Search

Background and purpose: Our previous studies showed that lugdunin activities are associated with Staphylococcus lugdunensis genotypes, and most isolates do not exhibit lugdunin activity. As a continuation of our previous analysis, we focused on the reasons for defects in lugdunin production in S. lugdunensis clinical isolates. Methods: A comparative analysis of 36 S. lugdunensis whole genome sequencing data revealed three major mutation types, unknown deletion mechanism that caused most of lug operon genes lost, mobile genetic element (MGE) insertion, and nonsense mutations, which potentially damaged lugdunin production. A total of 152 S. lugdunensis clinical isolates belonging to lugdunin nonproducers were further examined for the above three mutation types. PCR products were sequenced to examine these variations. Results: Forty-six of the 152 isolates were CRISPR-Cas IIC isolates, including 26 ST27, 14 ST4, and 6 ST29 isolates; further investigation confirmed that all of their lug operons had lost almost all lug operon genes except lugM. An IS256 insertion in lugA was identified in 16 isolates, and most isolates (15 over 16) belonged to ST3. In addition, three nonsense mutations caused by single nucleotide substitutions (an adenine deletion in lugB at the 361th and 1219th nucleotides and an adenine deletion in lugC at the 1612nd nucleotide) that were frequently observed among 36 S. lugdunensis whole genome sequencing data were further observed in our clinical isolates. These three nonsense mutations were frequently found in most of CRISPR-Cas IIIA strains, especially in ST6 isolates. Conclusion: Our findings suggest that the mechanisms affecting lugdunin production are associated with S. lugdunensis molecular types.

Carbapenemase-producing organisms (CPOs) present a major threat to public health, demanding precise diagnostic techniques for their detection. Discrepancies among the CPO tests have raised concerns, partly due to limitations in detecting bacterial diversity within host specimens. We explored the impact of an unbiased colony selection on carbapenemase testing and assessed its relevance to various tests. Using the FirstAll method for unbiased colony selection to reduce bias, we compared the results from different methods, namely the modified carbapenem inactivation method/EDTA-modified carbapenem inactivation method (mCIM/eCIM), the Carba5, the CPO panel, and the multiplex PCR (MPCR). We compared the FirstAll method to the conventional colony selection for MPCR with seven CPO species. In addition, we evaluated the test performance on seven CPO species using MPCR as a reference and the FirstAll method as the colony-selection method. The results revealed that the selections from the FirstAll method have improved rates of carbapenemase detection, in comparison to approximately 11.2% of the CPO isolates that were noted to be false negatives in the conventional colony-selection methods. Both the Carba5 test and the CPO panel showed suboptimal performance (sensitivity/specificity: Carba5 74.6%/89.5%, CPO panel 77.2%/74.4%) in comparison to the FirstAll method. The Carba5 test provided specific carbapenemase class assignments, but the CPO panel failed in 18.7% of the cases. The Carba5 test and the CPO panel results correlated well with ceftazidime–avibactam minimal inhibitory concentrations (MICs). The concordance for Class A/D with MICs was 94.7% for Carba5 and 92.7% for the CPO panel; whereas for Class B, it was 86.5% for Carba5 and 75.9% for the CPO panel. In conclusion, FirstAll, as the unbiased colony-selection method, was shown to impact carbapenemase testing. With FirstAll, the diagnostic performance of both the Carba5 and the CPO panel was found to be lower. Furthermore, the utilization of ceftazidime–avibactam guided by either the CPO panel or Carba5 was appropriate.

Background: In this study, our objective was to characterize Staphylococcus lugdunensis isolated from sterile body fluids (SBFs) in a medical center in Taiwan between 2009 and 2020. Methods: We used MALDI-TOF MS, disk diffusion testing, agar dilution assay, SCCmec typing, and antibiotic resistance gene screening to identify and investigate the characteristics of oxacillin-resistant S. lugdunensis (ORSL). Results: A total of 438 S. lugdunensis isolates were collected and 146 (33.3%) isolates were identified as ORSL. SCCmec type V was dominant (65.7%) in our ORSL isolates, followed by SCCmec type II (18.5%), and type IV (8.9%). After 2013, a slight increase in SCCmec types IV and V was revealed. Moreover, all ORSL isolates with type II and untypable SCCmec were highly resistant to oxacillin (MIC >32 μg/mL), compared to ORSL that had SCCmec types IV, V, and VT. All 146 ORSL isolates were resistant to penicillin and susceptible to teicoplanin and vancomycin. High resistance rates of ORSL to clindamycin (43.2%), erythromycin (43.2%), gentamicin (78.1%) and tetracycline (46.6%) was observed. Moreover, only two (1.4%) and six (4.1%) ORSL isolates were resistant to trimethoprim/sulfamethoxazole and ciprofloxacin, respectively. The erythromycin-resistant ORSL isolates mostly exhibited constitutive MLSB resistant phenotype (61/63, 96.8%) and contained either ermC alone (27/63, 42.9%) or a combination of ermC with ermA (28/63, 44.4%). Conclusion: Our present study showed a stable rate of ORSL from SBFs during 2009–2020. Moreover, teicoplanin, vancomycin, trimethoprim/sulfamethoxazole, and ciprofloxacin were shown to be highly efficient for the treatment of ORSL in vitro.

Our study optimized METex14 skipping mutation detection by analyzing 223 Oncomine™ Focus Assay-positive cases using Pan Lung Cancer PCR Panel and reverse transcription (RT)-PCR. Among the 11 METex14 skipping mutation-positive cases (average read counts: 1390), 2 with Oncomine™ Focus Assay read counts of 2540 and 10,177 were positive on all platforms. Those with Oncomine™ Focus Assay read counts ranging from 179 to 612 tested negative elsewhere. Specimens with low ratios (average ratio: 0.12% for nine cases) may yield false-positive results. Our results suggested that monitoring read counts and ratios and validating the results with RT-PCR are crucial to prevent false positives.

ABSTRACT Lugdunin produced by Staphylococcus lugdunensis has been shown to have broad inhibitory activity against Gram-positive bacteria; however, lugdunin activity among S. lugdunensis isolates and its association with different agr, SCCmec, and sequence types remain unclear. We used matrix-assisted laser desorption ionization-time-of-flight mass spectrometry to identify S. lugdunensis and collected 202 S. lugdunensis samples for further assays. Agar spot tests were performed to characterize S. lugdunensis lugdunin production and activity. Multilocus sequence typing, SCCmec, and agr genotyping were performed on S. lugdunensis. In all, 91 Staphylococcus aureus strains with varying vancomycin susceptibilities were used to examine lugdunin activity in S. lugdunensis. In total, 48 S. lugdunensis strains (23.8%) were found to be oxacillin-resistant S. lugdunensis (ORSL), whereas 154 (76.2%) were classified as oxacillin-sensitive S. lugdunensis (OSSL). Moreover, 16 (33.3%) ORSL and 35 (22.7%) OSSL strains showed antibacterial activity against S. aureus. Our data showed that most lugdunin-producing ORSL strains (14/48, 29.2%) were of ST3-SCCmec V-agr II genotypes, whereas most lugdunin-producing OSSL strains (15/154, 9.7%) were of ST3-agr II, followed by ST1-agr I (10/154, 6.5%). Our data also revealed that lugdunin exhibited weak inhibitory activity against the VISA ST239 isolate. In addition, we observed that ST239 VSSA was more resistant to lugdunin than ST5, ST59, and ST45 VSSA. Taken together, our data pioneered the epidemiology of lugdunin production in S. lugdunensis isolates and revealed its association with genotypes. However, further molecular and bioinformatics investigations are needed to elucidate the regulatory mechanisms of lugdunin production and activity. IMPORTANCE Lugdunin is active against both methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci by dissipating their membrane potential. However, the association of lugdunin activity with the genotypes of Staphylococcus lugdunensis has not been addressed. Here, we show the high prevalence of lugdunin-producing strains among ST1 (83.3%), ST2 (66.7%), and ST3 (53.3%) S. lugdunensis. Moreover, we identified the antibacterial activity of lugdunin-producing strains against VISA and hVISA. These results shed light on the potential application of lugdunin for the treatment of drug-resistant pathogens.

