Evidence map›Paper›PMID 24418497›Full record

ArticleJournal of clinical bioinformatics2014

Clinical detection of human probiotics and human pathogenic bacteria by using a novel high-throughput platform based on next generation sequencing.

Chih-Min Chiu, Feng-Mao Lin, Tzu-Hao Chang, Wei-Chih Huang, Chao Liang, Ting Yang, Wei-Yun Wu, Tzu-Ling Yang, Shun-Long Weng, Hsien-Da Huang

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 19 citations in OpenAlex.

  1. Vancomycin Resistance inThe Yale journal of biology and medicine · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Chih-Min Chiu *Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Feng-Mao Lin *Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Tzu-Hao ChangGraduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
Wei-Chih HuangInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Chao LiangInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Ting YangInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Wei-Yun WuInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Tzu-Ling YangInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Shun-Long WengInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
Hsien-Da HuangInstitute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan.
National Yang Ming Chiao Tung University · TWMackay Memorial Hospital · TWTaipei Medical University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe human body plays host to a vast array of bacteria, found in oral cavities, skin, gastrointestinal tract and the vagina. Some bacteria are harmful while others are beneficial to the host. Despite the availability of many methods to identify bacteria, most of them are only applicable to specific and cultivable bacteria and are also tedious. Based on high throughput sequencing technology, this work derives 16S rRNA sequences of bacteria and analyzes probiotics and pathogens species.

resultsWe constructed a database that recorded the species of probiotics and pathogens from literature, along with a modified Smith-Waterman algorithm for assigning the taxonomy of the sequenced 16S rRNA sequences. We also constructed a bacteria disease risk model for seven diseases based on 98 samples. Applicability of the proposed platform is demonstrated by collecting the microbiome in human gut of 13 samples.

conclusionsThe proposed platform provides a relatively easy means of identifying a certain amount of bacteria and their species (including uncultivable pathogens) for clinical microbiology applications. That is, detecting how probiotics and pathogens inhabit humans and how affect their health can significantly contribute to develop a diagnosis and treatment method.

Identifiers

PMID24418497
PMCPMC3901789
OpenAlexW2143889018

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.