Developmental roadmap for antimicrobial susceptibility testing systems

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

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.

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision

Methicillin-resistant Staphylococcus aureus (MRSA) is a clinical threat with high morbidity and mortality. Here, we describe a new simple, rapid identification method for MRSA using oxacillin sodium salt, a cell wall synthesis inhibitor, combined with Gram staining and machine vision (MV) analysis. Gram staining classifies bacteria as positive (purple) or negative (pink) according to the cell wall structure and chemical composition. In the presence of oxacillin, the integrity of the cell wall for methicillin-susceptible S. aureus (MSSA) was destroyed immediately and appeared Gram negative. In contrast, MRSA was relatively stable and appeared Gram positive. This color change can be detected by MV. The feasibility of this method was demonstrated in 150 images of the staining results for 50 clinical S. aureus strains. Based on effective feature extraction and machine learning, the accuracies of the linear linear discriminant analysis (LDA) model and nonlinear artificial neural network (ANN) model for MRSA identification were 96.7% and 97.3%, respectively. Combined with MV analysis, this simple strategy improved the detection efficiency and significantly shortened the time needed to detect antibiotic resistance. The whole process can be completed within 1 h. Unlike the traditional antibiotic susceptibility test, overnight incubation is avoided. This new strategy could be used for other bacteria and represents a new rapid method for detection of clinical antibiotic resistance. IMPORTANCE Oxacillin sodium salt destroys the integrity of the cell wall of MSSA immediately, appearing Gram negative, whereas MRSA is relatively stable and still appears Gram positive. This color change can be detected by microscopic examination and MV analysis. This new strategy has significantly reduced the time to detect resistance. The results show that using oxacillin sodium salt combined with Gram staining and MV analysis is a new, simple and rapid method for identification of MRSA.

Risk Factors for the In-Hospital Mortality in Pyogenic Vertebral Osteomyelitis: A Cross-Sectional Study on 9753 Patients

BACKGROUND

Pyogenic vertebral osteomyelitis represents a clinical challenge associated with significant morbidity and mortality. The aim of this study was to analyze potential risk factors for the in-hospital mortality of vertebral osteomyelitis (VO) patients.

METHODS

Based on the International Classification of Diseases, 10th Revision (ICD-10) codes for VO ("M46.2-", "M46.3-", and "M46.4-") data for total case numbers, secondary diagnoses, and numbers of in-hospital deaths were extracted from the Institute for the Hospital Remuneration System (InEK GmbH). Odds ratios (OR) for death were calculated for several secondary diseases and factors of interest.

RESULTS

Despite age, certain comorbidities were found to be strongly associated with increased mortality risk: Heart failure (OR = 2.80; 95% CI 2.45 to 3.20; p < 0.01), chronic kidney disease (OR = 1.83; 95% CI 1.57 to 2.13; p < 0.01), and diabetes with complications (OR = 1.86; 95% CI 1.46 to 2.38; p < 0.01). Among the complications, acute liver failure showed the highest risk for in-hospital mortality (OR = 42.41; 95% CI 23.47 to 76.62; p < 0.01). Additionally, stage III kidney failure (OR = 9.81; 95% CI 7.96 to 12.08; p < 0.01), sepsis (OR = 5.94; 95% CI 5.02 to 7.03; p < 0.01), acute respiratory failure (OR = 5.31; 95% CI 4.61 to 6.12; p < 0.01), and systemic inflammatory response syndrome (SIRS) (OR = 5.19; 95% CI 3.69 to 5.19; p < 0.01) were associated with in-hospital mortality. When analyzing the influence of pathogens, documented infection with Pseudomonas aeruginosa had the highest risk for mortality (OR = 2.74; 95% CI 2.07 to 3.63; p < 0.01), followed by Streptococci, Escherichia coli, and Staphylococcus aureus infections.

CONCLUSIONS

An early assessment of individual patient risk factors may be beneficial in the care and treatment of VO to help reduce the risks of mortality. These findings emphasize the importance of closely monitoring VO patients with chronic organ diseases, early detection and treatment of sepsis, and tailored empirical antibiotic therapy. The identification of specific pathogens and antibiotic susceptibility testing should be prioritized to improve patient outcomes in this high-risk population.

