scholarly journals Magnetic-Nanosensor-Based Virus and Pathogen Detection Strategies before and during COVID-19

2020 ◽  
Vol 3 (10) ◽  
pp. 9560-9580
Author(s):  
Kai Wu ◽  
Renata Saha ◽  
Diqing Su ◽  
Venkatramana D. Krishna ◽  
Jinming Liu ◽  
...  
Keyword(s):  
Pathogen Detection ◽  
Detection Strategies

Microbial Pathogen Detection Strategies

2010 ◽  
pp. 1-4
Author(s):  
Diana Vanegas ◽  
Jonathan Claussen ◽  
Eric McLamore ◽  
Carmen Gomes
Keyword(s):  
Pathogen Detection ◽  
Microbial Pathogen ◽  
Detection Strategies

Inkjet Printing platforms for DNA-based pathogen detection

2018 ◽  
Vol 2018 (1) ◽  
pp. 107-112 ◽  
Author(s):  
Min Zhao ◽  
Susana Diaz Amaya ◽  
Seon-ah Jin ◽  
Li-Kai Lin ◽  
Amanda J. Deering ◽  
...  
Keyword(s):  
Inkjet Printing ◽  
Pathogen Detection

Autonomous Pathogen Detection System (APDS) NYC Spring 2005 Field Test Report May to July 2005

2005 ◽  
Author(s):  
J M Dzenitis ◽  
A J Makarewicz ◽  
D R Hadley ◽  
D M Gutierrez ◽  
T R Metz ◽  
...  
Keyword(s):  
Field Test ◽  
Pathogen Detection ◽  
Detection System ◽  
Test Report

Biomedical Devices for Pathogen Detection Using Microfluidic Chips

2014 ◽  
Vol 11 (2) ◽  
pp. 116-120 ◽  
Author(s):  
Yung-Sheng Lin ◽  
Ming-Yuan-Lee ◽  
Chih-Hui Yang ◽  
Keng-Shiang Huang
Keyword(s):  
Pathogen Detection ◽  
Biomedical Devices ◽  
Microfluidic Chips

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan
Keyword(s):  
Antimicrobial Resistance ◽  
High Performance ◽  
Pathogen Detection ◽  
Bacterial Species ◽  
Workflow Management ◽  
Metagenomic Data ◽  
Antimicrobial Resistance Genes ◽  
Culture Independent ◽  
Automated Pipeline ◽  
User Friendly

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

scholarly journals Bacteremic sepsis leads to higher mortality when adjusting for confounders with propensity score matching

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lisa Mellhammar ◽  
Fredrik Kahn ◽  
Caroline Whitlow ◽  
Thomas Kander ◽  
Bertil Christensson ◽  
...  
Keyword(s):  
Intensive Care ◽  
Clinical Review ◽  
Pathogen Detection ◽  
Mortality Rates ◽  
Blood Cultures ◽  
Population Heterogeneity ◽  
Sepsis Diagnosis ◽  
Culture Positive ◽  
Culture Negative ◽  
Microbial Samples

AbstractOne can falsely assume that it is well known that bacteremia is associated with higher mortality in sepsis. Only a handful of studies specifically focus on the comparison of culture-negative and culture-positive sepsis with different conclusions depending on study design. The aim of this study was to describe outcome for critically ill patients with either culture-positive or -negative sepsis in a clinical review. We also aimed to identify subphenotypes of sepsis with culture status included as candidate clinical variables. Out of 784 patients treated in intensive care with a sepsis diagnosis, blood cultures were missing in 140 excluded patients and 95 excluded patients did not fulfill a sepsis diagnosis. Of 549 included patients, 295 (54%) had bacteremia, 90 (16%) were non-bacteremic but with relevant pathogens detected and in 164 (30%) no relevant pathogen was detected. After adjusting for confounders, 90-day mortality was higher in bacteremic patients, 47%, than in non-bacteremic patients, 36%, p = 0.04. We identified 8 subphenotypes, with different mortality rates, where pathogen detection in microbial samples were important for subphenotype distinction and outcome. In conclusion, bacteremic patients had higher mortality than their non-bacteremic counter-parts and bacteremia is more common in sepsis when studied in a clinical review. For reducing population heterogeneity and improve the outcome of trials and treatment for sepsis, distinction of subphenotypes might be useful and pathogen detection an important factor.

scholarly journals A Pandemic Early Warning System Decision Analysis Concept Utilizing a Distributed Network of Air Samplers via Electrostatic Air Precipitation

2021 ◽  
Vol 11 (11) ◽  
pp. 5308
Author(s):  
Joseph J. Bango ◽  
Sophia A. Agostinelli ◽  
Makayla Maroney ◽  
Michael Dziekan ◽  
Ruba Deeb ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Pathogen Detection ◽  
Clinical Laboratory ◽  
Culture Media ◽  
Morphological Characteristics ◽  
Warning System ◽  
Ambient Air ◽  
Aerosol Sampling ◽  
Distributed Network

The COVID-19 pandemic has highlighted the need for improved airborne infectious disease monitoring capability. A key challenge is to develop a technology that captures pathogens for identification from ambient air. While pathogenic species vary significantly in size and shape, for effective airborne pathogen detection the target species must be selectively captured from aerosolized droplets. Captured pathogens must then be separated from the remaining aerosolized droplet content and characterized in real-time. While improvements have been made with clinical laboratory automated sorting in culture media based on morphological characteristics of cells, this application has not extended to aerosol samples containing bacteria, viruses, spores, or prions. This manuscript presents a strategy and a model for the development of an airborne pandemic early warning system using aerosol sampling. 

Sign in / Sign up

Export Citation Format

Share Document