Development and validation of a quick, automated, and reproducible ATR FT-IR spectroscopy machine-learning model for Klebsiella pneumoniae typing.

The reliability of Fourier-transform infrared (FT-IR) spectroscopy for Klebsiella pneumoniae typing and outbreak control has been previously assessed, but issues remain in standardization and reproducibility. We developed and validated a reproducible FT-IR with attenuated total reflectance (ATR) workflow for the identification of K. pneumoniae lineages. We used 293 isolates representing multidrug-resistant K. pneumoniae lineages causing outbreaks worldwide (2002-2021) to train a random forest classification (RF) model based on capsular (KL)-type discrimination. This model was validated with 280 contemporaneous isolates (2021-2022), using wzi sequencing and whole-genome sequencing as references. Repeatability and reproducibility were tested in different culture media and instruments throughout time. Our RF model allowed the classification of 33 capsular (KL)-types and up to 36 clinically relevant K. pneumoniae lineages based on the discrimination of specific KL- and O-type combinations. We obtained high rates of accuracy (89%), sensitivity (88%), and specificity (92%), including from cultures obtained directly from the clinical sample, allowing to obtain typing information the same day bacteria are identified. The workflow was reproducible in different instruments throughout time (>98% correct predictions). Direct colony application, spectral acquisition, and automated KL prediction through Clover MS Data analysis software allow a short time-to-result (5 min/isolate). We demonstrated that FT-IR ATR spectroscopy provides meaningful, reproducible, and accurate information at a very early stage (as soon as bacterial identification) to support infection control and public health surveillance. The high robustness together with automated and flexible workflows for data analysis provide opportunities to consolidate real-time applications at a global level. IMPORTANCE We created and validated an automated and simple workflow for the identification of clinically relevant Klebsiella pneumoniae lineages by FT-IR spectroscopy and machine-learning, a method that can be extremely useful to provide quick and reliable typing information to support real-time decisions of outbreak management and infection control. This method and workflow is of interest to support clinical microbiology diagnostics and to aid public health surveillance.

Stability, electronic properties and CO adsorption properties of bimetallic PtAg/Pt(111) surfaces.

Aiming at a better fundamental understanding of the chemistry of bimetallic PtAg/Pt(111) surfaces, we have investigated the stability, electronic properties and CO adsorption properties of bimetallic PtAg surfaces, including pseudomorphic Ag film covered Pt(111) surfaces and PtxAg1-x/Pt(111) monolayer surface alloys, using periodic density functional theory calculations. The data provide detailed insights into the relative stabilities of different surface configurations, as indicated by their formation enthalpies and surface energies, and changes in their electronic properties, i.e., in the projected local densities of states and shifts in the d-band center. The adsorption properties of different Ptn ensembles were systematically tested using CO as a probe molecule. In addition to electronic ligand and strain effects, we were particularly interested in the role of different adsorption sites and of the local COad coverage, given by the number of CO molecules per Pt surface atom in the Ptn ensemble. Different from PdAg surfaces, variations in the adsorption energy with adsorption sites and with increasing local coverage are small up to one COad per Pt surface atom. Finally, formation of multicarbonyl species with more than one COad per Pt surface atom was tested for separated Pt1 monomers and can be excluded at finite temperatures. General trends and aspects are derived by comparison with comparable data for PdAg bimetallic surfaces. Fundamental insights relevant for applications of bimetallic Pt catalysts, specifically PtAg catalysts, are briefly discussed.

Direct Detection of Carbapenemase-Producing Klebsiella pneumoniae by MALDI-TOF Analysis of Full Spectra Applying Machine Learning.