Contemporary Considerations for Establishing Reference Methods for Antibacterial Susceptibility Testing

Antibacterial susceptibility testing (AST) is performed to guide therapy, perform resistance surveillance studies, and support development of new antibacterial agents. For 5 decades, broth microdilution (BMD) has served as the reference method to assess in vitro activity of antibacterial agents against which both novel agents and diagnostic tests have been measured. BMD relies on in vitro inhibition or killing of bacteria. It is associated with several limitations: it is a poor mimic of the in vivo milieu of bacterial infections, requires multiple days to perform, and is associated with subtle, difficult to control variability. In addition, new reference methods will soon be needed for novel agents whose activity cannot be evaluated by BMD (e.g., those that target virulence). Any new reference methods must be standardized, correlated with clinical efficacy and be recognized internationally by researchers, industry, and regulators. Herein, we describe current reference methods for in vitro assessment of antibacterial activity and highlight key considerations for the generation of novel reference methods.

Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

BACKGROUND

Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.

RESULTS

Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.

CONCLUSION

Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.

Addressing Consumer Misconceptions on Antibiotic Use and Resistance in the Context of Sore Throat: Learnings from Social Media Listening

A misunderstanding of the mechanism of action and bacterial targets of antibiotics by consumers may drive inappropriate antibiotic use and antimicrobial resistance (AMR). Tackling AMR requires an in-depth understanding of consumer beliefs and misconceptions. We explored consumer conversations on a number of social media platforms on antibiotic use and AMR in the context of sore throat and how coronavirus disease 2019 (COVID-19) affected online conversations between 1 January 2018 and 25 November 2021 across eight countries. Five distinct consumer groups were identified (antibiotic-preserving peer educators, antibiotic-cautious consumers, medication-resistant antibiotic opponents, believers in the strength of antibiotics, determined pro-antibiotic consumers) with a wide spectrum of beliefs around antibiotics in sore throat. Many opinions were based upon misconceptions, the most prominent of which was that antibiotics are strong medications that can treat all types of sore throat. COVID-19 had a multifaceted effect on the sore throat and AMR conversation. Sore throat triggered anxiety as consumers feared it may be a COVID-19 symptom while engagement in conversations around antibiotics for COVID-19 increased. Finally, consumers sought multiple routes to access antibiotics, such as directly from the pharmacy or by attempting to persuade physicians to prescribe. Knowledge obtained from this study could be used to develop focused approaches to dispel consumer misconceptions and mitigate AMR.

Rapid Phenotypic Antimicrobial Susceptibility Testing Using a Coulter Counter and Proliferation Rate Discrepancy

The rapid determination of antimicrobial susceptibility and evidence-based antimicrobial prescription is necessary to combat widespread antimicrobial resistance and promote effectively treatment for bacterial infections. This study developed a rapid phenotypic antimicrobial susceptibility determination method competent for seamless clinical implementation. A laboratory-friendly Coulter counter-based antimicrobial susceptibility testing (CAST) was developed and integrated with bacterial incubation, population growth monitoring, and result analysis to quantitatively detect differences in bacterial growth between resistant and susceptible strains following a 2 h exposure to antimicrobial agents. The distinct proliferation rates of the different strains enabled the rapid determination of their antimicrobial susceptibility phenotypes. We evaluated the performance efficacy of CAST for 74 clinically isolated Enterobacteriaceae subjected to 15 antimicrobials. The results were consistent with those obtained via the 24 h broth microdilution method, showing 90.18% absolute categorical agreement.

Regulation of probe density on upconversion nanoparticles enabling high-performance lateral flow assays

Upconversion nanoparticles (UCNPs)-based fluorescence probes have shown great potential in point-of-care testing (POCT) applications, due to UCNPs' features of high photostability and background-free fluorescence. Ceaseless improvements of UCNPs-probes have been carried out to increase detection sensitivity and to broaden detection range of UCNPs-based POCT. In this paper, we optimized UCNPs-probes by regulating probe density. The optimization was verified by a traditional lateral flow assay (LFA) platform for C-reactive protein (CRP) detection. Further, the optimized UCNPs-LFA integrating with a home-made benchtop fluorescence analyzer holds the capability to achieve high-performance POCT. Finally, nearly a 20 times sensitivity enhancement with a limit of detection of 0.046 ng/mL and a broad detection range of 0.2-300 ng/mL for CRP detection was obtained. Moreover, the optimized UCNPs-LFA was applied to detecting CRP in clinical serum samples and the detection results were consistent with the clinical test, validating its clinical practicability. The proposed optimization method is also expected to optimize other nanoparticles-based bio-probes for wider POCT application.

Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Analytical methods for assessing antimicrobial activity of nanomaterials in complex media: advances, challenges, and perspectives

Assessing the antimicrobial activity of engineered nanomaterials (ENMs), especially in realistic scenarios, is of great significance for both basic research and applications. Multiple analytical methods are available for analysis via off-line or on-line measurements. Real-world samples are often complex with inorganic and organic components, which complicates the measurements of microbial viability and/or metabolic activity. This article highlights the recent advances achieved in analytical methods including typical applications and specifics regarding their accuracy, cost, efficiency, and user-friendliness. Methodological drawbacks, technique gaps, and future perspectives are also discussed. This review aims to help researchers select suitable methods for gaining insight into antimicrobial activities of targeted ENMs in artificial and natural complex matrices.

