Detection of Rare Drug Resistance Mutations by Digital PCR in a Human Influenza A Virus Model System and Clinical Samples

Digital PCR (dPCR) is being increasingly used for the quantification of sequence variations, including single nucleotide polymorphisms (SNPs), due to its high accuracy and precision in comparison with techniques such as quantitative PCR (qPCR) and melt curve analysis. To develop and evaluate dPCR for SNP detection using DNA, RNA, and clinical samples, an influenza virus model of resistance to oseltamivir (Tamiflu) was used. First, this study was able to recognize and reduce off-target amplification in dPCR quantification, thereby enabling technical sensitivities down to 0.1% SNP abundance at a range of template concentrations, a 50-fold improvement on the qPCR assay used routinely in the clinic. Second, a method was developed for determining the false-positive rate (background) signal. Finally, comparison of dPCR with qPCR results on clinical samples demonstrated the potential impact dPCR could have on clinical research and patient management by earlier (trace) detection of rare drug-resistant sequence variants. Ultimately this could reduce the quantity of ineffective drugs taken and facilitate early switching to alternative medication when available. In the short term such methods could advance our understanding of microbial dynamics and therapeutic responses in a range of infectious diseases such as HIV, viral hepatitis, and tuberculosis. Furthermore, the findings presented here are directly relevant to other diagnostic areas, such as the detection of rare SNPs in malignancy, monitoring of graft rejection, and fetal screening.

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LC-MS Peak Assignment Based on Unanimous Selection by Six Machine Learning Algorithms

10.21203/rs.3.rs-845859/v1 ◽

2021 ◽

Author(s):

Hiroaki Ito ◽

Takashi Matsui ◽

Ryo Konno ◽

Makoto Itakura ◽

Yoshio Kodera

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

False Positive Rate ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Weak Signals ◽

Accuracy And Precision ◽

Peak Assignment ◽

Positive Rate ◽

Assignment Strategy

Abstract Recent Mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance resulted in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

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LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Scientific Reports ◽

10.1038/s41598-021-02899-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hiroaki Ito ◽

Takashi Matsui ◽

Ryo Konno ◽

Makoto Itakura ◽

Yoshio Kodera

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

False Positive Rate ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Weak Signals ◽

Accuracy And Precision ◽

Positive Rate ◽

Assignment Strategy ◽

Performance Results

AbstractRecent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

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SARSeq, a robust and highly multiplexed NGS assay for parallel detection of SARS-CoV2 and other respiratory infections

10.1101/2020.10.28.20217778 ◽

2020 ◽

Cited By ~ 2

Author(s):

Ramesh Yelagandula ◽

Aleksandr Bykov ◽

Alexander Vogt ◽

Robert Heinen ◽

Ezgi Özkan ◽

...

Keyword(s):

Influenza A ◽

Respiratory Infections ◽

False Positive Rate ◽

Cost Effective ◽

Rt Pcr ◽

Robust Performance ◽

Saliva Analysis ◽

Positive Rate ◽

Double Blinded ◽

Generation Sequencing

During a pandemic, mitigation as well as protection of system-critical or vulnerable institutions requires massively parallel, yet cost-effective testing to monitor the spread of agents such as the current SARS-CoV2 virus. Here we present SARSeq, saliva analysis by RNA sequencing, as an approach to monitor presence of SARS-CoV2 and other respiratory viruses performed on tens of thousands of samples in parallel. SARSeq is based on next generation sequencing of multiple amplicons generated in parallel in a multiplexed RT-PCR reaction. It relies on a two-dimensional unique dual indexing strategy using four indices in total, for unambiguous and scalable assignment of reads to individual samples. We calibrated this method using dilutions of synthetic RNA and virions to show sensitivity down to a few molecules, and applied it to hundreds of patient samples validating robust performance across various sample types. Double blinded benchmarking to gold-standard quantitative RT-PCR performed in a clinical setting and a human diagnostics laboratory showed robust performance up to a Ct of 36. The false positive rate, likely due to cross contamination during sample pipetting, was estimated at 0.04-0.1%. In addition to SARS-CoV2, SARSeq detects Influenza A and B viruses as well as human rhinovirus and can be easily expanded to include detection of other pathogens. In sum, SARSeq is an ideal platform for differential diagnostic of respiratory diseases at a scale, as is required during a pandemic.

