Integrated Bacterial Identification and Antimicrobial Susceptibility Testing Using PCR and High-Resolution Melt

FARPA-based tube array coupled with quick DNA extraction enables ultra-fast bedside detection of antibiotic-resistant pathogens

Rapid and accurate detection of pathogens and antimicrobial-resistant (AMR) genes of the pathogens are crucial for the clinical diagnosis and effective treatment of infectious diseases. However, the time-consuming steps of conventional culture-based methods inhibit the precise and early application of anti-infection therapy. For the prompt treatment of pathogen-infected patients, we have proposed a novel tube array strategy based on our previously reported FARPA (FEN1-aided recombinase polymerase amplification) principle for the ultra-fast detection of antibiotic-resistant pathogens on site. The entire process from "sample to result" can be completed in 25 min by combining quick DNA extraction from a urine sample with FARPA to avoid the usually complicated DNA extraction step. Furthermore, a 36-tube array made from commercial 384-well titre plates was efficiently introduced to perform FARPA in a portable analyser, achieving an increase in the loading sample throughput (from several to several tens), which is quite suitable for the point-of-care testing (POCT) of multiple pathogens and multiple samples. Finally, we tested 92 urine samples to verify the performance of our proposed method. The sensitivities for the detection of E. coli, K. pneumoniae, E. faecium, and E. faecalis were 92.7%, 93.8%, 100% and 88.9%, respectively. The specificities for the detection of the four pathogens were 100%. Consequently, our rapid, low-cost and user-friendly POCT method holds great potential for guiding the rational use of antibiotics and reducing bacterial resistance.

MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning

Introduction

Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs and the outcome of infections. Mass Spectrometry (MS), and more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories to identify bacterial species and detect AMR. The analysis of AMR with deep learning is still recent, and most models depend on filters and preprocessing techniques manually applied on spectra.

Methods

This study propose a deep neural network, MSDeepAMR, to learn from raw mass spectra to predict AMR. MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus under different antibiotic resistance profiles. Additionally, a transfer learning test was performed to study the benefits of adapting the previously trained models to external data.

Results

MSDeepAMR models showed a good classification performance to detect antibiotic resistance. The AUROC of the model was above 0.83 in most cases studied, improving the results of previous investigations by over 10%. The adapted models improved the AUROC by up to 20% when compared to a model trained only with external data.

Discussion

This study demonstrate the potential of the MSDeepAMR model to predict antibiotic resistance and their use on external MS data. This allow the extrapolation of the MSDeepAMR model to de used in different laboratories that need to study AMR and do not have the capacity for an extensive sample collection.

Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration

BACKGROUND

Bacterial infections, especially polymicrobial infections, remain a threat to global health and require advances in diagnostic technologies for timely and accurate identification of all causative species. Digital melt - microfluidic chip-based digital PCR combined with high resolution melt (HRM) - is an emerging method for identification and quantification of polymicrobial bacterial infections. Despite advances in recent years, existing digital melt instrumentation often delivers nonuniform temperatures across digital chips, resulting in nonuniform digital melt curves for individual bacterial species. This nonuniformity can lead to inaccurate species identification and reduce the capacity for differentiating bacterial species with similar digital melt curves.

RESULTS

We introduce herein a new temperature calibration method for digital melt by incorporating an unamplified, synthetic DNA fragment with a known melting temperature as a calibrator. When added at a tuned concentration to an established digital melt assay amplifying the commonly targeted 16S V1 - V6 region, this calibrator produced visible low temperature calibrator melt curves across-chip along with the target bacterial melt curves. This enables alignment of the bacterial melt curves and correction of heating-induced nonuniformities. Using this calibration method, we were able to improve the uniformity of digital melt curves from three causative species of bacteria. Additionally, we assessed calibration's effects on identification accuracy by performing machine learning identification of three polymicrobial mixtures comprised of two bacteria with similar digital melt curves in different ratios. Calibration greatly improved mixture composition prediction.

SIGNIFICANCE

To the best of our knowledge, this work represents the first DNA calibrator-supplemented assay and calibration method for nanoarray digital melt. Our results suggest that this calibration method can be flexibly used to improve identification accuracy and reduce melt curve variabilities across a variety of pathogens and assays. Therefore, this calibration method has the potential to elevate the diagnostic capabilities of digital melt toward polymicrobial bacterial infections and other infectious diseases.

The Rapid Phenotypic Susceptibility Testing in Real-Life Experience: How the MIC Values Impact on Sepsis Fast Diagnostic Workflow

The MIC value definition faithfully reflects antimicrobial sensitivity, profoundly impacting the infection's clinical outcome. Our study aimed to evaluate the Accelerate PhenoTM System in defining the importance of fast phenotypic susceptibility data. A number of 270 monomicrobial samples simultaneously underwent standard procedures and fast protocols after a contemporary Gram stain. Finally, we provided Turn-around Time (TAT) and statistical evaluations. The fast technology required a medium value of 7 h to complete ID and AST profiles. Although there were some spectrum limitations, it revealed an optimal success rate in microbial identification directly from positive blood cultures. The Gram-negative AST reached a 98.9% agreement between the Accelerate Pheno™ System and the standard method. In addition, the Gram-positive AST gathered a 98.7% agreement comparing the same systems. The chance to rapidly provide precise MIC values is one of the last frontiers in clinical microbiology, especially in high-prevalence antimicrobial resistance areas.

Rapid detection of antimicrobial resistance in methicillin-resistant Staphylococcus aureus using MALDI-TOF mass spectrometry

Antimicrobial resistance is a growing problem in modern healthcare. Most antimicrobial susceptibility tests (AST) require long culture times which delay diagnosis and effective treatment. Our group has previously reported a proof-of-concept demonstration of a rapid AST in Escherichia coli using deuterium labeling and MALDI mass spectrometry. Culturing bacteria in D2O containing media incorporates deuterium in newly synthesized lipids, resulting in a mass shift that can be easily detected by mass spectrometry. The extent of new growth is measured by the average mass of synthesized lipids that can be correlated with resistance in the presence of antimicrobials. In this work, we adapt this procedure to methicillin-resistant Staphylococcus aureus using the Bruker MALDI-TOF Biotyper, a low-cost instrument commonly available in diagnostic laboratories. The susceptible strain showed a significant decrease in average mass in on-target microdroplet cultures after 3 hours of incubation with 10 µg/mL methicillin, while the resistant strain showed consistent labeling regardless of methicillin concentration. This assay allows us to confidently detect methicillin resistance in S. aureus after only 3 hours of culture time and minimal sample processing, reducing the turn-around-time significantly over conventional assays. The success of this work suggests its potential as a rapid AST widely applicable in many clinical microbiology labs with minimal additional costs.