Background: The study aimed to assess the clinical characteristics of patients with nocardiosis, to evaluate the in vitro susceptibility of antimicrobial agents against Nocardia species, and to explore changes in antimicrobial susceptibilities in this era of multidrug resistance. Methods: Nocardia isolates were identified to the species level using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and 16S rRNA, hsp65, and secA1 gene sequencing, and minimum inhibitory concentrations (MICs) of 15 antimicrobial agents were assessed with the broth microdilution method. Results: Eighty-nine isolates from 68 patients were identified to species level. The most common species were Nocardia brasiliensis (n = 28, 31.5%), followed by N. farcinica (n = 24, 27%) and N. cyriacigeorgica (n = 16, 18%). Skin and soft tissue were the most common sites of nocardiosis. In multivariate analysis, cutaneous infection (OR, 0.052; p = 0.009), immunosuppressant use (OR, 16.006; p = 0.013) and Charlson combidity index (OR, 1.522; p = 0.029) were significant predictors for death. In total, 98.9% isolates were susceptible to trimethoprim-sulfamethoxazole and linezolid. Further, the MIC range and resistance rate of all Nocardia species to ceftriaxone, imipenem, and amoxicillin-clavulanic acid were found to generally increase over time. Conclusion: Considering that trimethoprim-sulfamethoxazole is effective against most Nocardia species, it is the antibiotic of choice in Taiwan. Besides, amikacin, tigecycline, and linezolid showed high activity against Nocardia species and are thus good alternatives or additional therapies to treat nocardiosis, depending on patient's underlying conditions and site of infection.

ABSTRACT In clinical microbiology, matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms−logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.

Background/purpose: Staphylococcus lugdunensis is a Gram-positive coagulase-negative bacterium and is recognized as a critical pathogenic species recently. Here, we aimed to evaluate the cefoxitin disk diffusion (CDD), oxacillin agar dilution (OAD), and mecA PCR for detecting oxacillin-resistant S. lugdunensis (ORSL) isolates. Methods: Multilocus sequence typing (MLST) analysis was performed to determine the clonality of 117 S. lugdunensis isolates isolated between May 2009 and Jul 2014. CDD, OAD, and mecA PCR were used to identify oxacillin-resistant S. lugdunensis (ORSL). Results: MLST results showed that the most common sequence type (ST) of our S. lugdunensis isolates was ST6 (35.9%) followed by ST3 (28.2%), ST27 (17.9%), and ST4 (6.8%). CDD and OAD showed that 39 and 43 isolates were ORSL, respectively. 4 ST3 CDD-susceptible S. lugdunensis (OSSL) isolates had MIC values ≥ 4 for oxacillin. mecA PCR results showed that 43 OAD-resistant S. lugdunensis and 3 OAD-susceptible ST27 S. lugdunensis had the mecA gene. Therefore, OAD was used as the gold standard to evaluate the performance of CDD and mecA PCR for identifying ORSL. The overall sensitivity, specificity, and accuracy of CCD for ORSL detection was 90.7%, 100%, and 96.8%, respectively. The sensitivity, specificity, and accuracy of mecA PCR for identifying ORSL was 100%, 95.9%, and 97.44%, respectively. Conclusion: Our results indicate that OAD shows higher accuracy for ORSL detection compared with CDD and mecA PCR.

A total of seventy VanA-type vancomycin-resistant enterococci (VRE) isolates obtained in Taiwan in the early 2000s were retrospectively characterized. Forty isolates were obtained from human patients and thirty from livestock. Of these VRE isolates, twenty-three (57.5%) of the human VRE and thirty (100%) of the livestock VRE were Enterococcus faecalis, and the remaining seventeen (42.5%) of the human VRE were E. faecium. Of the 53 E. faecalis isolates, twenty-two (96%) of the human VRE and thirty (100%) of the livestock VRE exhibited a high level of resistance to vancomycin and sensitivity to teicoplanin. They also had three amino acid substitutions in the N-terminal region of the deduced VanS sequence. The vancomycin resistance of all of the 22 human isolates, and 20 of the 30 livestock isolates, transferred to E. faecalis FA2-2 at a frequency of 10−5 to 10−3 per donor cell in broth. Each of the transconjugants responded to E. faecalis pheromone (i.e., E. faecalis FA2-2 culture filtrate), indicating that the conjugative plasmids were pheromone-responsive plasmids. Three of the conjugative plasmids originated from human isolates, and five plasmids from livestock isolates were corresponded and classified as type A plasmid. Two plasmids originated from human isolates and six plasmids from livestock isolates were corresponded and classified as type B plasmid. E. faecalis FA2-2 containing either the type A or type B plasmid responded to the synthetic pheromone cAD1. The type A and type B plasmids transferred between E. faecalis FA2-2 and JH2SS at a frequency of about 10−2 per donor cell and conferred vancomycin, bacitracin, and erythromycin resistances. The complete DNA sequence of the representative type A plasmid pTW9 (85,068 bp) showed that the plasmid carried a Tn1546-like element encoding vanA-type resistance, erythromycin resistance (ermB), and bacitracin resistance (bcrABDR). The plasmid contained the regulatory region found in the pheromone-responsive plasmid and encoded the genes traA, traD and iad1, which are the key negative regulatory elements, and traE1, a key positive regulator of plasmid pAD1, indicating that plasmid pTW9 was pAD1-type pheromone-responsive plasmid. PFGE analysis of SmaI-digested chromosomal DNAs showed that several E. faecalis strains harboring an identical type A pheromone-responsive plasmid were indistinguishable, and that these were identified both in human and livestock isolates, indicating the transmissions of the VRE strains between livestock and humans. These data showed that the multiple-drug-resistant pheromone-responsive conjugative plasmids have been widely spread in both human and livestock VRE, and there was high potential for transfers of VRE from food animals to humans in Taiwan in the early 2000s.

