Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics-Current State and Whole Genome Sequencing Implementation Perspectives

Harnessing technology for infectious disease response in conflict zones: Challenges, innovations, and policy implications

Epidemic outbreaks of infectious diseases in conflict zones are complex threats to public health and humanitarian activities that require creativity approaches of reducing their damage. This narrative review focuses on the technology intersection with infectious disease response in conflict zones, and complexity of healthcare infrastructure, population displacement, and security risks. This narrative review explores how conflict-related destruction is harmful towards healthcare systems and the impediments to disease surveillance and response activities. In this regards, the review also considered the contributions of technological innovations, such as the improvement of epidemiological surveillance, mobile health (mHealth) technologies, genomic sequencing, and surveillance technologies, in strengthening infectious disease management in conflict settings. Ethical issues related to data privacy, security and fairness are also covered. By advisement on policy that focuses on investment in surveillance systems, diagnostic capacity, capacity building, collaboration, and even ethical governance, stakeholders can leverage technology to enhance the response to infectious disease in conflict settings and, thus, protect the global health security. This review is full of information for researchers, policymakers, and practitioners who are dealing with the issues of infectious disease outbreaks in conflicts worn areas.

Thermal-Accelerated Urease-Driven Bowl-Like Polydopamine Nanorobot for Targeted Photothermal/Photodynamic Antibiotic-Free Antibacterial Therapy

The problem of antibiotic resistance seriously affects the treatment of bacterial infections, so there is an urgent need to develop novel antibiotic-independent antimicrobial strategies. Herein, a urease-driven bowl-like mesoporous polydopamine nanorobot (MPDA@ICG@Ur@Man) based on single-wavelength near-infrared (NIR) remote photothermal acceleration to achieve antibiotic-free phototherapy(photothermal therapy, PTT, plus photodynamic therapy, PDT) is first reported. The smart nanorobots can perform active movement by decomposing urea to produce carbon dioxide and ammonia. Particularly, the elevated local temperature during PTT can increase urease activity to enhance the autonomous movement and thus increase the contact between the antimicrobial substance and bacteria. Compared with a nanomotor propelled by urea only, the diffusion coefficient (De) of photothermal-accelerated nanorobots is increased from 1.10 to 1.26 µm2 s-1. More importantly, urease-driven bowl-like nanorobots with photothermal enhancement can specifically identify Escherichia coli (E. coli) and achieve simultaneous PTT/PDT at a single wavelength with 99% antibactericidal activity in vitro. In a word, the urease-driven bowl-like nanorobots guided by photothermal-accelerated strategy could provide a novel perspective for increasing PTT/PDT antibacterial therapeutic efficacy and be promising for various antibiotic-free sterilization applications.

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

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

Present and Future Non-Culture-Based Diagnostics: Stewardship Potentials and Considerations

The medical microbiologist plays a key role in the transition from culture-based to molecular test methods for diagnosis of infectious diseases. They must understand the scientific and technical bases underlying these tests along with their associated benefits and limitations and be able to educate administrators and patient providers on their proper use. Coordination of testing practices between clinical departments and the spectrum of public health and research laboratories is essential to optimize health care delivery.

Whole Genome Sequencing: Applications in Clinical Bacteriology

The success in determining the whole genome sequence of a bacterial pathogen was first achieved in 1995 by determining the complete nucleotide sequence of Haemophilus influenzae Rd using the chain-termination method established by Sanger et al. in 1977 and automated by Hood et al. in 1987. However, this technology was laborious, costly, and time-consuming. Since 2004, high-throughput next-generation sequencing technologies have been developed, which are highly efficient, require less time, and are cost-effective for whole genome sequencing (WGS) of all organisms, including bacterial pathogens. In recent years, the data obtained using WGS technologies coupled with bioinformatics analyses of the sequenced genomes have been projected to revolutionize clinical bacteriology. WGS technologies have been used in the identification of bacterial species, strains, and genotypes from cultured organisms and directly from clinical specimens. WGS has also helped in determining resistance to antibiotics by the detection of antimicrobial resistance genes and point mutations. Furthermore, WGS data have helped in the epidemiological tracking and surveillance of pathogenic bacteria in healthcare settings as well as in communities. This review focuses on the applications of WGS in clinical bacteriology.

16S rRNA Next-Generation Sequencing May Not Be Useful for Examining Suspected Cases of Spontaneous Bacterial Peritonitis

Background and Objectives: Ascites, often associated with liver cirrhosis, poses diagnostic challenges, particularly in detecting bacterial infections. Traditional methods have limitations, prompting the exploration of advanced techniques such as 16S rDNA next-generation sequencing (NGS) for improved diagnostics in such low-biomass fluids. The aim of this study was to investigate whether the NGS method enhances detection sensitivity compared to a conventional ascites culture. Additionally, we aimed to explore the presence of a microbiome in the abdominal cavity and determine whether it has a sterile condition. Materials and Methods: Ten patients with clinically suspected spontaneous bacterial peritonitis (SBP) were included in this study. A traditional ascites culture was performed, and all ascites samples were subjected to 16S ribosomal RNA gene amplification and sequencing. 16S rRNA gene sequencing results were interpreted by comparing them to positive and negative controls for each sample. Results: Differential centrifugation was applied to all ascites samples, resulting in very small or no bacterial pellets being harvested. The examination of the 16S amplicon sequencing libraries indicated that the target amplicon products were either minimally visible or exhibited lower intensity than their corresponding negative controls. Contaminants present in the reagents were also identified in the ascites samples. Sequence analysis of the 16S rRNA gene of all samples showed microbial compositions that were akin to those found in the negative controls, without any bacteria isolated that were unique to the samples. Conclusions: The peritoneal cavity and ascites exhibit low bacterial biomass even in the presence of SBP, resulting in a very low positivity rate in 16S rRNA gene sequencing. Hence, the 16S RNA sequencing method does little to enhance the rate of positive samples compared to traditional culture methods, including in SBP cases.