MALDI-TOF MS is considered to be an important tool for the future development of rapid microbiological techniques. We propose the application of MALDI-TOF MS as a dual technique for the identification of bacteria and the detection of resistance, with no extra hands-on procedures. We have developed a machine learning approach that uses the random forest algorithm for the direct prediction of carbapenemase-producing Klebsiella pneumoniae (CPK) isolates, based on the spectra of complete cells. For this purpose, we used a database of 4,547 mass spectra profiles, including 715 unduplicated clinical isolates that are represented by 324 CPK with 37 different ST. The impact of the culture medium was determinant in the CPK prediction, being that the isolates were tested and cultured in the same media, compared to the isolates used to build the model (blood agar). The proposed method has an accuracy of 97.83% for the prediction of CPK and an accuracy of 95.24% for the prediction of OXA-48 or KPC carriage. For the CPK prediction, the RF algorithm yielded a value of 1.00 for both the area under the receiver operating characteristic curve and the area under the precision-recall curve. The contribution of individual mass peaks to the CPK prediction was determined using Shapley values, which revealed that the complete proteome, rather than a series of mass peaks or potential biomarkers (as previously suggested), is responsible for the algorithm-based classification. Thus, the use of the full spectrum, as proposed here, with a pattern-matching analytical algorithm produced the best outcome. The use of MALDI-TOF MS coupled with machine learning algorithm processing enabled the identification of CPK isolates within only a few minutes, thereby reducing the time to detection of resistance.

Identification of Mycobacterium abscessus Subspecies by MALDI-TOF Mass Spectrometry and Machine Learning.

Mycobacterium abscessus is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 325 clinical isolates of M. abscessus was used for MALDI-TOF MS analysis and for the development of machine learning predictive models based on MALDI-TOF MS protein spectra. Overall, using a random forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 96.5% of isolates were correctly identified at the subspecies level. Moreover, an improved model with Spanish isolates was able to identify 88.9% of strains collected in other countries. In addition, differences in culture media, colony morphology, and geographic origin of the strains were evaluated, showing that the latter had an impact on the protein spectra. Finally, after studying all protein peaks previously reported for this species, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of M. abscessus using MALDI-TOF MS.

Automatic Discrimination of Species within the Enterobacter cloacae Complex Using Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry and Supervised Algorithms.

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may have different acquired antimicrobial resistance and virulence mechanisms, and their identification is challenging. This study aims to develop predictive models based on matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) profiles and machine learning for species-level identification. A total of 219 ECC and 118 Klebsiella aerogenes clinical isolates from three hospitals were included. The capability of the proposed method to differentiate the most common ECC species (Enterobacter asburiae, Enterobacter kobei, Enterobacter hormaechei, Enterobacter roggenkampii, Enterobacter ludwigii, and Enterobacter bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with principal-component analysis (PCA) preprocessing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development data set. Thus, we developed supervised, nonlinear predictive models (support vector machine with radial basis function and random forest). The external validation of these models with protein spectra from two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species; with data analyzed in the three participating centers, the accuracy was close to 100%. Similar results were obtained with the Mass Spectrometric Identification (MSI) database developed recently (https://msi.happy-dev.fr) except in the case of E. hormaechei, which was more accurately identified with the random forest algorithm. In short, MALDI-TOF MS combined with machine learning was demonstrated to be a rapid and accurate method for the differentiation of ECC species.

Occurrence of the p019 Gene in the blaKPC-Harboring Plasmids: Adverse Clinical Impact for Direct Tracking of KPC-Producing Klebsiella pneumoniae by Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.

Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been used for the direct detection of KPC-producing isolates by analysis of the 11,109 Da mass peak representing the P019 protein. In this study, we evaluate the presence of the 11,109 Da mass peak in a collection of 435 unduplicated Klebsiella pneumoniae clinical isolates. The prevalence of the P019 peak in the blaKPC K. pneumoniae isolates was 49.2% (32/65). The 11,109 Da mass peak was not observed in any of the other carbapenemase (319) or noncarbapenemase producers (116). Computational analysis of the presence of the p019 gene was performed in the aforementioned carbapenemase-producing K. pneumoniae isolates fully characterized by whole-genome sequencing (WGS) and in a further collection of 1,649 K. pneumoniae genomes included in EuSCAPE. Herein, we have demonstrated that the p019 gene is not exclusively linked to the pKpQil plasmid but that it is present in the following plasmids: IncFIB(K)/IncFII(K)/ColRNAI, IncFIB(pQil), IncFIB(pQil)/ColRNAI, IncFIB(pQil)/IncFII(K), IncFIB(K)/IncFII(K), and IncX3. In addition, we have proven the independent movement of the Tn4401 and the ISKpn31, of which the p019 gene is a component. The absence of the p019 gene was obvious in Col440I, Col(pHAD28), IncFIB(K)/IncX3/IncFII(K), and IncFIB(K)/IncFII(K) plasmids. In addition, we also observed another plasmid in which neither Tn4401 nor ISKpn31 was found, IncP6. In the EuSCAPE, the occurrence of p019 varied from 0% to 100% among the different geographical locations. The adverse clinical impact of the diminished prevalence of the p019 gene within the plasmid encoding KPC-producing Klebsiella pneumoniae puts forward the need for reconsideration when applying this technique in a clinical setting.

An Improved Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry Data Analysis Pipeline for the Identification of Carbapenemase-Producing Klebsiella pneumoniae.

The increasing emergence of carbapenemase-producing Klebsiella pneumoniae (CPK) isolates is a global health alarm. Rapid methods that require minimum sample preparation and rapid data analysis are urgently required. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has recently been used by clinical laboratories for identification of antibiotic-resistant bacteria; however, discrepancies have arisen regarding biological and technical issues. The aim of this study was to standardize an operating procedure and data analysis for identification of CPK by MALDI-TOF MS. To evaluate this approach, a series of 162 K. pneumoniae isolates (112 CPK and 50 non-CPK) were processed in the MALDI BioTyper system (Bruker Daltonik, Germany) following a standard operating procedure. The study was conducted in two stages; the first is denominated the "reproducibility stage" and the second "CPK identification." The first stage was designed to evaluate the biological and technical variation associated with the entire analysis of CPK and the second stage to assess the final accuracy of MALDI-TOF MS for the identification of CPK. Therefore, we present an improved MALDI-TOF MS data analysis pipeline using neural network analysis implemented in Clover MS Data Analysis Software (Clover Biosoft, Spain) that is designed to reduce variability, guarantee interlaboratory reproducibility, and maximize the information selected from the bacterial proteome. Using the random forest (RF) algorithm, 100% of CPK isolates were correctly identified when all the peaks in the spectra were selected as input features and total ion current (TIC) normalization was applied. Thus, we have demonstrated that real-time direct tracking of CPK is possible using MALDI-TOF MS.

MALDI-MS Protein Profiling of Chemoresistance in Extracellular Vesicles of Cancer Cells.

Cancer cells communicate with the whole organism via extracellular vesicles (EVs), which propagate molecular information in support of the malignant phenotype. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was employed for protein profiling of EVs derived from CCL-228 as the primary colon tumor, the lymph node metastasis CCL-227, and subclones resistant to 5, 25, and 125 µM 5-fluorouracil (FU). EVs were harvested from cell culture supernatant by ultracentrifugation to serve as a model for circulating cancer cell-derived biomarker carriers from body fluids (i.e., liquid biopsy). Protein mass spectra were recorded using standard MALDI matrixes (e.g., CHCA, sinapinic acid) in the range m/ z 2000-20000 on different MALDI-TOF-MS systems and subjected to multivariate data analysis . By using hierarchical clustering, PCA and PLS-DA, discriminatory protein patterns of the EVs from the different cell populations were obtained. Peaks in the range  m/ z 2000-6500 and m/ z 5500-15500 were found to be unique to EVs and the cells, respectively. This clearly demonstrates the differential expression of proteins in EVs as the result of an increasing chemoresistance of their parent cells. The sensitivity of the MALDI-MS based assay was in the low µg/mL (≈1.2-5 × 1010 particles/mL) range. Consequently, our MALDI-MS protein profiling approach shows the potential to serve as novel tool for minimally invasive cancer diagnostics and chemotherapy monitoring in the future, e.g., for early detection of therapy resistance without biopsy.

