Activity of mecillinam against carbapenem-resistant Enterobacterales.

BACKGROUND: Despite the fact that carbapenem-resistant Enterobacterales (CRE) mostly cause urinary tract infections (UTIs), only few studies have focused on the efficacity of mecillinam against these CRE. OBJECTIVES: To evaluate the mecillinam susceptibility of a huge collection of CRE, including carbapenemase-producing Enterobacterales (CPE) and non-CPE (ESBL and AmpC producers with decreased permeability of the outer membrane). METHODS: A total of 8310 non-duplicate clinical CRE, including 4042 OXA-48-like producers, 1094 NDM producers, 411 VIM producers, 174 KPC producers, 42 IMI producers, 153 multiple-carbapenemase producers and 45 isolates producing other types of carbapenemases (such as IMP-like enzymes or GES-5), were included in the study. WGS was performed on all CPE using Illumina technology. Categorization of susceptibility to mecillinam was performed using disc diffusion (mecillinam discs at 10 µg; I2A, France) according to EUCAST recommendations. The results were interpreted according to EUCAST guidelines (S ≥15 mm). RESULTS: Significantly higher susceptibility rates were observed for carbapenem-resistant Proteus spp. (85%) and carbapenem-resistant Escherichia coli (84%), which are the two most common species responsible for UTIs, than for Klebsiella pneumoniae (67%), Enterobacter cloacae complex (75%), Citrobacter spp. (65%), Serratia spp. (34%) and Morganella morganii (12%). Susceptibility rates were 84%, 71% and 91% for OXA-48-like, NDM and IMI producers and 70% for non-CPE CRE. Mecillinam was less active against VIM and KPC producers (14% and 0%, respectively). CONCLUSIONS: Mecillinam might be an alternative for the treatment of infections due to CRE, particularly UTIs, except for VIM and KPC producers and for M. morganii and Serratia spp species.

Diagnosis of mucormycosis using an intercalating dye-based quantitative PCR.

PCR-based methods applied to various body fluids emerged in recent years as a promising approach for the diagnosis of mucormycosis. In this study, we set up and assess the value of a qPCR to detect a wide variety of Mucorales species in a single tube. A pair of degenerated primers targeting the rDNA operon was used in a qPCR utilizing an intercalating fluorescent dye. Analytical assessment, using a wide variety of both Mucorales strains (8 genera, 11 species) and non-Mucorales strains (9 genera, 14 species), showed 100% sensitivity and specificity rates with a limit of detection at 3 rDNA copy/qPCR reaction. Subsequently, 364 clinical specimens from 166 at-risk patients were prospectively tested with the assay. All the seven patients classified as proven/probable mucormycosis using the EORTC-MSG criteria had a positive qPCR as well as a patient with a proven uncharacterized invasive mold infection. In addition, three out of seven patients with possible mold invasive infections had at least one positive qPCR test. Sensitivity was calculated between 73.33 and 100% and specificity between 98.10 and 100%. The qPCR method proposed showed excellent performances and would be an important adjunctive tool for the difficult diagnosis of mucormycosis diagnosis. LAY ABSTRACT: qPCR-based diagnosis is the most reliable approach for mucormycosis. We set up a pan-Mucorales qPCR able to detect in a single reaction not less than 11 different species. Both analytical and clinical performances support its use in the clinical setting.

Four-Hour Immunochromatographic Detection of Intestinal Carriage of Carbapenemase-Producing Enterobacteriaceae: a Validation Study.

The increasing incidence of carbapenemase-producing Gram-negative bacilli (C-PGNB) represents a major public health challenge. Rapid detection of digestive colonization with C-PGNB is fundamental to control their spread. We performed the validation of a rapid protocol for C-PGNB detection directly on rectal swabs. We developed a protocol combining enrichment by a rapid selective subculture of the rectal swab medium and realization of a Resist-4 O.K.N.V. K-SeT test on the bacterial pellet obtained. The limit of detection and performances of this protocol were validated in vitro on 52 C-PGNB strains spiked on a calibrated sample suspension and confirmed in clinical settings on 144 rectal swabs sampled from patients with C-PGNB digestive colonization (n = 48) and controls (patients with extended-spectrum beta-lactamase [ESBL] colonization [n = 48] and without carbapenemase/ESBL [n = 48]). The protocol detected, with 100% sensitivity, the presence of the 15 OXA-48-, 14 KPC-, 13 NDM-, and 10 VIM-producing GNB from 103 CFU/ml. The limit of detection was 2 × 102 CFU/ml. Among the 48 C-PGNB-containing rectal swabs of the validation cohort, 46 were accurately detected. False negative were observed for 1 NDM-producing Acinetobacter baumannii strain and 1 OXA-48-producing Escherichia coli strain. The 96 control swabs were negative. Sensitivity and specificity for C-PGNB detection were 97.7% (95% confidence interval [CI], 87.7 to 100) and 100% (95% CI, 96.2 to 100). The negative likelihood ratio was 0.04 (95% CI, 0.01 to 0.16). Considering a C-PGNB digestive colonization prevalence between 0.01% and 0.1%, positive and negative predictive values were 100%. Our protocol is a rapid and low-cost method detecting accurately the digestive colonization with carbapenemase-producing Enterobacteriaceae in 4 h without any requirement for specific equipment.

