Evidence map›Paper›PMID 38635063›Full record

ArticleMikrochimica acta2024

Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe

Wei-Hsiang Ma, Che-Chia Chang, Te-Sheng Lin, Yu-Chie Chen

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

Article in Mikrochimica acta, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 1 institution in 1 country.

Wei-Hsiang Ma *Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
Che-Chia Chang *Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
Te-Sheng LinDepartment of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. teshenglin@nycu.edu.tw.
Yu-Chie ChenDepartment of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. yuchie@nycu.edu.tw.ORCID http://orcid.org/0000-0003-2253-4049
National Yang Ming Chiao Tung University · TW

Funding

National Science and Technology Council 111-2113-M-A49-019-MY3National Science and Technology Council 111-2628-M-A49-008-MY4
6 · The paper itself

Abstract

Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe

Indexed as

Magnetite NanoparticlesMethicillin-Resistant Staphylococcus aureusHumansMachine LearningMethicillinSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationStaphylococcus aureusMagnetite NanoparticlesMethicillinFe3O4 MNPsMachine learningMALDI-MSMRSANeural networkStaphylococcus aureus

Identifiers

PMID38635063
PMCPMC11026280
OpenAlexW4394920578

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.