AI-based mobile application to fight antibiotic resistance

Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge

ABSTRACTImproved sanitation, increased access to health care, and advances in preventive and clinical medicine have reduced the mortality and morbidity rates of several infectious diseases. However, recent outbreaks of several emerging infectious diseases (EIDs) have caused substantial mortality and morbidity, and the frequency of these outbreaks is likely to increase due to pathogen, environmental, and population effects driven by climate change. Extreme or persistent changes in temperature, precipitation, humidity, and air pollution associated with climate change can, for example, expand the size of EID reservoirs, increase host-pathogen and cross-species host contacts to promote transmission or spillover events, and degrade the overall health of susceptible host populations leading to new EID outbreaks. It is therefore vital to establish global strategies to track and model potential responses of candidate EIDs to project their future behaviour and guide research efforts on early detection and diagnosis technologies and vaccine development efforts for these targets. Multi-disciplinary collaborations are demanding to develop effective inter-continental surveillance and modelling platforms that employ artificial intelligence to mitigate climate change effects on EID outbreaks. In this review, we discuss how climate change has increased the risk of EIDs and describe novel approaches to improve surveillance of emerging pathogens that pose the risk for EID outbreaks, new and existing measures that could be used to contain or reduce the risk of future EID outbreaks, and new methods to improve EID tracking during further outbreaks to limit disease transmission.

Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions

Antimicrobial resistance (AMR) is a global health challenge that threatens humans, animals and the environment. Evidence is emerging for a role of healthcare infrastructure, environments and patient pathways in promoting and maintaining AMR via direct and indirect mechanisms. Advances in vaccination and monoclonal antibody therapies together with integrated surveillance, rapid diagnostics, targeted antimicrobial therapy and infection control measures offer opportunities to address healthcare-associated AMR risks more effectively. Additionally, innovations in artificial intelligence, data linkage and intelligent systems can be used to better predict and reduce AMR and improve healthcare resilience. In this Review, we examine the mechanisms by which healthcare functions as a driver, reservoir and amplifier of AMR, contextualized within a One Health framework. We also explore the opportunities and innovative solutions that can be used to combat AMR throughout the patient journey. We provide a perspective on the current evidence for the effectiveness of interventions designed to mitigate healthcare-associated AMR and promote healthcare resilience within high-income and resource-limited settings, as well as the challenges associated with their implementation.

An escape from ESKAPE pathogens: A comprehensive review on current and emerging therapeutics against antibiotic resistance

The rise of antimicrobial resistance has positioned ESKAPE pathogens as a serious global health threat, primarily due to the limitations and frequent failures of current treatment options. This growing risk has spurred the scientific community to seek innovative antibiotic therapies and improved oversight strategies. This review aims to provide a comprehensive overview of the origins and resistance mechanisms of ESKAPE pathogens, while also exploring next-generation treatment strategies for these infections. In addition, it will address both traditional and novel approaches to combating antibiotic resistance, offering insights into potential new therapeutic avenues. Emerging research underscores the urgency of developing new antimicrobial agents and strategies to overcome resistance, highlighting the need for novel drug classes and combination therapies. Advances in genomic technologies and a deeper understanding of microbial pathogenesis are crucial in identifying effective treatments. Integrating precision medicine and personalized approaches could enhance therapeutic efficacy. The review also emphasizes the importance of global collaboration in surveillance and stewardship, as well as policy reforms, enhanced diagnostic tools, and public awareness initiatives, to address resistance on a worldwide scale.

Access to and utilisation of antimicrobials among forcibly displaced persons in Uganda, Yemen and Colombia: a pilot cross-sectional survey

OBJECTIVES

Identifying key barriers to accessing quality-assured and affordable antimicrobials among forcibly displaced persons in Uganda, Yemen and Colombia and investigating their (1) utilisation patterns of antibiotics, (2) knowledge about antimicrobial resistance (AMR) and (3) perception of the quality of antimicrobials received.

DESIGN

Pilot cross-sectional survey.

