Rapid Identification Of Pathogens Research Articles

Deep Learning-enhanced Hyperspectral Imaging for the Rapid Identification and Classification of Foodborne Pathogens

Background: Bacterial cellulose (BC) is a versatile biomaterial with numerous applications, and the identification of bacterial strains that produce it is of great importance. This study explores the effectiveness of a Stacked Autoencoder (SAE)-based deep learning method for the classification of bacterial cellulose-producing bacteria. Objective: The primary objective of this research is to assess the potential of SAE-based classification models in accurately identifying and classifying bacterial cellulose-producing bacteria, with a particular focus on strain GZ-01. objective: The primary objective of this research is to assess the potential of SAE-based classification models in accurately identifying and classifying bacterial cellulose-producing bacteria, with a particular focus on strain GZ-01. Methods: Strain GZ-01 was isolated and subjected to a comprehensive characterization process, including morphological observations, physiological and biochemical analysis, and 16S rDNA sequencing. These methods were employed to determine the identity of strain GZ-01, ultimately recognized as Acetobacter Okinawa. The study compares the performance of SAE-based classification models to traditional methods like Principal Component Analysis (PCA). Results: The SAE-based classifier exhibits outstanding performance, achieving an impressive accuracy of 94.9% in the recognition and classification of bacterial cellulose-producing bacteria. This approach surpasses the efficacy of conventional PCA in handling the complexities of this classification task. Conclusion: The findings from this research highlight the immense potential of utilizing nanotechnology- driven data analysis methods, such as Stacked Autoencoders, in the realm of bacterial cellulose research. These advanced techniques offer a promising avenue for enhancing the efficiency and accuracy of bacterial cellulose-producing bacteria classification, which has significant implications for various applications in biotechnology and materials science.

Clinical application of nanopore-targeted sequencing technology in bronchoalveolar lavage fluid from patients with pulmonary infections.

The rapid and effective identification of pathogens in patients with pulmonary infections has posed a persistent challenge in medicine, with conventional microbiological tests (CMTs) proving time-consuming and less sensitive, hindering early diagnosis of respiratory infections. While there has been some research on the clinical performance of targeted sequencing technologies, limited focus has been directed toward bronchoalveolar lavage fluid (BALF). This study primarily evaluates the pathogen detection capabilities of nanopore-targeted sequencing (NTS) in BALF, providing a comprehensive analysis. The retrospective study, spanning from January 2022 to November 2023, includes 223 patients exclusively sourced from a single center. We conducted a detailed comparative analysis among NTS, targeted next-generation sequencing (tNGS), and CMTs. Initially, we compared the detection capabilities of NTS and tNGS and found no significant differences in their sensitivity and specificity. Specifically, we observed that the sensitivity of NTS was significantly higher than that of CMTs (74.83% vs 33.11%, P < 0.001). Furthermore, NTS exhibited a higher positivity rate in common pulmonary infections (62.88% vs. 23.48%) and in clinically suspected tuberculosis patients compared to CMTs (87.18% vs. 48.72%). Additionally, NTS showed less susceptibility to antibiotic interference, indicating a more sensitive detection capability, especially in detecting fastidious organisms. It complements GeneXpert in tuberculosis diagnosis and offers excellent advantages in identifying pathogens challenging for CMTs, such as non-tuberculous mycobacteria and viruses. Moreover, NTS significantly shortens the reporting time and is only a quarter of the cost of metagenomic next-generation sequencing. Clearly, NTS can facilitate faster and more cost-effective early diagnosis of respiratory infections.IMPORTANCEThis study holds paramount significance in advancing the field of respiratory infection diagnostics. By assessing the pathogen detection capabilities in bronchoalveolar lavage fluid (BALF) of patients with pulmonary infections, we illuminate the promising potential of nanopore-targeted sequencing (NTS). The findings underscore NTS as a comparable yet distinct alternative to traditional methods like comprehensive conventional microbiological tests (CMTs). Notably, NTS demonstrates a pivotal edge, expanding the spectrum of identified pathogens, particularly excelling in the detection of challenging entities like non-tuberculous mycobacteria and viruses. The study also highlights the complementary role of NTS alongside GeneXpert in the identification of tuberculosis, providing a comprehensive overview of the diagnostic landscape for respiratory infections. This insight carries significant implications for clinicians seeking rapid, cost-effective, and accurate diagnostic tools in the realm of pulmonary infections.

Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning.

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

Accelerated microbial identification “directly” from positive blood cultures using MALDI-TOF MS: Local clinical laboratory challenges

Rapid identification of microbial pathogens “directly” from positive blood cultures (PBCs) is critical for prompt initiation of empirical antibiotic therapy and clinical outcomes. Towards higher microbial identification rates, we modified a published initial serum separator tubes-based MALDI-TOF-MS protocol, for blood culture specimens received at a non-hospital based standalone diagnostic laboratory, Bangalore, India: (a) “Initial” protocol #1: From 28 PBCs, identification= 39% (Gram-negative= 43%: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa; Gram-positive: 36%: Enterococcus faecalis, Staphylococcus aureus, Staphylococcus haemolyticus); mis-identification= 14%; non-identification= 47%. (b) “Modified” protocol #2: Quality controls (ATCC colonies spiked in negative blood cultures) From 7 analysis, identification= 100% (Escherichia coli, Klebsiella pneumonia, Klebsiella oxytoca, Pseudomonas aeruginosa, Enterococcus faecalis, Staphylococcus aureus); From 7 PBCs, identification= 57%; mis-identification= 14%; non-identification= 29%. Microbial preparations of highest quality and quantity for proteomic analysis and separate spectra matching reference databases for colonies and PBCs are needed for best clinical utility.

Application of the MALTI-TOF MS Method for Identification of Vibrio spp. in Aquaculture

Aquaculture is developing rapidly and plays an important role in providing animal protein to the world’s growing population. However, increasing mortality from bacterial disease outbreaks in important species poses a challenge to production progress in this sector. In order to reduce the impact of these diseases, rapid and accurate pathogen identification is essential for disease management, early detection and the continued health of aquaculture. The aim of this review is to summarise studies on the identification and diagnosis of Vibrio pathogens in aquatic organisms by MALTI-TOF MS (Matrix-Assisted Laser Desorption Ionisation Time-of-Flight Mass Spectrometry), a rapid identification method based on protein profiling of bacteria. The profiles of bacterial protein obtained are compared with a global microbial protein library for identification. This study demonstrates the potential of using MALDI-TOF MS for the detection of Vibrio pathogens in aquaculture in studies published between 2015 and 2024. While purchasing a time-of-flight mass spectrometer is expensive when compared to conventional and molecular identification methods. It also appears to be much more efficient in terms of time spent on identification. MALDI-TOF MS has been shown to be simple to use in fish identification laboratories.

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification.

Respiratory tract infections (RTIs) are among the most common problems in clinical settings. Rapid and accurate identification of bacterial pathogens will provide practical guidelines for managing and treating RTIs. This study describes a method for rapidly detecting bacterial pathogens that cause respiratory tract infections via multi-channel loop-mediated isothermal amplification (LAMP). LAMP is a sensitive and specific diagnostic tool that rapidly detects bacterial nucleic acids with high accuracy and reliability. The proposed method offers a significant advantage over traditional bacterial culturing methods, which are time-consuming and often require greater sensitivity for detecting low levels of bacterial nucleic acids. This article presents representative results of K. pneumoniae infection and its multiple co-infections using LAMP to detect samples (sputum, bronchial lavage fluid, and alveolar lavage fluid) from the lower respiratory tract. In summary, the multi-channel LAMP method provides a rapid and efficient means of identifying single and multiple bacterial pathogens in clinical samples, which can help prevent the spread of bacterial pathogens and aid in the appropriate treatment of RTIs.

Optimization and Validation of an FTIR-based, All-in-one System for Viable MDR Bacteria Detection in Combat-related Wound Infection.

