Diagnosis Of Infectious Diseases Research Articles

Clinical usefulness of metagenomic next-generation sequencing for Talaromyces marneffei diagnosis in China: a retrospective study.

Metagenomic next-generation sequencing (mNGS) has been widely used in the diagnosis of infectious diseases. However, studies on Talaromyces marneffei detection using mNGS remain scarce. Therefore, this study aimed to explore the diagnostic performance of mNGS in T. marneffei. Between March 2021 and June 2023, patients who were discharged with a final diagnosis of talaromycosis, or confirmed T. marneffei infection by mNGS, culture or pathological examination were included in the study. Culture and mNGS were performed simultaneously for all patients. Clinical data were retrieved for analysis. A total of 78 patients were enrolled, with 40 in the talaromycosis group and 38 in the suspected-talaromycosis group. In the talaromycosis group, mNGS showed a higher positivity rate(40/40, 100.0%) compared to culture(34/40, 85.0%)(P = 0.111). All patients in the suspected-talaromycosis group tested negative via culture, while mNGS yielded positive results. The T. marneffei reads in the talaromycosis group were significantly higher than in the suspected-talaromycosis group (4399 vs. 28, P < 0.001). In the suspected-talaromycosis group, of the four patients with low reads who did not receive antifungal therapy, one died and one lung lesion progressed; most patients(31/34, 91.2%) recovered after receiving appropriate antifungal therapy. mNGS proves to be a rapid and highly sensitive method for detecting T. marneffei. Higher reads of T. marneffei correspond to a higher likelihood of infection. However, cases with low reads necessitate a comprehensive approach, integrating clinical manifestations, laboratory tests, and imaging examinations to confirm T. marneffei infection.

Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI). This was a retrospective observational study from a single academic hospital over six calendar years (2016-2021). Machine learning was used to examine patients with an NSI compared to those without. Inclusion criteria: all adult inpatient admissions (2016-2021). Three approaches were used to analyze the NSI group compared to the No-NSI group. In the univariate analysis, descriptive statistics, and absolute standardized differences (ASDs) were employed to compare the demographics and clinical variables of patients who experienced a NSI and those who did not experience any NSIs. For the multivariate analysis, a light grading boosting machine (LightGBM) model was utilized to comprehensively examine the relationships associated with the development of an NSI. Lastly, a simulation study was conducted to quantify the strength of associations obtained from the machine learning model. From 163,507 admissions, 4643 (2.8%) were associated with at least one NSI. The mean, standard deviation (SD) age was 59.5 (18.2) years, males comprised 82,397 (50.4%). Non-Hispanic White 84,760 (51.8%), non-Hispanic Black 8703 (5.3%), non-Hispanic Asian 23,368 (14.3%), non-Hispanic Other 14,284 (8.7%), and Hispanic 30,271 (18.5%). Race and ethnicity alone were not associated with occurrence of an NSI. The NSI group had a statistically significant longer length of stay (LOS), longer intensive care unit (ICU) LOS, and was more likely to have an emergency admission compared to the group without an NSI. The simulation study results demonstrated that likelihood of NSI was higher in patients admitted under the major diagnostic categories (MDC) associated with circulatory, digestive, kidney/urinary tract, nervous, and infectious and parasitic disease diagnoses. In this study, race/ethnicity was not associated with the risk of an NSI event. The risk of an NSI event was associated with emergency admission, longer LOS, longer ICU-LOS and certain MDCs (circulatory, digestive, kidney/urinary, nervous, infectious, and parasitic diagnoses). Machine learning methodologies provide a new mechanism to investigate NSI events through the lens of health equity/disparity. Understanding which patients are at higher risk for adverse outcomes can help hospitals improve nursing care and prevent NSI injury and harm.