Streptococcus agalactiae (Group B Streptococcus, GBS) is an important pathogen of bacterial meningitis in neonates. We aimed to investigate the clinical and genetic characteristics of neonatal GBS meningitis. All neonates with GBS meningitis at a tertiary level medical center in Taiwan between 2003 and 2020 were analyzed. Capsule serotyping, multilocus sequence typing, antimicrobial resistance, and whole-genome sequencing (WGS) were performed on the GBS isolates. We identified 48 neonates with GBS meningitis and 140 neonates with GBS sepsis. Neonates with GBS meningitis had significantly more severe clinical symptoms; thirty-seven neonates (77.8%) had neurological complications; seven (14.6%) neonates died; and 17 (41.5%) survivors had neurological sequelae at discharge. The most common serotypes that caused meningitis in neonates were type III (68.8%), Ia (20.8%), and Ib (8.3%). Sequence type (ST) is highly correlated with serotypes, and ST17/III GBS accounted for more than half of GBS meningitis cases (56.3%, n = 27), followed by ST19/Ia, ST23/Ia, and ST12/Ib. All GBS isolates were sensitive to ampicillin, but a high resistance rates of 72.3% and 70.7% to erythromycin and clindamycin, respectively, were noted in the cohort. The virulence and pilus genes varied greatly between different GBS serotypes. WGS analyses showed that the presence of PezT; BspC; and ICESag37 was likely associated with the occurrence of meningitis and was documented in 60.4%, 77.1%, and 52.1% of the GBS isolates that caused neonatal meningitis. We concluded that GBS meningitis can cause serious morbidity in neonates. Further experimental models are warranted to investigate the clinical and genetic relevance of GBS meningitis. Specific GBS strains that likely cause meningitis requires further investigation and clinical attention.

ABSTRACT Streptococcus agalactiae (group B Streptococcus [GBS]) is well known to cause serious diseases in infants. A serotype Ib GBS strain has recently emerged and become prevalent in Southeast Asia. We aimed to investigate the clinical and genetic characteristics of this strain. All neonates with invasive GBS diseases from a tertiary-level medical center in Taiwan between 2003 and 2020 were analyzed. The capsule serotyping, multilocus sequence typing, and antimicrobial resistance analyses were performed on all the invasive GBS isolates, and whole-genome sequencing (WGS) was performed specifically on the type Ib GBS strain. A total of 188 neonates with invasive GBS disease during the study period were identified. The type Ib GBS strain accounted for 7.4% (n = 14) of neonatal GBS invasive diseases. Almost all type Ib GBS isolates belonged to sequence type 12 (13/14, 92.9%) and clonal complex 12. Neonates with type Ib GBS disease had a significantly higher rate of complicated sepsis (10/14, 71.4%; P

Background: The antiviral resistance of cytomegalovirus (CMV) infections is associated with mutations in the CMV UL54 and UL97 gene regions and is a serious problem in immunocompromised patients. However, the molecular epidemiology of UL54 and UL97 in Taiwan is unclear. Methods: We conducted a retrospective study of patients with CMV infections between January and December 2016 in two tertiary hospitals, one regional hospital in Taiwan. CMV DNAemia was confirmed by elevated CMV DNA titers. Then the regions of the UL54 and UL97 mutations were amplified by PCR and sequenced. Results: Of 729 patients with CMV syndrome, 112 CMV DNAemia patients were enrolled. Twelve novel variants in UL54 (P342S, S384F, K434R, S673F, T754M, R778H, C814S, M827I, G878E, S880L, E888K, and S976N) and one novel variant in UL97 (M615T) were discovered. UL97 antiviral resistance mutations (L595S, M460I, and M460V) were found in four patients (3.6%). In the drug resistance strains, the mutation events occurred after 83–150 days of therapy, and drug resistance was also observed in these patients. The following high frequency variants were observed: D605E in UL97 and A885T, N898D, V355A, N685S, and A688V in UL54. Conclusion: The results demonstrate that the positive rate of CMV DNAemia was 15.3% (112/729) among the patients with clinical CMV infection symptoms. The proportion of antiviral resistance CMV strains within CMV DNAemia patients was 3.6%. With the information of polymorphism incidence in the UL54 and UL97 patients from our study, determination of the genetic profile of UL54 and UL97 among immunocompromised populations with refractory CMV infection is recommended.

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium, which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VREfm) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VREfm using a deep learning algorithm.

Abstract The Mycobacterium tuberculosis complex (MTBC) remains one of the top 10 leading causes of death globally. The early diagnosis of MTBC can reduce mortality and mitigate disease transmission. However, current nucleic acid amplification diagnostic test methods are generally time-consuming and show suboptimal diagnostic performance, especially in extrapulmonary MTBC samples or acid-fast stain (AFS)-negative cases. Thus, development of an accurate assay for the diagnosis of MTBC is necessary, particularly under the above mentioned conditions. In this study, a single-tube nested real-time PCR assay (N-RTP) was developed and compared with a newly in-house-developed high-sensitivity real-time PCR assay (HS-RTP) using 134 clinical specimens (including 73 pulmonary and 61 extrapulmonary specimens). The amplification efficiency of HS-RTP and N-RTP was 99.8% and 100.7%, respectively. The sensitivity and specificity of HS-RTP and N-RTP for the diagnosis of MTBC in these specimens were 97.5% (77/79) versus 94.9% (75/79) and 80.0% (44/55) versus 89.1% (49/55), respectively. The sensitivity and specificity of HS-RTP and N-RTP for the diagnosis of MTBC in pulmonary specimens were 96.3% (52/54) versus 96.3% (52/54) and 73.7.0% (14/19) versus 89.5% (17/19), respectively; in extrapulmonary specimens, the sensitivity and specificity of HS-RTP and N-RTP were 100% (25/25) versus 92% (23/25) and 83.3% (30/36) versus 88.9% (32/36), respectively. Among the AFS-negative cases, the sensitivity and specificity of HS-RTP and N-RTP were 97.0% (32/33) versus 90.9% (30/33) and 88.0% (44/50) versus 92.0% (46/50), respectively. Overall, the sensitivity of HS-RTP was higher than that of N-RTP, and the performance was not compromised in extrapulmonary specimens and under AFS-negative conditions. In contrast, the specificity of the N-RTP assay was higher than that of the HS-RTP assay in all types of specimens. In conclusion, the HS-RTP assay would be useful for screening patients suspected of exhibiting an MTBC infection due to its higher sensitivity, while the N-RTP assay could be used for confirmation because of its higher specificity. Our results provide a two-step method (screen to confirm) that simultaneously achieves high sensitivity and specificity in the diagnosis of MTBC.

Multi-drug resistant Staphylococcus haemolyticus is a frequent nosocomial invasive bacteremia pathogen in hospitals. Our previous analysis showed one of the predominant strains, ST42 originated from ST3, had only one multilocus sequence typing (MLST) variation among seven loci in SH1431; yet no significant differences in biofilm formation observed between ST42 and ST3, suggesting that other factors influence clonal lineage change. Whole genome sequencing was conducted on two isolates from ST42 and ST3 to find phenotypic and genotypic variations, and these variations were further validated in 140 clinical isolates. The fusidic acid- and tetracycline-resistant genes (fusB and tetK) were found only in CGMH-SH51 (ST42). Further investigation revealed consistent resistant genotypes in all isolates, with 46% and 70% of ST42 containing fusB and tetK, respectively. In contrast, only 23% and 4.2% ST3 contained these two genes, respectively. The phenotypic analysis also showed that ST42 isolates were highly resistant to fusidic acid (47%) and tetracycline (70%), compared with ST3 (23% and 4%, respectively). Along with drug-resistant genes, three capsule-related genes were found in higher percentage distributions in ST42 than in ST3 isolates. Our findings indicate that ST42 could become endemic in Taiwan, further constitutive surveillance is required to prevent the spread of this bacterium.

