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

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

SUMMARYThis 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 maltophilia, 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. maltophilia, Inquilinus, Achromobacter, Ralstonia, and Pandoraea species, as well as yeasts and filamentous fungi, is recommended to understand their epidemiology and clinical importance in pwCF. However, conventional biochemical tests and automated platforms may not accurately identify CF pathogens. MALDI-TOF MS provides excellent genus-level identification, but databases may lack representation of CF pathogens to the species-level. Thus, DNA sequence analysis should be routinely available to laboratories for selected clinical circumstances. Antimicrobial susceptibility testing (AST) is not recommended for every routine surveillance culture obtained from pwCF, although selective AST may be helpful, e.g., for unusual pathogens or exacerbations unresponsive to initial therapy. While this guidance reflects current care paradigms for pwCF, recommendations will continue to evolve as CF research expands the evidence base for laboratory practices.

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

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.