Challenges in bimetallic multilayer structure formation: Pt growth on Cu monolayers on Ru(0001).

In a joint experimental and theoretical study, we investigated the formation and morphology of PtCu/Ru(0001) bimetallic surfaces grown at room and higher temperatures under UHV conditions. We obtained the PtCu/Ru(0001) surfaces by deposition of Pt atoms on a previously created Cu/Ru(0001) structure which includes only one Cu monolayer. Bimetallic surfaces prepared at different Pt coverages are investigated using STM imaging, revealing the existence of reconstruction lines and Cu islands. Although primarily created Cu islands continue growing in size by increasing Pt coverage, a continuous formation of new Cu islands is observed. This leads to an atypical exponential increase of the island density as well as to an atypical behavior of the average number of atoms per island for low Pt coverages. Although coalescence of the islands is observed for high Pt coverages, the island density remains almost constant in that regime. In order to understand the trends observed in the experiments, we study the stability of these surfaces, atom adsorption, and adatom diffusion using periodic density functional theory calculations. On the basis of the experimental observations and the first-principles calculations, we suggest a model that includes exchange of Pt adatoms with Cu surface atoms, Pt and Cu adatom diffusion, and attractive (repulsive) interactions between Cu (Pt) adatoms with substitutional Pt surface atoms, which explains the main trends in island formation and growth observed in the experiment.

Vibrational anharmonicity of small gold and silver clusters using the VSCF method.

We study the vibrational spectra of small neutral gold (Au2-Au10) and silver (Ag2-Au5) clusters using the vibrational self-consistent field method (VSCF) in order to account for anharmonicity. We report harmonic, VSCF, and correlation-corrected VSCF calculations obtained using a vibrational configuration interaction approach (VSCF/VCI). Our implementation of the method is based on an efficient calculation of the potential energy surfaces (PES), using periodic density functional theory (DFT) with a plane-wave pseudopotential basis. In some cases, we use an efficient technique (fast-VSCF) assisted by the Voter-Chen potential in order to get an efficient reduction of the number of pair-couplings between modes. This allows us to efficiently reduce the computing time of 2D-PES without degrading the accuracy. We found that anharmonicity of the gold clusters is very small with maximum rms deviations of about 1 cm(-1), although for some particular modes anharmonicity reaches values slightly larger than 2 cm(-1). Silver clusters show slightly larger anharmonicity. In both cases, large differences between calculated and experimental vibrational frequencies (when available) stem more likely from the quality of the electronic structure method used than from vibrational anharmonicity. We show that noble gas embedding often affects the vibrational properties of these clusters more than anharmonicity, and discuss our results in the context of experimental studies.

The nature and role of the gold-krypton interactions in small neutral gold clusters.

We investigate the nature and role of krypton embedding in small neutral gold clusters. For some of these clusters, we observe a particular site-dependent character of the Kr binding that does not completely follow the criterion of binding at low-coordinated sites, widely accepted for interaction of a noble gas with closed-shell metal systems such as metal surfaces. We aim at understanding the effect of low dimensionality and open-shell electronic structure of the odd-numbered clusters on the noble gas-metal cluster interaction. First, we investigate the role of attractive and repulsive forces, and the frontier molecular orbitals. Second, we investigate the Au-Kr interaction in terms of reactivity and bonding character. We use a reactivity index derived from Fukui formalism, and criteria provided by the electron localization function (ELF), in order to classify the type of bonding. We carry out this study on the minimum energy structures of neutral gold clusters, as obtained using pseudo potential plane-wave density functional theory (DFT). A model is proposed that includes the effect of attractive electrostatic, van der Waals and repulsive forces, together with effects originating from orbital overlap. This satisfactorily explains minimum configurations of the noble gas-gold cluster systems, the site preference of the noble gas atoms, and changes in electronic properties.