Study of Laser Light Scattering Methods in Rapid Viability Assessment of Microorganisms under Antibiotics Exposure for Adaptation in Lab-on-A-Chip Format

The antibiotic resistance (ABR) problem is becoming increasingly disturbing and it is important to implement express methods of ABR testing to allow operative antibiotic therapy decisions. The application of laser light scattering (LLS) in microbiological analysis for express ABR testing of microorganisms has been considered. The ways of miniaturization of laser light scattering for creating the bases of their integration into microbiological laboratory-on-a-chip (MLOC) for clinical express diagnostics have been analysed. The advantage of miniaturization in the context of clinical express analysis realization problems are investigated. A system of parallel measuring cells and illumination, enabling simultaneous testing of a group of antibiotics, was tested by splitting a laser beam with a two-dimensional collimator prepared of nanoporous anodic aluminum oxide. It has been demonstrated that the application of LLS methods, providing high concentration and mass sensitivity as well as a miniaturization potential, is an effective approach in the development of new generation diagnostic instruments. The studies have demonstrated the ability of methods to register effects of antibiotics on microbiological samples within 10 min. The following microorganisms were used in the study: Escherichia coli M-17, Lactobacillus plantarum, Bifidobacterium bifidum, Stenotrophomonas maltophilia.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

QMAC-dRAST for the direct testing of antibiotic susceptibility for Enterobacterales in positive blood-culture broth: a comparison of the performances with the MicroScan system and direct disc diffusion testing methods

OBJECTIVES

To evaluate the performances of the QMAC-dRAST GN (Gram-negative) kit for rapid antimicrobial sensitivity testing (AST) and two other methods, directly on positive blood-culture broth (PBCB), by comparison with a reference method: the MicroScan method based on broth microdilution on colonies isolated on PBCB subculture.

METHODS

In total, 156 samples were collected prospectively from blood cultures positive for a Gram-negative rod. Each sample was tested with four AST techniques: (i) the QMAC dRAST GN kit, (ii) the disc diffusion (DD) method, (iii) the MicroScan method applied directly to PBCB; and (iv) MicroScan with isolates from PBCB subculture, as a reference.

RESULTS

For 124 PBCB containing Enterobacterales, overall essential agreement (EA) and categorical agreement (CA) between the QMAC-dRAST on PBCB and the reference reached 95.7% and 93.5%, respectively. There were 3.0% very major errors (VME), 4.0% major errors (ME) and 2.8% minor errors (mE). A comparison of MicroScan on PBCB and the reference yielded 98.8% EA, 98.5% CA, and rates of 0.6% VME, 0.9% ME and 0.7% mE. The DD method on PBCB gave a CA of 95.8% and rates of 1.7% for VME, 2.0% for ME and 1.9% for mE. Results were obtained more rapidly for QMAC-dRAST (median of 6 h 37 min versus 18 h for the MicroScan and DD methods on PBCB).

CONCLUSIONS

The QMAC-dRAST system provided rapid results well correlated with the reference method on PBCB containing Enterobacterales. Given the shorter time-to-results, the QMAC-dRAST system constitutes a fast and reliable alternative to conventional AST methods.

Advances in the screening of antimicrobial compounds using electrochemical biosensors: is there room for nanomaterials?

The abusive use of antimicrobial compounds and the associated appearance of antimicrobial resistant strains are a major threat to human health. An improved antimicrobial administration involves a faster diagnosis and detection of resistances. Antimicrobial susceptibility testing (AST) are the reference techniques for this purpose, relying mainly in the use of culture techniques. The long time required for analysis and the lack of reproducibility of these techniques have fostered the development of high-throughput AST methods, including electrochemical biosensors. In this review, recent electrochemical methods used in AST have been revised, with particular attention on those used for the evaluation of new drug candidates. The role of nanomaterials in these biosensing platforms has also been questioned, inferring that it is of minor importance compared to other applications.