The U.S. Military members experiencing combat-related injuries have a higher chance of developing infections by multidrug-resistant (MDR) bacteria at admission to military hospitals. MDR wound infections result in higher amputation rates and greater risks for subsequent or chronic infections that require readmission or extended stay in the hospital. Currently, there is no FDA-clear, deployable early diagnostic system for suitable field use.We are reporting our efforts to improve a previously developed Rapid Label-free Pathogen Identification (RAPID) system to detect viable MDR bacteria in wound infections and perform antibiotic susceptibility testing (AST). Specifically, we added multiplex and automation capability and significantly simplified the sample preparation process. A functional prototype of the improved system was built, and its performance was validated using a variety of lab-prepared spiked samples and real-world samples. To access the baseline performance of the improved RAPID system in detecting bacteria presence, we selected 17 isolates, most of them from blood or wound infections, and prepared mono-strain spiked samples at 104 to 106 cfu/mL concentration. These samples were processed and analyzed by the RAPID system. To demonstrate the AST capability of the system, we selected 6 strains against 6 different antibiotics and compared the results from the system with the ones from the gold standard method.To validate the system's performance with real-world samples, we first investigated its performance on 3 swab samples from epicutaneous methicillin-resistant Staphylococcus aureus-exposed mouse model. The AST results from our system were compared with the ones from the gold standard method. All animal experiments were approved by the Johns Hopkins University Animal Care and Use Committee (Protocol No. MO21M378). Then, we obtained swab samples from 7 atopic dermatitis (AD) patients and compared our AST results with the ones from the gold standard method. The human subject protocol was approved by the Johns Hopkins Medicines Institutional Review Boards (Study No. CR00043438/IRB00307926) and by USAMRDC (Proposal Log Number/Study Number 20000251). High-quality data were obtained from the spiked samples of all 17 strains. A quantitative analysis model built using these data achieved 94% accuracy in predicting the species ID in 8 unknown samples. The AST results on the spiked samples had shown 100% matching with the gold standard method. Our system successfully detects the presence/absence of viable bacteria in all 3 mouse and 7 AD patient swab samples. Our system shows 100% and 85.7% (6 out of 7) accuracy when compared to the oxacillin susceptibility testing results for the mouse and the AD patient swabs, respectively. Our system has achieved excellent performance in detecting viable bacteria presence and in performing AST in a multiplex, automated, and easy-to-operate manner, on both lab-prepared and real samples. Our results have shown a path forward to a rapid (sample-to-answer time ≤3 hours), accurate, sensitive, species-specific, and portable system to detect the presence of MDR combat-related wound infections in the field environment. Our future efforts involve ruggedizing the RAPID system and evaluating performance under relevant environmental conditions.

Building Research Capacity in Low- and Middle-Income Countries and Pandemic Preparedness: Lessons Learned and Future Directions.

Research capacity is a critical component of pandemic preparedness, as highlighted by the challenges faced during the Ebola outbreak in West Africa. Recent global initiatives, such as the Research & Development Task Force of the Global Health Security Agenda and the World Health Assembly's resolution on strengthening clinical trials, emphasize the need for robust research capabilities. This Perspective discusses the experiences of leaders in infectious disease research and capacity building in low- and middle-income countries, focusing on Colombia, Jamaica, and Pakistan. These case studies underscore the importance of collaborative efforts, interdisciplinary training, and global partnerships in pandemic response. The experiences highlight the necessity for rapid pathogen identification, capacity for genomic sequencing, and proactive engagement with policymakers. Challenges faced, including the shortage of trained staff and reliance on imported reagents, emphasize the ongoing need for building research capacity.

Nucleic-Acid-Based Molecular Fungal Diagnostics: A Way to a Better Future.

The world has seen a tremendous increase in the number of fungal infections during the past two decades. Recently, the World Health Organisation released the pathogen priority list for fungal infections, signifying the importance of these infections in the fields of research and public health. Microbiology laboratories demand an upgrade in the diagnostic system to keep up with the increased burden of these infections. Diagnosis of fungal infections using conventional techniques has always faced limitations in terms of specificity, sensitivity, and turnaround time. Although these methods are the core pillars of the diagnosis, there is an increased need for molecular approaches. Molecular techniques have revolutionised the field of fungal diagnostics. The diverse array of molecular techniques, including techniques like Polymerase Chain Reaction (PCR), have emerged as a cornerstone in fungal diagnostics. Molecular techniques have transformed fungal diagnostics, providing powerful tools for the rapid and accurate identification of pathogens. As these technologies continue to evolve, their integration into routine clinical practice holds the promise of improving patient outcomes through timely and targeted antifungal interventions. This review will cover the molecular approaches involved in fungal diagnostics, moving from the basic techniques to the advanced-level nucleic-acid-based molecular approaches providing a high throughput and decreased turnaround time for the diagnosis of serious fungal infections.

