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Antibiotics
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Antibiotic Resistance: Moving from Individual Health to One Health and...
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Antibiotic Resistance: Moving from Individual Health to One Health and Global Health

A special issue of Antibiotics (ISSN 2079-6382).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 5070

Special Issue Editor

Dr. Francisco J. Roig

E-Mail Website
Guest Editor
Facultad de Ciencias de la Salud, Universidad San Jorge, Zaragoza, Spain
Interests: microbiology; epidemiology; genetics; bioinformatics; omics; antibiotics; taxonomy; health; zoonosis; environment

Special Issue Information

Dear Colleagues,

Antibiotics have been used for a long time to treat infections, although until the last century, people did not know that infections were caused by bacteria. The successful use of any therapeutic agent is compromised by the potential development of tolerance or resistance to that compound. Currently, antibiotic resistance is a global public health problem that is increasing. What can cause this resistance? Various things, such as natural mutation rate, or the selection and promotion of resistant strains through inappropriate prescription or unnecessary use. For example, it is estimated that 66% of all antibiotics are used in farm animals; much of this is routine to keep animals in poor condition and reduce the effect or possible infections, not for treatment, leading to a bioaccumulation environment of antibiotics generating a perfect field for the selection of bacteria resistant to antibiotics. Thus, we need a global strategy with interdisciplinary collaboration (medical personnel, veterinarians, researchers, etc.) for the care of the global health of people, animals, and the environment since human health and animal health are interdependent and linked to the ecosystems in which they coexist. This is known as the One Health concept. The One Health initiative studies the emergence, evolution and, spread of antibiotic-resistant microorganisms on a local and global scale as a significant risk factor for global health. In this Special Issue, we will share the latest advances in the knowledge of antibiotic resistance in relation to our globalized and interconnected world.

Dr. Francisco J. Roig
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Antibiotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • antibiotic
  • resistance
  • evolution
  • One Health
  • bioproducts
  • animal health
  • human health

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Published Papers (2 papers)

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Research

18 pages, 2327 KiB  
Article
Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm
by Claudia P. Barrera-Patiño, Jennifer M. Soares, Kate C. Branco, Natalia M. Inada and Vanderlei Salvador Bagnato
Antibiotics 2023, 12(10), 1502; https://doi.org/10.3390/antibiotics12101502 - 30 Sep 2023
Cited by 2 | Viewed by 2113
Abstract
FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial [...] Read more.
FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of Staphylococcus aureus, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of S. aureus samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of S. aureus bacteria for potential application in the detection of antibiotic resistance in clinical use. Full article
(This article belongs to the Special Issue Antibiotic Resistance: Moving from Individual Health to One Health and Global Health)
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16 pages, 649 KiB  
Article
Genomic Surveillance of Salmonella from the Comunitat Valenciana (Spain)
by Andrea Sánchez-Serrano, Lorena Mejía, Maria Luisa Camaró, Susana Ortolá-Malvar, Martín Llácer-Luna, Neris García-González and Fernando González-Candelas
Antibiotics 2023, 12(5), 883; https://doi.org/10.3390/antibiotics12050883 - 9 May 2023
Viewed by 2368
Abstract
Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these [...] Read more.
Salmonella enterica subspecies enterica is one of the most important foodborne pathogens and the causative agent of salmonellosis, which affects both humans and animals producing numerous infections every year. The study and understanding of its epidemiology are key to monitoring and controlling these bacteria. With the development of whole-genome sequencing (WGS) technologies, surveillance based on traditional serotyping and phenotypic tests of resistance is being replaced by genomic surveillance. To introduce WGS as a routine methodology for the surveillance of food-borne Salmonella in the region, we applied this technology to analyze a set of 141 S. enterica isolates obtained from various food sources between 2010 and 2017 in the Comunitat Valenciana (Spain). For this, we performed an evaluation of the most relevant Salmonella typing methods, serotyping and sequence typing, using both traditional and in silico approaches. We extended the use of WGS to detect antimicrobial resistance determinants and predicted minimum inhibitory concentrations (MICs). Finally, to understand possible contaminant sources in this region and their relationship to antimicrobial resistance (AMR), we performed cluster detection combining single-nucleotide polymorphism (SNP) pairwise distances and phylogenetic and epidemiological data. The results of in silico serotyping with WGS data were highly congruent with those of serological analyses (98.5% concordance). Multi-locus sequence typing (MLST) profiles obtained with WGS information were also highly congruent with the sequence type (ST) assignment based on Sanger sequencing (91.9% coincidence). In silico identification of antimicrobial resistance determinants and minimum inhibitory concentrations revealed a high number of resistance genes and possible resistant isolates. A combined phylogenetic and epidemiological analysis with complete genome sequences revealed relationships among isolates indicative of possible common sources for isolates with separate sampling in time and space that had not been detected from epidemiological information. As a result, we demonstrate the usefulness of WGS and in silico methods to obtain an improved characterization of S. enterica enterica isolates, allowing better surveillance of the pathogen in food products and in potential environmental and clinical samples of related interest. Full article
(This article belongs to the Special Issue Antibiotic Resistance: Moving from Individual Health to One Health and Global Health)
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