MALDI-TOF mass spectrometry technology for detecting biomarkers of antimicrobial resistance: current achievements and future perspectives

Nanomaterials for bacterial enrichment and detection in healthcare

Bacterial infections in the blood (sepsis) have been recognized as a leading cause of mortality in the clinical field due to limitations in the detection of bacteria at low concentration and their resistance to antibiotics by excessive misuse. Some of the common symptoms are fever, chills, rapid heartbeat, difficulty breathing, confusion, and changes in mental status with occasionally pale, clammy, and mottled skin. Early diagnosis and identification are the keys to a successful treatment for sepsis patients. Researchers have developed nanoparticles to enrich bacterial populations followed by detection and applied them to conventional methods such as phenotypic and molecular diagnostics to enhance different detectors' responses toward pathogens. This short review systematically overviews steps that are followed in clinical labs for bacterial detection, identification, and their drawbacks. In this context, we discuss the role that nanoparticles can play in overcoming the limits of traditional microbiology methods in terms of turnaround times (TATs) and accuracy. We believe that this short review will provide up-to-date information about the applications of nanoparticles in the enrichment, separation, and identification of bacterial infection in the clinical field and, therefore, a way of rapid treatment.

Nanoparticle-Supported, Point-of-Care Detection of Shiga Toxin-Producing E. coli Infection from Food and Human Specimens

Shiga toxin-producing Escherichia coli (STEC) are major foodborne pathogens responsible for severe infections, including the deadly hemolytic uremic syndrome (HUS). However, the current diagnostic methods lack the sensitivity and speed required for effective clinical and food safety applications. Early detection of Shiga toxin 2 (Stx2), a primary virulence factor of STEC, could potentially offer critical benefits for timely intervention. In this work, gold n anoparticles (AuNPs) are functionalized with a pair of high-affinity, d esigned a nkyrin repeat p roteins (DARPins) targeting the A and B subunits of Stx2, and used as multifunctional signal transductors for r apid and e lectronic d etection (RED). This DARPin-RED platform leverages active centrifugal forces and vortex agitation for signal enhancement within a short turnaround time (<30 minutes), achieving highly sensitive (attomolar to femtomolar) detection of Stx2 spiked in food matrices, such as milk, lettuce extract, and ground beef extract, as well as biological fluids, including whole blood, and serum. Additionally, DARPin-RED is capable of detecting multiple Stx2 subtypes without serious background interference, and successful in both differentiating high-toxin-producing E. coli strain (RM5856) from low toxin producer (RM9872) (p < 0.001) and analyzing different bacterial inoculation stages (p = 0.011) from STEC culture within 8 hours post-inoculation. The ability of DARPin-RED to detect Stx2 from food and human specimens at a high sensitivity and specificity using a point-of-care (POC) readout circuit presents a significant advancement for mitigating foodborne outbreaks and effective management of HUS progression.

Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

For the last decade, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been the reference method for species identification in clinical microbiology. Hampered by a historical lack of open data, machine learning research towards models specifically adapted to MALDI-TOF MS remains in its infancy. Given the growing complexity of available datasets (such as large-scale antimicrobial resistance prediction), a need for models that (1) are specifically designed for MALDI-TOF MS data, and (2) have high representational capacity, presents itself. Here, we introduce Maldi Transformer, an adaptation of the state-of-the-art transformer architecture to the MALDI-TOF mass spectral domain. We propose the first self-supervised pre-training technique specifically designed for mass spectra. The technique is based on shuffling peaks across spectra, and pre-training the transformer as a peak discriminator. Extensive benchmarks confirm the efficacy of this novel design. The final result is a model exhibiting state-of-the-art (or competitive) performance on downstream prediction tasks. In addition, we show that Maldi Transformer's identification of noisy spectra may be leveraged towards higher predictive performance. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/maldi-nn.

Development and validation of a nomogram to predict linezolid-induced thrombocytopenia in hospitalized adults

BACKGROUND

Linezolid (LZD) is used to treat infectious diseases caused by Gram-positive bacteria, but thrombocytopenia is one of the main adverse reactions to LZD administration. Early prediction of linezolid-induced thrombocytopenia (LI-TP) is of great importance to improve the clinical outcomes and prognoses. The aim of this study was to develop and validate a prediction model for LI-TP.

METHODS

A retrospective cohort of hospitalized adults receiving LZD therapy (January 2014-June 2022) was analyzed. Independent risk factors for LI-TP were identified via logistic regression in the training set (n = 757). A nomogram model for LI-TP were developed based on independent risk factors, and verified in validation set (n = 123).

RESULTS

The incidence of LI-TP was 13.5% (102/757). A logistic regression model was developed based on the seven independent risk factors, including age (≥ 60 y), duration of LZD therapy (> 11 d), bPLT (< 308 × 109/L), ALT (> 100 IU/L), Ccr (< 67.5 mL/min), and concomitant use with VPA or Tac (p < 0.01) and transformed into a quantifiable nomogram. The nomogram demonstrated strong discrimination with AUCs of 0.760 in training (95% CI: 0.709-0.812, P < 0.001) and 0.767 in validation (95% CI: 0.635-0.899, P < 0.001). The calibration curves and Hosmer-Lemeshow tests confirmed good reliability and specificity of the nomogram model.

CONCLUSION

This nomogram provides a practical tool for stratifying LI-TP risk, which provide an important reference for enabling timely clinical interventions to enhance LZD safety.

Detection of antimicrobial resistance via state-of-the-art technologies versus conventional methods

Antimicrobial resistance (AMR) is recognized as one of the foremost global health challenges, complicating the treatment of infectious diseases and contributing to increased morbidity and mortality rates. Traditionally, microbiological culture and susceptibility testing methods, such as disk diffusion and minimum inhibitory concentration (MIC) assays, have been employed to identify AMR bacteria. However, these conventional techniques are often labor intensive and time consuming and lack the requisite sensitivity for the early detection of resistance. Recent advancements in molecular and genomic technologies-such as next-generation sequencing (NGS), matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), lateral flow immunoassays (LFIAs), PCR-based diagnostic methods, and CRISPR-based diagnostics-have revolutionized the diagnosis of AMR. These innovative approaches provide increased sensitivity, reduced turnaround times, and the ability to identify genetic resistance mechanisms. This review seeks to examine the advantages and disadvantages of both emerging technologies and traditional methods for detecting AMR, emphasizing the potential benefits and limitations inherent to each. By understanding the strengths and limitations of these technologies, stakeholders, including researchers, healthcare professionals, regulatory agencies, health authorities, financial managers, and patients, can make informed decisions aimed at preventing the emergence and dissemination of antibiotic-resistant strains, thereby ultimately increasing patient safety.

