Evidence map›Paper›PMID 39334995›Full record

ArticleAntibiotics (Basel, Switzerland)2024

Machine Learning in FTIR Spectrum for the Identification of Antibiotic Resistance: A Demonstration with Different Species of Microorganisms.

Claudia Patricia Barrera Patiño, Jennifer Machado Soares, Kate Cristina Blanco, Vanderlei Salvador Bagnato

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Antibiotics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Machine learning-based typing ofMicrobiology spectrum · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. 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.

Claudia Patricia Barrera PatiñoSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense No. 400, Parque Arnold Schimidt, São Carlos CEP 13566-590, SP, Brazil.ORCID 0000-0002-3233-1109
Jennifer Machado SoaresSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense No. 400, Parque Arnold Schimidt, São Carlos CEP 13566-590, SP, Brazil.ORCID 0000-0002-2978-7076
Kate Cristina BlancoSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense No. 400, Parque Arnold Schimidt, São Carlos CEP 13566-590, SP, Brazil.
Vanderlei Salvador BagnatoSão Carlos Institute of Physics, University of São Paulo, Avenida Trabalhador São-Carlense No. 400, Parque Arnold Schimidt, São Carlos CEP 13566-590, SP, Brazil.ORCID 0000-0003-4833-239X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentiate antimicrobial resistance among microorganisms. Using advanced machine learning algorithms, we established a methodology capable of analyzing the FTIR structural profile of the samples of

Indexed as

antibiotic-resistant bacteriaEscherichia coliKlebsiella pneumoniaemachine learning algorithmsStreptococcus mutansStreptococcus pyogenes

Identifiers

PMID39334995
PMCPMC11428736

What Socratic holds

Textmetadata
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 Socratic graph.