SETTING

Data were collected from five health facilities in the Kiryandongo refugee settlement (Bweyale, Uganda), three camps for internally displaced persons (IDPs) in the Dar Sad district (Aden, Yemen) and a district with a high population of Venezuelan migrants (Kennedy district, Bogotá, Colombia). Data collection took place between February and May 2021. The three countries were selected due to their high number of displaced people in their respective continents.

PARTICIPANTS

South Sudanese refugees in Uganda, IDPs in Yemen and Venezuelan migrants in Colombia.

OUTCOME MEASURE

The most common barriers to access to quality-assured and affordable antimicrobials.

RESULTS

A total of 136 participants were enrolled in this study. Obtaining antimicrobials through informal pathways, either without a doctor's prescription or through family and friends, was common in Yemen (27/50, 54.0%) and Colombia (34/50, 68.0%). In Yemen and Uganda, respondents used antibiotics to treat (58/86, 67.4%) and prevent (39/86, 45.3%) a cold. Knowledge of AMR was generally low (24/136, 17.6%). Barriers to access included financial constraints in Colombia and Uganda, prescription requirements in Yemen and Colombia, and non-availability of drugs in Uganda and Yemen.

CONCLUSION

Our multicentred research identified common barriers to accessing quality antimicrobials among refugees/IDPs/migrants and common use of informal pathways. The results suggest that knowledge gaps about AMR may lead to potential misuse of antimicrobials. Due to the study's small sample size and use of non-probability sampling, the results should be interpreted with caution, and larger-scale assessments on this topic are needed. Future interventions designed for similar humanitarian settings should consider the interlinked barriers identified.

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

Combating antimicrobial resistance in malaria, HIV and tuberculosis

Antimicrobial resistance poses a significant threat to the sustainability of effective treatments against the three most prevalent infectious diseases: malaria, human immunodeficiency virus (HIV) infection and tuberculosis. Therefore, there is an urgent need to develop novel drugs and treatment protocols capable of reducing the emergence of resistance and combating it when it does occur. In this Review, we present an overview of the status and underlying molecular mechanisms of drug resistance in these three diseases. We also discuss current strategies to address resistance during the research and development of next-generation therapies. These strategies vary depending on the infectious agent and the array of resistance mechanisms involved. Furthermore, we explore the potential for cross-fertilization of knowledge and technology among these diseases to create innovative approaches for minimizing drug resistance and advancing the discovery and development of new anti-infective treatments. In conclusion, we advocate for the implementation of well-defined strategies to effectively mitigate and manage resistance in all interventions against infectious diseases.

Accurate estimation of the inhibition zone of antibiotics based on laser speckle imaging and multiple random speckle illumination

The antimicrobial susceptibility test (AST) plays a crucial role in selecting appropriate antibiotics for the treatment of bacterial infections in patients. The diffusion disk method is widely adopted AST method due to its simplicity, cost-effectiveness, and flexibility. It assesses antibiotic efficacy by measuring the size of the inhibition zone where bacterial growth is suppressed. Quantification of the zone diameter is typically achieved using tools such as rulers, calipers, or automated zone readers, as the inhibition zone is visually discernible. However, challenges arise due to inaccuracies stemming from human errors or image processing of intensity-based images. Here, we proposed a bacterial activity-based AST using laser speckle imaging (LSI) with multiple speckle illumination. LSI measures a speckle pattern produced by interferences of scattered light from the sample; therefore, LSI enables the detection of variation or movement within the sample such as bacterial activity. We found that LSI with multiple speckle illuminations provides consistent and uniform analysis of measured time-varying speckle images. Furthermore, our proposed method effectively identified the boundary of the inhibition zone using the k-means clustering algorithm, exploiting a result of speckle pattern analysis as features. Collectively, the proposed method offers a versatile analytical tool in the diffusion disk method.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Antimicrobial susceptibility prediction from genomes: a dream come true?

Genome-based diagnostics provides relevant information to guide patient treatment and support pathogen and resistance surveillance. Recently, Coll et al. introduced a curated database for predicting antimicrobial resistance (AMR) from Enterococcus faecium genomics data, offering excellent predictive values for susceptibility to important antimicrobials. Challenges to predict resistance to last-resort antimicrobials remain.