Inoculum size-insensitive susceptibility determination of urine sample based on in-situ measurement of inducible enzyme activity after 20 min of antibiotic exposure

BACKGROUND

The empirical antibiotic therapies for bacterial infections cause the emergence and propagation of multi-drug resistant bacteria, which not only impair the effectiveness of existing antibiotics but also raise healthcare costs. To reduce the empirical treatments, rapid antimicrobial susceptibility testing (AST) of causative microorganisms in clinical samples should be conducted for prescribing evidence-based antibiotics. However, most of culture-based ASTs suffer from inoculum effect and lack differentiation of target pathogen and commensals, hampering their adoption for evidence-based antibiotic prescription. Therefore, rapid ASTs which can specifically determine pathogens' susceptibilities, regardless of the bacterial load in clinical samples, are in urgent need.

RESULTS

We present a pathogen-specific and inoculum size-insensitive AST to achieve the reliable susceptibility determination on Escherichia coli (E. coli) in urine samples. The developed AST is featured with an 1 h sample-to-result workflow in a filter, termed on-filter AST. The AST results can be obtained by using an inducible enzymatic assay to in-situ measure the cell response of E. coli collected from urine after 20 min of antibiotic exposure. The calculated detection limit of our AST (1.95 × 104 CFU/mL) is much lower than the diagnosis threshold of urinary tract infections. The specific expression of the inducible enzyme enables on-filter AST to correctly profile the susceptibilities of target pathogen to multi-type antibiotics without the interference from commensals. We performed the on-filter AST on 1 mL urine samples with bacterial loads varying from 105 CFU/mL to 107 CFU/mL and compared the results to that of standard method, demonstrating its insensitivity to inoculum size.

SIGNIFICANCE

The developed AST is demonstrated to be of high sensitivity, specificity, and insensitive to inoculum size. With further developments for additional bacteria and clinical validation, on-filter AST is promising as a rapid and reliable surrogate of culture-based AST to promote the evidence-based prescription at the first visit and minimize the emergency of new multi-drug resistant microorganisms.

Enzymatic Antimicrobial Susceptibility Testing with Bacteria Identification in 30 min

Rapid antimicrobial susceptibility testing (AST) with the ability of bacterial identification is urgently needed for evidence-based antibiotic prescription. Herein, we propose an enzymatic AST (enzyAST) that employs β-d-glucuronidase as a biomarker to identify pathogens and profile phenotypic susceptibilities simultaneously. EnzyAST enables to offer binary AST results within 30 min, much faster than standard methods (>16 h). The general applicability of enzyAST was verified by testing the susceptibility of two Escherichia coli strains to three antibiotics with different action mechanisms. The pilot study also shows that the minimal inhibitory concentrations can be determined by enzyAST with the statistical analysis of enzymatic activity of the bacteria population exposed to varying antibiotic concentrations. With further development of multiple bacteria and sample treatment, enzyAST could be able to evaluate the susceptibility of pathogens in clinical samples directly to facilitate the evidence-based therapy.

Harnessing Variabilities in Digital Melt Curves for Accurate Identification of Bacteria

Digital PCR combined with high resolution melt (HRM) is an emerging method for identifying pathogenic bacteria with single cell resolution via species-specific digital melt curves. Currently, the development of such digital PCR-HRM assays entails first identifying PCR primers to target hypervariable gene regions within the target bacteria panel, next performing bulk-based PCR-HRM to examine whether the resulting species-specific melt curves possess sufficient interspecies variability (i.e., variability between bacterial species), and then digitizing the bulk-based PCR-HRM assays with melt curves that have high interspecies variability via microfluidics. In this work, we first report our discovery that the current development workflow can be inadequate because a bulk-based PCR-HRM assay that produces melt curves with high interspecies variability can, in fact, lead to a digital PCR-HRM assay that produces digital melt curves with unwanted intraspecies variability (i.e., variability within the same bacterial species), consequently hampering bacteria identification accuracy. Our subsequent investigation reveals that such intraspecies variability in digital melt curves can arise from PCR primers that target nonidentical gene copies or amplify nonspecifically. We then show that computational in silico HRM opens a window to inspect both interspecies and intraspecies variabilities and thus provides the missing link between bulk-based PCR-HRM and digital PCR-HRM. Through this new development workflow, we report a new digital PCR-HRM assay with improved bacteria identification accuracy. More broadly, this work can serve as the foundation for enhancing the development of future digital PCR-HRM assays toward identifying causative pathogens and combating infectious diseases.

Tesla valve-assisted biosensor for dual-mode and dual-target simultaneous determination of foodborne pathogens based on phage/DNAzyme co-modified zeolitic imidazolate framework-encoded probes

Sensitive and accurate detection of multiplex foodborne pathogens is crucial for food safety. In this work, a dual-mode and dual-target biosensor regulated by a Tesla valve was established for simultaneously determining Escherichia coli O157:H7 (E. coli) and Salmonella typhimurium (S. T). Two zeolitic imidazolate framework (ZIF-8) signal probes decorated with electroactive materials (ferrocene or methylene blue), DNAzyme, and different phages were synthesized to specifically recognize the targets and generate fluorescent/electrochemical dual-mode signals. In the presence of bacteria, they were captured and enriched on two individual working electrodes through the modified 4-mercaptophenylboric acid. The encoded signal probes added on different working electrodes could be conjugated with the corresponding target bacteria depending on the specificity of phages. Under the acidic condition, the DNAzyme could catalyze click chemistry for fluorescent signals. Simultaneously, the released ferrocene and methylene blue from ZIF-8 could generate electrochemical signals at different potentials. Benefiting from the flow regulation feature of the Tesla valve, the triggered fluorescent and electrochemical signals in the two individual electrodes would not influence each other, achieving simultaneous dual-mode and dual-target determination of foodborne pathogens. It depicted good linearity ranged 10-10⁷ CFU mL^-1. And the corresponding detection of limits were 5 CFU mL^-1 and 8 CFU mL^-1 for two bacteria, respectively. A low false positive was realized through the dual-mode strategy. The proposed biosensor can not only on-site, specifically, and sensitively determine E. coli and S. T, but also provide the wide prospect in rapid screening of other foodborne pathogens.