Resilient sustainable current and emerging technologies for foodborne pathogen detection

Foodborne pathogens such as Salmonella, Escherichia coli and Listeria pose significant risks to human health. The World Health Organization estimates that 2.2 million deaths per year are directly caused by foodborne and waterborne bacterial diseases worldwide. Accordingly, detecting pathogens in food is essential to ensure that our food is safe. This review explores the critical role of novel technologies in enhancing food safety practices whilst delving into adopting and integrating innovative, resilient and sustainable approaches in the food supply chain. Further, applying novel, emerging advanced analytical techniques such as Raman spectroscopy and nanotechnology based biosensors in food contamination detection is discussed. These advanced technologies show the promise of real-time monitoring, traceability, and predictive analytics to identify and mitigate potential hazards before they reach consumers. They can provide rapid and accurate results and ensure the integrity of food products. Furthermore, the herein-highlighted synergistic integration of these technologies offers a promising path toward a safer and more transparent food system, thereby addressing the challenges of today's globalised food market and laying the platform for developing multimodal technologies for affordable, sensitive and rapid pathogen detection along the different stages of the food chain, from "farm to fork".

Cockroaches as Reservoirs, Vectors, and Potential Sentinels of Multidrug-Resistant Bacteria in Ugandan Communities: A Retrospective Analysis

Background: Cockroaches could play a role in the transmission dynamics of antimicrobial-resistant bacteria (ARB) at variable interfaces in Ugandan communities, acting as both reservoirs and vectors. This study investigated the burden and diversity of ARB carried by cockroaches in human settlements in Uganda, so as to understand their role in the spread of these pathogens and their potential as sentinels in antimicrobial resistance (AMR) surveillance programs. Materials and Methods: A retrospective analysis was conducted on two unpublished studies by Makerere University students. Study one and study two sampled 58 and 110 cockroaches, respectively, from secondary schools in Kampala. Cockroach species identification was determined based on physical characteristics. Bacterial isolation and characterization were performed through microbiological analyses including standard culture methods, biochemical tests, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), disc diffusion method, and polymerase chain reaction (PCR). Results: Majority of the cockroaches (over 80%) were Periplaneta americana. Multidrug resistance (MDR) was prevalent among the isolates, with over 30% of the isolates being resistant to three or more antibiotic classes. Specifically, MDR (over 90%) was rampant in the extended spectrum beta-lactamase (ESBL)- or AmpC-producing Escherichia coli and enterococci isolates. Critical World Health Organization (WHO) priority pathogens, such as ESBL-/AmpC-producing Enterobacteriaceae, and carbapenem-resistant E. coli, were also identified. The most abundant resistance determinants (tetracycline and sulphonamide) were tetA, sul1, and sul2 for E. coli, and tetM and tetL for enterococci. Conclusion: The findings accentuate the potential role of cockroaches: (1) in transmitting multidrug-resistant bacteria at the human-animal-environment interface and (2) as sentinels in the surveillance of community-generated AMR.

MALDI-TOF MS profiling to predict resistance or biofilm production in gram-positive ESKAPE pathogens from healthcare-associated infections

Antimicrobial resistance and biofilm production in healthcare-associated infections is a health issue worldwide. This study aimed to identify potential biomarker peaks for resistance or biofilm production in ESKAPE pathogens using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Antimicrobial susceptibility and biofilm production were assessed on selected isolates. Biomarker peaks were identified using MALDI Biotyper and ClinProTools software. Among resistant strains, 90.0 % were carbapenem-resistant Acinetobacter baumannii (CRAB), 39.0 % were methicillin-resistant Staphylococcus aureus (MRSA), 17.98 % were multidrug-resistant (MDR) Pseudomonas aeruginosa, 21.6 % were vancomycinresistant Enterococcus (VRE), and 2.55 % were carbapenem-resistant Enterobacterales (CRE). Biofilm production was 40.0 % in VRE and 45.8 % in MRSA. Although no potential biomarker peaks for biofilm production were detected, several potential biomarker peaks for drug resistance in VRE (n=5), MRSA (n=4), and MDR P. aeruginosa (n=4) were detected, suggesting avenues for the development of rapid diagnostic tools.

Advances in Helicobacter pylori Antimicrobial Resistance Detection: From Culture-Based to Multi-Omics-Based Technologies

Helicobacter pylori (H. pylori), a proven carcinogenic microbe, necessitates antimicrobial treatment once infected. However, H. pylori worldwide currently faces serious antibiotic resistance (AMR), requiring infected patients to undergo antibiotic susceptibility testing (AST) to guide therapy. Currently, the recommended ASTs for H. pylori are culture-based methods, which are time-consuming, complicated, and expensive, impeding their widespread application. With in-depth researches on the AMR mechanisms of H. pylori, specific gene mutations and novel proteins have been confirmed as the cause of AMR and can serve as targets of ASTs. Accordingly, molecular biology detection has been developed and tremendously shortened the time and reduced difficulty of AST. However, these assays still struggle to meet the enormous testing demand and need for even faster, simpler, and more accurate methods. In recent years, researchers have developed various new platforms based on biosensors, transcriptomics, proteomics, and single-cell analysis. This review introduces the AMR mechanisms of H. pylori and summarizes the current ASTs from the working principles to application characteristics. Additionally, we draw attention to the potentially applicable techniques for AST of H. pylori from DNA, RNA, protein, and cell perspectives. By systematically recapitulating the past, present, and future of AST for H. pylori, this review provides valuable insights for developing novel assays.

Recent Advances in Antimicrobial Resistance: Insights from Escherichia coli as a Model Organism

Antimicrobial resistance (AMR) represents a critical global health threat, and a thorough understanding of resistance mechanisms in Escherichia coli is needed to guide effective treatment interventions. This review explores recent advances for investigating AMR in E. coli, including machine learning for resistance pattern analysis, laboratory evolution to generate resistant mutants, mutant library construction, and genome sequencing for in-depth characterization. Key resistance mechanisms are discussed, including drug inactivation, target modification, altered transport, and metabolic adaptation. Additionally, we highlight strategies to mitigate the spread of AMR, such as dynamic resistance monitoring, innovative therapies like phage therapy and CRISPR-Cas technology, and tighter regulation of antibiotic use in animal production systems. This review provides actionable insights into E. coli resistance mechanisms and identifies promising directions for future antibiotic development and AMR management.