Antimicrobial susceptibility testing: An updated primer for clinicians in the era of antimicrobial resistance: Insights from the Society of Infectious Diseases Pharmacists

Antimicrobial susceptibility testing (AST) is a critical function of the clinical microbiology laboratory and is essential for optimizing care of patients with infectious diseases, monitoring antimicrobial resistance (AMR) trends, and informing public health initiatives. Several methods are available for performing AST including broth microdilution, agar dilution, and disk diffusion. Technological advances such as the development of commercial automated susceptibility testing platforms and the advent of rapid diagnostic tests have improved the rapidity, robustness, and clinical application of AST. Numerous accrediting and regulatory agencies are involved in the process of AST and setting and revising breakpoints, including the U.S. Food and Drug Administration and the Clinical and Laboratory Standards Institute. Challenges to optimizing AST include the emergence of new resistance mechanisms, the development of new antimicrobial agents, and generation of new data requiring updates and revisions to established methods and breakpoints. Together, the challenges in AST methods and their interpretation create important opportunities for well-informed clinicians to improve patient outcomes and provide value to antimicrobial stewardship programs, especially in the setting of rapidly changing and increasing AMR. Addressing AST challenges will involve continued development of new technologies along with collaboration between clinicians and the laboratory to facilitate optimal antimicrobial use, combat the increasing burden of AMR, and inform the development of novel antimicrobials. This updated primer serves to reinforce important principles of AST, and to provide guidance on their implementation and optimization.

A sprayed graphene transistor platform for rapid and low-cost chemical sensing

We demonstrate a novel and versatile sensing platform, based on electrolyte-gated graphene field-effect transistors, for easy, low-cost and scalable production of chemical sensor test strips. The Lab-on-PCB platform is enabled by low-boiling, low-surface-tension sprayable graphene ink deposited on a substrate manufactured using a commercial printed circuit board process. We demonstrate the versatility of the platform by sensing pH and Na⁺ concentrations in an aqueous solution, achieving a sensitivity of 143 ± 4 μA per pH and 131 ± 5 μA per log₁₀Na⁺, respectively, in line with state-of-the-art graphene chemical sensing performance.

Optical photothermal infrared spectroscopy: A novel solution for rapid identification of antimicrobial resistance at the single-cell level via deuterium isotope labeling

The rise and extensive spread of antimicrobial resistance (AMR) has become a growing concern, and a threat to the environment and human health globally. The majority of current AMR identification methods used in clinical setting are based on traditional microbiology culture-dependent techniques which are time-consuming or expensive to be implemented, thus appropriate antibiotic stewardship is provided retrospectively which means the first line of treatment is to hope that a broad-spectrum antibiotic works. Hence, culture-independent and single-cell technologies are needed to allow for rapid detection and identification of antimicrobial-resistant bacteria and to support a more targeted and effective antibiotic therapy preventing further development and spread of AMR. In this study, for the first time, a non-destructive phenotyping method of optical photothermal infrared (O-PTIR) spectroscopy, coupled with deuterium isotope probing (DIP) and multivariate statistical analysis was employed as a metabolic fingerprinting approach to detect AMR in Uropathogenic Escherichia coli (UPEC) at both single-cell and population levels. Principal component-discriminant function analysis (PC-DFA) of FT-IR and O-PTIR spectral data showed clear clustering patterns as a result of distinctive spectral shifts (C-D signature peaks) originating from deuterium incorporation into bacterial cells, allowing for rapid detection and classification of sensitive and resistant isolates at the single-cell level. Furthermore, the single-frequency images obtained using the C-D signature peak at 2,163 cm-1 clearly displayed the reduced ability of the trimethoprim-sensitive strain for incorporating deuterium when exposed to this antibiotic, compared to the untreated condition. Hence, the results of this study indicated that O-PTIR can be employed as an efficient tool for the rapid detection of AMR at the single-cell level.

Nanotechnology Involved in Treating Urinary Tract Infections: An Overview

Considered as the most frequent contaminations that do not require hospitalization, urinary tract infections (UTIs) are largely known to cause significant personal burdens on patients. Although UTIs overall are highly preventable health issues, the recourse to antibiotics as drug treatments for these infections is a worryingly spread approach that should be addressed and gradually overcome in a contemporary, modernized healthcare system. With a virtually alarming global rise of antibiotic resistance overall, nanotechnologies may prove to be the much-needed 'lifebuoy' that will eventually suppress this prejudicial phenomenon. This review aims to present the most promising, currently known nano-solutions, with glimpses on clinical and epidemiological aspects of the UTIs, prospective diagnostic instruments, and non-antibiotic treatments, all of these engulfed in a comprehensive overview.

Management of Bacterial and Fungal Infections in the ICU: Diagnosis, Treatment, and Prevention Recommendations

Bacterial and fungal infections are common issues for patients in the intensive care unit (ICU). Large, multinational point prevalence surveys have identified that up to 50% of ICU patients have a diagnosis of bacterial or fungal infection at any one time. Infection in the ICU is associated with its own challenges. Causative organisms often harbour intrinsic and acquired mechanisms of drug-resistance, making empiric and targeted antimicrobial selection challenging. Infection in the ICU is associated with worse clinical outcomes for patients. We review the epidemiology of bacterial and fungal infection in the ICU. We discuss risk factors for acquisition, approaches to diagnosis and management, and common strategies for the prevention of infection.

