Dynamic monitoring of bacteriostatic process by SERS analysis based on a simple but effective detection strategy

Xiang, Songtao; Lan, YuXiang; Mai, Zhiliang; Tian, Feng; Mao, Hua

doi:10.1016/j.saa.2022.121611

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Sers Applications For the “Big 5” Antibiotic Resistance Chal...1

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“…Similarly, in a very recent study conducted by Xiang et al , antibacterial nanocomposite structure combined with AgNPs as a SERS substrate was applied as a synergistic approach for the dynamic monitoring of bacteriostatic process of MRSA species. 102 A curcumin liposome@AuNPs nanocomposite was designed and used as bacteriostatic agent, as well as a SERS probe. By means of electrostatic attraction between the nanocomposite and MRSA onto the surface of the AgNPs substrate, a specific detection of MRSA at the molecular level was simply realized by SERS.…”

Section: Sers Applications For the “Big 5” Antibiotic Resistance Chal...mentioning

confidence: 99%

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

Hassanain

Johnson

Faulds

et al. 2022

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Section: Sers Applications For the “Big 5” Antibiotic Resistance Chal...mentioning

confidence: 99%

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

Hassanain

Johnson

Faulds

et al. 2022

Analyst

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Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Al‐Shaebi,

Akdeniz,

Ahmed

et al. 2023

Adv Materials Inter

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Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are imperative. Surface‐enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide.

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Dynamic monitoring of bacteriostatic process by SERS analysis based on a simple but effective detection strategy

Cited by 2 publications

References 28 publications

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

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