Early detection of bacterial pneumonia by characteristic induced odor signatures

INTRODUCTION

The ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.

METHODS

A real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.

RESULTS

Characteristic changes in the VOCs emitted from the mice were detected as early as 4-6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.

CONCLUSION

the non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Recent developments in antibiotic resistance: an increasing threat to public health

Antibiotic resistance (ABR) is a major global health threat that puts decades of medical progress at risk. Bacteria develop resistance through various means, including modifying their targets, deactivating drugs, and utilizing efflux pump systems. The main driving forces behind ABR are excessive antibiotic use in healthcare and agriculture, environmental contamination, and gaps in the drug development process. The use of advanced detection technologies, such as next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR)-based diagnostics, and metagenomics, has greatly improved the identification of resistant pathogens. The consequences of ABR on public health are significant, increased mortality rates, the endangerment of modern medical procedures, and resulting in higher healthcare expenses. It has been expected that ABR could potentially drive up to 24 million individuals into extreme poverty by 2030. Mitigation strategies focus on antibiotic stewardship, regulatory measures, research incentives, and raising public awareness. Furthermore, future research directions involve exploring the potential of CRISPR-Cas9 (CRISPR-associated protein 9), nanotechnology, and big data analytics as new antibiotic solutions. This review explores antibiotic resistance, including mechanisms, recent trends, drivers, and technological advancements in detection. It also evaluates the implications for public health and presents strategies for mitigating resistance. The review emphasizes the significance of future directions and research needs, stressing the necessity for sustained and collaborative efforts to tackle this issue.

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavors to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged at https://github.com/gdewael/maldi-nn.

Diagnosis and treatment challenges in a rare Clostridium infection: A case report

The Clostridium genus includes >180 species of Gram-positive, anaerobic, sporulating bacteria. Under certain conditions, these can cause a wide range of invasive infections in humans. Clostridium paraputrificum occurs in the commensal intestinal flora and related bacteremia typically occurs secondary to an injury to the intestinal mucosa and in the presence of predisposing conditions, such as gastrointestinal disorders, malignancies, diabetes, HIV infection or neutropenia. The current study presents the case of a 70-year-old male patient, a rural resident living in poverty, with a history of alchohol consumption and cardiovascular pathology. Several initial and subsequent diagnoses were ruled out by successive investigations (e.g., stroke, meningitis, localized tetanus). Blood cultures were eventually found positive for Clostridium paraputrificum and the patient developed septic shock despite treatment with metronidazole and penicillin G. Once switched to carbapenem, the patient progressed favorably, suggesting that carbapenem could work as a first-line antibiotic treatment for Clostridium paraputrificum infections.

Towards integrated cross-sectoral surveillance of pathogens and antimicrobial resistance: Needs, approaches, and considerations for linking surveillance to action

Pathogenic and antimicrobial-resistant (AMR) microorganisms are continually transmitted between human, animal, and environmental reservoirs, contributing to the high burden of infectious disease and driving the growing global AMR crisis. The sheer diversity of pathogens, AMR mechanisms, and transmission pathways connecting these reservoirs create the need for comprehensive cross-sectoral surveillance to effectively monitor risks. Current approaches are often siloed by discipline and sector, focusing independently on parts of the whole. Here we advocate that integrated surveillance approaches, developed through transdisciplinary cross-sector collaboration, are key to addressing the dual crises of infectious diseases and AMR. We first review the areas of need, challenges, and benefits of cross-sectoral surveillance, then summarise and evaluate the major detection methods already available to achieve this (culture, quantitative PCR, and metagenomic sequencing). Finally, we outline how cross-sectoral surveillance initiatives can be fostered at multiple scales of action, and present key considerations for implementation and the development of effective systems to manage and integrate this information for the benefit of multiple sectors. While methods and technologies are increasingly available and affordable for comprehensive pathogen and AMR surveillance across different reservoirs, it is imperative that systems are strengthened to effectively manage and integrate this information.

Dynamic Changes in Soluble Triggering Receptor Expressed on Myeloid Cells-1 in Sepsis with Respect to Antibiotic Susceptibility

Purpose

Proper antibiotic administration is crucial for sepsis management. Given the escalating incidence of antimicrobial resistance, there is a pressing need for indicators of antimicrobial susceptibility with short turnaround times. This study aimed to investigate the potential of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) as an early biomarker for in vivo antibiotic susceptibility in patients with sepsis.

Patients and Methods

We conducted a retrospective analysis of plasma samples from patients enrolled in a pre-established study designed to investigate prognostic biomarkers in patients with sepsis or septic shock. Baseline and 6 h sTREM-1 levels were examined using enzyme-linked immunosorbent assays. The primary outcome of the study was the comparison of percentage changes in sTREM-1 levels at the 6 h relative to baseline with respect to antibiotic susceptibility.

Results

Of the 596 patients enrolled in the pre-established study, 29 with a median age of 75.8 and a 28-day mortality rate of 17.2% were included in the present analysis. Among these patients, 24 were classified into the susceptible group, whereas the remaining five were classified into the resistant group. The trend in plasma sTREM-1 levels differed with respect to antibiotic susceptibility. Moreover, percentage change in sTREM-1 levels at the 6 h relative to baseline was significantly higher in the resistant group (P = 0.028).

Conclusion

The trend in plasma sTREM-1 levels in patients with sepsis differed with respect to antibiotic susceptibility, with a higher percentage change in patients treated with inappropriate antibiotics. These findings indicate the potential utility of sTREM-1 as an early biomarker of antibiotic susceptibility.

Deciphering feedback regulation of prostaglandin F2α in blood stasis syndrome using nitrogen-doped porous transition metal carbides

Blood stasis syndrome (BSS) has persistent health risks; however, its pathogenesis remains elusive. This obscurity may result in missed opportunities for early intervention, increased susceptibility to chronic diseases, and reduced accuracy and efficacy of treatments. Metabolomics, employing the matrix-assisted laser desorption/ionization (MALDI) strategy, presents distinct advantages in biomarker discovery and unraveling molecular mechanisms. Nonetheless, the challenge is to develop efficient matrices for high-sensitivity and high-throughput analysis of diverse potential biomarkers in complex biosamples. This work utilized nitrogen-doped porous transition metal carbides and nitrides (NP-MXene) as a MALDI matrix to delve into the molecular mechanisms underlying BSS pathogenesis. Structural optimization yielded heightened peak sensitivity (by 1.49-fold) and increased peak numbers (by 1.16-fold) in clinical biosamples. Validation with animal models and clinical serum biosamples revealed significant differences in metabolic fingerprints between BSS and control groups, achieving an overall diagnostic efficacy of 0.905 (95% CI, 0.76-0.979). Prostaglandin F2α was identified as a potential biomarker (diagnostics efficiency of 0.711, specificity = 0.7, sensitivity = 0.6), and pathway enrichment analysis disclosed disruptions in arachidonic acid metabolism in BSS. This innovative approach not only advances comprehension of BSS pathogenesis, but also provides valuable insights for personalized treatment and diagnostic precision.

