Faculty Opinions recommendation of High sensitivity to aligner and high rate of false positives in the estimates of positive selection in the 12 Drosophila genomes.

Abstract Objectives The PIRADS Steering Committee has called for “higher quality data before making evidence-based recommendations on MRI without contrast enhancement as an initial diagnostic work up,” however, recognizing biparametric (bp) MRI as a reasonable option in a low-risk setting such as screening. With bpMRI, more men can undergo MRI at a lower cost and they can be spared the invasiveness of intravenous access. The aim of this study was to assess cancer detection in bpMRI vs mpMRI in sequential screening for prostate cancer (PCa). Methods Within the ongoing Göteborg PCa screening 2 trial, we assessed cancer detection in 551 consecutive participants undergoing prostate MRI. In the same session, readers first assessed bpMRI and then mpMRI. Four targeted biopsies were performed for lesions scored PIRADS 3–5 with bpMRI and/or mpMRI. Results Cancer was detected in 84/551 cases (15.2%; 95% CI: 12.4–18.4) with mpMRI and in 83/551 cases (15.1%; 95% CI: 12.3–18.2%) with bpMRI. The relative risk (RR) for cancer detection with bpMRI compared to mpMRI was 0.99 (95% one-sided CI: > 94.8); bpMRI was non-inferior to mpMRI (10% non-inferiority margin). bpMRI resulted in fewer false positives, 45/128 (35.2%), compared to mpMRI, 52/136 (38.2%), RR = 0.92; 95% CI: 0.84–0.98. Of 8 lesions scored positive only with mpMRI, 7 were false positives. The PPV for MRI and targeted biopsy was 83/128 (64.8%) for bpMRI and 84/136 (61.8%) for mpMRI, RR = 1.05, 95% CI: 1.01–1.10. Conclusions In a PSA-screened population, bpMRI was non-inferior to mpMRI for cancer detection and resulted in fewer false positives. Key Points • In screening for prostate cancer with PSA followed by MRI, biparametric MRI allows radiologists to detect an almost similar number of prostate cancers and score fewer false positive lesions compared to multiparametric MRI. • In a screening program, high sensitivity should be weighed against cost and risks for healthy men; a large number of men can be saved the exposure of gadolinium contrast medium by adopting biparametric MRI and at the same time allowing for a higher turnover in the MRI room.

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Automated detection of cribriform growth patterns in prostate histology images

Scientific Reports ◽

10.1038/s41598-020-71942-7 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Pierre Ambrosini ◽

Eva Hollemans ◽

Charlotte F. Kweldam ◽

Geert J. L. H. van Leenders ◽

Sjoerd Stallinga ◽

...

Keyword(s):

Deep Learning ◽

False Positive ◽

Clinical Decision Making ◽

False Negative ◽

Area Under The Curve ◽

High Sensitivity ◽

False Positives ◽

Growth Patterns ◽

Learning Method ◽

Method Performance

Abstract Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than $${0.0150}\,\hbox {mm}^{2}$$ 0.0150 mm 2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.

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Ethylene Measurements from Sweet Fruits Flowers Using Photoacoustic Spectroscopy

Molecules ◽

10.3390/molecules24061144 ◽

2019 ◽

Vol 24 (6) ◽

pp. 1144 ◽

Cited By ~ 5

Author(s):

Cristina Popa

Keyword(s):

Plant Development ◽

Photoacoustic Spectroscopy ◽

Plant Hormone ◽

Trace Gases ◽

Air Flow ◽

High Sensitivity ◽

High Rate ◽

Nitrogen Flow ◽

Tissue Sensitivity

Ethylene is a classical plant hormone and has appeared as a strong molecule managing many physiological and morphological reactions during the life of a plant. With laser-based photoacoustic spectroscopy, ethylene can be identified with high sensitivity, at a high rate and with very good selectivity. This research presents the dynamics of trace gases molecules for ethylene released by cherry flowers, apple flowers and strawberry flowers. The responses of distinctive organs to ethylene may fluctuate, depending on tissue sensitivity and the phase of plant development. From the determinations of this study, the ethylene molecules at the flowers in the nitrogen flow were established in lower concentrations when the value is correlated to the ethylene molecules at the flowers in synthetic air flow.