Risk assessment and pathogen profile of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy: A retrospective study.

Emergency craniotomy in patients with traumatic brain injury poses a significant risk for surgical site infections (SSIs). Understanding the risk factors and pathogenic characteristics of SSIs in this context is crucial for improving outcomes. This comprehensive retrospective analysis spanned from February 2020 to February 2023 at our institution. We included 25 patients with SSIs post-emergency craniotomy and a control group of 50 patients without SSIs. Data on various potential risk factors were collected, including demographic information, preoperative conditions, and intraoperative details. The BACT/ALERT3D Automated Bacterial Culture and Detection System was utilized for rapid bacterial pathogen identification. Statistical analyses included univariate and multivariate logistic regression to identify significant risk factors for SSIs. The study identified Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus as the most prevalent pathogens in SSIs. Significant risk factors for SSIs included the lack of preoperative antibiotic use, postoperative drainage tube placement, diabetes mellitus, and the incorporation of invasive procedures, all of which showed a significant association with SSIs in the univariate analysis. The multivariate analysis further highlighted the protective effect of preoperative antibiotics and the increased risks associated with anaemia, diabetes mellitus, postoperative drainage tube placement, and the incorporation of invasive procedures. Our research underscores the critical role of factors like insufficient preoperative antibiotics, postoperative drainage, invasive techniques, anaemia, and diabetes mellitus in elevating the risk of surgical site infections in traumatic brain injury patients undergoing emergency craniotomy. Enhanced focus on these areas is essential for improving surgical outcomes.

Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning

Bakanae disease is a common seed-borne disease of rice. Rapid and accurate detection of bakanae pathogens carried by rice seeds is essential for the health of rice germplasm resources and the safety of rice production. This study aims to propose a general framework for species identification of major bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning. Seven varieties of rice seeds and four kinds of bakanae pathogens were analyzed. One-dimensional deep convolution neural networks (DCNNs) were first constructed using complete datasets. They achieved accuracies larger than 96.5% on the testing sets of most datasets, exceeding the conventional SVM and PLS-DA models. Then the developed DCNNs were transferred to detect other complete training sets. Most of the deep transferred models achieved comparable or even better performance than the original DCNNs. Two smaller target training sets were further constructed by randomly selecting spectra from the complete training sets. As the size of the target training sets reduced, the accuracies of all models on the corresponding testing sets also decreased gradually. Visualization analysis were conducted using the t-distribution stochastic neighbor embedding (t-SNE) algorithm and a proposed gradient-weighted activation wavelength (Grad-AW) method. They all showed that deep transfer learning could utilize the representation patterns in the source datasets to improve the target tasks. The overall results indicated that the bakanae pathogens were all identified accurately under our proposed framework. Hyperspectral imaging combined with deep transfer learning provided a new idea for the quality detection of large-scale seeds in modern seed industry.

Metagenomic Next-generation Sequencing for Pathogen Identification in Bronchoalveolar Lavage Fluid From Neonates Receiving Extracorporeal Membrane Oxygenation.

Neonates on extracorporeal membrane oxygenation (ECMO) are at high risk of infection. Rapid and accurate identification of pathogens is essential to improve the prognosis of children on ECMO. Metagenome next-generation sequencing (mNGS) has been used in recent years to detect pathogenic bacteria, but evidence for its use in neonates on ECMO is lacking. This retrospective study was conducted using an electronic medical record system. We analyzed the results of mNGS and conventional microbiological tests (CMTs) in bronchoalveolar lavage fluid of neonates receiving ECMO support with pulmonary infections in our hospital from July 2021 to January 2023. We screened 18 ECMO-supported neonates with pneumonia for inclusion in the study. The median age of the included children was 2 (1-4) days, the median gestational age was 38.3 (33-40 +4 ) weeks, and the median weight was 3.3 (2.2-4.8) kg. The detection rate of mNGS was 77.8% (14/18), higher than the 44.4% (8/18) of CMT ( P = 0.04). A total of 20 pathogens were detected in mNGS, with the top 3 most common pathogens being Klebsiella pneumoniae , Acinetobacter baumannii and Escherichia coli . Mixed infections were found in 14 cases (77.8%), including 13 cases (72.2%) with mixed infections detected by mNGS and 7 cases (27.8%) with mixed infections detected by CMT. A total of 9 children underwent treatment changes based on mNGS results and all of them experienced relief of symptoms. Compared with CMT, mNGS can detect pathogens earlier and more sensitively, and may play an important role in ECMO-supported neonatal pneumonia pathogen detection and optimization of antibiotic therapy.