Short turnaround time of seven to nine hours from sample collection until informed decision for sepsis treatment using nanopore sequencing

Bloodstream infections (BSIs) and sepsis are major health problems, annually claiming millions of lives. Traditional blood culture techniques, employed to identify sepsis-causing pathogens and assess antibiotic susceptibility, usually take 2-4 days. Early and accurate antibiotic prescription is vital in sepsis to mitigate mortality and antibiotic resistance. This study aimed to reduce the wait time for sepsis diagnosis by employing shorter blood culture incubation times for BD BACTEC™ bottles using standard laboratory incubators, followed by real-time nanopore sequencing and data analysis. The method was tested on nine blood samples spiked with clinical isolates from the six most prevalent sepsis-causing pathogens. The results showed that pathogen identification was possible at as low as 102-104 CFU/mL, achieved after just 2 h of incubation and within 40 min of nanopore sequencing. Moreover, all the antimicrobial resistance genes were identified at 103-107 CFU/mL, achieved after incubation for 5 h and only 10 min to 3 h of sequencing. Therefore, the total turnaround time from sample collection to the information required for an informed decision on the right antibiotic treatment was between 7 and 9 h. These results hold significant promise for better clinical management of sepsis compared with current culture-based methods.

The Superior Effect of Radiofrequency With Targeted Ultrasound for Facial Rejuvenation by Inducing Hyaluronic Acid Synthesis: A Pilot Preclinical Study

Background

The level of dermal hyaluronic acid (HA) can be depleted by 75% at age 70. HA provides dermal hydration, volume, and thickness, making it a major component of the extracellular matrix. Restoration of dermal and epidermal HA can be achieved by combining radiofrequency (RF) energy and targeted ultrasound (TUS). The monopolar RF generates heat, with the TUS stimulating HA production. The heat induces a regenerative response in the skin, increasing the fibroblast activity and producing various extracellular matrix compounds, including HA.

Objectives

To investigate the effect of the simultaneous application of RF + TUS or RF + US on the stimulation of HA production.

Methods

Twelve animals underwent 4 treatments. Six were treated with transcutaneous RF + TUS and 6 with the combination RF + US. The opposite untreated side served as a control. Punch biopsies of the skin were taken at baseline, immediately posttreatment, 1 month, and 2 months posttreatment. The tissue was evaluated with real-time quantitative polymerase chain reaction (RT-qPCR), matrix-assisted laser desorption (MALDI) and time of flight (TOF), and confocal microscopy.

Results

The RT-qPCR focused on assessing the production of has1 and has2, enzymes responsible for HA synthesis. RT-qPCR results of the RF + TUS group revealed a +98% and +45% increase in hyaluronic synthetase (HAS) 1 and HAS2 production after the treatments, respectively. The MALDI-TOF revealed a +224% increase in measured HA 2 months after the treatments. The changes were also visible in the confocal microscopy. The control group showed no significant (P > .05) results in either of the evaluation methods.

Conclusions

Concurrent application of RF and TUS significantly enhances the natural regenerative processes in skin tissue.

Level of Evidence 5

In situ Tracking of Exoenzyme Activity Using Droplet Luminescence Concentrators for Ratiometric Detection of Bacteria

We demonstrate a novel, rapid, and cost-effective biosensing paradigm that is based on an in situ visualization of bacterial exoenzyme activity using biphasic Janus emulsion droplets. Sensitization of the droplets toward dominant extracellular enzymes of bacterial pathogens is realized via selective functionalization of one hemisphere of Janus droplets with enzyme-cleavable surfactants. Surfactant cleavage results in an interfacial tension increase at the respective droplet interface, which readily transduces into a microscopically detectable change of the internal droplet morphologies. A macroscopic fluorescence read-out of such morphological transitions is obtained via ratiometrically recording the angle-dependent anisotropic emission signatures of perylene-containing droplets from two different angles. The optical read-out method facilitates detection of marginal morphological responses of polydisperse droplet samples that can be easily produced in any environment. The performance of Janus droplets as powerful optical transducers and signal amplifiers is highlighted by rapid (<4 h) and cost-effective antibody and DNA-free identification of three major foodborne pathogens, with detection thresholds of below 10 CFU mL-1 for Salmonella and <102 to 103 CFU mL-1 for Listeria and Escherichia coli.

Current and Future Technologies for the Detection of Antibiotic-Resistant Bacteria

Antibiotic resistance is a global public health concern, posing a significant threat to the effectiveness of antibiotics in treating bacterial infections. The accurate and timely detection of antibiotic-resistant bacteria is crucial for implementing appropriate treatment strategies and preventing the spread of resistant strains. This manuscript provides an overview of the current and emerging technologies used for the detection of antibiotic-resistant bacteria. We discuss traditional culture-based methods, molecular techniques, and innovative approaches, highlighting their advantages, limitations, and potential future applications. By understanding the strengths and limitations of these technologies, researchers and healthcare professionals can make informed decisions in combating antibiotic resistance and improving patient outcomes.

A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.

Evaluation of an adapted method of relative growth to determine the susceptibility of Enterobacterales to polymyxin B by MALDI-TOF MS

Polymyxin B resistance is an emerging problem worldwide. The reference method to determine susceptibility to polymyxins is broth microdilution (BMD). As BMD is time consuming, it is necessary to develop new methodologies to provide faster evaluation of polymyxin susceptibility. This study aimed to evaluate polymyxin B susceptibility of Enterobacterales using an adapted methodology of relative growth (RG) by Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). A total of 60 isolates of Enterobacterales (22 resistant and 38 susceptible to polymyxin B by BMD) were evaluated. The adapted RG technique presented categorical agreement of 96.7% with only 2 major errors (3.3%) in comparison to BMD. Our findings demonstrate a high agreement between BMD and adapted RG, indicating that this methodology is promising for differentiating polymyxin B-susceptible isolates from polymyxin B-resistant isolates and could be implemented routinely in microbiology laboratories that already use the MALDI-TOF MS to identify bacteria.

