Rapid, simple, and inexpensive diagnostic point-of-care tests (POCTs) are essential for controlling infectious diseases in resource-limited settings. In this study, we developed a new detection system based on nanoparticle–DNA aggregation (STat aggregation of tagged DNA, STAT-DNA) to yield a visual change that can be easily detected by the naked eye. This simplified optical detection system was applied to detect hepatitis C virus (HCV). Reverse transcription-polymerase chain reaction (RT-PCR) was performed using primers labeled with biotin and digoxigenin. Streptavidin-coated magnetic particles (1 μm) and anti-digoxigenin antibody-coated polystyrene particles (250–350 nm) were added to form aggregates. The limit of detection (LoD) and analytical specificity were analyzed. The STAT-DNA results were compared with those of the standard real-time PCR assay using serum samples from 54 patients with hepatitis C. We achieved visualization of amplified DNA with the naked eye by adding nanoparticles to the PCR mixture without employing centrifugal force, probe addition, incubation, or dilution. The LoD of STAT-DNA was at least 10¹ IU/mL. STAT-DNA did not show cross-reactivity with eight viral pathogens. The detection using STAT-DNA was consistent with that using standard real-time PCR.

Background: Vancomycin is commonly dosed using standard weight–based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive model that personalizes initial dosing based on patient-specific pharmacokinetic factors prior to administration may enhance target attainment and minimize the need for subsequent dose adjustments. Objective: This study aimed to develop and evaluate a machine learning (ML)–based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under the curve to minimum inhibitory concentration, thereby optimizing the initial vancomycin dosage. Methods: A retrospective cohort study was conducted using hospitalized patients who received intravenous vancomycin and underwent pharmacokinetic TDM consultation (n=415). The cohort was randomly divided into training and testing datasets in a 7:3 ratio, and multiple ML techniques were used to develop an algorithm for optimizing initial vancomycin dosing. The optimal algorithm, referred to as the OPTIVAN algorithm, was selected and validated using an external cohort (n=268). We evaluated the performance of 4 ML models: gradient boosting machine, random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB). Additionally, a web-based clinical support tool was developed to facilitate real-time vancomycin TDM application in clinical practice. Results: The SVM algorithm demonstrated the best predictive performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.832 (95% CI 0.753-0.900) for the training dataset and 0.720 (95% CI 0.654-0.783) for the external validation dataset. The gradient boosting machine followed closely with AUROC scores of 0.802 (95% CI 0.667-0.857) for the training dataset and 0.689 (95% CI 0.596-0.733) for the validation dataset. In contrast, both XGB and RF exhibited relatively lower performance. XGB achieved AUROC values of 0.769 (95% CI 0.671-0.853) for the training set and 0.707 (95% CI 0.644-0.772) for the validation set, while RF recorded AUROC scores of 0.759 (95% CI 0.656-0.846) for the test dataset and 0.693 (95% CI 0.625-0.757) for the external validation set. The SVM model incorporated 7 covariates: age, BMI, glucose, blood urea nitrogen, estimated glomerular filtration rate, hematocrit, and daily dose per body weight. Subgroup analyses demonstrated consistent performance across different patient categories, such as renal function, sex, and BMI. A web-based TDM analysis tool was developed using the OPTIVAN algorithm. Conclusions: The OPTIVAN algorithm represents a significant advancement in personalized initial vancomycin dosing, addressing the limitations of current TDM practices. By optimizing the initial dose, this algorithm may reduce the need for subsequent dosage adjustments. The algorithm’s web-based app is easy to use, making it a practical tool for clinicians. This study highlights the potential of ML to enhance the effectiveness of vancomycin treatment.

The spread of delta variants (B.1.671.2) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe global threat. Multiplex real-time PCR is a common method for confirming SARS-CoV-2 infection, however, additional tests, such as whole genomic sequencing, are required to reveal the presence or type of viral mutation. Moreover, applying whole genomic sequencing to all SARS-CoV-2 positive samples is challenging due to time and cost constraints. Here, we report that the double or low amplification curve observed during RNA-dependent RNA polymerase (RdRp) gene/S gene amplification in the Allplex SARS-CoV-2 Assay is related to delta/alpha variants. We analyzed the RdRp/S gene amplification curve using 94 samples confirmed as SARS-CoV-2 infection by the Allplex SARS-CoV-2 Assay from January to August, 2021. These positive samples identified variant types using the Novaplex SARS-CoV-2 Variants I and IV Assays. Overall, 17 samples showing a double curve and 11 samples showing a low amplification pattern were associated with alpha-/delta-type strains with variants in the P681 region. The double or low curve shown in the RdRp gene amplification curve had 100% sensitivity and 100% specificity for diagnosing delta/alpha variants. During the SARS-CoV-2 virus diagnostic RT-PCR test using the Allplex SARS-CoV-2 Assay, we could consider the presence of delta/alpha variants in the samples with double or low amplification curve of the RdRp/S gene channel. This PCR amplification curve abnormality enables rapid and cost-effective variant type prediction during SARS-CoV-2 diagnostic testing in clinical laboratories.

Performances of the colistin antimicrobial susceptibility testing (AST) systems of Acinetobacter baumannii vary depending on the manufacturer, and data on colistin-resistant A. baumannii are limited. We evaluated the VITEK2 and Sensititre systems to determine colistin resistance and minimum inhibitory concentration (MIC) for A. baumannii isolated from a clinical microbiology laboratory. A total of 213 clinical A. baumannii isolates were tested, including 81 colistin-resistant A. baumannii. ASTs were performed using the VITEK2 and Sensititre systems according to the manufacturer’s instructions. Reference MICs for colistin were determined using the manual broth microdilution method (BMD). The results of the two AST methods were compared with the BMD results. VITEK2 and Sensititre systems showed category agreements of 95.3% and 99.1%, respectively. VITEK2 had a relatively high very major error (VME) rate (9.9%). Sensititre reported higher MICs than the reference method for the susceptible isolates and showed low essential agreement. In conclusion, the automated systems investigated in this study showed good category agreements for colistin AST of A. baumannii. However, VITEK2 had a high VME rate, and Sensititre had differences in MIC results. Colistin AST remains a challenging task in the clinical laboratory.