Machine learning to identify clinically relevant Candida yeast species

Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organisms such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically important Candida species. Wet-mounted images were captured using bright field live-cell microscopy followed by separating single-cells, budding-cells, and cell-group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predict Candida species. Among the six algorithms tested, the InceptionV3 model performed best in predicting Candida species from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells of C. albicans, C. auris, C. glabrata (Nakaseomyces glabrata), and C. haemulonii in 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells of C. albicans, C. auris, C. glabrata, and C. haemulonii InceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were 77.1% and 92.4%, respectively. Overall, this study provides proof of the concept that microscopy images from wet-mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.

Maximally stable extremal regions-based algorithm for automatic interpretation of disc-diffusion antibiotic sensitivity test

Antibiotic resistance causes a major threat to patients suffering from infectious diseases. Accurate and timely assessment of Antibiotic Susceptibility Test (AST) is of great importance to ensure adequate treatment for patients and for epidemiological monitoring. Disc Diffusion Test (DDT) is a standard and widely used method for AST. Manual interpretation of DDT results is a tedious task and susceptible to human errors. Computer vision-based automated interpretation of DDT results will speed up the process and reduces the manpower requirement. This would assist the physician to initiate the antibiotic treatment for the patients on time and results in saving the patient's life. The crucial step in automatic interpretation of DDT result is to measure and present the diameter of zone of inhibition without manual intervention. The existing methods require manual interventions at various stages during inhibition zone diameter measurement for some typical cases. This issue is addressed in the present work through maximally stable extremal regions (MSER) based algorithm. Dataset consisting of 60 agar plate images that includes different agar medium, images having different resolution and visual quality is used to validate the proposed method. Experimental results demonstrated that there is a strong correlation between standard method and the proposed method.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

What are the most relevant publications in Clinical Microbiology in the last two years?

This minireview describes some of the articles published in the last two years related to innovative technologies including CRISPR-Cas, surface-enhanced Raman spectroscopy, microfluidics, flow cytometry, Fourier transform infrared spectroscopy, and artificial intelligence and their application to microbiological diagnosis, molecular typing and antimicrobial susceptibility testing. In addition, some articles related to resistance to new antimicrobials (ceftazidime-avibactam, meropenem-vaborbactam, imipenem-relebactam, and cefiderocol) are also described.

Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician

BACKGROUND

Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.

OBJECTIVES

We summarise recent and potential future applications of AI and its relevance to clinical infection practice.

METHODS

1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.

RESULTS

There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.

CONCLUSIONS

Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.

A nested cohort 5-year Canadian surveillance of Gram-negative antimicrobial resistance for optimized antimicrobial therapy

We analyzed 5 years (2016-2020) of nested Canadian data from the Study for Monitoring Antimicrobial Resistance Trends (SMART) to identify pathogen predominance and antimicrobial resistance (AMR) patterns of adult Gram-negative infections in Canadian health care and to complement other public surveillance programs and studies in Canada. A total of 6853 isolates were analyzed from medical (44%), surgical (18%), intensive care (22%) and emergency units (15%) and from respiratory tract (36%), intra-abdominal (25%), urinary tract (24%) and bloodstream (15%) infections. Overall, E. coli (36%), P. aeruginosa (18%) and K. pneumoniae (12%) were the most frequent isolates and P. aeruginosa was the most common respiratory pathogen. 18% of Enterobacterales species were ESBL positive. Collective susceptibility profiles showed that P. aeruginosa isolates were highly susceptible (> 95%) to ceftolozane/tazobactam and colistin, though markedly less susceptible (58-74%) to other antimicrobials tested. Multi-drug resistance (MDR) was present in 10% of P. aeruginosa isolates and was more frequent in those from respiratory infections and from ICU than non-ICU locations. Of P. aeruginosa isolates that were resistant to combinations of ceftazidime, piperacillin/tazobactam and meropenem, 73-96% were susceptible to ceftolozane/tazobactam over the period of the study. These national data can now be combined with clinical prediction rules and genomic data to enable expert antimicrobial stewardship applications and guide treatment policies to optimize adult patient care.

