RESUMEN
Antimicrobial resistance (AMR) is one of the major global health and economic threats. There is growing concern about the emergence of AMR in food and the possibility of transmission of microorganisms possessing antibiotic resistance genes (ARGs) to the human gut microbiome. Shotgun sequencing and in vitro antimicrobial susceptibility testing were used in this study to provide a detailed characterization of the antibiotic resistance profile of bacteria and their ARGs in dromedary camel milk. Eight pooled camel milk samples, representative of multiple camels distributed in the Kuwait desert, were collected from retail stores and analyzed. The genotypic analysis showed the presence of ARGs that mediate resistance to 18 classes of antibiotics in camel milk, with the highest resistance to fluoroquinolones (12.48%) and disinfecting agents and antiseptics (9%). Furthermore, the results pointed out the possible transmission of the ARGs to other bacteria through mobile genetic elements. The in vitro antimicrobial susceptibility testing indicated that 80% of the isolates were resistant to different classes of antibiotics, with the highest resistance observed against three antibiotic classes: penicillin, tetracyclines, and carbapenems. Multidrug-resistant pathogens including Klebsiella pneumoniae, Escherichia coli, and Enterobacter hormaechei were also revealed. These findings emphasize the human health risks related to the handling and consumption of raw camel milk and highlight the necessity of improving the hygienic practices of farms and retail stores to control the prevalence of ARGs and their transmission.
RESUMEN
The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.
RESUMEN
BACKGROUND: Premature born infants or infants born sick require immediate medical attention and decreasing the stress imposed onto their body by the environment. Infant incubators provide an enclosed environment that can be controlled to fit the needs of the infant. As such, their performance must be consistent and without significant deviations. The only manner to ensure this is by post-market surveillance (PMS) focused on evaluation of both safety and performance. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods. OBJECTIVE: This paper proposes a novel method for conformity assessment testing of infant incubators for post-market surveillance purposes. METHOD: The method was developed based on guidelines for devices providing measurements laid out by the International Organisation of Legal Metrology (OIML). The methodology was validated during a four year period in healthcare institutions of all levels. RESULTS: The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infant incubators as a method used during PMS contributes to significant improvement in devices' accuracy and reliability. CONCLUSION: A standardized approach in conformity assessment testing of infant incubators during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.
