POC device for rapid oral pH determination based on a smartphone platform.

Salivary pH serves as a valuable and useful diagnostic marker for periodontal disease, as it not only plays a critical role in disease prevention but also in its development. Typically, saliva sampling is collected by draining and spitting it into collection tubes or using swabs. In this study, we have developed a Point-of-Care (POC) device for in situ determination of oral pH without the need for complex instruments, relying solely on a smartphone as the detection device. Our system utilizes a non-toxic vegetable colourimetric indicator, immobilized on a chitosan membrane located on a disposable stick, enabling direct sampling within the buccal cavity. An ad hoc designed 3D-printed attachment is used to ensure accurate positioning and alignment of the stick, as well as isolation from external lighting conditions. A custom-developed smartphone application captures and automatically processes the image of the sensing membrane, providing the salivary pH results. After optimizing the cocktail composition, the developed sensors demonstrated the capacity to determine pH within a range of 5.4 to 8.1 with a remarkable precision of 0.6%, achieving a very short analysis time of just 1 min. A stability study conducted on the sensing membranes revealed a lifetime of 50 days. To validate the performance of our analytical device, we compared its results against those obtained from a calibrated pH-meter, using a group of individuals. The device exhibited an average error of 2.4% when compared with the pH-meter results, confirming its reliability and accuracy.

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.

Discrimination between hypervirulent and non-hypervirulent ribotypes of Clostridioides difficile by MALDI-TOF mass spectrometry and machine learning.

Hypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/RT176, RT023, RT045/078/126/127). MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.

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.

A Rare Case of Non-IUD-Related Chronic Endometritis caused by Actinomyces Bacteria in a Postmenopausal Woman: A Case Report.

Pelvic actinomycosis is a rare condition, usually associated with intrauterine device (IUD) use. Its clinical presentation may vary from being asymptomatic to the mimicking of pelvic malignancy; it has been described as one of the most misdiagnosed diseases. A 78-year-old woman without a history of IUD use, arrived at our clinic complaining of chronic and intermittent postmenopausal bleeding associated with lower pelvic pain. An endometrial curettage was performed, and endometritis (caused by Actinomyces) identified. Treatment with intravenous piperacillin and tazobactam for 7 days, followed by 6 weeks of oral ampicillin, daily, decreased the bleeding and the pain. Although rare, it is important to consider Actinomyces-related endometritis as a differential diagnosis in cases of elderly woman with postmenopausal bleeding and without a history of IUD use.

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.

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.

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.

Comparison of sublingual therapeutic vaccine with antibiotics for the prophylaxis of recurrent urinary tract infections.

OBJECTIVE: To compare the clinical impact of a prophylactic treatment with sublingual immunostimulation in the prevention of recurrent urinary tract infections (rUTIs) with the use of antibiotics. MATERIAL AND METHODS: Retrospective cohort study evaluating the medical records of 669 women with rUTIs; 339 had a 6-month prophylaxis with antibiotics and 360 a 3-month prophylaxis with a sublingual bacterial preparation (MV 140-Uromune®). The time frame after the prophylaxis-period until the appearance of a new infection (assessed by uroculture) was scored and followed during 1 year. The absolute risk reduction (ARR) and number needed to treat (NNT) were also calculated. RESULTS: All patients treated with antibiotics experienced a new UTI during the scoring period of 12 months, being 19 days the median number of days free of UTIs (range 5-300). In the group treated with the bacterial preparation, 35 (9.7%) patients experienced an UTI in the same period. Kaplan-Meier curves comparing the accumulated survival (disease-free time) between both groups were significant different (P < 0.0001). The absolute risk reduction (ARR) was 90.28% (87.18-93.38) and the number needed to treat (NNT) 1.1 (1.1-1.1). CONCLUSIONS: These results suggest that the treatment with this bacterial preparation significantly reduces the incidence of rUTIs, arising as an effective strategy to reduce the frequency of rUTIs. It reduces antibiotic consumption, matching the current recommendations due to the raise of antimicrobial resistance. Randomized, double-blind and placebo-controlled, clinical trials are needed to establish, more accurately, the clinical impact of this bacterial preparation in patients with rUTIs.