Peracetic acid disinfection induces antibiotic-resistant E. coli into VBNC state but ineffectively eliminates the transmission potential of ARGs

The occurrence of a viable but nonculturable (VBNC) state in antibiotic-resistant E. coli (AR E. coli) and inefficient degradation of their antibiotic resistance genes (ARGs) may cause potential health risks during disinfection. Peracetic acid (PAA) is an alternative disinfectant for replacing chlorine-based oxidants in wastewater treatment, and the potential of PAA to induce a VBNC state in AR E. coli and to remove the transformation functionality of ARGs were investigated for the first time. Results show that PAA exhibits excellent performance in inactivating AR E. coli (over 7.0-logs) and persistently inhibiting its regeneration. After PAA disinfection, insignificant changes in the ratio of living to dead cells (∼4%) and the level of cell metabolism, indicating that AR E. coli were induced into VBNC states. Unexpectedly, PAA was found to induce AR E. coli into VBNC state by destroying the proteins containing reactive amino acids at thiol, thioether and imidazole groups, rather than the result of membrane damage, oxidative stress, lipid destruction and DNA disruption in the conventional disinfection processes. Moreover, the result of poor reactivity between PAA and plasmid strands and bases confirmed that PAA hardly reduced the abundance of ARGs and damaged the plasmid's integrity. Transformation assays and real environment validation indicated that PAA-treated AR E. coli could release large abundance of naked ARGs with high-efficiency transformation functionality (∼5.4 × 10-4 - ∼8.3 × 10-6) into the environment. This study has significant environmental implications for assessing the transmission of antimicrobial resistance during PAA disinfection.

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance is predicated on the surveillance of antibiotic resistance genes within an environment. Bioinformatics and artificial intelligence (AI) methods applied to metagenomic sequencing data offer the capacity to detect known and infer yet-unknown resistance mechanisms, and predict future outbreaks of antibiotic-resistant infections. Machine learning methods, in particular, could revive the waning antibiotic discovery pipeline by helping to predict the molecular structure and function of antibiotic resistance compounds, and optimising their interactions with target proteins. Consequently, AI has the capacity to play a central role in guiding antibiotic stewardship and future clinical decision-making around antibiotic resistance.

Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review

Artificial intelligence (AI) has received great attention since the concept was proposed, and it has developed rapidly in recent years with applications in many fields. Meanwhile, newer iterations of smartphone hardware technologies which have excellent data processing capabilities have leveraged on AI capabilities. Based on the desirability for portable detection, researchers have been investigating intelligent analysis by combining smartphones with AI algorithms. Various examples of the application of AI algorithm-based smartphone detection and analysis have been developed. In this review, we give an overview of this field, with a particular focus on bioanalytical detection applications. The applications are presented in terms of hardware design, software algorithms, and specific application areas. We also discuss the existing limitations of AI-based smartphone detection and analytical approaches, and their future prospects. The take-home message of our review is that the application of AI in the field of detection analysis is restricted by the limitations of the smartphone's hardware as well as the model building of AI for detection targets with insufficient data. Nevertheless, at this juncture, while bioanalytical diagnostics and health monitoring have set the pace for AI-based smartphone applicability, the future should see the technology making greater inroads into other fields. In relation to the latter, it is likely that the ordinary or average person will play a greater participatory role.

Chemical Basis of Combination Therapy to Combat Antibiotic Resistance

The antimicrobial resistance crisis is a global health issue requiring discovery and development of novel therapeutics. However, conventional screening of natural products or synthetic chemical libraries is uncertain. Combination therapy using approved antibiotics with inhibitors targeting innate resistance mechanisms provides an alternative strategy to develop potent therapeutics. This review discusses the chemical structures of effective β-lactamase inhibitors, outer membrane permeabilizers, and efflux pump inhibitors that act as adjuvant molecules of classical antibiotics. Rational design of the chemical structures of adjuvants will provide methods to impart or restore efficacy to classical antibiotics for inherently antibiotic-resistant bacteria. As many bacteria have multiple resistance pathways, adjuvant molecules simultaneously targeting multiple pathways are promising approaches to combat multidrug-resistant bacterial infections.