Towards an understanding of the vibrational spectrum of the neutral Au7 cluster.

We present a detailed theoretical study of the vibrational spectrum of the neutral Au(7) cluster, aimed at understanding its reported experimental spectrum [P. Gruene et al., Science, 2008, 321, 674]. We study the effect of vibrational anharmonicity, polymorphism, noble gas embedding, and the use of various electronic-structure methods. We use a vibrational configuration-interaction approach (VCI) with a vibrational self-consistent field (VSCF) basis, in order to study the effect of vibrational anharmonicity for the density functional theory (DFT) global minimum energy structure. Our implementation of the VSCF/VCI method is based on the direct calculation of the potential energy surface (PES) using pseudo potential plane-wave DFT. An efficient reduction of the number of mode-mode couplings between vibrational modes (fast-VSCF/VCI) is used to speed up calculations. We show that the rather small anharmonicity does not account for the difference between harmonic and experimental frequencies and consequently for the large global scaling factor, reported by the authors of the experiment. Instead, the use of different electronic structure methods allows for a significant reduction of the scaling factor. We also show that krypton embedding does not significantly change the vibrational frequencies of the Au(7) cluster.

Structure and local reactivity of PdAg/Pd(111) surface alloys.

Motivated by a recent detailed experimental study [Y. Ma et al., Phys. Chem. Chem. Phys., 2011, 13, 10741], the structure and local reactivity of PdAg/Pd(111) surface alloys were studied using periodic density functional theory calculations. As a probe of the local reactivity, CO adsorption energies were evaluated as a function of concentration and configuration of silver and palladium atoms and the CO coverage and related to the underlying electronic structure. According to the calculations, the formation of PdAg/Pd(111) surface alloys is found to be energetically stable. We find in accordance with the experiment that the adsorption on the surface alloy is dominated by ensemble effects, whereas electronic ligand and strain effects effectively cancel each other out. Furthermore, we elucidate the mechanism of CO adsorption on small Pd ensembles upon higher exposure.

Ultrasensitive and rapid identification of ESRI developer- and piperacillin/tazobactam-resistant Escherichia coli by the MALDIpiptaz test.

BACKGROUND: The excessive use of piperacillin/tazobactam (P/T) has promoted the emergence of P/T-resistant Enterobacterales. We reported that in Escherichia coli, P/T contributes to the development of extended-spectrum resistance to ß-lactam/ß-lactamase inhibitor (BL/BLI) (ESRI) in isolates that are P/T susceptible but have low-level resistance to BL/BLI. Currently, the detection of P/T resistance relying on conventional methods is time-consuming. To overcome this issue, we developed a cost-effective test based on MALDI-MS technology, called MALDIpiptaz, which aims to detect P/T resistance and ESRI developers in E. coli. METHODS: We used automated Clover MS Data Analysis software to analyse the protein profile spectra obtained by MALDI-MS from a collection of 248 E. coli isolates (91 P/T-resistant, 81 ESRI developers and 76 P/T-susceptible). This software allowed to preprocess all the spectra to build different peak matrices that were analysed by machine learning algorithms. RESULTS: We demonstrated that MALDIpiptaz can efficiently and rapidly (15 min) discriminate between P/T-resistant, ESRI developer and P/T-susceptible isolates and allowed the correct classification between ESRI developers from their isogenic resistance to P/T. CONCLUSION: The combination of excellent performance and cost-effectiveness are all desirable attributes, allowing the MALDIpiptaz test to be a useful tool for the rapid determination of P/T resistance in clinically relevant E. coli isolates.

Detection of azole resistance in Aspergillus fumigatus complex isolates using MALDI-TOF mass spectrometry.

