Biology subjects

Lin, T.-W.

Publications and source records attributed to Lin, T.-W..

3 recordsLinked to original sources

bioRxiv2023

Comparison of Multiple Carbapenemase Tests Based on an Unbiased Colony Selection Method

Carbapenemase-producing organisms (CPOs) present a major threat to public health, demanding precise diagnostic techniques their detection. Discrepancies among CPO tests have raised concerns, partly due to limitations in detecting bacterial diversity within host/specimen. We explored the impact of unbiased colony selection on carbapenemase testing and assessed its relevance on various tests. Based on "FirstAll" for unbiased colony selection to reduce bias, we compared modified carbapenem inactivation method/EDTA-modified carbapenem inactivation method (mCIM/eCIM), Carba5, the CPO panel, and multiplex PCR (M-PCR). Initially, we compared FirstAll to conventional colony selection for mCIM. Second, we used M-PCR as a reference, to evaluate test performance across seven CPO species. The results revealed that FirstAll selection improved carbapenemase detection, revising false-negative in 10.5% of K. pneumoniae isolates. In addition, 12.4% of CPOs tested positive for multiple carbapenemase genes. Both the Carba5 test and CPO panel showed suboptimal performance (sensitivity/specificity: Carba5 75.5%/89.0%, CPO panel 78.1%/74.0%). Carba5 test provided specific carbapenemase class assignments but CPO panel failed in 20.3% of cases. Carba5 test and the CPO panel results correlated well with ceftazidime-avibactam minimal inhibitory concentrations (MICs). Concordance for class A/D with MICs was 88.3% for Carba5 and 92.0% for the CPO panel; whereas for class B, it was 86.5% for Carba5 and 76.2% for the CPO panel. In conclusion, FirstAll as the unbiased colony selection impacted carbapenemase testing. With FirstAll, the diagnostic performance of either Carba5 or the CPO panel was compromised. The utilization of ceftazidime-avibactam guided by either the CPO panel or Carba5 was appropriate. ImportanceThe increasing carbapenemase-producing organisms (CPO) is concerning due to high mortality rates and limited treatment options. Precise testing for CPO is crucial not only for antibiotic treatments but also for infection control. However, discrepant results for an individual overtime or even intra-specimen are found in either phenotypic or genetic testing, posing considerable challenge in clinical management. Based on the colonization-infection model of CPO infections, there would be strain heterogeneity in an individual. On top of the heterogeneity, the single colony selection method in conventional CPO testing would be the source of discrepancy and bias. To test the hypothesis, we proposed FirstAll method as the unbiased colony selection method. We demonstrated that FirstAll corrected around 10% false-negative cases. Lower diagnostic performances of CPO tests were also found in comparison to previous related studies. The study revealed that colony selection would have considerable impacts on CPO testing.

microbiology↗

bioRxiv2022

Rapid and accurate discrimination of Mycobacterium abscessus subspecies based on matrix-assisted laser desorption ionization-time of flight spectrum and machine learning

BackgroundMycobacterium abscessus complex (MABC) has been reported to cause considerable complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance property amid the subspecies. However, long incubation days is needed for the traditional antibiotic susceptibility testing (AST) method. Effective antibiotics administration often delayed considerably and caused unfavorable outcomes. Thus, we proposed a novel and accurate method to identify subspecies and its potential antibiotics resistance, to guide clinical treatment within hours. MethodsSubspecies of the MABC isolates were determined by secA1, rpoB, and hsp65. AST was tested by using microdilution method, as well as sequencing of erm(41) and rrl genes. MALDI-TOF mass spectrometry (MS) spectra were analyzed. The informative peaks on MS spectra were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on MALDI-TOF spectrum. The models were developed and validated by nested five-fold cross-validation to avoid over-fitting. ResultsIn total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, 2805, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating subspecies abscessus from massiliense. ConclusionWe developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. The novel diagnostic tool would guide a more accurate and timely MABC subspecies-specific treatment.

microbiology↗

bioRxiv2020

Rapidly predicting vancomycin resistance of Enterococcus faecium through MALDI-TOF MS spectra obtained in real-world clinical microbiology laboratory

Enterococcus faecium is one of the leading pathogens in the world. In this study, we proposed a strategy to rapidly and accurately distinguish vancomycin-resistant Enterococcus faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) to help doctors correctly determine the use of vancomycin by a machine learning (ML)-based algorithm. A predictive model was developed and validated to distinguish VREfm and VSEfm by analyzing MALDI-TOF MS spectra of unique E. faecium isolates from different specimen types. Firstly, 5717 mass spectra, including 2795 VREfm and 2922 VSEfm, were used to develop the algorithm. And 2280 mass spectra of isolates, namely 1222 VREfm and 1058 VSEfm, were used to externally validate the algorithm. The random forest-based algorithm demonstrated good classification performances for overall specimens, whose mean AUROC in 5-fold cross validation, time-wise validation, and external validation was all greater than 0.84. For the detection of VREfm in blood, sterile body fluid, urinary tract, and wound, the AUROC in external validation was also greater than 0.84. The predictions with algorithms were significantly more accurate than empirical antibiotic use. The accuracy of antibiotics administration could be improved by 30%. And the algorithm could provide rapid antibiotic susceptibility results at least 24 hours ahead of routine laboratory tests. The turn-around-time of antibiotic susceptibility could be reduced by 50%. In conclusion, a ML algorithm using MALDI-TOF MS spectra obtained in routine workflow accurately differentiated VREfm from VSEfm, especially in blood and sterile body fluid, which can be applied to facilitate the clinical testing process due to its accuracy, generalizability, and rapidness.

microbiology↗