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Catching the Sugars: Electrochemical Aptasensors for the Detection of Cancer-Related Glycosylation Changes in Prostate-Specific Antigen

Proceedings ◽

10.3390/iecb2020-07023 ◽

2020 ◽

Vol 60 (1) ◽

pp. 47

Author(s):

Ana Díaz-Fernández ◽

Rebeca Miranda-Castro ◽

Pedro Estrela ◽

Noemí de-los-Santos-Álvarez ◽

María Jesús Lobo-Castañón

Keyword(s):

Prostate Cancer ◽

Prostate Specific Antigen ◽

Specific Antigen ◽

False Positives ◽

High Rate ◽

Enzyme Linked Immunosorbent Assay ◽

Glycan Structure ◽

Specific Cancer ◽

Different Levels ◽

Selection Of

Prostate-specific Antigen (PSA) is the biomarker that is used for prostate cancer (PCa) detection, although its lack of specificity results in a high rate of false-positives and many unnecessary biopsies. Therefore, there is a need for more specific cancer biomarkers for PCa. Recent studies have shown that the aberrant glycosylation of proteins is a common feature of the presence of cancer. In the case of prostate cancer, there are changes in core-fucose and sialic acids in the glycan structure of PSA. In this work, we describe two different strategies to direct the selection of aptamers toward the glycans of PSA. From these strategies, we identified two aptamers (PSA-1 and PSAG-1) that bind to the glycan structure of PSA with high affinity. Both aptamers were applied in the design of electrochemical aptasensors, in sandwich and direct formats, in order to detect the changes in the glycosylation of PSA. The sensors responded to different levels of PSA in serum, and they showed higher potential to discriminate clinically-meaningful PCa than the ELISA (Enzyme-linked immunosorbent assay) test used in hospitals (reducing the number of false positives), although validation on more samples is needed.

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Steady-State Kinetics of Enzyme-Catalyzed Hydrolysis of Echothiophate, a P–S Bonded Organophosphorus as Monitored by Spectrofluorimetry

Molecules ◽

10.3390/molecules25061371 ◽

2020 ◽

Vol 25 (6) ◽

pp. 1371 ◽

Cited By ~ 1

Author(s):

Irina V. Zueva ◽

Sofya V. Lushchekina ◽

David Daudé ◽

Eric Chabrière ◽

Patrick Masson

Keyword(s):

Steady State ◽

High Efficiency ◽

High Sensitivity ◽

High Rate ◽

Paraoxonase 1 ◽

Brevundimonas Diminuta ◽

Steady State Kinetics ◽

Steady State Kinetic ◽

Thiol Reagent ◽

Hydrolysis Of

Enzyme-catalyzed hydrolysis of echothiophate, a P–S bonded organophosphorus (OP) model, was spectrofluorimetrically monitored, using Calbiochem Probe IV as the thiol reagent. OP hydrolases were: the G117H mutant of human butyrylcholinesterase capable of hydrolyzing OPs, and a multiple mutant of Brevundimonas diminuta phosphotriesterase, GG1, designed to hydrolyze a large spectrum of OPs at high rate, including V agents. Molecular modeling of interaction between Probe IV and OP hydrolases (G117H butyrylcholinesterase, GG1, wild types of Brevundimonas diminuta and Sulfolobus solfataricus phosphotriesterases, and human paraoxonase-1) was performed. The high sensitivity of the method allowed steady-state kinetic analysis of echothiophate hydrolysis by highly purified G117H butyrylcholinesterase concentration as low as 0.85 nM. Hydrolysis was michaelian with Km = 0.20 ± 0.03 mM and kcat = 5.4 ± 1.6 min−1. The GG1 phosphotriesterase hydrolyzed echothiophate with a high efficiency (Km = 2.6 ± 0.2 mM; kcat = 53400 min−1). With a kcat/Km = (2.6 ± 1.6) × 107 M−1min−1, GG1 fulfills the required condition of potential catalytic bioscavengers. quantum mechanics/molecular mechanics (QM/MM) and molecular docking indicate that Probe IV does not interact significantly with the selected phosphotriesterases. Moreover, results on G117H mutant show that Probe IV does not inhibit butyrylcholinesterase. Therefore, Probe IV can be recommended for monitoring hydrolysis of P–S bonded OPs by thiol-free OP hydrolases.