Stability of Routine Biochemical Analytes in Whole Blood and Plasma From Lithium Heparin Gel Tubes During 6-hr Storage.

BACKGROUND: The stability of biochemical analytes has already been investigated, but results strongly differ depending on parameters, methodologies, and sample storage times. We investigated the stability for many biochemical parameters after different storage times of both whole blood and plasma, in order to define acceptable pre- and postcentrifugation delays in hospital laboratories. METHODS: Twenty-four analytes were measured (Modular® Roche analyzer) in plasma obtained from blood collected into lithium heparin gel tubes, after 2-6 hr of storage at room temperature either before (n = 28: stability in whole blood) or after (n = 21: stability in plasma) centrifugation. Variations in concentrations were expressed as mean bias from baseline, using the analytical change limit (ACL%) or the reference change value (RCV%) as acceptance limit. RESULTS: In tubes stored before centrifugation, mean plasma concentrations significantly decreased after 3 hr for phosphorus (-6.1% [95% CI: -7.4 to -4.7%]; ACL 4.62%) and lactate dehydrogenase (LDH; -5.7% [95% CI: -7.4 to -4.1%]; ACL 5.17%), and slightly decreased after 6 hr for potassium (-2.9% [95% CI: -5.3 to -0.5%]; ACL 4.13%). In plasma stored after centrifugation, mean concentrations decreased after 6 hr for bicarbonates (-19.7% [95% CI: -22.9 to -16.5%]; ACL 15.4%), and moderately increased after 4 hr for LDH (+6.0% [95% CI: +4.3 to +7.6%]; ACL 5.17%). Based on RCV, all the analytes can be considered stable up to 6 hr, whether before or after centrifugation. CONCLUSION: This study proposes acceptable delays for most biochemical tests on lithium heparin gel tubes arriving at the laboratory or needing to be reanalyzed.

Contribution of MALDI-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains.

Clostridioides difficile (CD) infections are defined by toxins A (TcdA) and B (TcdB) along with the binary toxin (CDT). The emergence of the 'hypervirulent' (Hv) strain PR 027, along with PR 176 and 181, two decades ago, reshaped CD infection epidemiology in Europe. This study assessed MALDI-TOF mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) and Deep Learning (DL) to identify toxigenic strains (producing TcdA, TcdB with or without CDT) and Hv strains. In total, 201 CD strains were analysed, comprising 151 toxigenic (24 ToxA+B+CDT+, 22 ToxA+B+CDT+ Hv+ and 105 ToxA+B+CDT-) and 50 non-toxigenic (ToxA-B-) strains. The DL-based classifier exhibited a 0.95 negative predictive value for excluding ToxA-B- strains, showcasing accuracy in identifying this strain category. Sensitivity in correctly identifying ToxA+B+CDT- strains ranged from 0.68 to 0.91. Additionally, all classifiers consistently demonstrated high specificity (>0.96) in detecting ToxA+B+CDT+ strains. The classifiers' performances for Hv strain detection were linked to high specificity (≥0.96). This study highlights MALDI-TOF MS enhanced by ML techniques as a rapid and cost-effective tool for identifying CD strain virulence factors. Our results brought a proof-of-concept concerning the ability of MALDI-TOF MS coupled with ML techniques to detect virulence factor and potentially improve the outbreak's management.

Investigation of a Mycobacterium fortuitum catheter-related bloodstream infection in an oncology unit.

We describe a case of healthcare-associated bloodstream infection due to Mycobacterium fortuitum. Whole-genome sequencing showed that the same strain was isolated from the shared shower water of the unit. Nontuberculous mycobacteria frequently contaminate hospital water networks. Preventative actions are needed to reduce the exposure risk for immunocompromised patients.

Improving the Detection of Epidemic Clones in Candida parapsilosis Outbreaks by Combining MALDI-TOF Mass Spectrometry and Deep Learning Approaches.