Under-oil open microfluidic systems for rapid phenotypic antimicrobial susceptibility testing

Antimicrobial susceptibility testing (AST) remains the cornerstone of effective antimicrobial selection and optimization in patients. Despite recent advances in rapid pathogen identification and resistance marker detection with molecular diagnostics (e.g., qPCR, MALDI-TOF MS), phenotypic (i.e., microbial culture-based) AST methods - the gold standard in hospitals/clinics - remain relatively unchanged over the last few decades. Microfluidics-based phenotypic AST has been growing fast in recent years, aiming for rapid (i.e., turnaround time <8 h), high-throughput, and automated species identification, resistance detection, and antibiotics screening. In this pilot study, we describe the application of a multi-liquid-phase open microfluidic system, named under-oil open microfluidic systems (UOMS), to achieve a rapid phenotypic AST. UOMS provides an open microfluidics-based solution for rapid phenotypic AST (UOMS-AST) by implementing and recording a pathogen's antimicrobial activity in micro-volume testing units under an oil overlay. UOMS-AST allows free physical access (e.g., by standard pipetting) to the system and label-free, single-cell resolution optical access. UOMS-AST can accurately and rapidly determine antimicrobial activities [including susceptibility/resistance breakpoint and minimum inhibitory concentration (MIC)] from nominal sample/bacterial cells in a system aligned with clinical laboratory standards where open systems and optical microscopy are predominantly adopted. Further, we combine UOMS-AST with a cloud lab data analytic technique for real-time image analysis and report generation to provide a rapid (<4 h) sample-to-report turnaround time, shedding light on its utility as a versatile (e.g., low-resource setting and manual laboratory operation, or high-throughput automated system) phenotypic AST platform for hospital/clinic use.

Machine Learning-Assisted Real-Time Polymerase Chain Reaction and High-Resolution Melt Analysis for SARS-CoV-2 Variant Identification

Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.

All-In-One Escherichia coli Viability Assay for Multi-dimensional Detection of Uncomplicated Urinary Tract Infections

The urinary tract infections by antibiotic-resistant bacteria have been a serious public health problem and increase the healthcare costs. The conventional technologies of diagnosis and antimicrobial susceptibility testing (AST) relying on multiple culture-based assays are time-consuming and labor-intensive and thus compel the empirical antimicrobial therapies to be prescribed, fueling the prevalence of antimicrobial resistance. Herein, we propose an all-in-one Escherichia coli viability assay in an enclosed 3D microwell array chip, termed digital β-d-glucuronidase (GUS)-AST assay. It employs GUS, a specific metabolism-related enzyme, to convert the presence of E. coli into bright fluorescence. The random distribution of single bacteria in microwell array enables to quantify the E. coli concentrations by counting the positive microwells. We incorporate the most probable number with digital quantification to lower the limit of detection and expand the dynamic range to 7 orders. The digital GUS-AST assay is able to indicate the potency of antibiotics and determine the minimum inhibitory concentrations. A streamlined procedure of urine removal, bacterial separation, and digital GUS-AST is established to perform the direct analysis of bacteria population in urine. The sample-to-result workflow can be finished in 4.5 h with a limit of detection of 39 CFU/mL. With further development for additional pathogens and multiple antibiotic conditions, the digital GUS-AST assay could help physicians to prescribe timely targeted therapies for better patient outcomes and the minimum emergence of resistant bacteria.

Rapid Antimicrobial Susceptibility Testing Based on a Bio-Inspired Chemiluminescence Sensor

Indiscriminate usage of antibiotics has caused accelerating growth and global expansion of antimicrobial resistance. Therefore, rapid antimicrobial susceptibility testing (AST) for guiding antibiotic prescription and preventing the spread of antimicrobial resistance is in urgent need. Phenotypic AST is the clinical gold standard method; however, no phenotypic AST has realized a colony-to-answer at about 1 h by utilizing the chemiluminescence sensor to detect the enzyme expressed by bacteria. Inspired by the bubble formation in the mixture of Escherichia coli and H₂O₂, we demonstrate a strategy based on the chemiluminescence sensor for rapid AST. Compared with the gold standard methods, the values of AUC are 0.960 for E. coli and 0.950 for Staphylococcus aureus, close to 1, indicating superb diagnostic performance as an AST method. The whole process from colonies to answer is 55 min for E. coli and 70 min for S. aureus. The chemiluminescence readout is based on the common equipment in the laboratory of the hospital, which is conducive to follow-up clinical promotion. Our sensor promises great potential in rapid AST, facilitating antimicrobial stewardship.

Programmable molecular circuit discriminates multidrug-resistant bacteria

Recognizing multidrug-resistant (MDR) bacteria with high accuracy and precision from clinical samples has long been a difficulty. For reliable detection of MDR bacteria, we investigated a programmable molecular circuit called the Background-free isothermal circuital kit (BRICK). The BRICK method provides a near-zero background signal by integrating four inherent modules equivalent to the conversion, amplification, separation, and reading modules. Interference elimination is largely owing to a molybdenum disulfide nanosheets-based fluorescence nanoswitch and non-specific suppression mediated by molecular inhibitors. In less than 70 ​min, an accurate distinction of various MDR bacteria was achieved without bacterial lysis. The BRICK technique detected 6.73 ​CFU/mL of methicillin-resistant Staphylococcus aureus in clinical samples in a proof-of-concept trial. By simply reprogramming the sequence panel, such a high signal-to-noise characteristic has been proven in the four other superbugs. The proposed BRICK method can provide a universal platform for infection surveillance and environmental management thanks to its superior programmability.

Bioinspired Plasmonic Nanosensor for on-Site Antimicrobial Susceptibility Testing in Urine Samples

Delayed use of appropriate antibiotics for superbugs, particularly for extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (E. coli) and Klebsiella pneumoniae (K. pn), has caused extensive morbidity and mortality worldwide. Therefore, rapid and on-site antimicrobial susceptibility testing (AST) is urgently required. Unfortunately, currently, no phenotypic AST can realize a sample-to-answer result within 2 h directly from a clinical sample and without using laboratory equipment or customized devices. Inspired by observing that E. coli and K. pn can rapidly catalyze H₂O₂, we developed a plasmonic nanosensor that responds to the proliferation of bacteria for realizing rapid AST. The results can be determined with the naked eye, digitized using a smartphone, and validated using ultraviolet-visible spectrometry. Our assay achieved superb area under the curves of 0.9752 and 1 in a receiver operating characteristic analysis directly obtained from uncultured clinical urine samples infected by E. coli and K. pn, respectively. The entire process from sample collection to analysis takes 100 min for E. coli and 85 min for K. pn detection. Our platform provides a practical approach for performing on-site AST in clinics to improve the survival of patients. It releases the burden of superbugs and avoids the abuse of antibiotics.