Multidrug resistance has become a phenotype that commonly exists among Staphylococcus aureus and is a serious concern for infection treatment. Nowadays, to detect the antibiotic susceptibility, antibiotic testing is generated based on the level of genomic for cure decision consuming huge of time and labor, while matrix-assisted laser desorption-ionization (MALDI) time-of-flight mass spectrometry (TOF/MS) shows its possibility in high-speed and effective detection on the level of proteomic. In this study, on the basis of MALDI-TOF spectra data of discovery cohort with 26,852 samples and replication cohort with 4,963 samples from Taiwan area and their corresponding susceptibilities to oxacillin and clindamycin, a multi-label prediction model against double resistance using Lowest Power set ensemble with XGBoost is constructed for rapid susceptibility prediction. With the output of serial susceptibility prediction, the model performance can realize 77% of accuracy for the serial prediction, the area under the receiver characteristic curve of 0.93 for oxacillin susceptibility prediction, and the area under the receiver characteristic curve of 0.89 for clindamycin susceptibility prediction. The generated multi-label prediction model provides serial antibiotic resistance, such as the susceptibilities of oxacillin and clindamycin in this study, for S. aureus-infected patients based on MALDI-TOF, which will provide guidance in antibiotic usage during the treatment taking the advantage of speed and efficiency.

Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability.

Background: Pneumocystis pneumonia (PCP) is a disease caused by the opportunistic infection of the fungus Pneumocystis jirovecii. Several PCR methods have been developed to aid in the diagnosis of PCP. In this study, we evaluated the performance of a real-time PCR in the diagnosis of PCP, in patients with various underlying diseases. Methods: Ninety-seven BAL samples and 94 sputum samples from 191 patients were used in the study. Patients were classified as PCP (121 patients) or non-PCP (70 patients) based on their clinical and radiological presentations. Results: Real time PCR amplified the P. jirovecii mitochondrial large-subunit rRNA gene with a detection limit of 68 copies of DNA per reaction. Non-PCP pathogens including 32 different fungi and bacteria were also evaluated. Overall, 71.9% of the samples from PCP patients and 14.5% of those from non-PCP patients were positive for the PCR test with a CT value of the real-time PCR below 45. The main underlying diseases of the patients were hematological or solid malignancies (47.1%) and HIV infection (8.9%). The CT values of the test were significantly lower in BAL samples from PCP patients than those from non-PCP patients (p = 0.024). No non-PCP patient had a CT value below 30, whereas samples from 24.8% of PCP patients with underlying diseases had a CT value below 30. Conclusion: Since false positive PCR results were obtained, perhaps due to colonization, we suggest that the diagnosis of PCP should be based on a combination of clinical symptoms, underlying diseases, and PCR results.

Abstract Traditional Chinese Medicine (TCM) has been extensively used to ameliorate diseases in Asia for over thousands of years. However, owing to a lack of formal scientific validation, the absence of information regarding the mechanisms underlying TCMs restricts their application. After oral administration, TCM herbal ingredients frequently are not directly absorbed by the host, but rather enter the intestine to be transformed by gut microbiota. The gut microbiota is a microbial community living in animal intestines, and functions to maintain host homeostasis and health. Increasing evidences indicate that TCM herbs closely affect gut microbiota composition, which is associated with the conversion of herbal components into active metabolites. These may significantly affect the therapeutic activity of TCMs. Microbiota analyses, in conjunction with modern multiomics platforms, can together identify novel functional metabolites and form the basis of future TCM research.

BackgroundThe use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. ObjectiveThe aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms—logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms—when applied to clinical laboratory datasets. MethodsWe applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. ResultsThe experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. ConclusionsThe XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods––FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods––to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Abstract Background Group B streptococcus (GBS) is an important pathogen that is responsible for invasive infections, including sepsis and meningitis. GBS serotyping is an essential means for the investigation of possible infection outbreaks and can identify possible sources of infection. Although it is possible to determine GBS serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming and labor-intensive. In recent years, the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported as an effective tool for the determination of GBS serotypes in a more rapid and accurate manner. Thus, this work aims to investigate GBS serotypes by incorporating machine learning techniques with MALDI-TOF MS to carry out the identification. Results In this study, a total of 787 GBS isolates, obtained from three research and teaching hospitals, were analyzed by MALDI-TOF MS, and the serotype of the GBS was determined by a geno-serotyping experiment. The peaks of mass-to-charge ratios were regarded as the attributes to characterize the various serotypes of GBS. Machine learning algorithms, such as support vector machine (SVM) and random forest (RF), were then used to construct predictive models for the five different serotypes (Types Ia, Ib, III, V, and VI). After optimization of feature selection and model generation based on training datasets, the accuracies of the selected models attained 54.9–87.1% for various serotypes based on independent testing data. Specifically, for the major serotypes, namely type III and type VI, the accuracies were 73.9 and 70.4%, respectively. Conclusion The proposed models have been adopted to implement a web-based tool (GBSTyper), which is now freely accessible at http://csb.cse.yzu.edu.tw/GBSTyper/, for providing efficient and effective detection of GBS serotypes based on a MALDI-TOF MS spectrum. Overall, this work has demonstrated that the combination of MALDI-TOF MS and machine intelligence could provide a practical means of clinical pathogen testing.

As antibiotics resistance on superbugs has risen, more and more studies have focused on developing rapid antibiotics susceptibility tests (AST). Meanwhile, identification of multiple antibiotics resistance on Staphylococcus aureus provides instant information which can assist clinicians in administrating the appropriate prescriptions. In recent years, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has emerged as a powerful tool in clinical microbiology laboratories for the rapid identification of bacterial species. Yet, lack of study devoted on providing efficient methods to deal with the MS shifting problem, not to mention to providing tools incorporating the MALDI-TOF MS for the clinical use which deliver the instant administration of antibiotics to the clinicians. In this study, we developed a web tool, MDRSA, for the rapid identification of oxacillin-, clindamycin-, and erythromycin-resistant Staphylococcus aureus. Specifically, the kernel density estimation (KDE) was adopted to deal with the peak shifting problem, which is critical to analyze mass spectra data, and machine learning methods, including decision trees, random forests, and support vector machines, which were used to construct the classifiers to identify the antibiotic resistance. The areas under the receiver operating the characteristic curve attained 0.8 on the internal (10-fold cross validation) and external (independent testing) validation. The promising results can provide more confidence to apply these prediction models in the real world. Briefly, this study provides a web-based tool to provide rapid predictions for the resistance of antibiotics on Staphylococcus aureus based on the MALDI-TOF MS data. The web tool is available at: http://fdblab.csie.ncu.edu.tw/mdrsa/.

