Evaluation of different phenotypic methods to detect methicillin resistance in Staphylococcus aureus isolates recovered from cystic fibrosis patients

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

show abstract

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Koul

Bawa²,

Kumar³

2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

Livestock associated Staphylococcus aureus in cystic fibrosis patients in Spain: Detection of MRSA and MSSA CC398

Eguizábal,

López-Calleja,

Arias

et al. 2024

Microbial Pathogenesis

View full text Add to dashboard Cite

Practical Guidance for Clinical Microbiology Laboratories: Updated guidance for processing respiratory tract samples from people with cystic fibrosis

Saiman,

Waters,

LiPuma

et al. 2024

Clin Microbiol Rev

View full text Add to dashboard Cite

SUMMARY This guidance presents recommendations for clinical microbiology laboratories for processing respiratory samples from people with cystic fibrosis (pwCF). Appropriate processing of respiratory samples is crucial to detect bacterial and fungal pathogens, guide treatment, monitor the epidemiology of cystic fibrosis (CF) pathogens, and assess therapeutic interventions. Thanks to CF transmembrane conductance regulator modulator therapy, the health of pwCF has improved, but as a result, fewer pwCF spontaneously expectorate sputum. Thus, the collection of sputum samples has decreased, while the collection of other types of respiratory samples such as oropharyngeal and bronchoalveolar lavage samples has increased. To optimize the detection of microorganisms, including Pseudomonas aeruginosa , Staphylococcus aureus , Haemophilus influenzae , and Burkholderia cepacia complex; other less common non-lactose fermenting Gram-negative bacilli, e.g., Stenotrophomonas maltopholia , Inquilinus , Achromobacter , Ralstonia , and Pandoraea species; and yeasts and filamentous fungi, non-selective and selective culture media are recommended for all types of respiratory samples, including samples obtained from pwCF after lung transplantation. There are no consensus recommendations for laboratory practices to detect, characterize, and report small colony variants (SCVs) of S. aureus , although studies are ongoing to address the potential clinical impact of SCVs. Accurate identification of less common Gram-negative bacilli, e.g., S. maltopholia , Inquilinus , Achromobacter , Ralstonia , and Pandoraea species, as well as yeasts and filamentous fungi, is recommended to understand their epidemiology and clinical import

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Evaluation of different phenotypic methods to detect methicillin resistance in Staphylococcus aureus isolates recovered from cystic fibrosis patients

Cited by 3 publications

References 21 publications

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Livestock associated Staphylococcus aureus in cystic fibrosis patients in Spain: Detection of MRSA and MSSA CC398

Practical Guidance for Clinical Microbiology Laboratories: Updated guidance for processing respiratory tract samples from people with cystic fibrosis

Contact Info

Product

Resources

About