Ratiometric PCR in a Portable Sample-to-Result Device for Broad-Based Pathogen Identification

Polymerase chain reaction (PCR)-based diagnostic testing is the gold standard method for pathogen identification (ID) with recent developments enabling automated PCR tests for point-of-care (POC) use. However, multiplexed identification of several pathogens in PCR assays typically requires optics for an equivalent number of fluorescence channels, increasing instrumentation's complexity and cost. In this study, we first developed ratiometric PCR that surpassed one target per color barrier to allow multiplexed identification while minimizing optical components for affordable POC use. We realized it by amplifying pathogenic targets with fluorescently labeled hydrolysis probes with a specific ratio of red-to-green fluorophores for each bacterial species. We then coupled ratiometric PCR and automated magnetic beads-based sample preparation within a thermoplastic cartridge and a portable droplet magnetofluidic platform. We named the integrated workflow POC-ratioPCR. We demonstrated that the POC-ratioPCR could detect one out of six bacterial targets related to urinary tract infections (UTIs) in a single reaction using only two-color channels. We further evaluated POC-ratioPCR using mock bacterial urine samples spiked with good agreement. The POC-ratioPCR presents a simple and effective method for enabling broad-based POC PCR identification of pathogens directly from crude biosamples with low optical instrumentation complexity.

Multiplexed and Rapid AST for Escherichia coli Infection by Simultaneously Pyrosequencing Multiple Barcodes Each Specific to an Antibiotic Exposed to a Sample

Antimicrobial susceptibility testing (AST) is an effective way to guide antibiotic selection. However, conventional culture-based phenotypic AST is time-consuming. The key point to shorten the test is to quantify the small change in the bacterial number after the antibiotic exposure. To achieve rapid AST, we proposed a combination of multiplexed PCR with barcoded pyrosequencing to significantly shorten the time for antibiotic exposure. First, bacteria exposed to each antibiotic were labeled with a unique barcode. Then, the pool of the barcoded products was amplified by PCR with a universal primer pair. Finally, barcodes in the amplicons were individually and quantitatively decoded by pyrosequencing. As pyrosequencing is able to discriminate as low as 5% variation in target concentrations, as short as 7.5 min was enough for cultivation to detect the susceptibility of Escherichia coli to an antibiotic. The barcodes enable more than six kinds of drugs or six kinds of concentrations of a drug to be tested at a time. The susceptibility of 6 antibiotics to 43 E. coli-positive samples from 482 clinical urine samples showed a consistency of 99.3% for drug-resistant samples and of 95.7% for drug-sensitive samples in comparison with the conventional method. In addition, the minimum inhibitory concentration (MIC) of 29 E. coli samples was successfully measured. The proposed AST is dye free (pyrosequencing), multiplexed (six antibiotics), fast (a half-working day for reporting the results), and able to detect the MIC, thus having a great potential for clinical use in quick antibiotic selection.

Single bacteria detection by droplet DNAzyme-coupled rolling circle amplification

We described a new system termed droplet DNAzyme-coupled rolling circle amplification (dDRCA) that can selectively detect bacteria from clinical urine samples with single-cell sensitivity within 1.5 h compared with the several hours needed for traditionally used culture-based methods.

Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood-while maintaining, if not improving-the current assay's sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

A comprehensive review on LED-induced fluorescence in diagnostic pathology

Sensitivity, specificity, mobility, and affordability are important criteria to consider for developing diagnostic instruments in common use. Fluorescence spectroscopy has been demonstrating substantial potential in the clinical diagnosis of diseases and evaluating the underlying causes of pathogenesis. A higher degree of device integration with appropriate sensitivity and reasonable cost would further boost the value of the fluorescence techniques in clinical diagnosis and aid in the reduction of healthcare expenses, which is a key economic concern in emerging markets. Light-emitting diodes (LEDs), which are inexpensive and smaller are attractive alternatives to conventional excitation sources in fluorescence spectroscopy, are gaining a lot of momentum in the development of affordable, compact analytical instruments of clinical relevance. The commercial availability of a broad range of LED wavelengths (255-4600 nm) has opened up new avenues for targeting a wide range of clinically significant molecules (both endogenous and exogenous), thereby diagnosing a range of clinical illnesses. As a result, we have specifically examined the uses of LED-induced fluorescence (LED-IF) in preclinical and clinical evaluations of pathological conditions, considering the present advancements in the field.

Combating Antimicrobial Resistance via Single-Cell Diagnostic Technologies Powered by Droplet Microfluidics

Antimicrobial resistance is a global threat that if left unchecked could lead to 10 million annual mortalities by 2050. One factor contributing to the rise of multi-drug-resistant (MDR) pathogens is the reliance on traditional culture-based pathogen identification (ID) and antimicrobial susceptibility testing (AST) that typically takes several days. This delay of objective pathogen ID and AST information to inform clinical decision making results in clinicians treating patients empirically often using first-line, broad-spectrum antibiotics, contributing to the misuse/overuse of antibiotics. To combat the rise in MDR pathogens, there is a critical demand for rapid ID and AST technologies. Among the advances in ID and AST technologies in the past decade, single-cell diagnostic technologies powered by droplet microfluidics offer great promise due to their potential for high-sensitivity detection and rapid turnaround time. Our laboratory has been at the forefront of developing such technologies and applying them to diagnosing urinary tract infections (UTIs), one of the most common infections and a frequent reason for the prescription of antimicrobials. For pathogen ID, we first demonstrated the highly sensitive, amplification-free detection of single bacterial cells by confining them in picoliter-scale droplets and detection with fluorogenic peptide nucleic acid (PNA) probes that target their 16S rRNA (rRNA), a well-characterized marker for phylogenic classification. We subsequently improved the PNA probe design and enhanced detection sensitivity. For single-cell AST, we first employed a growth indicator dye and engineered an integrated device that allows us to detect growth from single bacterial cells under antibiotic exposure within 1 h, equivalent to two to three bacterial replications. To expand beyond testing a single antibiotic condition per device, a common limitation for droplet microfluidics, we developed an integrated programmable droplet microfluidic device for scalable single-cell AST. Using the scalable single-cell AST platform, we demonstrated the generation of up to 32 droplet groups in a single device with custom antibiotic titers and the capacity to scale up single-cell AST, and providing reliable pathogen categories beyond a binary call embodies a critical advance. Finally, we developed an integrated ID and AST platform. To this end, we developed a PNA probe panel that can identify nearly 90% of uropathogens and showed the quantitative detection of 16S rRNA from single bacterial cells in droplet-enabled AST after as little as 10 min of antibiotic exposure. This platform achieved both ID and AST from minimally processed urine samples in 30 min, representing one of the fastest turnaround times to date. In addition to tracing the development of our technologies, we compare them with contemporary research advances and offer our perspectives for future development, with the vision that single-cell ID and AST technologies powered by droplet microfluidics can indeed become a useful diagnostic tool for combating antimicrobial resistance.