A novel inductively coupled capacitor wireless sensor system for rapid antibiotic susceptibility testing

BACKGROUND

The increasing prevalence and severity of antimicrobial resistance (AMR) present a major challenge to our healthcare system. Rapid detection of AMR is essential for lifesaving under emergent conditions such as sepsis. The current gold standard phenotypic antibiotic susceptibility testing (AST) takes more than a day to obtain results. Genotypic ASTs are faster (hours) in detecting the presence of resistance genes but require specific probes/knowledge of each AMR gene and do not provide specific information at the phenotype level. To address this unmet challenge, we developed a new rapid phenotypic AST.

RESULT

We designed a new electrochemical biosensor based on the concept of magnetically coupled LC sensors. The engineered LC sensors can be placed in 96-well plates and communicate the reading remotely with a receiver coil for signal analysis. The sensors were validated by monitoring the growth of Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa in the presence and absence of different antibiotics. Drug-resistant strains were used as controls. Bacterial growth was detected within 30 min after inoculation, allowing rapid determination of antibiotic susceptibility at the phenotype level. The sensor also functions in the presence of host proteins when tested with 2% FBS in growth media.

CONCLUSIONS

With the compatibility with 96-well plates, this label-free rapid 30-min AST has the potential for low-cost applications with simple integration into the existing workflow in clinical settings.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.

Current and Future Flow Cytometry Applications Contributing to Antimicrobial Resistance Control

Antimicrobial resistance is a global threat to human health and welfare, food safety, and environmental health. The rapid detection and quantification of antimicrobial resistance are important for both infectious disease control and public health threat assessment. Technologies such as flow cytometry can provide clinicians with the early information, they need for appropriate antibiotic treatment. At the same time, cytometry platforms facilitate the measurement of antibiotic-resistant bacteria in environments impacted by human activities, enabling assessment of their impact on watersheds and soils. This review focuses on the latest applications of flow cytometry for the detection of pathogens and antibiotic-resistant bacteria in both clinical and environmental samples. Novel antimicrobial susceptibility testing frameworks embedding flow cytometry assays can contribute to the implementation of global antimicrobial resistance surveillance systems that are needed for science-based decisions and actions.

MALDI-TOF MS Indirect Beta-Lactamase Detection in Ampicillin-Resistant Haemophilus influenzae

Rapid identification of beta-lactamase-producing strains of Haemophilus influenzae plays key role in diagnostics in clinical microbiology. Therefore, the aim of this study was the rapid determination of beta-lactamase's presence in H. influenzae isolates via indirect detection of degradation ampicillin products using MALDI-TOF MS. H. influenzae isolates were subjected to antibiotic resistance testing using disk diffusion and MIC methodologies. Beta-lactamase activity was tested using MALDI-TOF MS, and results were compared to spectral analysis of alkaline hydrolysis. Resistant and susceptible strains of H. influenzae were distinguished, and strains with a high MIC level were identified as beta-lactamase-producing. Results indicate that MALDI-TOF mass spectrometry is also suitable for the rapid identification of beta-lactamase-producing H. influenzae. This observation and confirmation can accelerate identification of beta-lactamase strains of H. influenzae in clinical microbiology, which can have an impact on health in general.

Thymosin β4, a potential marker of malignancy and prognosis in hepatocellular carcinoma

BACKGROUND

The lack of effective early diagnostic markers is an obstacle in clinical diagnosis and treatment of hepatocellular carcinoma (HCC). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is an increasing popular approach for identification of clinically relevant parameters including biomarkers.

PATIENTS AND METHODS

540 subjects, including 274 HCC, 119 liver cirrhosis, 89 hepatitis, and 58 healthy volunteers were enrolled. MALDI-TOF MS was used to select potential novel biomarkers from serum of HCC patients. Its clinical application was evaluated by experiments and clinical data analysis.

RESULTS

We identified Thymosin β4 (Tβ4) in serum by MALDI-TOF MS. The expression of Tβ4 was detected up-regulating in HCC cells and tissues which enhanced motility of HCC cells. More important, the level of serum Tβ4 was significantly elevated in HCC patients. The AUROC showed the optimum diagnostic cut-off was 1063.6 ng/mL, ROC and 95% CI of Tβ4 (0.908; 0.880-0.935) were larger than that of serum AFP (0.712; 0.662-0.762; p < 0.001). The sensitivity (91.3% vs 83.1%) and specificity (81.2% vs 20.3%) of serum Tβ4 were higher than alpha-fetoprotein (AFP). In AFP-negative HCC, the sensitivity could reach to 80.5%. ROC analysis showed serum Tβ4 had a better performance compared with AFP in distinguishing early-stage and small HCC. Tβ4 is correlated with TNM stage (p = 0.016) and vascular invasion (p = 0.005). Survival analysis indicated the survival time of Tβ4 positive patients was shorter (p < 0.001). Cox analysis suggested Tβ4 could be an independent factor for HCC prognosis.

CONCLUSION

Tβ4 may serve as a novel biomarker for HCC diagnosis and prognosis.

Performance of lipid fingerprint by routine matrix-assisted laser desorption/ionization time of flight for the diagnosis of Mycobacterium tuberculosis complex species

OBJECTIVES

Rapid detection of bacterial pathogens at species and sub-species levels is crucial for appropriate treatment, infection control, and public health management. Currently, one of the challenges in clinical microbiology is the discrimination of mycobacterial sub-species within the M. tuberculosis complex (MTBC). Our objective was to evaluate the ability of a biosafe mycobacterial lipid-based approach to identify MTBC cultures and sub-species.

METHODS

A blinded study was conducted using 90 mycobacterial clinical isolate strains comprising MTBC strains sub-cultured in Middlebrook 7H11 medium supplemented with 10% oleic-acid, dextrose, catalase growth supplement and incubated for up to 6 weeks at 37°C and using the following seven reference strains (M. tuberculosis H37Rv, M canettii, M. africanum, M. pinnipedii, M. caprae, M. bovis, and M. bovis BCG) grown under the same conditions, to set the reference lipid database and test it against the 90 MTBC clinical isolates. Cultured mycobacteria were heat-inactivated and loaded onto the matrix-assisted laser desorption/ionization target followed by the addition of the matrix. Acquisition of the data was performed using the positive ion mode.