Artificial intelligence as a smart approach to develop antimicrobial drug molecules: A paradigm to combat drug-resistant infections

Antimicrobial resistance (AMR) is a silent pandemic with the third highest global mortality. The antibiotic development pipeline is scarce even though AMR has escalated uncontrollably. Artificial intelligence (AI) is a revolutionary approach, accelerating drug discovery because of its fast pace, cost efficiency, lower labor requirements, and fewer chances of failure. AI has been used to discover several beta-lactamase inhibitors and antibiotic alternatives from antimicrobial peptides (AMPs), nonribosomal peptides, bacteriocins, and marine natural products. The significant recent increase in the use of AI platforms by pharmaceutical companies could result in the discovery of efficient antibiotic alternatives with lower chances of resistance generation.

Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study

Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform doctors about their results. The aim of this study was to investigate how healthcare professionals prefer to receive predictions generated by machine learning algorithms. A systematic search in MEDLINE, via PubMed, EMBASE and Web of Science was first performed. We developed a mobile app, RandomIA, to predict the occurrence of clinical outcomes, initially for COVID-19 and later expected to be expanded to other diseases. A questionnaire called System Usability Scale (SUS) was selected to assess the usability of the mobile app. A total of 69 doctors from the five regions of Brazil tested RandomIA and evaluated three different ways to visualize the predictions. For prognostic outcomes (mechanical ventilation, admission to an intensive care unit, and death), most doctors (62.9%) preferred a more complex visualization, represented by a bar graph with three categories (low, medium, and high probability) and a probability density graph for each outcome. For the diagnostic prediction of COVID-19, there was also a majority preference (65.4%) for the same option. Our results indicate that doctors could be more inclined to prefer receiving detailed results from predictive machine learning algorithms.

Metagenomics insights into the profiles of antibiotic resistome in combined sewage overflows from reads to metagenome assembly genomes

Combined sewage overflows (CSOs) have become an important source of antibiotic resistance genes (ARGs) in the environment, while the distribution and dynamics of antibiotic resistome in the CSOs events have not been well understood. This study deciphered the profiles of antibiotic resitome in the CSOs based on metagenomics analysis from reads to metagenome assembly genomes (MAGs), and the dynamical changes of ARGs were clarified through continuous monitoring of the CSO event. Results showed that antibiotic inactivation was the dominant resistance mechanism, and sulfonamide, aminoglycoside along with multidrug resistance were the dominant antibiotic resistance types. It was speculated that the antibiotic resistome were generally determined by sewer sediment flushed out along with the CSOs not domestic sewage in the pipes. The host range and mobility of the antibiotic resistome were determined at contigs level, and the hosts mainly belonged to the Proteobacteria with the genus of Pseudomonas, Escherichia, Enterobacter and Aeromonas being dominant. The transposase (tnpA), IS91 and integrons were mobile genetic elements (MGEs) located together with ARGs, and a MAG carrying 32 ARGs and 140 VFGs was assembled. Although microbial community contributed most to the changes of antibiotic resistome in the CSOs directly, the risks caused by the MGEs should be paid more attention.

Advancing pharmacy and healthcare with virtual digital technologies

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.