Electrochemical and spectroelectrochemical characterization of bacteria and bacterial systems

Microbes, such as bacteria, can be described, at one level, as small, self-sustaining chemical factories. Based on the species, strain, and even the environment, bacteria can be useful, neutral or pathogenic to human life, so it is increasingly important that we be able to characterize them at the molecular level with chemical specificity and spatial and temporal resolution in order to understand their behavior. Bacterial metabolism involves a large number of internal and external electron transfer processes, so it is logical that electrochemical techniques have been employed to investigate these bacterial metabolites. In this mini-review, we focus on electrochemical and spectroelectrochemical methods that have been developed and used specifically to chemically characterize bacteria and their behavior. First, we discuss the latest mechanistic insights and current understanding of microbial electron transfer, including both direct and mediated electron transfer. Second, we summarize progress on approaches to spatiotemporal characterization of secreted factors, including both metabolites and signaling molecules, which can be used to discern how natural or external factors can alter metabolic states of bacterial cells and change either their individual or collective behavior. Finally, we address in situ methods of single-cell characterization, which can uncover how heterogeneity in cell behavior is reflected in the behavior and properties of collections of bacteria, e.g. bacterial communities. Recent advances in (spectro)electrochemical characterization of bacteria have yielded important new insights both at the ensemble and the single-entity levels, which are furthering our understanding of bacterial behavior. These insights, in turn, promise to benefit applications ranging from biosensors to the use of bacteria in bacteria-based bioenergy generation and storage.

Double-site recognition of Staphylococcus aureus using a metal-organic framework material with an alkaline hydrolysis property as a sensitive fluorescent probe

A metal-organic framework (MOF) material was prepared from 2-aminoterephthalic acid and aluminum chloride with a solvothermal synthesis protocol. The as-prepared MOF material named NH₂-MIL-53(Al) emitted a very intensive fluorescent (FL) signal after it was hydrolyzed in alkaline solution for releasing numerous FL ligands NH₂-H₂BDC. Thus it can be considered as a sensitive FL probe for studying biorecognition events. In this proof-of-principle work, a double-site recognition method was established to quantify Staphylococcus aureus (S. aureus) relying on the alkaline hydrolysis property of the MOF material. In particular, magnetic beads (MBs) modified with pig IgG were adopted for binding S. aureus based on the strong affinity between pig IgG and protein A on the bacterial surface. Meanwhile, MOF NH₂-MIL-53(Al)-tagged teicoplanin (TEI) was adopted for tracing the target bacteria. By hydrolyzing the MOF material bound on the MBs to trigger the FL signal, S. aureus can be quantified with a dynamic range of 3.3 × 10³-3.3 × 10⁷ CFU mL^-1 and a detection limit of 5.3 × 10² CFU mL^-1 (3σ). The method can exclude efficiently the interference from other common bacteria. It has been applied to quantify S. aureus in saliva, pomegranate green tea, glucose injection and milk samples with satisfactory results, verifying the application potential for analyzing various types of real samples contaminated with S. aureus.

RNA markers for ultra-rapid molecular antimicrobial susceptibility testing in fluoroquinolone-treated Klebsiella pneumoniae

OBJECTIVES

Traditional antimicrobial susceptibility testing (AST) is growth dependent and time-consuming. With rising rates of drug-resistant infections, a novel diagnostic method is critically needed that can rapidly reveal a pathogen's antimicrobial susceptibility to guide appropriate treatment. Recently, RNA sequencing has been identified as a powerful diagnostic tool to explore transcriptional gene expression and improve AST.

METHODS

RNA sequencing was used to investigate the potential of RNA markers for rapid molecular AST using Klebsiella pneumoniae and ciprofloxacin as a model. Downstream bioinformatic analysis was applied for optimal marker selection. Further validation on 11 more isolates of K. pneumoniae was performed using quantitative real-time PCR.

RESULTS

From RNA sequencing, we identified RNA signatures that were induced or suppressed following exposure to ciprofloxacin. Significant shifts at the transcript level were observed as early as 10 min after antibiotic exposure. Lastly, we confirmed marker expression profiles with concordant MIC results from traditional culture-based AST and validated across 11 K. pneumoniae isolates. recA, coaA and metN transcripts harbour the most sensitive susceptibility information and were selected as our top markers.

CONCLUSIONS

Our results suggest that RNA signature is a promising approach to AST development, resulting in faster clinical diagnosis and treatment of infectious disease. This approach is potentially applicable in other models including other pathogens exposed to different classes of antibiotics.

A multiplex bacterial assay using an element-labeled strategy for 16S rRNA detection

A multiplex bacterial assay method that combines S1 nuclease pretreatment and ICP-MS-based elemental labels is presented in this work. Six intestinal related bacteria were identified at the species level and quantified simultaneously without isolation culturing. This method could be extended to assay a mixed bacterial community for point-of-care diagnosis.

Rapid Antimicrobial Susceptibility Testing on Clinical Urine Samples by Video-Based Object Scattering Intensity Detection

To combat the ongoing public health threat of antibiotic-resistant infections, a technology that can quickly identify infecting bacterial pathogens and concurrently perform antimicrobial susceptibility testing (AST) in point-of-care settings is needed. Here, we develop a technology for point-of-care AST with a low-magnification solution scattering imaging system and a real-time video-based object scattering intensity detection method. The low magnification (1-2×) optics provides sufficient volume for direct imaging of bacteria in urine samples, avoiding the time-consuming process of culture-based bacterial isolation and enrichment. Scattering intensity from moving bacteria and particles in the sample is obtained by subtracting both spatial and temporal background from a short video. The time profile of scattering intensity is correlated with the bacterial growth rate and bacterial response to antibiotic exposure. Compared to the image-based bacterial tracking and counting method we previously developed, this simple image processing algorithm accommodates a wider range of bacterial concentrations, simplifies sample preparation, and greatly reduces the computational cost of signal processing. Furthermore, development of this simplified processing algorithm eases implementation of multiplexed detection and allows real-time signal readout, which are essential for point-of-care AST applications. To establish the method, 130 clinical urine samples were tested, and the results demonstrated an accuracy of ∼92% within 60-90 min for UTI diagnosis. Rapid AST of 55 positive clinical samples revealed 98% categorical agreement with both the clinical culture results and the on-site parallel AST validation results. This technology provides opportunities for prompt infection diagnosis and accurate antibiotic prescriptions in point-of-care settings.