OBJECTIVES: The main goal of this study was to accurately detect azole resistance in species of the Aspergillus fumigatus complex by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). METHODS: Identification of isolates (n = 868) was done with MALDI-TOF MS using both commercial and in-house libraries. To determine azole susceptibility, the EUCAST E.Def. 9.3.2 method was applied as the reference standard. Identification of resistant isolates was confirmed by DNA sequence analysis. Protein spectra obtained by MALDI-TOF MS were analysed to differentiate species within the A. fumigatus complex and to detect azole-resistant A. fumigatus sensu stricto isolates. RESULTS: Correct discrimination of A. fumigatus sensu stricto from cryptic species was accomplished in 100% of the cases applying principal component analysis (PCA) to protein spectra generated by MALDI-TOF MS. Furthermore, a specific peak (4586 m/z) was found to be present only in cryptic species. The application of partial least squares (PLS) discriminant analysis allowed 98.43% (±0.038) discrimination between susceptible and azole-resistant A. fumigatus sensu stricto isolates. Finally, based on PLS and SVM, A. fumigatus sensu stricto isolates with different cyp51A gene mutations were correctly clustered in 91.5% of the cases. CONCLUSIONS: MALDI-TOF MS combined with peak analysis is a novel tool that allows the differentiation of A. fumigatus sensu stricto from other species within the A. fumigatus complex, as well as the detection of azole-resistant A. fumigatus sensu stricto. Although further studies are still needed, the results reported here show the great potential of MALDI-TOF and machine learning for the rapid detection of azole-resistant Aspergillus fumigatus isolates from clinical origins.

Rapid and Reproducible MALDI-TOF-Based Method for the Detection of Vancomycin-Resistant Enterococcus faecium Using Classifying Algorithms.

Vancomycin-resistant Enterococcus faecium represents a health threat due to its ability to spread and cause outbreaks. MALDI-TOF MS has demonstrated its usefulness for E. faecium identification, but its implementation for antimicrobial resistance detection is still under evaluation. This study assesses the repeatability of MALDI-TOF MS for peak analysis and its performance in the discrimination of vancomycin-susceptible (VSE) from vancomycin-resistant isolates (VRE). The study was carried out on protein spectra from 178 E. faecium unique clinical isolates-92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Technical and biological repeatability were assayed. Unsupervised (principal component analysis, (PCA)) and supervised algorithms (support vector machine (SVM), random forest (RF) and partial least squares-discriminant analysis (PLS-DA)) were applied. The repeatability assay was performed with 18 peaks common to VSE and VRE with intensities above 1.0% of the maximum peak intensity. It showed lower variability for normalized data and for the peaks within the 3000-9000 m/z range. It was found that 80.9%, 79.2% and 77.5% VSE vs. VRE discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct internal differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.6% cases. The implementation of MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.

Multicenter Performance Evaluation of MALDI-TOF MS for Rapid Detection of Carbapenemase Activity in Enterobacterales: The Future of Networking Data Analysis With Online Software.

In this study, we evaluate the performance of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid detection of carbapenemase activity in Enterobacterales in clinical microbiology laboratories during a multicenter networking validation study. The study was divided into three different stages: "software design," "intercenter evaluation," and "clinical validation." First, a standardized procedure with an online software for data analysis was designed. Carbapenem resistance was detected by measuring imipenem hydrolysis and the results were automatically interpreted using the Clover MS data analysis software (Clover BioSoft, Spain). Second, a series of 74 genotypically characterized Enterobacterales (46 carbapenemase-producers and 28 non carbapenemase-producers) were analyzed in 8 international centers to ensure the reproducibility of the method. Finally, the methodology was evaluated independently in all centers during a 2-month period and results were compared with the reference standard for carbapenemase detection used in each center. The overall agreement rate relative to the reference method for carbapenemase resistance detection in clinical samples was 92.5%. The sensitivity was 93.9% and the specificity, 100%. Results were obtained within 60 min and accuracy ranged from 83.3 to 100% among the different centers. Further, our results demonstrate that MALDI-TOF MS is an outstanding tool for rapid detection of carbapenemase activity in Enterobacterales in clinical microbiology laboratories. The use of a simple in-house procedure with online software allows routine screening of carbapenemases in diagnostics, thereby facilitating early and appropriate antimicrobial therapy.