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Detection of Subclonal SETBP1 and JAK3 Mutations in Patients with Juvenile Myelomonocytic Leukemia Using Droplet Digital PCR

Blood ◽

10.1182/blood-2019-125354 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4213-4213

Author(s):

Manabu Wakamatsu ◽

Hideki Muramatsu ◽

Norihiro Murakami ◽

Yusuke Okuno ◽

Hironobu Kitazawa ◽

...

Keyword(s):

Disease Progression ◽

False Positive ◽

Somatic Mutations ◽

False Positive Rate ◽

Digital Pcr ◽

Droplet Digital Pcr ◽

Juvenile Myelomonocytic Leukemia ◽

Positive Rate ◽

Hotspot Mutations ◽

Myelomonocytic Leukemia

Background Juvenile myelomonocytic leukemia (JMML) is a rare pediatric myelodysplastic/myeloproliferative disease. Approximately 85% of patients with JMML harbor germline and/or somatic mutations in RAS pathway genes, such as PTPN11, NF1, CBL, NRAS, and KRAS. In a subset of patients with JMML, SETBP1 and JAK3 mutations were identified as secondary mutations in addition to primary RAS mutations. These secondary mutations are associated with the disease progression and poor　clinical outcome. Recently, it has been reported that subclonal SETBP1 mutation also correlates with a dismal prognosis. Therefore, we hypothesized that subclonal JAK3 mutation is present in a higher than expected number of patients with JMML and associated with poor prognosis. The aim of this study is to identify patients with subclonal SETBP1 and/or JAK3 mutations at the diagnosis using droplet digital PCR (ddPCR) and to elucidate their clinical outcomes. Patients and Methods We enrolled 128 patients with JMML and 15 with Noonan syndrome-associated myeloproliferative disorder (NS/MPD). Using bone marrow (BM) or peripheral blood derived genomic DNA, ddPCR was performed in the 143 patients to detect SETBP1 p.D868N and JAK3 p.R657Q hotspot mutations with low variant allele frequencies (VAF). The study was approved by the institutional review board of Nagoya University Graduate School of Medicine. Results To assess the false-positive rate of the ddPCR assay for each mutation, the assay was also performed in 30 healthy volunteers. Among these, the false-positive rate (mean ± standard deviation) for SETBP1 and JAK3 mutations was 0.010% ± 0.010% and 0.013% ± 0.012%, respectively. Due to the presence of false-positive droplets, the sensitivity and the quantitative linearity was evaluated for >0.01% VAF. The significant correlation between the expected and the observed VAF in SETBP1 and JAK3 was observed (R-squared, 0.9923 and 0.9922, respectively). Therefore, 0.05% VAF was defined as the cut-off value in this assay. Among the 143 patients, ddPCR detected SETBP1 and JAK3 mutations in nine (6.3%) and fifteen (10.5%), respectively. SETBP1 and JAK3 mutations, including variants with low allele frequencies, were not detected in NS/MPD. Among patients with SETBP1 and/or JAK3 mutations, two and six patients harbored less than 1.0% VAF. Patients with less than 1.0% VAF in SETBP1 or JAK3 mutation exhibited a significantly poorer 2-year transplantation-free survival than those without SETBP1 and JAK3 mutations (P = 3.05 × 10-3). JMML is genetically characterized by an extremely small number of somatic mutations (an average of 0.8 mutations/exome/patient). However, we demonstrated that among 19 patients with SETBP1 and/or JAK3 mutations, five patients (26.3%) harbored both the mutations. This finding suggested a statistically significant co-occurrence of SETBP1 and JAK3 mutations in JMML. In order to determine whether SETBP1 and JAK3 mutations were present in the same clone or not, we performed colony formation assays using BM cells in one of the five patients with both SETBP1 and JAK3 mutations. This case harbored 0.9% VAF in JAK3 and 41.9% in SETBP1 in addition to 46.0% in PTPN11 mutation (c.227A>C, p.E76A), respectively. In total, 93 colonies were collected and individually analyzed by Sanger sequencing, of which two colonies (2.1%) were identified with both SETBP1 and JAK3 mutations. Conclusions ddPCR is a useful tool to assess subclonal SETBP1 and JAK3 hotspot mutations and to estimate the prognosis. It would be better to start preparing for hematopoietic stem cell transplantation when patients with JMML harbored subclonal SETBP1 and/or JAK3 mutations at the diagnosis. While JMML is characterized by a paucity of somatic mutations, clones harboring SETBP1 and JAK3 mutations were identified. This finding suggests that SETBP1 and JAK3 mutation are susceptible to each other. Furthermore, the serial acquisition of SETBP1 and JAK3 mutations might correlate with the disease progression. Disclosures No relevant conflicts of interest to declare.

