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      Clinical application and evaluation of metagenomic next-generation sequencing in pathogen detection for suspected central nervous system infections

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          Abstract

          Central nervous system Infections (CNSIs) is a disease characterized by complex pathogens, rapid disease progression, high mortality rate and high disability rate. Here, we evaluated the clinical value of metagenomic next generation sequencing (mNGS) in the diagnosis of central nervous system infections and explored the factors affecting the results of mNGS. We conducted a retrospective study to compare mNGS with conventional methods including culture, smear and etc. 111 suspected CNS infectious patients were enrolled in this study, and clinical data were recorded. Chi-square test were used to evaluate independent binomial variables, taking p < 0.05 as statistically significant threshold. Of the 111 enrolled cases, 57.7% (64/111) were diagnosed with central nervous system infections. From these cases, mNGS identified 39.6% (44/111) true-positive cases, 7.2% (8/111) false-positive case, 35.1% (39/111) true-negative cases, and 18.0% (20/111) false-negative cases. The sensitivity and specificity of mNGS were 68.7% (44/64) and 82.9% (39/47), respectively. Compared with culture, mNGS provided a higher pathogen detection rate in CNSIs patients (68.7% (44/64) vs. 26.5% (17/64), p < 0.0001). Compared to conventional methods, positive percent agreement and negative percent agreement was 84.60% (44/52) and 66.1% (39/59) separately. At a species-specific read number (SSRN) ≥ 2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] 0.758, 95% confidence interval [CI] 0.663–0.854). In bacterial CNSIs patients with significant CSF abnormalities (CSF WBC > 300*10 6/L), the positive rate of CSF mNGS is higher. To sum up, conventional microbiologic testing is insufficient to detect all neuroinvasive pathogens, and mNGS exhibited satisfactory diagnostic performance in CNSIs and with an overall detection rate higher than culture ( p < 0.0001).

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          Most cited references35

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            fastp: an ultra-fast all-in-one FASTQ preprocessor

            Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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              Quality control and preprocessing of metagenomic datasets

              Summary: Here, we present PRINSEQ for easy and rapid quality control and data preprocessing of genomic and metagenomic datasets. Summary statistics of FASTA (and QUAL) or FASTQ files are generated in tabular and graphical form and sequences can be filtered, reformatted and trimmed by a variety of options to improve downstream analysis. Availability and Implementation: This open-source application was implemented in Perl and can be used as a stand alone version or accessed online through a user-friendly web interface. The source code, user help and additional information are available at http://prinseq.sourceforge.net/. Contact: rschmied@sciences.sdsu.edu; redwards@cs.sdsu.edu
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                Author and article information

                Contributors
                ndyfy05038@ncu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 July 2024
                23 July 2024
                2024
                : 14
                : 16961
                Affiliations
                Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, ( https://ror.org/042v6xz23) Nanchang, 330006 People’s Republic of China
                Article
                68034
                10.1038/s41598-024-68034-1
                11266612
                39043813
                4d341175-1334-4962-91d0-13e00893adc5
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 13 March 2024
                : 18 July 2024
                Funding
                Funded by: Science and Technology Program of Jiangxi Traditional Chinese Medicine
                Award ID: grant number 2021B723
                Award Recipient :
                Funded by: the Natural Science Foundation of Jiangxi Province
                Award ID: SKJP220212485
                Award Recipient :
                Funded by: the National Natural Science Fund of China
                Award ID: 82260085
                Award Recipient :
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                © Springer Nature Limited 2024

                Uncategorized
                metagenomic next-generation sequencing,central nervous system infection,application,pathogens,diagnosis,neuroscience,signs and symptoms

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