ABSTRACT Clustered regularly interspaced short palindromic repeats (CRISPRs) and CRISPR-associated (Cas) genes (CRISPR-Cas) are present in many bacterial genomes with functions beyond adaptive immunity. We aimed to characterize the CRISPR-Cas system in the pathogenic Gram-positive bacterium Staphylococcus lugdunensis and determine its association with sequence types (STs) determined by multilocus sequence typing (MLST) and oxacillin susceptibility. Primers were designed to detect and sequence types IIIA and IIC CRISPR-Cas in 199 S. lugdunensis isolates. MLST and oxacillin susceptibility tests were also performed on the isolates. We found that 84 S. lugdunensis isolates had type IIIA CRISPR-Cas, while 46 had type IIC. The results showed a strong association between STs and CRISPR-Cas types. The ST1, ST6, ST12, and ST15 isolates had type IIIA CRISPR-Cas systems, and the ST4, ST27, and ST29 isolates had type IIC CRISPR-Cas. Interestingly, of 83 isolates containing type IIIA CRISPR-Cas, 17 (20.5%) were oxacillin-resistant S. lugdunensis (ORSL), and all of these ORSL isolates belonged to ST6 cluster 1. Moreover, spacers 23 and 21 were found in 16 and 17 ORSL isolates, respectively. In contrast, all 46 isolates with type IIC CRISPR-Cas were susceptible to oxacillin. Our results showed that 41.3% of CRISPR-Cas IIIA spacers were homologous to plasmids and 20.2% were homologous to phages. However, in type IIC CRISPR-Cas, 11.8% and 39.9% of spacers showed sequence homology with plasmids and phages, respectively. In conclusion, we found that the distribution and composition of the CRISPR-Cas system in S. lugdunensis was associated with STs and oxacillin susceptibility. IMPORTANCE CRISPR-Cas systems have been characterized as playing several biological roles in many bacterial genomes. Moreover, CRISPR-Cas systems are useful for epidemiological, diagnostic, and evolutionary studies of pathogenic bacteria. However, the characteristics of CRISPR-Cas systems in Staphylococcus lugdunensis have been rarely reported. In this study, we revealed that type IIIA CRISPR-Cas was dominant in S. lugdunensis isolates, followed by type IIC CRISPR-Cas. Moreover, the composition of CRISPR-Cas spacers was strongly associated with multilocus sequence typing and oxacillin susceptibility of S. lugdunensis. These results advance our understanding of the evolution of CRISPR-Cas systems; however, the biological functions of CRISPR-Cas systems in S. lugdunensis remain to be further characterized.

ABSTRACT Enterococcus faecium is a clinically important pathogen that can cause significant morbidity and death. In this study, we aimed to develop a machine learning (ML) algorithm-based rapid susceptibility method to distinguish vancomycin-resistant E. faecium (VREfm) and vancomycin-susceptible E. faecium (VSEfm) strains. A predictive model was developed and validated to distinguish VREfm and VSEfm strains by analyzing the matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry (MS) spectra of unique E. faecium isolates from different specimen types. The algorithm used 5,717 mass spectra, including 2,795 VREfm and 2,922 VSEfm mass spectra, and was externally validated with 2,280 mass spectra of isolates (1,222 VREfm and 1,058 VSEfm strains). A random forest-based algorithm demonstrated overall good classification performances for the isolates from the specimens, with mean accuracy, sensitivity, and specificity of 0.78, 0.79, and 0.77, respectively, with 10-fold cross-validation, timewise validation, and external validation. Furthermore, the algorithm provided rapid results, which would allow susceptibility prediction prior to the availability of phenotypic susceptibility results. In conclusion, an ML algorithm designed using mass spectra obtained from the routine workflow may be able to rapidly differentiate VREfm strains from VSEfm strains; however, susceptibility results must be confirmed by routine methods, given the demonstrated performance of the assay. IMPORTANCE A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on a large-scale MS data set of clinical VREfm and VSEfm isolates, including 2,795 VREfm and 2,922 VSEfm isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, the accuracy of which was 0.50, based on the local epidemiology. The algorithm improved the accuracy of antibiotic administration, compared to empirical antibiotic prescription. An ML algorithm designed using MALDI-TOF MS spectra obtained from the routine workflow accurately differentiated VREfm strains from VSEfm strains, especially in blood and sterile body fluid samples, and can be applied to facilitate the rapid and accurate clinical testing of pathogens.

Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI–TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072–0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI–TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.

The von Willebrand factor binding protein in Staphylococcus lugdunensis (vWbl) comprises four major regions: the signal peptide (S), the non-repetitive (A) region, the repeat (R) region, and the wall-associated (W) region. Previous studies have demonstrated that the R region contains 10 copies of repeating sequences; however, we reveal that the copy number of repeats in the vWbl gene varies among different S. lugdunensis isolates. In this study, an epidemiological surveillance was conducted to determine whether the copy number of repeats in vWbl in different isolates of S. lugdunensis correlates with their infectivity. The number of repeats was estimated in a total of 212 isolates, consisting of 162 isolates of oxacillin-sensitive S. lugdunensis (OSSL) and 50 isolates of oxacillin-resistant S. lugdunensis (ORSL). Our data showed that 72.5% (116/162) of OSSL isolates contained 9 (25, 15.4%), 12 (43, 26.5%), or 13 (48, 29.6%) repeats, and 90% (45/50) of ORSL isolates had 9 (32, 64%) or 13 (13, 26%) repeats. In addition, 89.6% (26 of 29) of the sequence type (ST)27 strain had 12 repeats, and 86.8% (13 of 15) of the ST4 strain had 14 repeats. Twenty-seven of the 28 isolates with nine repeats were of the staphylococcal cassette chromosome mec (SCCmec) V or Vt type and belonged to ST3, and all isolates with 13 repeats were of SCCmec II type and belonged to ST6. All isolates with nine repeats had a stop codon at the 18th codon of the third repeat, suggesting that these isolates coded for nonfunctional vWbl. Further, western blot analysis confirmed that all strains translated vWbl, and only vWbl proteins coded by genes with nine repeats were exported outside the cell. These results suggest that number of vWbl repeats in S. lugdunensis have clonal specificities and may correlate with potential pathogenicity.

Oxacillin-resistant Staphylococcus lugdunensis (ORSL) is considered a life-threatening isolate in healthcare settings. Among ORSL clones, ST6-SCCmec II strains are associated with an endemic spread in hospitals. We analyzed the complete genome of ORSL CGMH-SL118, a representative strain. Results revealed that this strain contained three MGEs (two prophages and one plasmid) other than the SCCmec II element, which showed remarkable differences in genome organization compared to the reference strains from NCBI. Eight multidrug-resistant genes were identified. All but blaZ were carried by MGEs, such as the SCCmec II element [mecA, ant (9)-Ia, and ermA] and the prophage φSPbeta [aac (6')-aph (2'), aph (3')-III, and ant (6)-Ia], indicating that MGEs carrying multidrug-resistant genes may be important for ST6 strains. The prophage φSPbeta contains sasX gene, which was responsible for the pathogenesis of Staphylococcus aureus. A phage-mediated resistant island containing fusB (SlRIfusB-118) was found near φSPbeta, which was highly homologous to type III SeRIfusB-5907 of Staphylococcus epidermidis. In contrast to previous studies, over 20% of ST6 isolates showed a fusidic acid-resistant phenotype, suggesting that phage-mediated intraspecies transmission of resistant islands may become an important issue for ST6 strains. Sixty-eight clinical isolates of ST6 Staphylococcus lugdunensis (50 OSSL, oxacillin-sensitive S. lugdunensis, and 18 ORSL, including CGMH-SL118) collected from various types of specimens in the hospital were studied. Among these isolates in this study, ORSL showed similar drug-resistant genes and phenotypes as CGMH-SL118. The comparative genomic analyses highlight the contribution of MGEs in the development and dissemination of antimicrobial resistance in ST6 strains, suggesting that resistance determinants and virulence factors encoded by MGEs provide a survival advantage for successful colonization and spread in healthcare settings.

