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Cited by 661

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Alternatives to Conventional Antibiotics in the Era of Antimicrobial Resistance

Ghosh, Chandradhish, Sarkar, Paramita, Issa, Rahaf, Haldar, Jayanta·2019·Trends in Microbiology

Abstract

As more antibiotics are rendered ineffective by drug-resistant bacteria, focus must be shifted towards alternative therapies for treating infections. Although several alternatives already exist in nature, the challenge is to implement them in clinical use. Advancements within biotechnology, genetic engineering, and synthetic chemistry have opened up new avenues towards the search for therapies that can substitute for antibiotics. This review provides an introduction to the various promising approaches that have been adopted in this regard. Whilst the use of bacteriophages and antibodies has been partly implemented, other promising strategies, such as probiotics, lysins, and antimicrobial peptides, are in various stages of development. Propitious concepts such as genetically modified phages, antibacterial oligonucleotides, and CRISPR-Cas9 are also discussed.

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Crossref · 10.1016/j.tim.2018.12.010

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Metadata

Authors
Ghosh, C., Sarkar, P., Issa, R., & Haldar, J.
Venue
Trends in Microbiology
Publisher
Elsevier BV
Published
2019-04
Volume
27
Issue
4
Pages
323-338
ISSN
0966-842X (ISSN-L)
OpenAlex
W2912354751
Cited by
661
References
5
FWCI
8.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Alternatives to Conventional Antibiotics in the Era of Antimicrobial Resistance. https://app.cochat.ai/papers/10.1016/j.tim.2018.12.010
The paper
Ghosh, C., Sarkar, P., Issa, R., & Haldar, J. (2019). Alternatives to Conventional Antibiotics in the Era of Antimicrobial Resistance. Trends in Microbiology. https://doi.org/10.1016/j.tim.2018.12.010

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.