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Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

Nuklearmedizin ◽

10.1055/s-0038-1623994 ◽

2002 ◽

Vol 41 (01) ◽

pp. 37-41 ◽

Cited By ~ 3

Author(s):

S. Shung-Shung ◽

S. Yu-Chien ◽

Y. Mei-Due ◽

W. Hwei-Chung ◽

A. Kao

Keyword(s):

Acute Appendicitis ◽

False Positive ◽

False Positive Rate ◽

Accurate Method ◽

Clinical Findings ◽

Pathological Findings ◽

Lower Quadrant ◽

Predictive Values ◽

Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

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Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

Methods of Information in Medicine ◽

10.1055/s-0038-1634910 ◽

1993 ◽

Vol 32 (02) ◽

pp. 175-179 ◽

Cited By ~ 7

Author(s):

B. Brambati ◽

T. Chard ◽

J. G. Grudzinskas ◽

M. C. M. Macintosh

Keyword(s):

Logistic Regression ◽

General Population ◽

Likelihood Ratio ◽

False Positive ◽

False Positive Rate ◽

Ratio Method ◽

Detection Rates ◽

Gaussian Distributions ◽

Positive Rate ◽

Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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Identification of and Correction for Publication Bias: Comment

10.31222/osf.io/dh87m ◽

2019 ◽

Author(s):

Amanda Kvarven ◽

Eirik Strømland ◽

Magnus Johannesson

Keyword(s):

Publication Bias ◽

False Positive ◽

Large Scale ◽

Meta Analysis ◽

False Positive Rate ◽

Effect Sizes ◽

Replication Studies ◽

Moderate Reduction ◽

Positive Rate ◽

Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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Forms, Importance, and Ineffability of Factor Interactions to Define Personality Disorders: Comment on Lilienfeld et al. (2019)

10.31234/osf.io/v8t7q ◽

2019 ◽

Author(s):

Stephen D Benning ◽

Edward Smith

Keyword(s):

Personality Disorders ◽

False Positive Rate ◽

Empirical Work ◽

Design Studies ◽

Linear Threshold ◽

Positive Rate ◽

Saturation Effects ◽

Factor Interactions ◽

Main Effects ◽

Criterion Variables

The emergent interpersonal syndrome (EIS) approach conceptualizes personality disorders as the interaction among their constituent traits to predict important criterion variables. We detail the difficulties we have experienced finding such interactive predictors in our empirical work on psychopathy, even when using uncorrelated traits that maximize power. Rather than explaining a large absolute proportion of variance in interpersonal outcomes, EIS interactions might explain small amounts of variance relative to the main effects of each trait. Indeed, these interactions may necessitate samples of almost 1,000 observations for 80% power and a false positive rate of .05. EIS models must describe which specific traits’ interactions constitute a particular EIS, as effect sizes appear to diminish as higher-order trait interactions are analyzed. Considering whether EIS interactions are ordinal with non-crossing slopes, disordinal with crossing slopes, or entail non-linear threshold or saturation effects may help researchers design studies, sampling strategies, and analyses to model their expected effects efficiently.