Autism spectrum disorder (ASD) is characterized by cognitive inflexibility and social deficits. Probiotics have been demonstrated to play a promising role in managing the severity of ASD. However, there are no effective probiotics for clinical use. Identifying new probiotic strains for ameliorating ASD is therefore essential. Using the maternal immune activation (MIA)-based offspring ASD-like mouse model, a probiotic-based intervention strategy was examined in female mice. The gut commensal microbe Parabacteroides goldsteinii MTS01, which was previously demonstrated to exert multiple beneficial effects on chronic inflammation-related-diseases, was evaluated. Prenatal lipopolysaccharide (LPS) exposure induced leaky gut-related inflammatory phenotypes in the colon, increased LPS activity in sera, and induced autistic-like behaviors in offspring mice. By contrast, P. goldsteinii MTS01 treatment significantly reduced intestinal and systemic inflammation and ameliorated disease development. Transcriptomic analyses of MIA offspring indicated that in the intestine, P. goldsteinii MTS01 enhanced neuropeptide-related signaling and suppressed aberrant cell proliferation and inflammatory responses. In the hippocampus, P. goldsteinii MTS01 increased ribosomal/mitochondrial and antioxidant activities and decreased glutamate receptor signaling. Together, significant ameliorative effects of P. goldsteinii MTS01 on ASD relevant behaviors in MIA offspring were identified. Therefore, P. goldsteinii MTS01 could be developed as a next-generation probiotic for ameliorating ASD.

Backgound: Conventional diagnosis of invasive fungal disease from blood cultures is often notoriously delayed and inadequately sensitive. We aimed to develop a universal primers-based polymerase chain reaction (PCR) assay and restriction fragment length polymorphisms (RFLP) for rapid identification of invasive fungal disease (IFD). Methods: We evaluated 16 clinical fungal species using a combination of PCR assays with 3 different restriction endonucleases targeting various internal transcribed spacer (ITS) regions and high resolution melting analysis (HRMA). Serial samples from 75 patients suspected to have IFD were analyzed for clinical verification. Results: We have designed a universal PCR capable of amplifying a portion of the 18S rRNA gene of 16 clinically important fungal species. The restriction patterns of most PCR products generated by EcoRI or double digested by ClaI and AvaI were different, except Aspergillus niger and Aspergillus flavus had a similar pattern, and Aspergillus fumigatus and Aspergillus terreus had a similar pattern. All these species had a unique melting curve shape using the HRMA. Both HRMA and universal PCR had adequate sensitivity, and all sixteen reference fungal species can be clearly distinguished by the universal PCR-RFLP-HRMA assay. With a reference library of 176 clinically relevant fungal strains, and 75 clinical samples from patients with suspicious IFD were tested, our assay identified 100% and 61.1% of isolates from the reference library and clinical samples, respectively. Conclusions: Universal PCR and RFLP coupled with HRMA could be a highly discriminative and useful molecular diagnostic that could enhance the current diagnostic, treatment, and surveillance methods of invasive fungal disease.

Abstract Trichomonas vaginalis (T. vaginalis) detection remains an unsolved problem in using of automated instruments for urinalysis. The study proposes a machine learning (ML)-based strategy to increase the detection rate of T. vaginalis in urine. On the basis of urinalysis data from a teaching hospital during 2009–2013, individuals underwent at least one urinalysis test were included. Logistic regression, support vector machine, and random forest, were used to select specimens with a high risk of T. vaginalis infection for confirmation through microscopic examinations. A total of 410,952 and 428,203 specimens from men and women were tested, of which 91 (0.02%) and 517 (0.12%) T. vaginalis-positive specimens were reported, respectively. The prediction models of T. vaginalis infection attained an area under the receiver operating characteristic curve of more than 0.87 for women and 0.83 for men. The Lift values of the top 5% risky specimens were above eight. While the most risky vigintile was picked out by the models and confirmed by microscopic examination, the incremental cost-effectiveness ratios for T. vaginalis detection in men and women were USD$170.1 and USD$29.7, respectively. On the basis of urinalysis, the proposed strategy can significantly increase the detection rate of T. vaginalis in a cost-effective manner.

Abstract Background Group B Streptococcus (GBS) is an important pathogen that causes high mortality and morbidity in young infants. However, data on clinical manifestations between different GBS serotypes and correlation with molecular epidemiology are largely incomplete. The aim of this study was to determine the serotype distribution, antimicrobial resistance, clinical features and molecular characteristics of invasive GBS isolates recovered from Taiwanese infants. Methods From 2003 to 2017, 182 non-duplicate GBS isolates that caused invasive disease in infants less than one year of age underwent serotyping, multilocus sequence typing (MLST) and antibiotic susceptibility testing. The clinical features of these infants with GBS disease were also reviewed. Results Of the 182 patients with invasive GBS disease, 41 (22.5%) were early-onset disease, 121 (66.5%) were late-onset disease and 20 (11.0%) were late late-onset disease (> 90 days of age). All these patients were treated with effective antibiotics on time. Among them, 51 (28.0%) had meningitis, 29 (16.0%) had neurological complications, 12 (6.6%) died during hospitalization, and 15 (8.8%) out of 170 patients who survived had long-term neurological sequelae at discharge. Serotype III GBS strains accounted for 64.8%, followed by serotype Ia (18.1%) and Ib (8.2%). MLST analysis revealed 11 different sequence types among the 182 isolates and ST-17 was the most dominant sequence type (56.6%). The correlation between serotype III and ST17 was evident, as ST17 accounted for 87.3% of all serotype III isolates. There was an obvious increasing trend of type III/ST-17 GBS that caused invasive disease in infants. All isolates were susceptible to penicillin, cefotaxime, and vancomycin, while 68.1 and 65.9% were resistant to erythromycin and clindamycin, respectively. Conclusions Despite timely and appropriate antibiotic treatment, a significant proportion of invasive GBS disease still inevitably causes adverse outcomes. Further study to explore preventive strategies and development of serotype-based vaccines will be necessary in the future.

Background: Group B Streptococcus (GBS) is an important invasive pathogen in neonates, pregnant women and the elderly. Serotype VI GBS, which has been rarely reported globally, has emerged as a significant pathogen in Asia. However, traditional serologic latex agglutination (LA) methods may fail to type isolates that lack of or low expression of CPS. Methods: A total of 104 GBS strains were analyzed by MALDI-TOF MS. Multiplex PCR and multilocus sequence typing (MLST) were also performed to confirm their strains. The protein markers were purified with gel electrophoresis and LC-column, followed by identification with nanoLC–MS/MS analysis. Results: Protein peak of 6251-Da was appeared in most (20/24, 92%) serotypes VI (94% ST-1 or single locus variant of ST-1), and protein peak of 6891-Da was appeared in most serotypes III (15/18, 83%) and Ib (19/23, 83%) strains. The protein peak of 6251-Da and 6891-Da were identified as CsbD family protein and UPF0337 protein gbs0600, respectively. Conclusions: The protein peak of 6251 Da may play a role of emergence of ST-1 clone, serotype VI GBS in central Taiwan and could be useful in rapid identifying invasive serotype VI from III isolates, which is hardly achieved by LA. Keywords: Group B Streptococcus, Multilocus sequence typing, Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, Sequence type