Efficacy of 16S rRNA variable regions high-resolution melt analysis for bacterial pathogens identification in periprosthetic joint infections

BACKGROUND

Accurate and rapid identification of microorganisms causing periprosthetic joint infections (PJIs) are necessary for choosing an appropriate antibiotic therapy. Therefore, molecular techniques are suggested for diagnosis in suspected PJIs. The Broad-range PCR and High-Resolution Melt Analysis (HRMA) were evaluated for the identification of causative organisms of PJIs in this study.

RESULTS

For 47 of 63 specimens, both the culture and broad-range PCR were positive. The culture was found to be able of organism's detection in 74.6% (47/63) of patients. Of 47 positive cultures, 11 (23.4%) were polymicrobial and 36 (76.59%) were monomicrobial cultures, in which 34 (91.89%) cases were detected by HRM assay. The sensitivity, specificity of HRMA vs monomicrobial culture were 91.89, 93.75%, respectively. The sensitivity, specificity of total HRMA (mono + poly) vs culture were 82.92, 93.75%.

CONCLUSIONS

HRM assay coupled with broad-range PCR are effective screening, rapid, and relatively cost-effective methods for discrimination of PJIs especially in aiding culture method. Using computer programs such as the Matlab-2018b program for HRM data analysis is also valuable and helpful in diagnosis.

Digital electrical impedance analysis for single bacterium sensing and antimicrobial susceptibility testing

Single-molecule and single-cell analysis techniques have opened new opportunities for characterizing and analyzing heterogeneity within biological samples. These detection methods are often referred to as digital assays because the biological sample is partitioned into many small compartments and each compartment contains a discrete number of targets (e.g. cells). Using digital assays, researchers can precisely detect and quantify individual targets, and this capability has made digital techniques the basis for many modern bioanalytical tools (including digital PCR, single cell RNA sequencing, and digital ELISA). However, digital assays are dominated by optical analysis systems that typically utilize microscopy to analyze partitioned samples. The utility of digital assays may be dramatically enhanced by implementing cost-efficient and portable electrical detection capabilities. Herein, we describe a digital electrical impedance sensing platform that enables direct multiplexed measurement of single cell bacterial cells. We outline our solutions to the challenge of multiplexing impedance sensing across many culture compartments and demonstrate the potential for rapidly differentiating antimicrobial resistant versus susceptible strains of bacteria.

Droplet-Based Single-Cell Measurements of 16S rRNA Enable Integrated Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Testing from Clinical Samples in 30 min

Empiric broad-spectrum antimicrobial treatments of urinary tract infections (UTIs) have contributed to widespread antimicrobial resistance. Clinical adoption of evidence-based treatments necessitates rapid diagnostic methods for pathogen identification (ID) and antimicrobial susceptibility testing (AST) with minimal sample preparation. In response, a microfluidic droplet-based platform is developed for achieving both ID and AST from urine samples within 30 min. In this platform, fluorogenic hybridization probes are utilized to detect 16S rRNA from single bacterial cells encapsulated in picoliter droplets, enabling molecular identification of uropathogenic bacteria directly from urine in as little as 16 min. Moreover, in-droplet single-bacterial measurements of 16S rRNA provide a surrogate for AST, shortening the exposure time to 10 min for gentamicin and ciprofloxacin. A fully integrated device and screening workflow were developed to test urine specimens for one of seven unique diagnostic outcomes including the presence/absence of Gram-negative bacteria, molecular ID of the bacteriaas Escherichia coli, an Enterobacterales, or other organism, and assessment of bacterial susceptibility to ciprofloxacin. In a 50-specimen clinical comparison study, the platform demonstrates excellent performance compared to clinical standard methods (areas-under-curves, AUCs >0.95), within a small fraction of the turnaround time, highlighting its clinical utility.

Microdroplet Impact-Induced Spray Ionization Mass Spectrometry (MISI MS) for Online Reaction Monitoring and Bacteria Discrimination

Microdroplet impact-induced spray ionization (MISI) is demonstrated involving the impact of microdroplets produced from a paper and their impact on another, leading to the ionization of analytes deposited on the latter. This cascaded process is more advantageous in comparison to standard spray ionization as it performs reactions and ionization simultaneously in the absence of high voltage directly applied on the sample. In MISI, we apply direct current (DC) potential only to the terminal paper, used as the primary ion source. Charge transfer due to microdroplet/ion deposition on the flowing analyte solution on the second surface generates secondary charged microdroplets from it carrying the analytes, which ionize and get detected by a mass spectrometer. In this way, up to three cascaded spray sources could be assembled in series. We show the detection of small molecules and proteins in such ionization events. MISI provides a method to understand chemical reactions by droplet impact. The C-C bond formation reactions catalyzed by palladium and alkali metal ion encapsulation using crown ether were studied as our model reactions. To demonstrate the application of our ion source in a bioanalytical context, we studied the noninvasive in situ discrimination of bacteria samples under ambient conditions.

Direct Antimicrobial Susceptibility Testing on Clinical Urine Samples by Optical Tracking of Single Cell Division Events

With the increasing prevalence of antibiotic resistance, the need to develop antimicrobial susceptibility testing (AST) technologies is urgent. The current challenge has been to perform the antibiotic susceptibility testing in short time, directly with clinical samples, and with antibiotics over a broad dynamic range of clinically relevant concentrations. Here, a technology for point-of-care diagnosis of antimicrobial-resistant bacteria in urinary tract infections, by imaging the clinical urine samples directly with an innovative large volume solution scattering imaging (LVSi) system and analyzing the image sequences with a single-cell division tracking method is developed. The high sensitivity of single-cell division tracking associated with large volume imaging enables rapid antibiotic susceptibility testing directly on the clinical urine samples. The results demonstrate direct detection of bacterial infections in 60 clinical urine samples with a 60 min LVSi video, and digital AST of 30 positive clinical samples with 100% categorical agreement with both the clinical culture results and the on-site agar plating validation results. This technology provides opportunities for precise antibiotic prescription and proper treatment of the patient within a single clinic visit.

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

Facile Coupling of Droplet Magnetofluidic-Enabled Automated Sample Preparation for Digital Nucleic Acid Amplification Testing and Analysis

Digital nucleic acid amplification testing (dNAAT) and analysis techniques, such as digital polymerase chain reaction (PCR), have become useful clinical diagnostic tools. However, nucleic acid (NA) sample preparation preceding dNAAT is generally laborious and performed manually, thus creating the need for a simple sample preparation technique and a facile coupling strategy for dNAAT. Therefore, we demonstrate a simple workflow which automates magnetic bead-based extraction of NAs with a one-step transfer to dNAAT. Specifically, we leverage droplet magnetofluidics (DM) to automate the movement of magnetic beads between small volumes of reagents commonly employed for NA extraction and purification. Importantly, the buffer typically used to elute the NAs off the magnetic beads is replaced by a carefully selected PCR solution, enabling direct transfer from sample preparation to dNAAT. Moreover, we demonstrate the potential for multiplexing using a digital high-resolution melt (dHRM) after the digital PCR (dPCR). The utility of this workflow is demonstrated with duplexed detection of bacteria in a sample imitating a coinfection. We first purify the bacterial DNA into a PCR solution using our DM-based sample preparation. We then transfer the purified bacterial DNA to our microfluidic nanoarray to amplify 16S rRNA using dPCR and then perform dHRM to identify the two bacterial species.