Risk of Multidrug-Resistant Pathogens in Severe Community-Acquired Pneumonia.

Severe community-acquired pneumonia (SCAP) is difficult to treat when caused by difficult-to-treat (DTR) pathogens because of limited treatment options and poorer clinical outcomes. Over time, several predictive scoring systems based on risk factors for infection with multidrug resistant pathogens have been developed. We reviewed the available tools for identifying DTR pathogens as the cause of SCAP, both predictive scoring systems and rapid diagnostic methods, to develop management strategies aimed at early identification of DTR pathogens, reducing broad-spectrum antibiotic use and improving clinical outcomes. The scoring systems reviewed show considerable heterogeneity among them at the level of the region studied, the definition of risk factors, as well as which DTR pathogens are the target pathogens. The models described have shown limited effectiveness in reducing inappropriate antibiotic treatment or improving patient outcomes by themselves. However, predictive models could serve as a first step in identifying DTR pathogen infections as part of a larger detection algorithm. Rapid diagnostic tools, such as multiplex polymerase chain reaction, would be useful for the rapid identification of pneumonia-causing pathogens and their resistance mechanisms. In resource-limited settings, rapid tests should be limited to patients at high risk of developing SCAP due to DTR pathogens. We propose an integrative algorithm based on the different scores, taking into account local epidemiological data, where ideally each center should have an antimicrobial stewardship program.

“Index Case of Multiresistant Ochrobactrum Anthropi in an Immunocompetent Postpartum Female with Posterior Reversible Encephalopathy Syndrome: A Unique Case at a Tertiary Care Centre in India”

Ochrobactrum anthropi, an emerging nosocomial pathogen associated with medical devices, presents a distinct challenge in healthcare. While prevalent in patients with invasive devices, such as central venous catheters, its occurrence in hemodialysis (HD) patients is rarely reported. This study details a case of O. anthropi bacteremia-induced septic shock in an immunocompetent, non-diabetic, post-LSCS-postpartum female undergoing intermittent dialysis at AIIMS Rishikesh, a tertiary care center in Uttarakhand, India. In addition to the clinical case, we provide a concise review of O. anthropi infections in HD patients, emphasizing the need for rapid pathogen identification, susceptibility testing, and tailored antimicrobial therapy for favorable outcomes.

Nanopore sequencing of infectious fluid is a promising supplement for gold-standard culture in real-world clinical scenario.

Infectious diseases are major causes of morbidity and mortality worldwide, necessitating the rapid identification and accurate diagnosis of pathogens. While unbiased metagenomic next-generation sequencing (mNGS) has been extensively utilized in clinical pathogen identification and scientific microbiome detection, there is limited research about the application of nanopore platform-based mNGS in the diagnostic performance of various infectious fluid samples. In this study, we collected 297 suspected infectious fluids from 10 clinical centers and detected them with conventional microbiology culture and nanopore platform-based mNGS. The objective was to assess detective and diagnostic performance of nanopore-sequencing technology (NST) in real-world scenarios. Combined with gold-standard culture and clinical adjudication, nanopore sequencing demonstrated nearly 100% positive predictive agreements in microbial-colonized sites, such as the respiratory and urinary tracts. For samples collected from initially sterile body sites, the detected microorganisms were highly suspected pathogens, and the negative predictive agreements were relatively higher than those in the microbial-colonized sites, particularly with 100% in abscess and 95.7% in cerebrospinal fluid. Furthermore, consistent performance was also observed in the identification of antimicrobial resistance genes and drug susceptibility testing of pathogenic strains of Escherichia coli, Staphylococcus aureus, and Acinetobacter baumannii. Rapid NST is a promising clinical tool to supplement gold-standard culture, and it has the potential improve patient prognosis and facilitate clinical treatment of infectious diseases.