Unmet needs for management of drug-resistant infections: low- and middle-income countries' viewpoint

Drug-resistant infections are a serious threat globally which demands cost-effective solutions to meet the unmet needs in their diagnosis and treatment. Gram-negative pathogens, drug-resistant tuberculosis, and multidrug-resistant Salmonella typhi have been reported as cause of resistant infections in developing countries. Here, we discuss the priority pathogens and conditions for which feasible solutions adaptable to low-resource settings are required to address the antimicrobial resistance in pathogens. These solutions will be helpful in containing the spread of antimicrobial resistance and better patient outcomes.

Overcoming Antibiotic Resistance with Novel Paradigms of Antibiotic Selection

Conventional antimicrobial susceptibility tests, including phenotypic and genotypic methods, are insufficiently accurate and frequently fail to identify effective antibiotics. These methods predominantly select therapies based on the antibiotic response of only the lead bacterial pathogen within pure bacterial culture. However, this neglects the fact that, in the majority of human infections, the lead bacterial pathogens are present as a part of multispecies communities that modulate the response of these lead pathogens to antibiotics and that multiple pathogens can contribute to the infection simultaneously. This discrepancy is a major cause of the failure of antimicrobial susceptibility tests to detect antibiotics that are effective in vivo. This review article provides a comprehensive overview of the factors that are missed by conventional antimicrobial susceptibility tests and it explains how accounting for these methods can aid the development of novel diagnostic approaches.

Recent Developments in Electrochemical Sensors for the Detection of Antibiotic-Resistant Bacteria

The development of efficient point-of-care (POC) diagnostic tools for detecting infectious diseases caused by destructive pathogens plays an important role in clinical and environmental monitoring. Nevertheless, evolving complex and inconsistent antibiotic-resistant species mire their drug efficacy. In this regard, substantial effort has been expended to develop electrochemical sensors, which have gained significant interest for advancing POC testing with rapid and accurate detection of resistant bacteria at a low cost compared to conventional phenotype methods. This review concentrates on the recent developments in electrochemical sensing techniques that have been applied to assess the diverse latent antibiotic resistances of pathogenic bacteria. It deliberates the prominence of biorecognition probes and tailor-made nanomaterials used in electrochemical antibiotic susceptibility testing (AST). In addition, the bimodal functional efficacy of nanomaterials that can serve as potential transducer electrodes and the antimicrobial agent was investigated to meet the current requirements in designing sensor module development. In the final section, we discuss the challenges with contemporary AST sensor techniques and extend the key ideas to meet the demands of the next POC electrochemical sensors and antibiotic design modules in the healthcare sector.

The landscape of bacterial antibiotic susceptibility in a multi-tier ophthalmology network in India: an electronic medical record driven analytics report

Introduction. Injudicious use of antibiotics often leads to antibiotic resistance which contributes to significant morbidity and mortality. Knowledge of local antibiogram is important in informing appropriate empiric antimicrobial therapy.

Hypothesis / Gap. It is the first and largest study that examines bacterial isolates from multiple ocular infections across South India and provides a overview of susceptibility patterns for each individual bacteria in the detailed interactive dashboard.

Aim. To study the landscape of bacterial antibiotic susceptibility patterns of ocular infections in patients undergoing microbiological investigations across a multi-tier ophthalmology network in India.

Methodology. This cross-sectional hospital-based study included 15 822 patients in whom microbiology samples were analysed between September 2013 and December 2021. Ocular tissue of patients in whom a microbiology sample was processed in at least one eye were included as cases. The data were collected using an indigenously developed electronic medical record (EMR) system.

Results. Among the 15 822 patients, bacteria (51.06 %) was the most common aetiology, followed by fungus (38.27 %). The majority of the patients were male (68.10 %) and the majority were adults (90.01 %). The most common age group was during the sixth decade with 2914 (18.42 %) patients. The patients were more commonly from the lower socio-economic status (51.61 %) and rural geography (46.82 %). The majority of the specimens sent for microbiological analysis were corneal scraping (68.61 %). Gram-positive cocci and Gram-positive bacilli were most sensitive towards vancomycin (86.83 and 92.89% respectively) followed by cefazolin (80.88 %) and amikacin, while Gram-negative bacilli were most sensitive towards ofloxacin (65.24%).