Identifying fungal clones propagated during outbreaks in hospital settings is a problem that increasingly confronts biologists. Current tools based on DNA sequencing or microsatellite analysis require specific manipulations that are difficult to implement in the context of routine diagnosis. Using deep learning to classify the mass spectra obtained during the routine identification of fungi by MALDI-TOF mass spectrometry could be of interest to differentiate isolates belonging to epidemic clones from others. As part of the management of a nosocomial outbreak due to Candida parapsilosis in two Parisian hospitals, we studied the impact of the preparation of the spectra on the performance of a deep neural network. Our purpose was to differentiate 39 otherwise fluconazole-resistant isolates belonging to a clonal subset from 56 other isolates, most of which were fluconazole-susceptible, collected during the same period and not belonging to the clonal subset. Our study carried out on spectra obtained on four different machines from isolates cultured for 24 or 48 h on three different culture media showed that each of these parameters had a significant impact on the performance of the classifier. In particular, using different culture times between learning and testing steps could lead to a collapse in the accuracy of the predictions. On the other hand, including spectra obtained after 24 and 48 h of growth during the learning step restored the good results. Finally, we showed that the deleterious effect of the device variability used for learning and testing could be largely improved by including a spectra alignment step during preprocessing before submitting them to the neural network. Taken together, these experiments show the great potential of deep learning models to identify spectra of specific clones, providing that crucial parameters are controlled during both culture and preparation steps before submitting spectra to a classifier.

Differential Performance of the FilmArray Meningitis/Encephalitis Assay To Detect Bacterial and Viral Pathogens in Both Pediatric and Adult Populations.

Meningitis/encephalitis (ME) syndromic diagnostic assays can be applied for the rapid one-step detection of the most common pathogens in cerebrospinal fluid (CSF). However, the comprehensive performance of multiplex assays is still under evaluation. In our multisite university hospital of eastern Paris, France, ME syndromic testing has been gradually implemented since 2017 for patients with neurological symptoms presenting to an adult or pediatric emergency unit. We analyzed the results from the BioFire FilmArray ME panel versus standard routine bacteriology and virology techniques, together with CSF cytology and clinical data, over a 2.5-year period to compare the diagnostic accuracy of the FilmArray ME panel to that of the reference methods. In total, 1,744 CSF samples from 1,334 pediatric and 336 adult patients were analyzed. False-positive (mostly bacterial) and false-negative (mostly viral) cases were deciphered with the help of clinical data. The performance of the FilmArray ME panel in our study was better for bacterial detection (specificity >99%, sensitivity 100%) than viral detection (specificity >99%, sensitivity 75% for herpes simplex virus 1 [HSV-1] and 89% for enterovirus), our study being one of the largest, to date, concerning enteroviruses. The use of a threshold of 10 leukocytes/mm3 considerably increased the positive agreement between the results of the FilmArray ME panel and the clinical features, especially for bacterial pathogens, for which agreement increased from 58% to 87%, avoiding two-thirds of inappropriate testing. Based on this analysis, we propose an algorithm for the use of both syndromic and specific assays for the optimal management of suspected meningitis/encephalitis in adult and pediatric patients. IMPORTANCE Based on our comparative analysis of performances of the diagnostic assays, we propose an algorithm for the use of both syndromic and specific assays, for an optimal care of the meningitis/encephalitis threat in adult and pediatric patients.

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning.

The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced.

Revisiting Species Identification within the Enterobacter cloacae Complex by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is commonly used by clinical microbiology laboratories to identify pathogens, despite some limitations of the technique. The Enterobacter cloacae complex (ECC) taxonomy has recently been expanded, leading to uncertain identification of some species within the ECC when commercial MALDI-TOF MS is used. This technique is especially unsuited in the case of E. hormaechei, the main species responsible for infections and one of the most prone, within the ECC, to acquire antibiotic resistance. Hence, rapid and reliable identification at the species level could improve patient management. Here, we evaluated the performance of the Bruker Microflex MALDI-TOF MS instrument to identify ECC isolates using two databases and algorithms in comparison to the hsp60 gene sequencing reference method: the Bruker database included in the MALDI Biotyper software and an extensive online database coupled to an original Mass Spectrometric Identification (MSI) algorithm. Among a panel of 94 ECC isolates tested in triplicate, the online database coupled to MSI software allowed the highest rate of identification at the species level (92%) compared to the MALDI Biotyper database (25%), especially for the species E. hormaechei (97% versus 20%). We show that by creating a database of MALDI-TOF reference spectral profiles with a high number of representatives associated with the performant MSI software, we were able to substantially improve the identification of the E. cloacae complex members, with only 8% of isolates misidentified at the species level. This online database is available through a free online MSI application (https://msi.happy-dev.fr/). IMPORTANCE Creation of a database of MALDI-TOF reference spectral profiles with a high number of representatives associated with the performant MSI software enables substantial improvement in identification of E. cloacae complex members. Moreover, this online database is available through a free online MSI application (https://msi.happy-dev.fr/).