Dataset Information

MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples.

ABSTRACT: Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene bla_NDM and bla_KPC, colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-enhanced recombinase polymerase amplification (RPA) and a refined Chelex-100 lysis method with HRP-catalyzed lateral flow immunoassay (LFIA), we developed an MCL-PRPA-HLFIA cascade assay system for detecting these genes in UTI samples. The refined Chelex-100 lysis method extracts target DNA from UTI samples in 20 min without high-speed centrifugation or pre-incubation of urine samples. Following optimization, the cascade detection system achieved an LOD of 10² CFU/mL with satisfactory specificity and could detect these genes in both simulated and actual UTI samples. It takes less than an hour to complete the process without the use of high-speed centrifuges or other specialized equipment, such as PCR amplifiers. The MCL-PRPA-HLFIA cascade assay system provides new ideas for the construction of rapid detection methods for pan-drug-resistant genes in clinical UTI samples and provides the necessary medication guidance for UTI treatment.

SUBMITTER: Tao J

PROVIDER: S-EPMC10095522 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples.

Tao Jin J Liu Dejun D Xiong Jincheng J Shan Wenchong W Dou Leina L Zhai Weishuai W Wang Yang Y Shen Jianzhong J Wen Kai K

International journal of molecular sciences 20230405 7

Recently, urinary tract infection (UTI) triggered by bacteria carrying pan-drug-resistant genes, including carbapenem resistance gene blaNDM and blaKPC, colistin resistance gene mcr-1, and tet(X) for tigecycline resistance, have been reported, posing a serious challenge to the treatment of clinical UTI. Therefore, point-of-care (POC) detection of these genes in UTI samples without the need for pre-culturing is urgently needed. Based on PEG 200-en ...[more]

PMID: 37047757

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Project description:ObjectiveVarious point-of-care testing (POCT) urine analysers are commercially available for routine urine analysis in general practice. The present study compares analytical performance, agreement and user-friendliness of six different POCT urine analysers for diagnosing urinary tract infection in general practice.SettingAll testing procedures were performed at a diagnostic centre for primary care in the Netherlands. Urine samples were collected at four general practices.Primary and secondary outcome measuresAnalytical performance and agreement of the POCT analysers regarding nitrite, leucocytes and erythrocytes, with the laboratory reference standard, was the primary outcome measure, and analysed by calculating sensitivity, specificity, positive and negative predictive value, and Cohen's κ coefficient for agreement. Secondary outcome measures were the user-friendliness of the POCT analysers, in addition to other characteristics of the analysers.ResultsThe following six POCT analysers were evaluated: Uryxxon Relax (Macherey Nagel), Urisys 1100 (Roche), Clinitek Status (Siemens), Aution 11 (Menarini), Aution Micro (Menarini) and Urilyzer (Analyticon). Analytical performance was good for all analysers. Compared with laboratory reference standards, overall agreement was good, but differed per parameter and per analyser. Concerning the nitrite test, the most important test for clinical practice, all but one showed perfect agreement with the laboratory standard. For leucocytes and erythrocytes specificity was high, but sensitivity was considerably lower. Agreement for leucocytes varied between good to very good, and for the erythrocyte test between fair and good. First-time users indicated that the analysers were easy to use. They expected higher productivity and accuracy when using these analysers in daily practice.ConclusionsThe overall performance and user-friendliness of all six commercially available POCT urine analysers was sufficient to justify routine use in suspected urinary tract infections in general practice.

Project description:Simple Summary It’s difficult to detect upper tract urothelial carcinoma at early stage. Invasive testing may increase risk of cancer recurrences in the bladder after radical nephroureterectomy. Thus, in the present study, we incorporated two-gene mutation and methylation biomarkers to conduct the diagnostic tool of upper tract urothelial carcinoma and performed external validation to investigate the utility and stability of the optimal panel. It showed a highly specific and robust performance. It may be used as a replaceable approach for early detection of upper tract urothelial carcinoma, resulting in less extensive examinations in patients at low risk. Abstract Background: To improve the selection of patients for ureteroscopy, avoid excessive testing and reduce costs, we aimed to develop and validate a diagnostic urine assay for upper tract urinary carcinoma (UTUC). Methods: In this cohort study we recruited 402 patients from six Hunan hospitals who underwent ureteroscopy for hematuria, including 95 patients with UTUC and 307 patients with non-UTUC findings. Midstream morning urine samples were collected before ureteroscopy and surgery. DNA was extracted and qPCR was used to analyze mutations in TERT and FGFR3 and the methylation of NRN1. In the training set, the random forest algorithm was used to build an optimal panel. Lastly, the Beijing cohort (n = 76) was used to validate the panel. Results: The panel combining the methylation with mutation markers led to an AUC of 0.958 (95% CI: 0.933–0.975) with a sensitivity of 91.58% and a specificity of 94.79%. The panel presented a favorable diagnostic value for UTUC vs. other malignant tumors (AUC = 0.920) and UTUC vs. benign disease (AUC = 0.975). Furthermore, combining the panel with age revealed satisfactory results, with 93.68% sensitivity, 94.44% specificity, AUC = 0.970 and NPV = 98.6%. In the external validation process, the model showed an AUC of 0.971, a sensitivity of 95.83% and a specificity of 92.31, respectively. Conclusions: A novel diagnostic model for analyzing hematuria patients for the risk of UTUC was developed, which could lead to a reduction in the need for invasive examinations. Combining NRN1 methylation and gene mutation (FGFR3 and TERT) with age resulted in a validated accurate prediction model.

Project description:BACKGROUND:The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS:We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS:the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.

Dataset Information

MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples.

Publications

MC-PRPA-HLFIA Cascade Detection System for Point-of-Care Testing Pan-Drug-Resistant Genes in Urinary Tract Infection Samples.

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