Conclusion. The most common aetiology of infection in ocular disease is bacterial and the majority of the patients presented from the rural geography and from lower socio-economic status. While vancomycin continues to be the drug of choice for Gram-positive organisms, increasing resistance to fluoroquinolones and ceftazidime was observed. Adoption of this model will enable access to antimicrobial susceptibility data, leading to reliable decisions and better clinical outcomes.

Fluorescent metabolic labeling-based quick antibiotic susceptibility test for anaerobic bacteria

Because of the advancements in medicine and science, the numbers of patients surviving complicated diseases are continuously increasing, which in turn leads to elevated chances of anaerobic infections by endogenous bacteria. Traditional growth yield-based antibiotic susceptibility tests (ASTs) against anaerobic bacteria are very time-consuming (≥48 h) and labor intensive, which delays the timely guidance of antibiotic prescription and increases the mortality of patients. Inspired by a fluorescent d-amino acid (FDAA) labeling-based AST (FaAST) that we recently developed for quick determination of aerobic bacteria's susceptibilities, here we report an accurate and fast AST method for anaerobic pathogens. Based on flow cytometry analysis of anaerobes that have been treated with various doses of antibiotics and metabolically labeled with FDAA, the intensities of which can reflect their affected metabolic status by the drugs, the MICs of each drug can then be determined. The whole process can be completed in 5 h. After testing 40 combinations of the representative anaerobic bacteria and antibiotics, our method demonstrates a high susceptibility category accuracy of 95.0%. This FaAST-based protocol is helpful in accurately and quickly guiding antibiotic decisions when treating critical infections caused by anaerobic bacteria.

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Antimicrobial resistance (AMR) by bacteria is a serious global threat, and a rapid, high-throughput, and easy-to-use phenotypic antimicrobial susceptibility testing (AST) method is essential for making timely treatment decisions and controlling the spread of antibiotic resistant micro-organisms. Traditional culture-based methods are time-consuming, and their capability to screen against a large number of different conditions is limited; meanwhile genotypic based methods, including sequencing and PCR based methods, are constrained by rarely identified resistance genes and complicated resistance mechanisms. Here, a combinatorial-screening SlipChip (cs-SlipChip) containing 192 nanoliter-sized compartments is developed which can perform high-throughput phenotypic AST within three hours by monitoring the bacterial growth within nanoliter-sized droplets with bright-field imaging and analyzing the changes in bacterial number and morphology. The minimum inhibitory concentration (MIC) of Escherichia coli ATCC 25922 against four antibiotics (ampicillin, ciprofloxacin, ceftazidime, and nitrofurantoin) can be measured in one chip within 3 hours. Furthermore, five antibiotic-resistant E. coli strains were isolated from patients diagnosed with urinary tract infections (UTIs), and an individual isolate was tested using four antibiotics and eleven antibiotic combinations simultaneously with three different concentrations of each. The results from the cs-SlipChip agree with those of a VITEK 2 automated system. This cs-SlipChip provides a practical high-throughput and rapid phenotypic method for AST and can also be used to screen different chemicals and antibiotic combinations for the treatment of multiple antibiotic-resistant 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.

Microfluidics for antibiotic susceptibility testing

The rise of antibiotic resistance is a threat to global health. Rapid and comprehensive analysis of infectious strains is critical to reducing the global use of antibiotics, as informed antibiotic use could slow down the emergence of resistant strains worldwide. Multiple platforms for antibiotic susceptibility testing (AST) have been developed with the use of microfluidic solutions. Here we describe microfluidic systems that have been proposed to aid AST. We identify the key contributions in overcoming outstanding challenges associated with the required degree of multiplexing, reduction of detection time, scalability, ease of use, and capacity for commercialization. We introduce the reader to microfluidics in general, and we analyze the challenges and opportunities related to the field of microfluidic AST.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.

Antimicrobial susceptibility testing for Gram positive cocci towards vancomycin using scanning electron microscopy

The rapid detection of resistant bacteria has become a challenge for microbiologists worldwide. Numerous pathogens that cause nosocomial infections are still being treated empirically and have developed resistance mechanisms against key antibiotics. Thus, one of the challenges for researchers has been to develop rapid antimicrobial susceptibility testing (AST) to detect resistant isolates, ensuring better antibiotic stewardship. In this study, we established a proof-of-concept for a new strategy of phenotypic AST on Gram-positive cocci towards vancomycin using scanning electron microscopy (SEM). Our study evaluated the profiling of Enterococcus faecalis, Enterococcus faecium and Staphylococcus aureus after brief incubation with vancomycin. Sixteen isolates were analysed aiming to detect ultrastructural modifications at set timepoints, comparing bacteria with and without vancomycin. After optimising slides preparation and micrographs acquisition, two analytical strategies were used. The high magnification micrographs served to analyse the division of cocci based on the ratio of septa, along with the bacterial size. Susceptible strains with vancomycin showed a reduced septa percentage and the average surface area was consequently double that of the controls. The resistant bacteria revealed multiple septa occurring at advanced timepoints. Low magnification micrographs made it possible to quantify the pixels at different timepoints, confirming the profiling of cocci towards vancomycin. This new phenotypic AST strategy proved to be a promising tool to discriminate between resistant and susceptible cocci within an hour of contact with vancomycin. The analysis strategies applied here would potentially allow the creation of artificial intelligence algorithms for septa detection and bacterial quantification, subsequently creating a rapid automated SEM-AST assay.