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

Background

Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.

Methods

We aimed to predict the minimum inhibitory concentrations (MICs) of 13 antimicrobial agents against Acinetobacter baumannii using three machine learning algorithms (random forest, support vector machine, and XGBoost) combined with k-mer features extracted from WGS data.

Results

A cohort of 339 isolates was used for model construction. The average essential agreement and category agreement of the best models exceeded 90.90% (95%CI, 89.03-92.77%) and 95.29% (95%CI, 94.91-95.67%), respectively; the exceptions being levofloxacin, minocycline and imipenem. The very major error rates ranged from 0.0 to 5.71%. We applied feature selection pipelines to extract the top-ranked 11-mers to optimise training time and computing resources. This approach slightly improved the prediction performance and enabled us to obtain prediction results within 10 min. Notably, when employing these top-ranked 11-mers in an independent test dataset (120 isolates), we achieved an average accuracy of 0.96.

Conclusion

Our study is the first to demonstrate that AMR prediction for A. baumannii using machine learning methods based on k-mer features has competitive performance over traditional workflows; hence, sequence-based AMR prediction and its application could be further promoted. The k-mer-based workflow developed in this study demonstrated high recall/sensitivity and specificity, making it a dependable tool for MIC prediction in clinical settings.

Molecular antimicrobial susceptibility testing in sepsis

Rapidly detecting and identifying pathogens is crucial for appropriate antimicrobial therapy in patients with sepsis. Conventional diagnostic methods have been a great asset to medicine, though they are time consuming and labor intensive. This work will enable healthcare professionals to understand the bacterial community better and enhance their diagnostic capacity by using novel molecular methods that make obtaining quicker, more precise results possible. The authors discuss and critically assess the merits and drawbacks of molecular testing and the added value of these tests, including the shift turnaround time, the implication for clinicians' decisions, gaps in knowledge, future research directions and novel insights or innovations. The field of antimicrobial molecular testing has seen several novel insights and innovations to improve the diagnosis and management of infectious diseases.

Wild-type distributions of minimum inhibitory concentrations and epidemiological cut-off values-laboratory and clinical utility

The characterization of wild-type minimum inhibitory concentration (MIC) and zone diameter distributions with the setting of epidemiological cut-off values (ECOFFs or ECVs) provides a reference for the otherwise relative MIC values in the international system for antimicrobial susceptibility testing. Distributions of MIC values for a species and an agent follow a log-normal distribution, which in the absence of resistance mechanisms is monomodal and designated wild type (WT). The upper end of the WT distribution, the ECOFF, can be identified with statistical methods. In the presence of phenotypically detectable resistance, the distribution has at least one more mode (the non-WT), but despite this, the WT is most often identifiable using the same methods. The ECOFF provides the most sensitive measure of resistance development in a species against an agent. The WT and non-WT modes are independent of the organism´s response to treatment, but when the European Committee on Antimicrobial Susceptibility Testing (EUCAST) determines the clinical breakpoints, the committee avoids breakpoints that split WT distributions of target species. This is to avoid the poorer reproducibility of susceptibility categorization when breakpoints split major populations but also because the EUCAST has failed to identify different clinical outcomes for isolates with different MIC values inside the wild-type distribution. In laboratory practice, the ECOFF is used to screen for and exclude resistance and allows the comparison of resistance between systems with different breakpoints from different breakpoint organizations, breakpoints evolving over time, and different breakpoints between human and animal medicine. The EUCAST actively encourages colleagues to question MIC distributions as presented on the website (https://www.eucast.org/mic_and_zone_distributions_and_ecoffs) and to contribute MIC and inhibition zone diameter data.

Organically Modified Mesoporous Silica Nanoparticles against Bacterial Resistance

Bacterial antimicrobial resistance is posed to become a major hazard to global health in the 21st century. An aggravating issue is the stalled antibiotic research pipeline, which requires the development of new therapeutic strategies to combat antibiotic-resistant infections. Nanotechnology has entered into this scenario bringing up the opportunity to use nanocarriers capable of transporting and delivering antimicrobials to the target site, overcoming bacterial resistant barriers. Among them, mesoporous silica nanoparticles (MSNs) are receiving growing attention due to their unique features, including large drug loading capacity, biocompatibility, tunable pore sizes and volumes, and functionalizable silanol-rich surface. This perspective article outlines the recent research advances in the design and development of organically modified MSNs to fight bacterial infections. First, a brief introduction to the different mechanisms of bacterial resistance is presented. Then, we review the recent scientific approaches to engineer multifunctional MSNs conceived as an assembly of inorganic and organic building blocks, against bacterial resistance. These elements include specific ligands to target planktonic bacteria, intracellular bacteria, or bacterial biofilm; stimuli-responsive entities to prevent antimicrobial cargo release before arriving at the target; imaging agents for diagnosis; additional constituents for synergistic combination antimicrobial therapies; and aims to improve the therapeutic outcomes. Finally, this manuscript addresses the current challenges and future perspectives on this hot research area.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.