RESULTS

Based on the identification of clear and defined lipid signatures from the seven reference strains, the method that we developed was fast (<10 minutes) and produced interpretable profiles for all but four isolates, caused by poor ionization giving an n = 86 with interpretable spectra. The sensitivity and specificity of the matrix-assisted laser desorption/ionization time of flight were 94.4 (95% CI, 86.4-98.5) and 94.4 (95% CI, 72.7-99.9), respectively.

CONCLUSIONS

Mycobacterial lipid profiling provides a means of rapid, safe, and accurate discrimination of species within the MTBC.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Comparison of four endotracheal tube cleaning protocols in anesthetized dogs

OBJECTIVE

To compare the efficacy of 4 cleaning protocols applied to endotracheal tubes (ETTs) collected from anesthetized dogs.

SAMPLE

100 ETTs (25 per protocol).

PROCEDURES

A 10-question survey designed to determine ETT reuse and cleaning practices was distributed via email to a sample of veterinary anesthesiologists. Informed by survey results, 4 ETT cleaning protocols were selected for use in a prospective clinical study. Dogs were intubated with sterile polyvinyl chloride ETTs. At extubation, each ETT was cultured for bacterial growth, randomly assigned to 1 of 4 protocols [water scrub (P1), detergent scrub (P2), detergent scrub and chlorhexidine gluconate (CHG) soak (P3), or detergent scrub and bleach soak (P4)], and cultured again after drying. Bacterial genera were identified using mass spectrometry and 16s rRNA sequencing. Proportions of ETTs exhibiting no post-cleaning growth were compared between protocols using the Fisher exact test with Bonferroni correction.

RESULTS

Half of survey respondents that reused ETTs did not sterilize them before reuse, cleaning methods varied widely, and no reported methods were evidence-based. After use, the number of ETTs exhibiting no post-cleaning bacterial growth were 15/25 (60%), 14/25 (56%), 20/25 (80%), and 17/25 (68%) for protocols P1, P2, P3, and P4, respectively. Pairwise comparisons did not reveal any statistically significant differences between protocols.

CLINICAL RELEVANCE

In small animal patients, some veterinary anesthesiologists reuse ETTs without sterilization and cleaning protocols vary widely. No differences between the studied protocols were identified. Further research is necessary to identify a safe, efficacious ETT cleaning protocol for use in small animal practice.

Ultraviolet light oxidation of fresh hemoglobin eliminates aggregate formation seen in commercially sourced hemoglobin

Elevated levels of circulating cell-free hemoglobin (CFH) are an integral feature of several clinical conditions including sickle cell anemia, sepsis, hemodialysis and cardiopulmonary bypass. Oxidized (Fe3+, ferric) hemoglobin contributes to the pathophysiology of these disease states and is therefore widely studied in experimental models, many of which use commercially sourced CFH. In this study, we treated human endothelial cells with commercially sourced ferric hemoglobin and observed the appearance of dense cytoplasmic aggregates (CAgg) over time. These CAgg were intensely autofluorescent, altered intracellular structures (such as mitochondria), formed in multiple cell types and with different media composition, and formed regardless of the presence or absence of cells. An in-depth chemical analysis of these CAgg revealed that they contain inorganic components and are not pure hemoglobin. To oxidize freshly isolated hemoglobin without addition of an oxidizing agent, we developed a novel method to convert ferrous CFH to ferric CFH using ultraviolet light without the need for additional redox agents. Unlike commercial ferric hemoglobin, treatment of cells with the fresh ferric hemoglobin did not lead to CAgg formation. These studies suggest that commercially sourced CFH may contain stabilizers and additives which contribute to CAgg formation.

Diagnosis and Treatment for Shiga Toxin-Producing Escherichia coli Associated Hemolytic Uremic Syndrome

Shiga toxin-producing Escherichia coli (STEC)-associated hemolytic uremic syndrome (STEC-HUS) is a clinical syndrome involving hemolytic anemia (with fragmented red blood cells), low levels of platelets in the blood (thrombocytopenia), and acute kidney injury (AKI). It is the major infectious cause of AKI in children. In severe cases, neurological complications and even death may occur. Treating STEC-HUS is challenging, as patients often already have organ injuries when they seek medical treatment. Early diagnosis is of great significance for improving prognosis and reducing mortality and sequelae. In this review, we first briefly summarize the diagnostics for STEC-HUS, including history taking, clinical manifestations, fecal and serological detection methods for STEC, and complement activation monitoring. We also summarize preventive and therapeutic strategies for STEC-HUS, such as vaccines, volume expansion, renal replacement therapy (RRT), antibiotics, plasma exchange, antibodies and inhibitors that interfere with receptor binding, and the intracellular trafficking of the Shiga toxin.

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.

The Development of Technology to Prevent, Diagnose, and Manage Antimicrobial Resistance in Healthcare-Associated Infections

There is a growing risk of antimicrobial resistance (AMR) having an adverse effect on the healthcare system, which results in higher healthcare costs, failed treatments and a higher death rate. A quick diagnostic test that can spot infections resistant to antibiotics is essential for antimicrobial stewardship so physicians and other healthcare professionals can begin treatment as soon as possible. Since the development of antibiotics in the last two decades, traditional, standard antimicrobial treatments have failed to treat healthcare-associated infections (HAIs). These results have led to the development of a variety of cutting-edge alternative methods to combat multidrug-resistant pathogens in healthcare settings. Here, we provide an overview of AMR as well as the technologies being developed to prevent, diagnose, and control healthcare-associated infections (HAIs). As a result of better cleaning and hygiene practices, resistance to bacteria can be reduced, and new, quick, and accurate instruments for diagnosing HAIs must be developed. In addition, we need to explore new therapeutic approaches to combat diseases caused by resistant bacteria. In conclusion, current infection control technologies will be crucial to managing multidrug-resistant infections effectively. As a result of vaccination, antibiotic usage will decrease and new resistance mechanisms will not develop.