Application of metagenomic next-generation sequencing in optimizing the diagnosis of ascitic infection in patients with liver cirrhosis

BackgroundMetagenomic next-generation sequencing (mNGS) is an emerging technique for the clinical diagnosis of infectious disease that has rarely been used for the diagnosis of ascites infection in patients with cirrhosis. This study compared mNGS detection with conventional culture methods for the on etiological diagnosis of cirrhotic ascites and evaluated the clinical effect of mNGS.MethodsA total of 109 patients with ascites due to cirrhosis were included in the study. We compared mNGS with conventional culture detection by analyzing the diagnostic results, pathogen species and clinical effects. The influence of mNGS on the diagnosis and management of ascites infection in patients with cirrhosis was also evaluated.ResultsAscites cases were classified into three types: spontaneous bacterial peritonitis (SBP) (16/109, 14.7%), bacterascites (21/109, 19.3%) and sterile ascites (72/109, 66.1%). In addition, 109 patients were assigned to the ascites mNGS-positive group (80/109, 73.4%) or ascites mNGS-negative group (29/109, 26.6%). The percentage of positive mNGS results was significantly greater than that of traditional methods (73.4% vs. 28.4%, P < 0.001). mNGS detected 43 strains of bacteria, 9 strains of fungi and 8 strains of viruses. Fourteen bacterial strains and 3 fungal strains were detected via culture methods. Mycobacteria, viruses, and pneumocystis were detected only by the mNGS method. The mNGS assay produced a greater polymicrobial infection rate than the culture method (55% vs. 16%). Considering the polymorphonuclear neutrophil (PMN) counts, the overall percentage of pathogens detected by the two methods was comparable, with 87.5% (14/16) in the PMN ≥ 250/mm3 group and 72.0% (67/93) in the PMN < 250/mm3 group (P > 0.05). Based on the ascites PMN counts combined with the mNGS assay, 72 patients (66.1%) were diagnosed with ascitic fluid infection (AFI) (including SBP and bacterascites), whereas based on the ascites PMN counts combined with the culture assay, 37 patients (33.9%) were diagnosed with AFI (P < 0.05). In 60 (55.0%) patients, the mNGS assay produced positive clinical effects; 40 (85.7%) patients had their treatment regimen adjusted, and 48 patients were improved. The coincidence rate of the mNGS results and clinical findings was 75.0% (60/80).ConclusionsCompared with conventional culture methods, mNGS can improve the detection rate of ascites pathogens, including bacteria, viruses, and fungi, and has significant advantages in the diagnosis of rare pathogens and pathogens that are difficult to culture; moreover, mNGS may be an effective method for improving the diagnosis of ascites infection in patients with cirrhosis, guiding early antibiotic therapy, and for reducing complications related to abdominal infection. In addition, explaining mNGS results will be challenging, especially for guiding the treatment of infectious diseases.

Clinical applications of metagenomics next-generation sequencing in infectious diseases.

Infectious diseases are a great threat to human health. Rapid and accurate detection of pathogens is important in the diagnosis and treatment of infectious diseases. Metagenomics next-generation sequencing (mNGS) is an unbiased and comprehensive approach for detecting all RNA and DNA in a sample. With the development of sequencing and bioinformatics technologies, mNGS is moving from research to clinical application, which opens a new avenue for pathogen detection. Numerous studies have revealed good potential for the clinical application of mNGS in infectious diseases, especially in difficult-to-detect, rare, and novel pathogens. However, there are several hurdles in the clinical application of mNGS, such as: (1) lack of universal workflow validation and quality assurance; (2) insensitivity to high-host background and low-biomass samples; and (3) lack of standardized instructions for mass data analysis and report interpretation. Therefore, a complete understanding of this new technology will help promote the clinical application of mNGS to infectious diseases. This review briefly introduces the history of next-generation sequencing, mainstream sequencing platforms, and mNGS workflow, and discusses the clinical applications of mNGS to infectious diseases and its advantages and disadvantages.

Molecular Tools for Diagnosis of Pediatric Infectious Diseases

Molecular Tools for Diagnosis of Pediatric Infectious Diseases

Clinical applications and challenges of metagenomic next-generation sequencing in the diagnosis of pediatric infectious disease

Abstract Infectious diseases seriously threaten the lives of children. Timely and accurate detection of pathogenic microorganisms and targeted medication are the keys to the diagnosing and treatment of infectious diseases in children. The next-generation metagenomic sequencing technology has attracted great attention in infectious diseases because of its characteristics such as no culture, high throughput, short detection cycle, wide coverage, and a good application prospect. In this paper, we review the studies of metagenomic next-generation sequencing in pediatric infectious diseases and analyze the challenges of its application in pediatric diseases.