Abstract The purpose of this study was to identify the microbial communities that colonize peri-implantitis pockets using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Subjects having at least one implant with peri-implantitis, no diabetes, and not taking antibiotics in the previous 3 months were selected. Peri-implantitis was defined when surrounding bone loss ≥0.5 mm and bleeding on probing was found. Microbial samples were collected from peri-implantitis pockets using paper points. After incubation and isolation, the colonies were analyzed by MALDI-TOF MS. A total of 126 isolates were cultivated and identified from 12 samples, in identification rates of 82.5% at the species level and 12.72% at the genus level. Although the compositions were highly variable, major habitants in different peri-implant pockets could be identified. Among them the most distinguished were Neisseria flavescens (87%), Streptococcus constellatus (56%), Slackia exigua (46%), Streptococcus intermedius (45%), Fusobacterium nucleatum (45%) and Gemella morbillorum (43%). This preliminary study provides comprehensive and reliable data for future study designs involving MALDI-TOF MS and peri-implantitis in a more specific, easy, rapid and economical way. MALDI-TOF MS could be a new clinical method to evaluate and monitor oral microbiota associated with the disease.

Background: ABO subgroups would be considered when discrepancies in ABO grouping occur. Serological methods including adsorption-elution test, salivary ABH inhibition test, and anti-A1 (lectin) saline method could be used. However, these serological methods are laboring and obscure. Therefore, reliable and affordable method to assess the ABO subgroups is of particular interest. Methods: To solve this problem, the multiplex SNaPshot-based assays were designed to determine rare A and B subgroups. Primers used as probes for determination of rare ABO blood groups known in Taiwanese population were designed. Many ABO subtype samples were used to validate the accuracy and reproducibility of our SNaPshot panel. Results: A panel of primer probes were successfully designed in determining 8 SNP sites (261, 539, 838, 820, 745, 664, IVS6 +5, and 829 in exon 6 and 7) for A phenotype and 6 SNP sites (261, 796, IVS3 +5, 247, 523, and 502 in exon 2, 6 and 7 and intron 3) for B phenotype. SNaPshot analysis for defining blood group A alleles (A1, A2, A3, Am and Ael) and blood group B alleles (B1, B3, Bw and Bel) was therefore available. Conclusion: SNaPshot analysis could be used in reference laboratories for typing known rare subgroups of A and B without DNA cloning and traditional sequencing. Moreover, this method would help to construct databases of genotyped blood donors, and it potentially plays a role in determining fetal–maternal ABO incompatibility. Keywords: ABO grouping, Adsorption-elution test, SNaPshot-based assays, Rare ABO blood groups, Fetal–maternal ABO incompatibility, Single nucleotide polymorphisms (SNPs)

Background/purpose: We reported an outbreak of Staphylococcus haemolyticus (SH) infection in a group of young patients (mean age 21.6) simultaneously hospitalized due to a mass-burn incident. This study analyzed the clinical features of these patients and the microbiological characteristics of the outbreak. Methods: All 50 patients hospitalized for burns were enrolled, and their clinical differences were analyzed based on culture results. A drug sensitivity test and pulsed-field gel electrophoresis (PFGE) were conducted to analyze the microbiological difference between SH isolates from the mass-burn casualty patients (the study group) and SH isolates from other patients hospitalized during the same period (the control group) with the intention of identifying the strain of SH outbreak. Results: Patients with isolated SH (N = 36) had a significantly higher disease severity (higher revised Baux score, APACHE II score, and concurrent bacteremia rate), and a significantly poorer clinical outcome (longer ICU and hospital stay, and longer MV usage). Significant differences in the phenotype (antibiotics drug sensitivity test) and genotype (PFGE typing) were observed between the study and control groups. The dominant PFGE type C identified among the study group was related to poorer outcomes in a subgroup analysis. Conclusion: A dominant PFGE type of SH infection was found in these mass-burn casualty patients. Pathogenesis or virulence factors may have contributed to our results. Further study of isolated SH should be conducted. Keywords: Burn, Outbreak, Outcome, Pulsed-field gel electrophoresis (PFGE), Staphylococcus haemolyticus (SH)

Background/purposes: Vancomycin resistance increased significantly to 31.3% among Enterococcus faecium in 2006 and remained high thereafter at a university hospital in Taiwan. A longitudinal study was retrospectively conducted to characterize these vancomycin-resistant E. faecium (VRE-fm). Methods: A total of 378 non-repetitive VRE-fm blood isolates collected during 2002–2015 were studied. Multilocus sequence typing, pulsed-field gel electrophoresis, analysis of van genes and the Tn1546 structure, and conjugation experiments were performed. Results: The majority (78.0%) of the isolates were associated with hospital-acquired infections. Molecular typing revealed nine major pulsotypes and five predominant sequence types (STs): ST17 (33.9%), ST78 (18.3%), ST414 (14.6%), ST18 (10.6%), and ST203 (7.4%). Fluctuation of these prevailing STs among the study years in association with some major pulsotypes was noted. All isolates carried vanA genes, except that in four isolates vanB genes were found. Among the vanA-carrying Tn1546-like elements, one predominant structure type (Type I, 55.9%) was noted throughout the study years. Since 2009, another predominant structure type (Type II, 40.1%) has emerged firstly in ST414 and gradually spread to other 11 STs in subsequent years. Isolates carrying these Type II Tn1546-like elements have become the most predominant population since 2014, majorly found in ST78 and ST17. Preliminary experiments indicated that plasmids carrying the Type II Tn1546-like elements demonstrated ten-fold higher efficiency than those carrying the Type I Tn1546-like elements. Conclusion: Dissemination of some major STs and horizontal transfer of plasmids carrying two major structure types of Tn1546-like elements may have together contributed to the increase of VRE-fm infection. Keywords: Bayesian analysis of population structure, Clonal complex 17, Enterococcus faecium, Tn1546-like element, Vancomycin resistance

Graves’ disease (GD) is a systemic autoimmune disease characterized by hyperthyroidism. Evidence suggests that alterations to the gut microbiota may be involved in the development of autoimmune disorders. The aim of this study was to characterize the composition of gut microbiota in GD patients. Fecal samples were collected from 55 GD patients and 48 healthy controls. Using 16S rRNA gene amplification and sequencing, the overall bacterial richness and diversity were found to be similar between GD patients and healthy controls. However, principal coordinate analysis and partial least squares-discriminant analysis showed that the overall gut microbiota composition was significantly different (ANOSIM; p < 0.001). The linear discriminant analysis effect size revealed that Firmicutes phylum decreased in GD patients, with a corresponding increase in Bacteroidetes phylum compared to healthy controls. In addition, the families Prevotellaceae, and Veillonellaceae and the genus Prevotella_9 were closely associated with GD patients, while the families Lachnospiraceae and Ruminococcaceae and the genera Faecalibacterium, Lachnospira, and Lachnospiraceae NK4A136 were associated with healthy controls. Metagenomic profiles analysis yielded 22 statistically significant bacterial taxa: 18 taxa were increased and 4 taxa were decreased. Key bacterial taxa with different abundances between the two groups were strongly correlated with GD-associated clinical parameters using Spearman’s correlation analysis. Importantly, the discriminant model based on predominant microbiota could effectively distinguish GD patients from healthy controls (AUC = 0.825). Thus, the gut microbiota composition between GD patients and healthy controls is significantly difference, indicating that gut microbiota may play a role in the pathogenesis of GD. Further studies are needed to fully elucidate the role of gut microbiota in the development of GD.