How MALDI-TOF Mass Spectrometry Technology Contributes to Microbial Infection Control in Healthcare Settings

Healthcare settings have been utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) since 2010. MALDI-TOF MS has various benefits over the conventional method of biochemical identification, including ease of use, speed, accuracy, and low cost. This approach can solve many of the obstacles to identifying bacteria, fungi and viruses. As technology advanced, more and more databases kept track of spectra, allowing species with similar morphological, genotypic, and biochemical traits to be identified. Using MALDI-TOF MS for identification has become more accurate and quicker due to advances in sample preparation and database enrichment. Rapid sample detection and colony identification using MALDI-TOF MS have produced promising results. A key application of MALDI-TOF MS is quickly identifying highly virulent and drug-resistant diseases. Here, we present a review of the scientific literature assessing the effectiveness of MALDI-TOF MS for locating clinically relevant pathogenic bacteria, fungi, and viruses. MALDI-TOF MS is a useful strategy for locating clinical pathogens, however, it also has some drawbacks. A small number of spectra in the database and inherent similarities among organisms can make it difficult to distinguish between different species, which can result in misidentifications. The majority of the time additional testing may correct these problems, which happen very seldom. In conclusion, infectious illness diagnosis and clinical care are being revolutionized by the use of MALDI-TOF MS in the clinical microbiology laboratory.

Rapid detection of Staphylococcus aureus and Streptococcus pneumoniae by real-time analysis of volatile metabolites

Early detection of pathogenic bacteria is needed for rapid diagnostics allowing adequate and timely treatment of infections. In this study, we show that secondary electrospray ionization–high resolution mass spectrometry (SESI-HRMS) can be used as a diagnostic tool for rapid detection of bacterial infections as a supportive system for current state-of-the-art diagnostics. Volatile organic compounds (VOCs) produced by growing S. aureus or S. pneumoniae cultures on blood agar plates were detected within minutes and allowed for the distinction of these two bacteria on a species and even strain level within hours. Furthermore, we obtained a fingerprint of clinical patient samples within minutes of measurement and predominantly observed a separation of samples containing live bacteria compared to samples with no bacterial growth. Further development of this technique may reduce the time required for microbiological diagnosis and should help to improve patient’s tailored treatment.

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Real-time mass spectrometry shows potential as a tool for microbiological diagnosis

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Bacterial volatile metabolites from 1 × 10³ CFUs are detected within minutes

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S. aureus and S. pneumoniae can be distinguished on species and even strain level

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Complex clinical samples cluster according to presence or absence of viable bacteria

Early appropriate diagnostics and treatment of MDR Gram-negative infections

The term difficult-to-treat resistance has been recently coined to identify Gram-negative bacteria exhibiting resistance to all fluoroquinolones and all β-lactam categories, including carbapenems. Such bacteria are posing serious challenges to clinicians trying to identify the best therapeutic option for any given patient. Delayed appropriate therapy has been associated with worse outcomes including increase in length of stay, increase in total in-hospital costs and ∼20% increase in the risk of in-hospital mortality. In addition, time to appropriate antibiotic therapy has been shown to be an independent predictor of 30 day mortality in patients with resistant organisms. Improving and anticipating aetiological diagnosis through optimizing not only the identification of phenotypic resistance to antibiotic classes/agents, but also the identification of specific resistance mechanisms, would have a major impact on reducing the frequency and duration of inappropriate early antibiotic therapy. In light of these considerations, the present paper reviews the increasing need for rapid diagnosis of bacterial infections and efficient laboratory workflows to confirm diagnoses and facilitate prompt de-escalation to targeted therapy, in line with antimicrobial stewardship principles. Rapid diagnostic tests currently available and future perspectives for their use are discussed. Early appropriate diagnostics and treatment of MDR Gram-negative infections require a multidisciplinary approach that includes multiple different diagnostic methods and further consensus of algorithms, protocols and guidelines to select the optimal antibiotic therapy.

Mass spectrometry in research laboratories and clinical diagnostic: a new era in medical mycology

Diagnosis by clinical mycology laboratory plays a critical role in patient care by providing definitive knowledge of the cause of infection and antimicrobial susceptibility data to physicians. Rapid diagnostic methods are likely to improve patient. Aggressive resuscitation bundles, adequate source control, and appropriate antibiotic therapy are cornerstones for success in the treatment of patients. Routine methods for identifying clinical specimen fungal pathogen are based on the cultivation on different media with the subsequent examination of its phenotypic characteristics comprising a combination of microscopic and colony morphologies. As some fungi cannot be readily identified using these methods, molecular diagnostic methods may be required. These methods are fast, but it can cost a lot. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is suitable for high-throughput and rapid diagnostics at low costs. It can be considered an alternative for conventional biochemical and molecular identification systems in a microbiological laboratory. The reliability and accuracy of this method have been scrutinized in many surveys and have been compared with several methods including sequencing and molecular methods. According to these findings, the reliability and accuracy of this method are very high and can be trusted. With all the benefits of this technique, the libraries of MALDI-TOF MS need to be strengthened to enhance its performance. This review provides an overview of the most recent research literature that has investigated the applications and usage of MT-MS to the identification of microorganisms, mycotoxins, antifungal susceptibility examination, and mycobiome research.

Discrimination of Methicillin-resistant Staphylococcus aureus by MALDI-TOF Mass Spectrometry with Machine Learning Techniques in Patients with Staphylococcus aureus Bacteremia

Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains' data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.

MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification

The term “proteome” refers to the total of all proteins expressed in an organism. The term “proteomics” refers to the field of research that includes not only information on the expression levels of individual proteins, but also their higher-order structures, intermolecular interactions, and post-translational modifications. The core technology, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), is available for protein analysis thanks to the work of Koichi Tanaka and John Fenn, who were awarded the Nobel Prize in Chemistry in 2002. The most successful proteome analysis in clinical practice is rapid microbial identification. This method determines the bacterial species by comparing the proteome profile of the bacteria obtained by matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) with a database. MS is superior in simplicity, speed, and accuracy to classic speciation by staining and phenotyping. In clinical microbiology, MS has had a large impact on the diagnosis and treatment of infectious disease. Early diagnosis and treatment of infectious disease are important, and rapid identification by MALDI-TOF MS has made a major contribution to this field.

MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus. Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/.

Clinically Applicable System for Rapidly Predicting Enterococcus faecium Susceptibility to Vancomycin

Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

Future potential of metagenomics in clinical laboratories

INTRODUCTION

Rapid and sensitive diagnostic strategies are necessary for patient care and public health. Most of the current conventional microbiological assays detect only a restricted panel of pathogens at a time or require a microbe to be successfully cultured from a sample. Clinical metagenomics next-generation sequencing (mNGS) has the potential to unbiasedly detect all pathogens in a sample, increasing the sensitivity for detection and enabling the discovery of unknown infectious agents.