Implementation of MALDI-TOF Mass Spectrometry and Peak Analysis: Application to the Discrimination of Cryptococcus neoformans Species Complex and Their Interspecies Hybrids.

Matrix-assisted laser desorption-ionization/time of flight mass spectrometry (MALDI-TOF MS) has been widely implemented for the rapid identification of microorganisms. Although most bacteria, yeasts and filamentous fungi can be accurately identified with this method, some closely related species still represent a challenge for MALDI-TOF MS. In this study, two MALDI-TOF-based approaches were applied for discrimination at the species-level of isolates belonging to the Cryptococcus neoformans complex, previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region: (i) an expanded database was built with 26 isolates from the main Cryptococcus species found in our setting (C. neoformans, C. deneoformans and AFLP3 interspecies hybrids) and (ii) peak analysis and data modeling were applied to the protein spectra of the analyzed Cryptococcus isolates. The implementation of the in-house database did not allow for the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of supervised classifiers (partial least squares-discriminant analysis and support vector machine) in a two-step analysis allowed for the 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. In addition, PCA analysis prior to support vector machine (SVM) provided 98.45% correct discrimination of the three analyzed species in a one-step analysis. This novel method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.

Image restoration using space-variant Gaussian scale mixtures in overcomplete pyramids.

In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image restoration. Its strength relies on providing a simple and, yet, very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation, and spatial location. Here, we present an enhancement of the model by introducing a coarser adaptation level, where a larger neighborhood is used to estimate the local signal covariance within every subband. We formulate our model as a BLS estimator using space-variant GSM. The model can be also applied to image deconvolution, by first performing a global blur compensation, and then doing local adaptive denoising. We demonstrate through simulations that the proposed method, besides being model-based and noniterative, it is also robust and efficient. Its performance, measured visually and in L2-norm terms, is significantly higher than the original BLS-GSM method, both for denoising and deconvolution.

Characterization of Yeasts and Filamentous Fungi using MALDI Lipid Phenotyping.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) becomes the method of choice for the rapid identification of microorganisms (i.e. protein biotyping). Although bacterial identification is already quite advanced, biotyping of other microbes including yeasts and fungi are still under development. In this context, lipids (e.g. membrane phospholipids) represent a very important group of molecules with essential functions for cell survival and adaptation to specific environments and habitats of the microorganisms. Therefore, lipids show the potential to serve as additional molecular parameters to be used for biotyping purposes. In this paper we present a molecular characterisation of yeasts and filamentous fungi based on the analysis of lipid composition by MALDI-MS (i.e. MALDI lipid phenotyping). Using a combination of Principal Component Analysis (PCA) and Hierarchical Clustering we could demonstrate that this approach allowed a classification and differentiation of several groups of yeasts (e.g. Saccharomyces) and filamentous fungi (e.g. Aspergillus, Penicillium, Trichoderma) at the species/strain level. By analysing the MALDI lipid profiles we were able to differentiate 26 closely related yeast strains, for which discrimination via genotypic methods like AFLP in this case are relatively more elaborate. Moreover, employing statistical analysis we could identify those lipid parameters (e.g. PCs and LPCs), which were responsible for the differentiation of the strains, thus providing insights into the molecular basis of our results. In summary, MALDI lipid phenotyping represents a suitable method for fungal characterization and shows the potential to be used as companion tool to genotyping and/or protein biotyping for the characterization and identification of yeasts and fungi in diverse areas (e.g. environmental, pharmaceutical, clinical applications, etc.).