Biological units of antimicrobial resistance and strategies for their containment in animal production

The increasing prevalence of antimicrobial resistant bacterial infections has ushered in a major global public health crisis. Judicious or restricted antimicrobial use in animal agriculture, aiming to confine the use for the treatment of infections, is the most commonly proposed solution to reduce selection pressure for resistant bacterial strains and resistance genes. However, a multifaceted solution will likely be required to make acceptable progress in reducing antimicrobial resistance, due to other common environmental conditions maintaining antimicrobial resistance and limited executionary potential as human healthcare and agriculture will continue to rely heavily on antimicrobials in the foreseeable future. Drawing parallels from systematic approaches to the management of infectious disease agents and biodiversity loss, we provide examples that a more comprehensive approach is required, targeting antimicrobial resistance in agroecosystems on multiple fronts simultaneously. We present one such framework, based on nested biological units of antimicrobial resistance, and describe established or innovative strategies targeting units. Some of the proposed strategies are already in use or ready to be implemented, while some require further research and discussion among scientists and policymakers. We envision that antimicrobial resistance mitigation strategies for animal agriculture combining multiple tools would constitute powerful ecosystem-level interventions necessary to mitigate antimicrobial resistance.

Metabolism-Triggered Colorimetric Sensor Array for Fingerprinting and Antibiotic Susceptibility Testing of Bacteria

The rapid identification and antibiotic susceptibility testing (AST) of bacteria would help us to accurately identify the infectious sources as well as guide the use of antibiotics, which are crucial for improving the survival rate and antimicrobial resistance. Herein, a colorimetric sensor array for bacteria fingerprinting was constructed with d-amino acid (d-AA)-modified gold nanoparticles (AuNPs) as probes (Au/d-AA). Bacteria can metabolize the d-AA, triggering the aggregation of AuNPs. Making use of different metabolic capabilities of bacteria toward different d-AA, eight kinds of bacteria including antibiotic-resistant bacteria and strains of the same bacterial species are successfully differentiated via learning the response patterns. Meanwhile, the sensor array also performs well in quantitative analysis of single bacterium and differentiation of bacteria mixtures. More interestingly, a rapid colorimetric AST approach has been developed based on the Au/d-AA nanoprobes by monitoring the d-AA metabolic activity of bacteria toward various antibiotic treatments. In this regard, the outlined work here would promote clinical practicability and facilitate antibiotic stewardship.

Identification of antibiotic resistance and virulence-encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning

Klebsiella pneumoniae has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical K. pneumoniae strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical K. pneumoniae strains. Rapid differentiation of clinical K. pneumoniae with high resistance/hypervirulence from classical K. pneumoniae would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence-encoding factors and resistance phenotypes from K. pneumoniae strains. A total of 71 K. pneumoniae strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on K. pneumoniae strains were determined. Seven thousand four hundred fifty-five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning-based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence-encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify K. pneumoniae with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back-testing the Raman-CNN platform on 71 K. pneumoniae strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.

Antimicrobial Susceptibility Testing: A Comprehensive Review of Currently Used Methods

Antimicrobial resistance (AMR) has emerged as a major threat to public health globally. Accurate and rapid detection of resistance to antimicrobial drugs, and subsequent appropriate antimicrobial treatment, combined with antimicrobial stewardship, are essential for controlling the emergence and spread of AMR. This article reviews common antimicrobial susceptibility testing (AST) methods and relevant issues concerning the advantages and disadvantages of each method. Although accurate, classic technologies used in clinical microbiology to profile antimicrobial susceptibility are time-consuming and relatively expensive. As a result, physicians often prescribe empirical antimicrobial therapies and broad-spectrum antibiotics. Although recently developed AST systems have shown advantages over traditional methods in terms of testing speed and the potential for providing a deeper insight into resistance mechanisms, extensive validation is required to translate these methodologies to clinical practice. With a continuous increase in antimicrobial resistance, additional efforts are needed to develop innovative, rapid, accurate, and portable diagnostic tools for AST. The wide implementation of novel devices would enable the identification of the optimal treatment approaches and the surveillance of antibiotic resistance in health, agriculture, and the environment, allowing monitoring and better tackling the emergence of AMR.