A Comparative Study of "Grafting to" and "Grafting from" Conjugation Methods for the Preparation of Antibody-Temperature-Responsive Polymer Conjugates.

Early diagnosis of infectious diseases is still challenging particularly in a nonlaboratory environment or limited resources areas. Thus, sensitive, inexpensive, and easily handled diagnostic approaches are required. The lateral flow immunoassay (LFIA) is commonly used in the screening of infectious diseases despite its poor sensitivity, especially with low pathogenic loads (early stages of infection). This article introduces a novel polymeric material that might help in the enrichment and concentration of pathogens to overcome the LFIA misdiagnosis. To achieve this, we evaluated the efficiency of introducing poly(N-isopropylacrylamide) (PNIPAAm) into immunoglobulin G (IgG) as a model antibody using two different conjugation methods: grafting to (GT) and grafting from (GF). The IgG-PNIPAAm conjugates were characterized using SDS-PAGE, DLS, and temperature-responsive phase transition behavior. SDS-PAGE analysis revealed that the GF method was more efficient in introducing the polymer than the GT method, with calculated polymer introduction ratios of 61% and 34%, respectively. The GF method proved to be less susceptible to steric hindrance and more efficient in introducing high-molecular-weight polymers into proteins. These results are consistent with previous studies comparing the GT and GF methods in similar systems. This study represents an important step toward understanding how the choice of polymer incorporation method affects the properties of IgG-PNIPAAm conjugates. The synthesized polymer allowed binding and enrichment of mouse IgG that was used as a model antigen with a clear LFIA band. On the basis of our findings, this system might help in improving the sensitivity of simple diagnostics.

Rapid Diagnosis of Pneumocystis jirovecii Pneumonia and Respiratory Tract Colonization by Next-Generation Sequencing

ObjectivesTo describe the epidemiology of Pneumocystis jirovecii pneumonia and colonization diagnosed by next-generation sequencing (NGS) and explore the usefulness of the number of P. jirovecii sequence reads for the diagnosis of P. jirovecii pneumonia.MethodsWe examined the NGS results for P. jirovecii in respiratory samples collected from patients and analysed their clinical, radiological and microbiological characteristics.ResultsAmong 285 respiratory samples collected over a 12-month period (January to December 2022), P. jirovecii sequences were detected in 56 samples from 53 patients. Fifty (94.3%) of the 53 patients were HIV-negative. Following our case definitions, 37 (69.8%) and 16 (30.2%) of the 53 patients had P. jirovecii infection and colonization respectively. P. jirovecii infection was associated with presence of underlying disease with immunosuppression (94.6% vs 18.8%, P < 0.05), positive serum 1,3-β-D-glucan (41.2% vs 0%, P < 0.01) and higher number of P. jirovecii sequence reads (P < 0.005). In contrast, P. jirovecii colonization was associated with the male sex (93.8% vs 54.1%, P < 0.01), another definitive infectious disease diagnosis of the respiratory tract (43.8% vs 2.7%, P < 0.001) and higher survival (100% vs 67.6%, P < 0.01). Although P. jirovecii pneumonia was associated with higher number of P. jirovecii reads in respiratory samples, only a sensitivity of 82.14% and a specificity of 68.75% could be achieved.ConclusionDetection of P. jirovecii sequences in respiratory samples has to be interpreted discreetly. A combination of clinical, radiological and laboratory findings is still the most crucial in determining whether a particular case is genuine P. jirovecii pneumonia.