Reduction of the Carbapenemase Inactivation Method (CIM) assay time by real-time PCR

Carbapenemase Inactivation Method (CIM) is a test to detect presence of the carbapenemase in Gram-negative bacteria. Determination of the carbapenemase production by inactivation of meropenem requires that a zone of control E. coli inhibition be measured approximately 6-24 h after plating. We have modified the CIM test by developing a rapid method which instead measures the growth of E. coli indicator strain ATCC 25922 using real-time PCR, referred to as a nucleic acid testing CIM (natCIM). Our natCIM, therefore reduces the detecting time from 6 to 24 h to approximately 4 h.

Fast antimicrobial susceptibility testing on Escherichia coli by metabolic heat nanocalorimetry

Fast spreading of antimicrobial resistance is now considered a major global health threat. New technologies are required, enabling rapid diagnostics of bacterial infection combined with fast antimicrobial susceptibility testing (AST) for evaluating the efficiency and dosage of antimicrobial compounds in vitro. This work presents an integrated chip-based isothermal nanocalorimetry platform for direct microbial metabolic heat measurements and evaluates its potential for fast AST. Direct detection of the bacteria-generated heat allows monitoring of metabolic activity and antimicrobial action at subinhibitory concentrations in real-time. The high heat sensitivity of the platform enables bacterial growth detection within only a few hours of incubation, whereas growth inhibition upon administration of antibiotics is revealed by a decrease or the absence of the heat signal. Antimicrobial stress results in lag phase extension and metabolic energy spilling. Oxygen consumption and optical density measurements provide a more holistic insight of the metabolic state and the evolution of bacterial biomass. As a proof-of-concept, a metabolic heat-based AST study on Escherichia coli as model organism with 3 clinically relevant antibiotics is performed and the minimum inhibitory concentrations are determined.

Modern Tools for Rapid Diagnostics of Antimicrobial Resistance

Fast, robust, and affordable antimicrobial susceptibility testing (AST) is required, as roughly 50% of antibiotic treatments are started with wrong antibiotics and without a proper diagnosis of the pathogen. Validated growth-based AST according to EUCAST or CLSI (European Committee on Antimicrobial Susceptibility Testing, Clinical Laboratory Standards Institute) recommendations is currently suggested to guide the antimicrobial therapy. Any new AST should be validated against these standard methods. Many rapid diagnostic techniques can already provide pathogen identification. Some of them can additionally detect the presence of resistance genes or resistance proteins, but usually isolated pure cultures are needed for AST. We discuss the value of the technologies applying nucleic acid amplification, whole genome sequencing, and hybridization as well as immunodiagnostic and mass spectrometry-based methods and biosensor-based AST. Additionally, we evaluate the potential of integrated systems applying microfluidics to integrate cultivation, lysis, purification, and signal reading steps. We discuss technologies and commercial products with potential for Point-of-Care Testing (POCT) and their capability to analyze polymicrobial samples without pre-purification steps. The purpose of this critical review is to present the needs and drivers for AST development, to show the benefits and limitations of AST methods, to introduce promising new POCT-compatible technologies, and to discuss AST technologies that are likely to thrive in the future.

Real-time kinetics and high-resolution melt curves in single-molecule digital LAMP to differentiate and study specific and non-specific amplification

Isothermal amplification assays, such as loop-mediated isothermal amplification (LAMP), show great utility for the development of rapid diagnostics for infectious diseases because they have high sensitivity, pathogen-specificity and potential for implementation at the point of care. However, elimination of non-specific amplification remains a key challenge for the optimization of LAMP assays. Here, using chlamydia DNA as a clinically relevant target and high-throughput sequencing as an analytical tool, we investigate a potential mechanism of non-specific amplification. We then develop a real-time digital LAMP (dLAMP) with high-resolution melting temperature (HRM) analysis and use this single-molecule approach to analyze approximately 1.2 million amplification events. We show that single-molecule HRM provides insight into specific and non-specific amplification in LAMP that are difficult to deduce from bulk measurements. We use real-time dLAMP with HRM to evaluate differences between polymerase enzymes, the impact of assay parameters (e.g. time, rate or florescence intensity), and the effect background human DNA. By differentiating true and false positives, HRM enables determination of the optimal assay and analysis parameters that leads to the lowest limit of detection (LOD) in a digital isothermal amplification assay.

Innovative and rapid antimicrobial susceptibility testing systems

Antimicrobial resistance (AMR) is a major threat to human health worldwide, and the rapid detection and quantification of resistance, combined with antimicrobial stewardship, are key interventions to combat the spread and emergence of AMR. Antimicrobial susceptibility testing (AST) systems are the collective set of diagnostic processes that facilitate the phenotypic and genotypic assessment of AMR and antibiotic susceptibility. Over the past 30 years, only a few high-throughput AST methods have been developed and widely implemented. By contrast, several studies have established proof of principle for various innovative AST methods, including both molecular-based and genome-based methods, which await clinical trials and regulatory review. In this Review, we discuss the current state of AST systems in the broadest technical, translational and implementation-related scope.

Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test

Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment-machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

OBJECTIVES

To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the "Ten threats to global health in 2019" report published by the World Health Organization.

METHODS

We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme.

RESULTS

In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures.

CONCLUSIONS

As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.

Emerging Analytical Techniques for Rapid Pathogen Identification and Susceptibility Testing

In the face of looming threats from multi-drug resistant microorganisms, there is a growing need for technologies that will enable rapid identification and drug susceptibility profiling of these pathogens in health care settings. In particular, recent progress in microfluidics and nucleic acid amplification is pushing the boundaries of timescale for diagnosing bacterial infections. With a diverse range of techniques and parallel developments in the field of analytical chemistry, an integrative perspective is needed to understand the significance of these developments. This review examines the scope of new developments in assay technologies grouped by key enabling domains of research. First, we examine recent development in nucleic acid amplification assays for rapid identification and drug susceptibility testing in bacterial infections. Next, we examine advances in microfluidics that facilitate acceleration of diagnostic assays via integration and scale. Lastly, recentdevelopments in biosensor technologies are reviewed. We conclude this review with perspectives on the use of emerging concepts to develop paradigm-changing assays.