Comparative study of the bacterial distribution and antimicrobial susceptibility of uropathogens in older and younger patients with urinary stones

Background

This study compared the bacterial spectrum and antibiotic susceptibility of uropathogens in older and younger patients with urinary stones to provide appropriate antibiotic management.

Methods

We retrospectively reviewed urinary tract infection patients with urolithiasis, presented to Xiangya Hospital from March 2014 to April 2021. Patients were divided into older and younger groups according to 60 years of age. The bacterial spectrum and drug sensitivity of uropathogens were compared.

Results

A total of 542 strains of uropathogens (177 in older and 365 in younger groups) were isolated from 507 patients. E. coli (41.8% vs 43.6%) remains the most common pathogen, followed by E. faecalis (6.2% vs 9.6%) in older and younger groups, respectively. Particularly, K. pneumoniae was significantly more frequent in older (9.6%) than in younger group (4.7%, P < .05). E. faecium was substantially more prevalent in older group (6.2%) than in younger group (2.7%, P < .05). The proportion of males increased in older patients (47.3%) than in younger patients (34.9%, P = 0.007). In both groups, major Gram-negative bacteria (E. coli and K. pneumoniae) revealed a high sensitivity over 70% to piperacillin/tazobactam, imipenem and amikacin, whereas the resistance level was high to penicillin, tetracycline and vancomycin. Major Gram-positive (E. faecalis and E. faecium) isolates demonstrated high sensitivity of over 50% to gentamicin and vancomycin in both groups. Furthermore, uropathogens isolated from younger urolithiasis patients were more susceptible to antimicrobials than those isolated from older patients.

Conclusions

The male increased in the older urolithiasis patients with UTI and uropathogens microbial spectrum in older urolithiasis patients are different from younger. High susceptibility and age should be utilized in empirical antibiotic selection to avoid increased multidrug-resistant bacteria.

A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing

Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

To slow and prevent the spread of antimicrobial resistant infections, rapid antimicrobial susceptibility testing (AST) is in urgent need to quantitatively determine the antimicrobial effects on pathogens. It typically takes days to complete the AST by conventional methods based on the long-time culture, and they do not work directly for clinical samples. Here, we report a rapid AST method enabled by stimulated Raman scattering (SRS) imaging of deuterium oxide (D2O) metabolic incorporation. Metabolic incorporation of D2O into biomass and the metabolic activity inhibition upon exposure to antibiotics at the single bacterium level are monitored by SRS imaging. The single-cell metabolism inactivation concentration (SC-MIC) of bacteria upon exposure to antibiotics can be obtained after a total of 2.5 h of sample preparation and detection. Furthermore, this rapid AST method is directly applicable to bacterial samples in complex biological environments, such as urine or whole blood. SRS metabolic imaging of deuterium incorporation is transformative for rapid single-cell phenotypic AST in the clinic.

Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance

The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

Research Progress in the Medical Application of Heavy Water, Especially in the Field of D2O-Raman Spectroscopy

Heavy water is an ideal contrast agent for metabolic activity and can be adapted to a wide range of biological systems owing to its non-invasiveness, universal applicability, and cost-effectiveness. As a new type of probe, the heavy isotope of water has been widely used in the study of cell development, metabolism, tissue homeostasis, aging, and tumor heterogeneity. Herein, we review findings supporting the applications of and research on heavy water in monitoring of bacterial metabolism, rapid detection of drug sensitivity, identification of tumor cells, precision medicine, and evaluation of skin barrier function and promote the use of heavy water as a suitable marker for the development of detection and treatment methodologies.

Recent Advances in Rapid Antimicrobial Susceptibility Testing

Background

Antimicrobial susceptibility testing (AST) is classically performed using growth-based techniques that essentially require viable bacterial matter to become visible to the naked eye or a sophisticated densitometer.

Content

Technologies based on the measurement of bacterial density in suspension have evolved marginally in accuracy and rapidity over the 20th century, but assays expanded for new combinations of bacteria and antimicrobials have been automated, and made amenable to high-throughput turn-around. Over the past 25 years, elevated AST rapidity has been provided by nucleic acid-mediated amplification technologies, proteomic and other “omic” methodologies, and the use of next-generation sequencing. In rare cases, AST at the level of single-cell visualization was developed. This has not yet led to major changes in routine high-throughput clinical microbiological detection of antimicrobial resistance.

Summary

We here present a review of the new generation of methods and describe what is still urgently needed for their implementation in day-to-day management of the treatment of infectious diseases.