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Atropos: specific, sensitive, and speedy trimming of sequencing reads

10.7287/peerj.preprints.2452v2 ◽

2016 ◽

Author(s):

John P Didion ◽

Francis S Collins

Keyword(s):

High Performance ◽

Simulated Data ◽

Leading Edge ◽

High Sensitivity ◽

Fold Increase ◽

Parallel Execution ◽

High Rate ◽

Data Sets ◽

Sequencing Data ◽

Broad Feature

A key step in the transformation of raw sequencing reads into biological insights is the trimming of adapter sequences and low-quality bases. Read trimming has been shown to increase the quality and reliability while decreasing the computational requirements of downstream analyses. Many read trimming software tools are available; however, no tool simultaneously provides the accuracy, computational efficiency, and feature set required to handle the types and volumes of data generated in modern sequencing-based experiments. Here we introduce Atropos and show that it trims reads with high sensitivity and specificity while maintaining leading-edge speed. Compared to other state-of-the-art read trimming tools, Atropos achieves a four-fold increase in trimming accuracy and a decrease in execution time of ~50% (using 16 parallel execution threads). Furthermore, Atropos maintains high accuracy even when trimming simulated data with a high rate of error. The accuracy, high performance, and broad feature set offered by Atropos makes it an appropriate choice for the pre-processing of most current-generation sequencing data sets. Atropos is open source and free software written in Python and available at https://github.com/jdidion/atropos.

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Challenging a bioinformatic tool’s ability to detect microbial contaminants usingin silicowhole genome sequencing data

PeerJ ◽

10.7717/peerj.3729 ◽

2017 ◽

Vol 5 ◽

pp. e3729 ◽

Cited By ~ 6

Author(s):

Nathan D. Olson ◽

Justin M. Zook ◽

Jayne B. Morrow ◽

Nancy J. Lin

Keyword(s):

Binary Mixtures ◽

Genome Sequencing ◽

Pathogen Detection ◽

A Priori ◽

High Sensitivity ◽

False Positives ◽

Sequencing Data ◽

Materials Used ◽

The Individual ◽

Genome Assemblies

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need fora prioriassumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, withStaphylococcus,Escherichia, andShigellahaving the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in thein-silicodatasets at the equivalent of 1 in 1,000 cells, thoughF. tularensiswas not detected in any of the simulated contaminant mixtures andY. pestiswas only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

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Dog Savior: Immediate Scent-Detection of SARS-COV-2 by Trained Dogs

10.1101/2020.06.17.158105 ◽

2020 ◽

Author(s):

Omar Vesga ◽

Andres F. Valencia ◽

Alejandro Mira ◽

Felipe Ossa ◽

Esteban Ocampo ◽

...

Keyword(s):

Infected Individual ◽

High Sensitivity ◽

The Body ◽

High Rate ◽

Diagnostic Tools ◽

Predictive Values ◽

Infected People ◽

Molecular Tests ◽

Production And Distribution ◽

Sensitivity Specificity

AbstractMolecular tests for viral diagnostics are essential to confront the COVID-19 pandemic, but their production and distribution cannot satisfy the current high demand. Early identification of infected people and their contacts is the key to being able to isolate them and prevent the dissemination of the pathogen; unfortunately, most countries are unable to do this due to the lack of diagnostic tools. Dogs can identify, with a high rate of precision, unique odors of volatile organic compounds generated during an infection; as a result, dogs can diagnose infectious agents by smelling specimens and, sometimes, the body of an infected individual. We trained six dogs of three different breeds to detect SARS-CoV-2 in respiratory secretions of infected patients and evaluated their performance experimentally, comparing it against the gold standard (rRT-PCR). Here we show that viral detection takes one second per specimen. After scent-interrogating 9,200 samples, our six dogs achieved independently and as a group very high sensitivity, specificity, predictive values, accuracy, and likelihood ratio, with very narrow confidence intervals. The highest metric was the negative predictive value, indicating that with a disease prevalence of 7.6%, 99.9% of the specimens indicated as negative by the dogs did not carry the virus. These findings demonstrate that dogs could be useful to track viral infection in humans, allowing COVID-19 free people to return to work safely.

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