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Sensitive quantitative and rapid immunochromatographic diagnosis of clinical samples by scanning electron microscopy - preparing for future outbreaks (Preprint)

10.2196/preprints.21941 ◽

2020 ◽

Author(s):

Hideya Kawasaki ◽

Hiromi Suzuki ◽

Masato Maekawa ◽

Takahiko Hariyama

Keyword(s):

Electron Microscopy ◽

Statistical Analysis ◽

Confidence Interval ◽

General Hospital ◽

Influenza A ◽

T Test ◽

Clinical Samples ◽

School Of Medicine ◽

P Gene ◽

Scanning Electron

BACKGROUND As pathogens such as influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can easily cause pandemics, rapid diagnostic tests are crucial for implementing efficient quarantine measures, providing effective treatments to patients, and preventing or containing a pandemic infection. Here, we developed the immunochromatography-NanoSuit® method, an improved immunochromatography method combined with a conventional scanning electron microscope (SEM), which enables observation of immunocomplexes labeled with a colloidal metal. OBJECTIVE A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. METHODS Immunochromatography kit The ImunoAce® Flu kit (NP antigen detection), a human influenza commercial diagnosis kit, was purchased from TAUNS Laboratories, Inc. (Shizuoka, Japan). Au/Pt nanoparticles were utilized to visualize the positive lines. A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. After macroscopic diagnosis using the Flu kit, the samples were stored in a biosafety box at room temperature (20-25 °C / 68 - 77 °F). The IgM detection immunochromatography kit against SARS-CoV-2 was obtained from Kurabo Industries, Ltd. (Osaka, Japan). One step rRT-PCR for influenza A rRT-PCR for influenza A was performed as described previously using Flu A universal primers. A Ct within 38.0 was considered as positive according to the CDC protocol. The primer/probe set targeted the human RNase P gene and served as an internal control for human nucleic acid as described previously. SEM image acquisition The immunochromatography kit was covered with a modified NanoSuit® solution based on previously published components (Nisshin EM Co., Ltd., Tokyo, Japan), placed first onto the wide stage of the specimen holder, and then placed in an Lv-SEM (TM4000Plus, Hitachi High-Technologies, Tokyo, Japan). Images were acquired using backscattered electron detectors with 10 or 15 kV at 30 Pa. Particle counting In fields containing fewer than 50 particles/field, the particles were counted manually. Otherwise, ImageJ/Fiji software was used for counting. ImageJ/Fiji uses comprehensive particle analysis algorithms that effectively count various particles. Images were then processed and counting was performed according to the protocol. Diagnosis and statistics The EM diagnosis and criteria for a positive test were defined as follows: particle numbers from 6 fields from the background area and test-line were statistically analyzed using the t-test. If there were more than 5 particles in one visual field and a significant difference (P < 0.01) was indicated by the t-test, the result was considered positive. Statistical analysis using the t-test was performed in Excel software. Statistical analysis of the assay sensitivity and specificity with a 95% confidence interval (95% CI) was performed using the MedCalc statistical website. The approximate line, correlation coefficient, and null hypothesis were calculated with Excel software. RESULTS Our new immunochromatography-NanoSuit® method suppresses cellulose deformity and makes it possible to easily focus and acquire high-resolution images of gold/platinum labeled immunocomplexes of viruses such as influenza A, without the need for conductive treatment as with conventional SEM. Electron microscopy (EM)-based diagnosis of influenza A exhibited 94% clinical sensitivity (29/31) (95% confidence interval [95%CI]: 78.58–99.21%) and 100% clinical specificity (95%CI: 97.80–100%). EM-based diagnosis was significantly more sensitive (71.2%) than macroscopic diagnosis (14.3%), especially in the lower influenza A-RNA copy number group. The detection ability of our method is comparable to that of real-time reverse transcription-polymerase chain reaction. CONCLUSIONS This simple and highly sensitive quantitative analysis method involving immunochromatography can be utilized to diagnose various infections in humans and livestock, including highly infectious diseases such as COVID-19.

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