QCML: Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images

The COVID-19 pandemic has had a terrible effect on human health, and computer-aided diagnostic (CAD) systems for chest computed tomography have emerged as a potential alternative for COVID-19 diagnosis. Yet, since the cost of data annotation may be excessively costly in the medical area, there is a shortage of data that has been annotated. A considerable quantity of labelled data is required in order to train a CAD system to a high level of accuracy. The study aims to describe an automatic and precise COVID-19 diagnostic method that utilizes a restricted amount of labelled CT images to solve this problem. The framework of the system is known as Qualified Contrastive Machine Learning (QCML), and the improvements that we have made may be summed up as follows: 1) In order to make use of all of the image's characteristics, we combine features with a two-dimensional discrete wavelet transform. 2) We employ the COVID-Net encoder with a redesign that focuses on the efficiency of learning and the task specificity of the data. 3) In order to strengthen our capacity to generalize, we have implemented a novel pertaining technique that is based on Qualified Contrastive Machine Learning. 4) In order to get better categorization results, we have included an extra auxiliary work. The application of Qualified Contrastive Machine Learning methodology for infectious disease diagnosis in CT images offers an accuracy of 93.55%, a recall of 91.59%, a precision of 96.92%, and an F1-score of 94.18%, demonstrating the potential for accurate and efficient COVID-19 diagnosis with limited labelled data.

Simultaneous detection of bovine viral diarrhea virus (BVDV) and bovine herpesvirus 1 (BoHV-1) using recombinase polymerase amplification

Bovine viral diarrhea virus (BVDV) is considered to be the most common agent of severe diarrhea in cattle worldwide, causing fever, diarrhea, ulcers, and abortion. Bovine herpesvirus 1 (BoHV-1) is also a major bovine respiratory disease agent that spreads worldwide and causes extensive damage to the livestock industry. Recombinase polymerase amplification (RPA) is a novel nucleic acid amplification method with the advantages of high efficiency, rapidity and sensitivity, which has been widely used in the diagnosis of infectious diseases. A dual RPA assay was developed for the simultaneous detection of BVDV and BoHV-1. The assay was completed at a constant temperature of 37 °C for 30 min. It was highly sensitive and had no cross-reactivity with other common bovine viruses. The detection rate of BVDV RPA in clinical samples (36.67%) was higher than that of PCR (33.33%), the detection rate of BoHV-1 RPA and PCR were equal. Therefore, the established dual RPA assay for BVDV and BoHV-1 could be a potential candidate for use as an immediate diagnostic.

Identification of Microorganism in Infected Wounds by Positively Charged Selective Sensor Array and Deep Learning Algorithm.

Microorganism are ubiquitous and intimately connected with human health and disease management. The accurate and fast identification of pathogenic microorganisms is especially important for diagnosing infections. Herein, three tetraphenylethylene derivatives (S-TDs: TBN, TPN, and TPI) featuring different cationic groups, charge numbers, emission wavelengths, and hydrophobicities were successfully synthesized. Benefiting from distinct cell wall binding properties, S-TDs were collectively utilized to create a sensor array capable of imaging various microorganisms through their characteristic fluorescent signatures. Furthermore, the interaction mechanism between S-TDs and different microorganisms was explored by calculating the binding energy between S-TDs and cell membrane/wall constituents, including phospholipid bilayer and peptidoglycan. Using a combination of the fluorescence sensor array and a deep learning model of residual network (ResNet), readily differentiation of Gram-negative bacteria (G-), Gram-positive bacteria (G+), fungi, and their mixtures was achieved. Specifically, by extensive training of two ResNet models with large quantities of images data from 14 kinds of microorganism stained with S-TDs, identification of microorganism was achieved at high-level accuracy: over 92.8% for both Gram species and antibiotic-resistant species, with 90.35% accuracy for the detection of mixed microorganism in infected wound. This novel method provides a rapid and accurate method for microbial classification, potentially aiding in the diagnosis and treatment of infectious diseases.

Development of a decision support tool to compare diagnostic strategies for establishing the herd status for infectious diseases: an example with Salmonella Dublin infection in dairies