The Impact of Pathogens on Sepsis Prevalence and Outcome.

Sepsis, a severe global healthcare challenge, is characterized by significant morbidity and mortality. The 2016 redefinition by the Third International Consensus Definitions Task Force emphasizes its complexity as a "life-threatening organ dysfunction caused by a dysregulated host response to infection". Bacterial pathogens, historically dominant, exhibit geographic variations, influencing healthcare strategies. The intricate dynamics of bacterial immunity involve recognizing pathogen-associated molecular patterns, triggering innate immune responses and inflammatory cascades. Dysregulation leads to immunothrombosis, disseminated intravascular coagulation, and mitochondrial dysfunction, contributing to the septic state. Viral sepsis, historically less prevalent, saw a paradigm shift during the COVID-19 pandemic, underscoring the need to understand the immunological response. Retinoic acid-inducible gene I-like receptors and Toll-like receptors play pivotal roles, and the cytokine storm in COVID-19 differs from bacterial sepsis. Latent viruses like human cytomegalovirus impact sepsis by reactivating during the immunosuppressive phases. Challenges in sepsis management include rapid pathogen identification, antibiotic resistance monitoring, and balancing therapy beyond antibiotics. This review highlights the evolving sepsis landscape, emphasizing the need for pathogen-specific therapeutic developments in a dynamic and heterogeneous clinical setting.

An aptamer-based SERS method for rapid screening and identification of pathogens assisted by machine learning technique with robustness evaluation

Pathogen testing is one of the compulsory items for the quality control of pharmaceutical products. Up to now, rapid screening and identification of low-concentration pathogen contaminations is still full of challenge. Herein, an aptamer-coated magnetic beads (Apt-MBs)-based isolation strategy combined with culture medium turbidity observing and label-free SERS detection technique has been developed for rapid screening and detection of Escherichia coli, Salmonella Typhimurium and Staphylococcus aureus. Apt-MBs were employed for capturing and isolation of target pathogens. Then, with a simple enrichment process, the potential pathogens could be screened out by observing the turbidity of liquid culture medium. The presumptive bacteria species was furtherly identified via a silicon-based SERS chip couple with an optimized machine learning model, which was selected by directly evaluating the model’s generalization ability on test data. The results demonstrated that SoftMax classifier had the optimal performance with the recognition accuracy of 100 % for all samples. Finally, the method was successfully applied for identification of pathogens at ∼1 CFU spiked level in drug samples within 24 h, which is much shorter than that of the standard culture-based method (3–5 d). Therefore, the developed method showed a promising application in monitoring pathogen levels in pharmaceutical industries.

Comparison of three rapid diagnostic tests for bloodstream infections using Benefit-risk Evaluation Framework (BED-FRAME).

Rapid diagnostic tests (RDTs) for bloodstream infections have the potential to reduce time to appropriate antimicrobial therapy and improve patient outcomes. Previously, an in-house, lipid-based, matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) method, Fast Lipid Analysis Technique (FLAT MS), has shown promise as a rapid pathogen identification method. In this study, FLAT MS for direct from blood culture identification was evaluated and compared to FDA-cleared identification methods using the Benefit-risk Evaluation Framework (BED-FRAME) analysis. FLAT MS was evaluated and compared to Bruker Sepsityper and bioMérieux BioFire FilmArray BCID2 using results from a previous study. For this study, 301 positive blood cultures were collected from the University of Maryland Medical Center. The RDTs were compared by their sensitivities, time-to-results, hands-on time, and BED-FRAME analysis. The overall sensitivity of all platforms compared to culture results from monomicrobial-positive blood cultures was 88.3%. However, the three RDTs differed in their accuracy for identifying Gram-positive bacteria, Gram-negative bacteria, and yeast. Time-to-results for FLAT MS, Sepsityper, and BioFire BCID2 were all approximately one hour. Hands-on times for FLAT MS, Sepsityper, and BioFire BCID2 were 10 (±1.3), 40 (±2.8), and 5 (±0.25) minutes, respectively. BED-FRAME demonstrated that each RDT had utility at different pathogen prevalence and relative importance. BED-FRAME is a useful tool that can used to determine which RDT is best for a healthcare center.