AREAS COVERED

High expectations have been built around mNGS; however, this technique is far from widely available. This review highlights the advances and currently available options in terms of costs, turnaround time, sensitivity, specificity, validation, and reproducibility of mNGS as a diagnostic tool in clinical microbiology laboratories.

EXPERT OPINION

The need for a novel diagnostic tool to increase the sensitivity of microbial diagnostics is clear. mNGS has the potential to revolutionise clinical microbiology. However, its role as a diagnostic tool has yet to be widely established, which is crucial for successfully implementing the technique. A clear definition of diagnostic algorithms that include mNGS is vital to show clinical utility. Similarly to real-time PCR, mNGS will one day become a vital tool in any testing algorithm.

Paving the Way to Overcome Antifungal Drug Resistance: Current Practices and Novel Developments for Rapid and Reliable Antifungal Susceptibility Testing

The past year has established the link between the COVID-19 pandemic and the global spread of severe fungal infections; thus, underscoring the critical need for rapid and realizable fungal disease diagnostics. While in recent years, health authorities, such as the Centers for Disease Control and Prevention, have reported the alarming emergence and spread of drug-resistant pathogenic fungi and warned against the devastating consequences, progress in the diagnosis and treatment of fungal infections is limited. Early diagnosis and patient-tailored therapy are established to be key in reducing morbidity and mortality associated with fungal (and cofungal) infections. As such, antifungal susceptibility testing (AFST) is crucial in revealing susceptibility or resistance of these pathogens and initiating correct antifungal therapy. Today, gold standard AFST methods require several days for completion, and thus this much delayed time for answer limits their clinical application. This review focuses on the advancements made in developing novel AFST techniques and discusses their implications in the context of the practiced clinical workflow. The aim of this work is to highlight the advantages and drawbacks of currently available methods and identify the main gaps hindering their progress toward clinical application.

Using Molecular Diagnostics to Develop Therapeutic Strategies for Carbapenem-Resistant Gram-Negative Infections

Infections caused by multidrug-resistant Gram-negative organisms have become a global threat. Such infections can be very difficult to treat, especially when they are caused by carbapenemase-producing organisms (CPO). Since infections caused by CPO tend to have worse outcomes than non-CPO infections, it is important to identify the type of carbapenemase present in the isolate or at least the Ambler Class (i.e., A, B, or D), to optimize therapy. Many of the newer beta-lactam/beta-lactamase inhibitor combinations are not active against organisms carrying Class B metallo-enzymes, so differentiating organisms with Class A or D carbapenemases from those with Class B enzymes rapidly is critical. Using molecular tests to detect and differentiate carbapenem-resistance genes (CRG) in bacterial isolates provides fast and actionable results, but utilization of these tests globally appears to be low. Detecting CRG directly in positive blood culture bottles or in syndromic panels coupled with bacterial identification are helpful when results are positive, however, even negative results can provide guidance for anti-infective therapy for key organism-drug combinations when linked to local epidemiology. This perspective will focus on the reluctance of laboratories to use molecular tests as aids to developing therapeutic strategies for infections caused by carbapenem-resistant organisms and how to overcome that reluctance.

Establishing a quantitative index of meropenem hydrolysis for the detection of KPC- and NDM-producing bacteria by MALDI-TOF MS

BACKGROUND

Matrix Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS), commonly used for microorganism identification, can also be applied for the detection of carbapenemase-producing bacteria by the evaluation of carbapenem hydrolysis. Since KPC- and NDM-producing bacteria are related to high mortality rates, diagnostic assays for its detection are essential. The aim of this study was to develop and evaluate a method to establish a quantitative measure (hydrolysis index - HI) to detect meropenem hydrolysis by MLADI-TOF MS.

METHODS

bla_KPC and bla_NDM positive and negative Klebsiella pneumoniae isolates and Escherichia coli ATCC 25922 (control) were incubated in a meropenem solution for 2 h. Protein extraction from these suspensions were submitted to MALDI-TOF MS analysis. The intensity of peaks at 384 m/z and 379 m/z of each isolate were used to establish the HI as follows: HI = (Peak intensity_{384 Test} / Peak intensity_{379 Test}) / (Peak intensity_{384 Control} / Peak intensity_{379 Control}). Receiver Operating Characteristic curve was used to determine a cutoff value to differentiate carbapenemase-producing from carbapenemase non-producing bacteria.

RESULTS

As all carbapenemase-producing K. pneumoniae presented HI ≤0.55 and all carbapenemase non-producing isolates presented a HI ≥0.57, the index of 0.56 was established as a cutoff value to differentiate carbapenemase (KPC and NDM) producing and non-producing bacteria.

Performance of lipid fingerprint-based MALDI-ToF for the diagnosis of mycobacterial infections

OBJECTIVES

Bacterial diagnosis of mycobacteria is often challenging because of the variability of the sensitivity and specificity of the assay used, and it can be expensive to perform accurately. Although matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) has become the workhorse of clinical laboratories, the current MALDI methodology (which is based on cytosolic protein profiling) for mycobacteria is still challenging due to the number of steps involved (up to seven) and potential biosafety concerns. Knowing that mycobacteria produce surface-exposed species-specific lipids, we here hypothesized that the detection of those molecules could offer a rapid, reproducible and robust method for mycobacterial identification.

METHODS

We evaluated the performance of an alternative methodology based on characterized species-specific lipid profiling of intact bacteria, without any sample preparation, by MALDI MS; it uses MALDI-time-of-flight (ToF) MS combined with a specific matrix (super-2,5-dihydroxybenzoic acid solubilized in an apolar solvent system) to analyse lipids of intact heat-inactivated mycobacteria. Cultured mycobacteria are heat-inactivated and loaded directly onto the MALDI target followed by addition of the matrix. Acquisition of the data is done in both positive and negative ion modes. Blinded studies were performed using 273 mycobacterial strains comprising both the Mycobacterium tuberculosis (Mtb) complex and non-tuberculous mycobacteria (NTMs) subcultured in Middlebrook 7H9 media supplemented with 10% OADC (oleic acid/dextrose/catalase) growth supplement and incubated for up to 2 weeks at 37°C.

RESULTS

The method we have developed is fast (<10 mins) and highly sensitive (<1000 bacteria required); 96.7% of the Mtb complex strains (204/211) were correctly assigned as MTB complex and 91.7% (22/24) NTM species were correctly assigned based only on intact bacteria species-specific lipid profiling by MALDI-ToF MS.

CONCLUSIONS

Intact bacterial lipid profiling provides a biosafe and unique route for rapid and accurate mycobacterial identification.