The diagnosis of infectious diseases at herd level can be challenging as different stakeholders can have conflicting priorities. The current study proposes a “proof of concept” of an approach that considers a reasonable number of criteria to rank plausible diagnostic strategies using multi-criteria decision analysis (MCDA) methods. The example of Salmonella Dublin diagnostic in Québec dairy herds is presented according to two epidemiological contexts: (i) in herds with no history of S. Dublin infection and absence of clinical signs, (ii) in herds with a previous history of infection, but absence of clinical signs at the moment of testing.Multiple multiparty exchanges were conducted to determine: 1) stakeholders’ groups; 2) the decision problem; 3) solutions to the problem (options) or diagnostic strategies to be ordered; 4) criteria and indicators; 5) criteria weights; 6) the construction of a performance matrix for each option; 7) the multi-criteria analyses using the visual preference ranking organization method for enrichment of evaluations approach; 8) the sensitivity analyses, and 9) the final decision.A total of nine people from four Québec’s organizations (the dairy producers provincial association along with the DHI company, the ministry of agriculture, the association of veterinary practitioners, and experts in epidemiology) composed the MCDA team. The decision problem was “What is the optimal diagnostic strategy for establishing the status of a dairy herd for S. Dublin infection when there are no clinical signs of infection?”. Fourteen diagnostic strategies composed of the three following parameters were considered: 1) biological samples (bulk tank milk or blood from 10 heifers aged over three months); 2) sampling frequencies (one to three samples collection visits); 3) case definitions to conclude to a positive status using imperfect milk- or blood-ELISA tests. The top-ranking diagnostic strategy was the same in the two contexts: testing the bulk tank milk and the blood samples, all samples collected during one visit and the herd being assigned a S. Dublin positive status if one sample is ELISA-positive. The final decision favored the top-ranking option for both contexts.This MCDA approach and its application to S. Dublin infection in dairy herds allowed a consensual, rational, and transparent ranking of feasible diagnostic strategies while taking into account the diagnostic tests accuracy, socio-economic, logistic, and perception considerations of the key actors in the dairy industry. This promising tool can be applied to other infectious diseases that lack a well-established diagnostic procedure to define a herd status.

Integrative analyses identify opportunistic pathogens of patients with lower respiratory tract infections based on metagenomic next-generation sequencing

Lower respiratory tract infections (LRTIs) represent some of the most globally prevalent and detrimental diseases. Metagenomic next-generation sequencing (mNGS) technology has effectively addressed the requirement for the diagnosis of clinical infectious diseases. This study aimed at identifying and classifying opportunistic pathogens from the respiratory tract-colonizing microflora in LRTI patients using data acquired from mNGS analyses. A retrospective study was performed employing the mNGS data pertaining to the respiratory samples derived from 394 LRTIs patients. Linear discriminant analysis effect size (LEfSe) analysis was conducted to discern the discriminant bacteria. Receiver operating characteristic curves (ROC) were established to demonstrate discriminant bacterial behavior to distinguish colonization from infection. A total of 443 discriminant bacteria were identified and segregated into three cohorts contingent upon their correlation profiles, detection frequency, and relative abundance in order to distinguish pathogens from colonizing microflora. Among them, 119 emerging opportunistic pathogens (cohort 2) occupied an average area under the curve (AUC) of 0.976 for exhibiting the most prominent predictability in distinguishing colonization from infection, 39 were colonizing bacteria (cohort 1, 0.961), and 285 were rare opportunistic pathogens (cohort 3, 0.887). The LTRIs patients appeared modular in the form of cohorts depicting complex microbial co-occurrence networks, reduced diversity, and a high degree of antagonistic interactions in the respiratory tract microbiome. The study findings indicate that therapeutic interventions should target interaction networks rather than individual microbes, providing an innovative perspective for comprehending and combating respiratory infections. Conclusively, this study reports a profile of LRTIs-associated bacterial colonization and opportunistic pathogens in a relatively large-scale cohort, which might serve as a reference panel for the interpretation of mNGS results in clinical practice.

Present and Future Applications of Digital PCR in Infectious Diseases Diagnosis.

Infectious diseases account for about 3 million deaths per year. The advent of molecular techniques has led to an enormous improvement in their diagnosis, both in terms of sensitivity and specificity and in terms of the speed with which a clinically useful result can be obtained. Digital PCR, or 3rd generation PCR, is based on a series of technical modifications that result in more sensitive techniques, more resistant to the action of inhibitors and capable of direct quantification without the need for standard curves. This review presents the main applications that have been developed for the diagnosis of viral, bacterial, and parasitic infections and the potential prospects for the clinical use of this technology.

Application value of metagenomic next-generation sequencing in hematological patients with high-risk febrile neutropenia.