Microbiology Assessments in Critically Ill Patients

The prevalence of suspected or proven infections in critically ill patients is high, with a substantial attributable risk to in-hospital mortality. Coordinated guidance and interventions to improve the appropriate microbiological assessment for diagnostic and therapeutic decisions are therefore pivotal. Conventional microbiology follows the paradigm of "best practice" of specimen selection and collection, governed by laboratory processing and standard operating procedures, and informed by the latest developments and trends. In this regard, the preanalytical phase of a microbiological diagnosis is crucial since inadequate sampling may result in the incorrect diagnosis and inappropriate management. In addition, the isolation and detection of contaminants interfere with multiple intensive care unit (ICU) processes, which confound the therapeutic approach to critically ill patients. To facilitate bedside enablement, the microbiology laboratory should provide expedited feedback, reporting, and interpretation of results. Compared with conventional microbiology, novel rapid and panel-based diagnostic strategies have the clear advantages of a rapid turnaround time, the detection of many microorganisms including antimicrobial resistant determinants and thus promise substantial improvements in health care. However, robust data on the clinical evaluation of rapid diagnostic tests in presumed sepsis, sepsis and shock are extremely limited and more rigorous intervention studies, focusing on direct benefits for critically ill patients, are pivotal before widespread adoption of their use through the continuum of ICU stay. Advocating the use of these diagnostics without firmly establishing which patients would benefit most, how to interpret the results, and how to treat according to the results obtained, could in fact be counterproductive with regards to diagnostic "best practice" and antimicrobial stewardship. Thus, for the present, they may supplement but not yet supplant conventional microbiological assessments.

Lab-on-a-Disc for Point-of-Care Infection Diagnostics

Reliable, inexpensive, and rapid diagnostic tools are essential to control and prevent the spread of infectious diseases. Many commercial kits for coronavirus disease 2019 (COVID-19) diagnostics have played a crucial role in the fight against the COVID-19 pandemic. Most current standard in vitro diagnostic (IVD) protocols for infectious diseases are sensitive but time-consuming and require sophisticated laboratory equipment and specially trained personnel. Recent advances in biosensor technology suggest the potential to deliver point-of-care (POC) diagnostics that are affordable and provide accurate results in a short time. The ideal "sample-in-answer-out" type fully integrated POC infection diagnostic platforms are expected to be autonomous or easy-to-operate, equipment-free or infrastructure-independent, and high-throughput or easy to upscale. In this Account, we detail the recent progress made by our group and others in the development of centrifugal microfluidic devices or lab-on-a-disc (LOAD) systems. Unlike conventional pump-based fluid actuation, the centrifugal force generated by spinning the disc induces liquid pumping and no external fluidic interconnects are required. This allows a total fluidic network required for multiple steps of biological assays to be integrated on a disc, enabling fully automated POC diagnostics. Various applications have been demonstrated, including liquid biopsy for personalized cancer management, food applications, and environmental monitoring; here, we focus on IVD for infectious disease. First, we introduce various on-disc unit operation technologies, including reagent storage, sedimentation, filtration, valving, decanting, aliquoting, mixing, separation, serial dilution, washing, and calibration. Such centrifugal microfluidic technologies have already proved promising for micro-total-analysis systems for automated IVD ranging from molecular detection of pathogens to multiplexed enzyme-linked immunosorbent assays (ELISAs) that use raw samples such as whole blood or saliva. Some recent examples of LOAD systems for molecular diagnostics in which some or all steps of the assays are integrated on a disc, including pathogen enrichment, nucleic acid extraction, amplification, and detection, are discussed in detail. We then introduce fully automated ELISA systems with enhanced sensitivity. Furthermore, we demonstrate a toy-inspired fidget spinner that enables electricity-free and rapid analysis of pathogens from undiluted urine samples of patients with urinary tract infection symptoms and a phenotypic antimicrobial susceptibility test for an extreme POC diagnostics application. Considering the urgent need for cost-effective and reliable POC infection diagnostic tools, especially in the current pandemic crisis, the current limitations and future directions of fast and broad adaptation in real-world settings are also discussed. With proper attention to key challenges and leverage with recent advances in bio-sensing technologies, molecular biology, nanomaterials, analytical chemistry, miniaturization, system integration, and data management, LOAD systems hold the potential to deliver POC infection diagnostic tools with unprecedented performance regarding time, accuracy, and cost. We hope the new insight and promise of LOAD systems for POC infection diagnostics presented in this Account can spark new ideas and inspire further research and development to create better healthcare systems for current and future pandemics.

AI-based mobile application to fight antibiotic resistance

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present an artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phone’s camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our application’s reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The application’s performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients’ access to AST worldwide.

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. Here, the authors present an offline smartphone application for automated and standardized antibiotic susceptibility testing, to be deployed in resource-limited settings.