Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation

Caioni, Giulia; Benedetti, Elisabetta; Perugini, Monia; Amorena, Michele; Merola, Carmine

doi:10.3390/antibiotics12040724

Cited by 9 publications

(3 citation statements)

References 99 publications

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“…Cosmetic components (e.g., parabens, triclocarban, triclosan) co-select and facilitate acquisition of AR [144].…”

Section: Antibiotics Increase Conjugation Frequencymentioning

confidence: 99%

Non-Canonical Aspects of Antibiotics and Antibiotic Resistance

Amábile-Cuevas,

Lund-Zaina

2024

Antibiotics

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The understanding of antibiotic resistance, one of the major health threats of our time, is mostly based on dated and incomplete notions, especially in clinical contexts. The “canonical” mechanisms of action and pharmacodynamics of antibiotics, as well as the methods used to assess their activity upon bacteria, have not changed in decades; the same applies to the definition, acquisition, selective pressures, and drivers of resistance. As a consequence, the strategies to improve antibiotic usage and overcome resistance have ultimately failed. This review gathers most of the “non-canonical” notions on antibiotics and resistance: from the alternative mechanisms of action of antibiotics and the limitations of susceptibility testing to the wide variety of selective pressures, lateral gene transfer mechanisms, ubiquity, and societal factors maintaining resistance. Only by having a “big picture” view of the problem can adequate strategies to harness resistance be devised. These strategies must be global, addressing the many aspects that drive the increasing prevalence of resistant bacteria aside from the clinical use of antibiotics.

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“…Cosmetic components (e.g., parabens, triclocarban, triclosan) co-select and facilitate acquisition of AR [144].…”

Section: Antibiotics Increase Conjugation Frequencymentioning

confidence: 99%

Non-Canonical Aspects of Antibiotics and Antibiotic Resistance

Amábile-Cuevas,

Lund-Zaina

2024

Antibiotics

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“…These new developments in the prevention and treatment of pediatric infectious diseases address several key challenges. They aim to overcome antibiotic resistance, improve the efficacy and specificity of treatment options, and enhance the overall management of infectious diseases in pediatric patients [21][22][23].…”

Section: Innovations In the Treatment Of Pediatric Infectious Diseasesmentioning

confidence: 99%

New Developments in Prevention and Treatment of Pediatric Infectious Diseases

Fadl,

Mudawi,

Alqahtani

et al. 2023

JAZ

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Both adults and children are regularly afflicted by infectious illnesses. We now have a better understanding of their epidemiology, pathophysiology, prevention, and treatment because of new diagnostic techniques. Recently, emerging infections have surfaced, prompting health officials to issue warnings. Furthermore, novel anti-microbial therapies have been identified; however, because of the potential for adverse effects on antimicrobial resistance, it is critical to understand when to utilize these therapies in order to prevent overuse and misuse. In addition, new vaccinations have entered the market, and it is crucial that medical professionals understand how they may affect public health. Also the use of prebiotics and probiotics has emerged now as a preventive measure for pediatric infectious diseases.

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“…Ultimately, the goal is to develop a system that autonomously improves its performance and accuracy without programming [6]. Such systems would substantially contribute to easing the burden of antimicrobial resistance [7].…”

Section: Introductionmentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation

Cited by 9 publications

References 99 publications

Non-Canonical Aspects of Antibiotics and Antibiotic Resistance

Non-Canonical Aspects of Antibiotics and Antibiotic Resistance

New Developments in Prevention and Treatment of Pediatric Infectious Diseases

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

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