Metagenomic next-generation sequencing (mNGS) is a novel non-invasive and comprehensive technique for etiological diagnosis of infectious diseases. However, its practical significance has been seldom reported in the context of hematological patients with high-risk febrile neutropenia, a unique patient group characterized by neutropenia and compromised immune responses. This retrospective study evaluated the results of plasma cfDNA sequencing in 164 hematological patients with high-risk febrile neutropenia. We assessed the diagnostic efficacy and clinical impact of mNGS, comparing it with conventional microbiological tests. mNGS identified 68 different pathogens in 111 patients, whereas conventional methods detected only 17 pathogen types in 36 patients. mNGS exhibited a significantly higher positive detection rate than conventional methods (67.7% vs. 22.0%, P < 0.001). This improvement was consistent across bacterial (30.5% vs. 9.1%), fungal (19.5% vs. 4.3%), and viral (37.2% vs. 9.1%) infections (P < 0.001 for all comparisons). The anti-infective treatment strategies were adjusted for 51.2% (84/164) of the patients based on the mNGS results. mNGS of plasma cfDNA offers substantial promise for the early detection of pathogens and the timely optimization of anti-infective therapies in hematological patients with high-risk febrile neutropenia.

Chemically engineered mTOR-nanoparticle blockers enhance antitumour efficacy

Hepatocellular carcinoma (HCC) is a highly prevalent and deadly type of cancer, and although pharmacotherapy remains the cornerstone of treatment, therapeutic outcomes are often unsatisfactory. Pharmacological inhibition of mammalian target of rapamycin (mTOR) has been closely associated with HCC regression. Herein, we covalently conjugated AZD8055, a potent mTORC1/2 blocker, with a small panel of unsaturated fatty acids via a dynamically activating linkage to enable aqueous self-assembly of prodrug conjugates to form mTOR nanoblockers. Cell-based experiments were carried out to evaluate the effects of the nanoblocker against hepatocellular carcinoma (HCC) cells. The orthotopic and subcutaneous HCC mouse models were established to examine its antitumour activity. Among several fatty acids as promoieties, linoleic acid-conjugated self-assembling nanoblocker exhibited optimal size distribution and superior physiochemical properties. Compared with free agents, PEGylated AZD8055 nanoblocker (termed AZD NB) was pharmacokinetically optimized after intravenous administration. Invivo investigations confirmed that AZD NB significantly suppressed tumour outgrowth in subcutaneous HCCLM3 xenograft, Hepatoma-22, and orthotopic Hepa1-6 liver tumour models. Strikingly, treatment with AZD NB, but not free agent, increased intratumour infiltration of IFN-γ+CD8+ T cells and CD8+ memory T cells, suggesting a potential role of the mTOR nanoblocker to remodel the tumour microenvironment. Overall, a single conjugation with fatty acid transformed a hydrophobic mTOR blocker into a systemically injectable nanomedicine, representing a facile and generalizable strategy for improving the therapeutic index of mTOR inhibition-based cancer therapy. The mTOR inhibition by chemically engineered nanoblocker presented here had enhanced efficacy against tumours compared with the pristine drug and thus has the potential to improve the survival outcomes of patients with HCC. Additionally, this new nanosystem derived from co-assembling of small-molecule prodrug entities can serve as a delivery platform for the synergistic co-administration of distinct pharmaceutical agents. This work was supported by the National Natural Science Foundation of China (32171368,81721091), the Zhejiang Provincial Natural Science Foundation of China (LZ21H180001), the Jinan Provincial Laboratory Research Project of Microecological Biomedicine (JNL-2022039c and JNL-2022010B), State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (zz202310), and Natural Science Foundation of Shandong Province (ZR2023ZD59).

High-Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer-Antigen Recognition in Clinical Samples and Machine Learning.

High-precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral concentrations makes it a huge challenge to develop simple diagnostic devices that do not require any sample processing and yet are capable of meeting performance metrics such as very high sensitivity and specificity. Herein we describe a new single-pot and single-step electrochemical method that uses real-time kinetic profiling of the interaction between a high-affinity aptamer and an antigen on a viral surface. This method generates many data points per sample, which when combined with machine learning, can deliver highly accurate test results in a short testing time. We demonstrate this concept using both SARS-CoV-2 and Influenza A viruses as model viruses with specifically engineered high-affinity aptamers. Utilizing this technique to diagnose COVID-19 with 37 real human saliva samples results in a sensitivity and specificity of both 100 % (27 true negatives and 10 true positives, with 0 false negative and 0 false positive), which showcases the superb diagnostic precision of this method.

