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Members of the Enterobacter (E.) cloacae complex have emerged as important pathogens frequently encountered in nosocomial infections. Several outbreaks with E. cloacae complex have been reported in recent years, especially in neonatal units. Fast and reliable strain typing methods are crucial for real-time surveillance and outbreak analysis to detect pathogen reservoirs and transmission routes. The aim of this study was to evaluate the performance of Fourier-transform infrared (FTIR) spectroscopy as a fast method for typing of clinical E. cloacae complex isolates, when whole genome sequencing (WGS) analysis was used as reference. First, the technique was used retrospectively on 24 first isolates of E. cloacae complex strains from neonatal patients and showed good concordance with SNP-based clustering [adjusted rand index (ARI) = 0.818] and with the sequence type (ST) (ARI = 0.801). 29 consecutive isolates from the same patients were shown by WGS analysis to almost always belong to the same SNP cluster as the first isolates, which was only inconsistently recognized by FTIR spectroscopy. Training of an artificial neural network (ANN) with all FTIR spectra from sequenced strains markedly improved the recognition of related and unrelated isolate spectra. In a second step, FTIR spectroscopy was applied on 14 strains during an outbreak with E. cloacae complex and provided fast typing results that were confirmed by WGS analysis. In conclusion, FTIR spectroscopy is a promising tool for strain typing of clinical E. cloacae complex strains. Discriminatory power can be improved by implementing an ANN for spectrum analysis. Due to its low costs and fast turnaround times, the method presents a valuable tool for real-time surveillance as well as outbreak analysis. [ABSTRACT FROM AUTHOR]

Study Design: Retrospective, mono-centric cohort research study., Objectives: The analysis of cervical sagittal balance parameters is essential for preoperative planning and dependent on the physician's experience. A fully automated artificial intelligence-based algorithm could contribute to an objective analysis and save time. Therefore, this algorithm should be validated in this study., Methods: Two surgeons measured C2-C7 lordosis, C1-C7 Sagittal Vertical Axis (SVA), C2-C7-SVA, C7-slope and T1-slope in pre- and postoperative lateral cervical X-rays of 129 patients undergoing anterior cervical surgery. All parameters were measured twice by surgeons and compared to the measurements by the AI algorithm consisting of 4 deep convolutional neural networks. Agreement between raters was quantified, among other metrics, by mean errors and single measure intraclass correlation coefficients for absolute agreement., Results: ICC-values for intra- (range: .92-1.0) and inter-rater (.91-1.0) reliability reflect excellent agreement between human raters. The AI-algorithm could determine all parameters with excellent ICC-values (preop:0.80-1.0; postop:0.86-.99). For a comparison between the AI algorithm and 1 surgeon, mean errors were smallest for C1-C7 SVA (preop: -.3 mm (95% CI:-.6 to -.1 mm), post: .3 mm (.0-.7 mm)) and largest for C2-C7 lordosis (preop:-2.2° (-2.9 to -1.6°), postop: 2.3°(-3.0 to -1.7°)). The automatic measurement was possible in 99% and 98% of pre- and postoperative images for all parameters except T1 slope, which had a detection rate of 48% and 51% in pre- and postoperative images., Conclusion: This study validates that an AI-algorithm can reliably measure cervical sagittal balance parameters automatically in patients suffering from degenerative spinal diseases. It may simplify manual measurements and autonomously analyze large-scale datasets. Further studies are required to validate the algorithm on a larger and more diverse patient cohort., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This research was a cooperation with Raylytic as part of the AIQNET initiative and was supported by the German Federal Ministry for Economic Affairs and Climate Action as a DLR project. This does not in any way affect the scientific output of this study.

Klebsiella pneumoniae and related species are frequent causes of nosocomial infections and outbreaks. Therefore, quick and reliable strain typing is crucial for the detection of transmission routes in the hospital. The aim of this study was to evaluate Fourier transform infrared spectroscopy (FTIR) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) as rapid methods for typing clinical Klebsiella isolates in comparison to whole-genome sequencing (WGS), which was considered the gold standard for typing and identification. Here, 68 clinical Klebsiella strains were analyzed by WGS, FTIR, and MALDI-TOF MS. FTIR showed high discriminatory power in comparison to the WGS reference, whereas MALDI-TOF MS exhibited a low ability to type the isolates. MALDI-TOF mass spectra were further analyzed for peaks that showed high specificity for different Klebsiella species. Phylogenetic analysis revealed that the Klebsiella isolates comprised three different species: K. pneumoniae , K. variicola , and K. quasipneumoniae Genome analysis showed that MALDI-TOF MS can be used to distinguish K. pneumoniae from K. variicola due to shifts of certain mass peaks. The peaks were tentatively identified as three ribosomal proteins (S15p, L28p, L31p) and one stress response protein (YjbJ), which exhibit amino acid differences between the two species. Overall, FTIR has high discriminatory power to recognize the clonal relationship of isolates, thus representing a valuable tool for rapid outbreak analysis and for the detection of transmission events due to fast turnaround times and low costs per sample. Furthermore, specific amino acid substitutions allow the discrimination of K. pneumoniae and K. variicola by MALDI-TOF MS., (Copyright © 2018 American Society for Microbiology.)