Rapid nanopore sequencing and predictive susceptibility testing of positive blood cultures from intensive care patients with sepsis.

We aimed to evaluate the performance of Oxford Nanopore Technologies (ONT) sequencing from positive blood culture (BC) broths for bacterial identification and antimicrobial susceptibility prediction. Patients with suspected sepsis in four intensive care units were prospectively enrolled. Human-depleted DNA was extracted from positive BC broths and sequenced using ONT (MinION). Species abundance was estimated using Kraken2, and a cloud-based system (AREScloud) provided in silico predictive antimicrobial susceptibility testing (AST) from assembled contigs. Results were compared to conventional identification and phenotypic AST. Species-level agreement between conventional methods and AST predicted from sequencing was 94.2% (49/52), increasing to 100% in monomicrobial infections. In 262 high-quality AREScloud AST predictions across 24 samples, categorical agreement (CA) was 89.3%, with major error (ME) and very major error (VME) rates of 10.5% and 12.1%, respectively. Over 90% CA was achieved for some taxa (e.g., Staphylococcus aureus) but was suboptimal for Pseudomonas aeruginosa. In 470 AST predictions across 42 samples, with both high quality and exploratory-only predictions, overall CA, ME, and VME rates were 87.7%, 8.3%, and 28.4%. VME rates were inflated by false susceptibility calls in a small number of species/antibiotic combinations with few representative resistant isolates. Time to reporting from sequencing could be achieved within 8-16 h from BC positivity. Direct sequencing from positive BC broths is feasible and can provide accurate predictive AST for some species. ONT-based approaches may be faster but significant improvements in accuracy are required before it can be considered for clinical use.IMPORTANCESepsis and bloodstream infections carry a high risk of morbidity and mortality. Rapid identification and susceptibility prediction of causative pathogens, using Nanopore sequencing direct from blood cultures, may offer clinical benefit. We assessed this approach in comparison to conventional phenotypic methods and determined the accuracy of species identification and susceptibility prediction from genomic data. While this workflow holds promise, and performed well for some common bacterial species, improvements in sequencing accuracy and more robust predictive algorithms across a diverse range of organisms are required before this can be considered for clinical use. However, results could be achieved in timeframes that are faster than conventional phenotypic methods.

The Value of Next-Generation Sequencing in Diagnosis and Therapy of Critically Ill Patients with Suspected Bloodstream Infections: A Retrospective Cohort Study.

Bloodstream infection (BSI), a frequent cause of severe sepsis, is a life-threatening complication in critically ill patients and still associated with a high mortality rate. Rapid pathogen identification from blood is crucial for an early diagnosis and the treatment of patients with suspected BSI. For this purpose, novel diagnostic tools on the base of genetic analysis have emerged for clinical application. The aim of this study was to assess the diagnostic value of additional next-generation sequencing (NGS) pathogen test for patients with suspected BSI in a surgical ICU and its potential impact on antimicrobial therapy. In this retrospective single-centre study, clinical data and results from blood culture (BC) and NGS pathogen diagnostics were analysed for ICU patients with suspected BSI. Consecutive changes in antimicrobial therapy and diagnostic procedures were evaluated. Results: 41 cases with simultaneous NGS and BC sampling were assessed. NGS showed a statistically non-significant higher positivity rate than BC (NGS: 58.5% (24/41 samples) vs. BC: 21.9% (9/41); p = 0.056). NGS detected eight different potentially relevant bacterial species, one fungus and six different viruses, whereas BC detected four different bacterial species and one fungus. NGS results affected antimicrobial treatment in 7.3% of cases. Conclusions: NGS-based diagnostics have the potential to offer a higher positivity rate than conventional culture-based methods in patients with suspected BSI. Regarding the high cost, their impact on anti-infective therapy is currently limited. Larger randomized prospective clinical multicentre studies are required to assess the clinical benefit of this novel diagnostic technology.