High‐Precision Viral Detection Using Electrochemical Kinetic Profiling of Aptamer‐Antigen Recognition in Clinical Samples and Machine Learning

AbstractHigh‐precision viral detection at point of need with clinical samples plays a pivotal role in the diagnosis of infectious diseases and the control of a global pandemic. However, the complexity of clinical samples that often contain very low viral concentrations makes it a huge challenge to develop simple diagnostic devices that do not require any sample processing and yet are capable of meeting performance metrics such as very high sensitivity and specificity. Herein we describe a new single‐pot and single‐step electrochemical method that uses real‐time kinetic profiling of the interaction between a high‐affinity aptamer and an antigen on a viral surface. This method generates many data points per sample, which when combined with machine learning, can deliver highly accurate test results in a short testing time. We demonstrate this concept using both SARS‐CoV‐2 and Influenza A viruses as model viruses with specifically engineered high‐affinity aptamers. Utilizing this technique to diagnose COVID‐19 with 37 real human saliva samples results in a sensitivity and specificity of both 100 % (27 true negatives and 10 true positives, with 0 false negative and 0 false positive), which showcases the superb diagnostic precision of this method.

Illumina and Nanopore sequencing in culture-negative samples from suspected lower respiratory tract infection patients.

To evaluate the diagnostic value of metagenomic sequencing technology based on Illumina and Nanopore sequencing platforms for patients with suspected lower respiratory tract infection whose pathogen could not be identified by conventional microbiological tests. Patients admitted to the Respiratory and Critical Care Medicine in Shanghai Ruijin Hospital were retrospectively studied from August 2021 to March 2022. Alveolar lavage or sputum was retained in patients with clinically suspected lower respiratory tract infection who were negative in conventional tests. Bronchoalveolar lavage fluid (BALF) samples were obtained using bronchoscopy. Sputum samples were collected, while BALF samples were not available due to bronchoscopy contraindications. Samples collected from enrolled patients were simultaneously sent for metagenomic sequencing on both platforms. Thirty-eight patients with suspected LRTI were enrolled in this study, consisting of 36 parts of alveolar lavage and 2 parts of sputum. According to the infection diagnosis, 31 patients were confirmed to be infected with pathogens, while 7 patients were diagnosed with non-infectious disease. With regard to the diagnosis of infectious diseases, the sensitivity and specificity of Illumina and Nanopore to diagnose infection in patients were 80.6% vs. 93.5% and 42.9 vs. 28.6%, respectively. In patients diagnosed with bacterial, Mycobacterium, and fungal infections, the positive rates of Illumina and Nanopore sequencer were 71.4% vs. 78.6%, 36.4% vs. 90.9%, and 50% vs. 62.5%, respectively. In terms of pathogen diagnosis, the sensitivity and specificity of pathogens detected by Illumina and Nanopore were 55.6% vs. 77.8% and 42.9% vs. 28.6%, respectively. Among the patients treated with antibiotics in the last 2 weeks, 61.1% (11/18) and 77.8% (14/18) cases of pathogens were accurately detected by Illumina and Nanopore, respectively, among which 8 cases were detected jointly. The consistency between Illumina and diagnosis was 63.9% (23/36), while the consistency between Nanopore and diagnosis was 83.3% (30/36). Between Illumina and Nanopore sequencing methods, the consistency ratio was 55% (22/42) based on pathogen diagnosis. Both platforms play a certain value in infection diagnosis and pathogen diagnosis of CMT-negative suspected LRTI patients, providing a theoretical basis for clinical accurate diagnosis and symptomatic treatment. The Nanopore platform demonstrated potential advantages in the identification of Mycobacterium and could further provide another powerful approach for patients with suspected Mycobacterium infection.

Molecular Imaging Techniques in the Diagnosis and Monitoring of Infectious Diseases

Molecular Imaging Techniques in the Diagnosis and Monitoring of Infectious Diseases