Current and Emerging Methods of Antibiotic Susceptibility Testing

Antibiotic susceptibility testing (AST) specifies effective antibiotic dosage and formulates a profile of empirical therapy for the proper management of an individual patient's health against deadly infections. Therefore, rapid diagnostic plays a pivotal role in the treatment of bacterial infection. In this article, the authors review the socio-economic burden and emergence of antibiotic resistance. An overview of the phenotypic, genotypic, and emerging techniques for AST has been provided and discussed, highlighting the advantages and limitations of each. The historical perspective on conventional methods that have paved the way for modern AST like disk diffusion, Epsilometer test (Etest), and microdilution, is presented. Several emerging methods, such as microfluidic-based optical and electrochemical AST have been critically evaluated. Finally, the challenges related with AST and its outlook in the future are presented.

3D impedimetric sensors as a tool for monitoring bacterial response to antibiotics

The presence of antimicrobial contaminants like antibiotics in the environment is a major concern because they promote the emergence and the spread of multidrug resistant bacteria. Since the conventional systems for the determination of bacterial susceptibility to antibiotics rely on culturing methods that require long processing times, the implementation of novel strategies is highly required for fast and point-of-care applications. Here the development and characterization of a novel label-free biosensing platform based on a microbial biosensor approach to perform antibiotic detection bioassays in diluted solution is presented. The microbial biosensor is based on a three-dimensional interdigitated electrode array (3D-IDEA) impedimetric transducer with immobilized E. coli bacteria. In 3D-IDEA to increase the sensitivity to superficial impedance changes the electrode digits are separated by insulating barriers. A novel strategy is employed to selectively immobilize bacteria in the spaces over the electrode digits between the barriers, referred to here as trenches, in order to concentrate bacteria, improve the reproducibility of the E. coli immobilization and increase the sensitivity for monitoring bacterial response. For effective attachment of bacteria within the trenches an initial anchoring layer of a highly charged polycation, polyethyleneimine (PEI), was used. To facilitate immobilization of bacteria within the trenches and prevent their deposition on top of the barriers an important novelty is the use of poly(N-isopropylmethacrylamide) p(NIPMAM) microgels working as antifouling agents, deposited on top of the barriers by microcontact printing. The reported microbial biosensor approach allows the bacterial response to ampicillin, a bacteriolytic antibiotic, to be registered by means of impedance variations in a rapid and label-free operation that enables new possibilities in bioassays for toxicity testing.

Single-color multiplexing by the integration of high-resolution melting pattern recognition with loop-mediated isothermal amplification

We develop a single-color multiplexing strategy by the integration of high-resolution melting pattern recognition with loop-mediated isothermal amplification (LAMP). This strategy can identify multiple amplicons with a small DNA melting temperature (Tm) difference (∼0.2 °C) without the involvement of either multicolor labels or parallelized multiplexing, and it can sensitively detect LAMP amplicons with the initial DNA concentrations ranging from 10 to 108 copies.

Nanotube assisted microwave electroporation for single cell pathogen identification and antimicrobial susceptibility testing

A nanotube assisted microwave electroporation (NAME) technique is demonstrated for delivering molecular biosensors into viable bacteria for multiplex single cell pathogen identification to advance rapid diagnostics in clinical microbiology. Due to the small volume of a bacterial cell (~femtoliter), the intracellular concentration of the target molecule is high, which results in a strong signal for single cell detection without amplification. The NAME procedure can be completed in as little as 30 minutes and can achieve over 90% transformation efficiency. We demonstrate the feasibility of NAME for identifying clinical isolates of bloodborne and uropathogenic pathogens and detecting bacterial pathogens directly from patient's samples. In conjunction with a microfluidic single cell trapping technique, NAME allows single cell pathogen identification and antimicrobial susceptibility testing concurrently. Using this approach, the time for microbiological analysis reduces from days to hours, which will have a significant impact on the clinical management of bacterial infections.

Rapid Microbiology Screening in Pharmaceutical Workflows

Recently advances in miniaturization and automation have been utilized to rapidly decrease the time to result for microbiology testing in the clinic. These advances have been made due to the limitations of conventional culture-based microbiology methods, including agar plate and microbroth dilution, which have long turnaround times and require physicians to treat patients empirically with antibiotics before test results are available. Currently, there exist similar limitations in pharmaceutical sterility and bioburden testing, where the long turnaround times associated with standard microbiology testing drive costly inefficiencies in workflows. These include the time lag associated with sterility screening within drug production lines and the warehousing cost and time delays within supply chains during product testing. Herein, we demonstrate a proof-of-concept combination of a rapid microfluidic assay and an efficient cell filtration process that enables a path toward integrating rapid tests directly into pharmaceutical microbiological screening workflows. We demonstrate separation and detection of Escherichia coli directly captured and analyzed from a mammalian (i.e., CHO) cell culture with a 3.0 h incubation. The demonstration is performed using a membrane filtration module that is compatible with sampling from bioreactors, enabling in-line sampling and process monitoring.

A Rapid and Low-Cost Pathogen Detection Platform by Using a Molecular Agglutination Assay

Rapid and low-cost pathogen diagnostic approaches are critical for clinical decision-making procedures. Cultivating bacteria often takes days to identify pathogens and provide antimicrobial susceptibilities. The delay in diagnosis may result in compromised treatment and inappropriate antibiotic use. Over the past decades, molecular-based techniques have significantly shortened pathogen identification turnaround time with high accuracy. However, these assays often use complex fluorescent labeling and nucleic acid amplification processes, which limit their use in resource-limited settings. In this work, we demonstrate a wash-free molecular agglutination assay with a straightforward mixing and incubation step that significantly simplifies procedures of molecular testing. By targeting the 16S rRNA gene of pathogens, we perform a rapid pathogen identification within 30 min on a dark-field imaging microfluidic cytometry platform. The dark-field images with low background noise can be obtained using a narrow beam scanning technique with off-the-shelf complementary metal oxide semiconductor (CMOS) imagers such as smartphone cameras. We utilize a machine learning algorithm to deconvolute topological features of agglutinated clusters and thus quantify the abundance of bacteria. Consequently, we unambiguously distinguish Escherichia coli positive from other E. coli negative among 50 clinical urinary tract infection samples with 96% sensitivity and 100% specificity. Furthermore, we also apply this quantitative detection approach to achieve rapid antimicrobial susceptibility testing within 3 h. This work exhibits easy-to-use protocols, high sensitivity, and short turnaround time for point-of-care testing uses.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?