Japanese Patients ◽  
False Negatives ◽  
Transrectal Biopsy ◽  
High False Negative Rate ◽  
Accurate Imaging ◽  
Preoperative Factors

Background. The aim of this study was to determine concordance rates for prostatectomy specimens and transrectal needle biopsy samples in various areas of the prostate in order to assess diagnostic accuracy of the transrectal biopsy approach, especially for presurgical detection of cancer in the prostatic apex.Materials and Methods. From 2006 to 2011, 158 patients whose radical prostatectomy specimens had been evaluated were retrospectively enrolled in this study. Concordance rates for histopathology results of prostatectomy specimens and needle biopsy samples were evaluated in 8 prostatic sections (apex, middle, base, and transitional zones bilaterally) from 73 patients diagnosed at this institution, besides factors for detecting apex cancer in total 118 true positive and false negative apex cancers.Results. Prostate cancer was found most frequently (85%) in the apex of all patients. Of 584 histopathology sections, 153 (49%) from all areas were false negatives, as were 45% of apex biopsy samples. No readily available preoperative factors for detecting apex cancer were identified.Conclusions. In Japanese patients, the most frequent location of prostate cancer is in the apex. There is a high false negative rate for transrectal biopsy samples. To improve the detection rate, transperitoneal biopsy or more accurate imaging technology is needed.

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