Gut microbiome maintains local gut integrity and systemic host homeostasis, where optimal control of intestinal lipopolysaccharides (LPS) activity may play an important role. LPS mainly produced from gut microbiota are a group of lipid-polysaccharide chemical complexes existing in the outer membrane of Gram-negative bacteria. Traditionally, LPS mostly produced from Proteobacteria are well known for their ability in inducing strong inflammatory responses (proinflammatory LPS, abbreviated as P-LPS), leading to septic shock or even death in animals and humans. Although the basic structures and chemical properties of P-LPS derived from different bacterial species generally show similarity, subtle and differential immune activation activities are observed. On the other hand, frequently ignored, a group of LPS molecules mainly produced by certain microbiota bacteria such as Bacteroidetes show blunt or even antagonistic activity in initiating pro-inflammatory responses (anti-inflammatory LPS, abbreviated as A-LPS). In this review, besides the immune activation properties of P-LPS, we also focus on the description of anti-inflammatory effects of A-LPS, and their potential antagonistic mechanism. We address the possibility of using native or engineered A-LPS for immune modulation in prevention or even treatment of P-LPS induced chronic inflammation related diseases. Understanding the exquisite interactive relationship between structure-activity correlation of P- and A-LPS not only contributes to molecular understanding of immunomodulation and homeostasis, but also re-animates the development of novel LPS-based pharmacological strategy for prevention and therapy of chronic inflammation related diseases.

The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the “grey zone”. We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.

Abstract Many uncommon Candida spp. (species other than C. albicans, C. parapsilosis, C. glabrata, C. tropicalis, and C. krusei) have been shown to emerge in tertiary care facilities. We aimed to investigate these uncommon candidemia in children. Forty-six cases of candidemia caused by uncommon Candida spp. were identified during 2003–2015 from a medical center in Taiwan. The most common specie was C. guilliermondii (31.2%), followed by C. lusitaniae (18.8%) and C. metapsilosis (18.8%). These cases were analyzed and compared with 148 episodes of C. albicans candidemia. The incidence density of uncommon Candida spp. candidemia and the proportion to all candidemia episodes increased substantively during the study period. Prior exposure to azoles was uncommon in the 30 days prior to infection, but fluconazole resistant strains were significantly more common (n = 19, 41.3%). The increased incidence density of uncommon Candida spp. candidemia was associated with increasing use of antifungal agents. No differences in demographics, underlying comorbidities, risk factors, clinical features, dissemination, and 30-day mortality were found between uncommon Candida spp. and C. albicans candidemia. Patients with uncommon Candida spp. candidemia were more likely to require modifications in antifungal treatment and receive echinocandin drugs (43.5% vs 21.6%, p = 0.007). Candidemia caused by uncommon Candida spp. had poorer response to antifungal treatment, led to longer duration of candidemia (median 4.0 versus 2.5 days, p = 0.008), and had a higher treatment failure rate (56.5% vs 38.5%, p = 0.040).

Background and purpose: Rapid identification of microbes in the bloodstream is crucial in managing septicemia because of its high disease severity, and direct identification from positive blood culture bottles through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) can shorten the turnaround time. Therefore, we developed a simple method for rapid microbiological identification from positive blood cultures by using MALDI-TOF MS. Methods: We modified previously developed methods to propose a faster, simpler and more economical method, which includes centrifugation and hemolysis. Specifically, our method comprises two-stage centrifugation with gravitational acceleration (g) at 600g and 3000g, followed by the addition of a lysis buffer and another 3000g centrifugation. Results: In total, 324 monomicrobial bacterial cultures were identified. The success rate of species identification was 81.8%, which is comparable with other complex methods. The identification success rate was the highest for Gram-negative aerobes (85%), followed by Gram-positive aerobes (78.2%) and anaerobes (67%). The proposed method requires less than 10 min, costs less than US$0.2 per usage, and facilitates batch processing. Conclusion: We conclude that this method is feasible for clinical use in microbiology laboratories, and can serve as a reference for treatments or further complementary diagnostic testing. Keywords: Blood culture, Direct identification, MALDI-TOF MS

Coinfection with Candida and Staphylococcus results in higher mortality in animal studies. However, the pathogenesis and interplay between C. albicans and S. aureus in bloodstream infections (BSIs) is unclear. This study determines the clinical features and outcomes of mixed C. albicans/S. aureus (CA/SA) BSIs and biofilm formation on pathogenesis during coinfection. Demographics and outcomes for mixed BSIs and monomicrobial candidemia were compared. Compared to 115 monomicrobial C. albicans BSIs, 22 patients with mixed CA/SA BSIs exhibited a significantly higher mortality rate and shorter survival time. In vitro and in vivo biofilm analysis showed that C. albicans accounted for the main biofilm architecture, and S. aureus increased its amount. Antibiotic tolerance in S. aureus, which adhered to Candida hyphae observed by scanning electron microscope, was demonstrated by the presence of wild-type C. albicans co-biofilm. Upregulation in exotoxin genes of S. aureus was evidenced by quantitative RT-PCR when a co-biofilm was formed with C. albicans. Mixed CA/SA BSIs result in a higher mortality rate in patients and in vivo surrogate models experiments. This study demonstrates that the virulence enhancement of C. albicans and S. aureus during co-biofilm formation contributes to the high mortality rate.

Background: Antibiotic-resistant type III/ST-17 Streptococcus agalactiae (group B Streptococcus, GBS) strain is predominant in neonatal invasive GBS diseases. We aimed to investigate the antibiotic resistance profiles and genetic characteristics of type III/ST-17 GBS strains. Methods: A total of 681 non-duplicate GBS isolates were typed (MLST, capsular types) and their antibiotic resistances were performed. Several molecular methods (WGS, PCR, sequencing and sequence analysis) were used to determine the genetic context of antibiotic resistant genes and pili genes. Results: The antibiotic resistant rates were significantly higher in type Ib (90.1%) and type III (71.1%) GBS isolates. WGS revealed that the loss of PI-1 genes and absence of ISSag5 was found in antibiotic-resistant III/ST-17 GBS isolates, which is replaced by a ~75-kb integrative and conjugative element, ICESag37, comprising multiple antibiotic resistance and virulence genes. Among 190 serotype III GBS isolates, the most common pilus island was PI-2b (58.4%) alone, which was found in 81.3% of the III/ST-17 GBS isolates. Loss of PI-1 and ISSag5 was significantly associated with antibiotic resistance (95.5% vs. 27.8%, p < 0.001). The presence of ICESag37 was found in 83.6% of all III/ST-17 GBS isolates and 99.1% (105/106) of the antibiotic-resistant III/ST-17 GBS isolates. Conclusions: Loss of PI-1 and ISSag5, which is replaced by ICESag37 carrying multiple antibiotic resistance genes, accounts for the high antibiotic resistance rate in III/ST-17 GBS isolates. The emerging clonal expansion of this hypervirulent strain with antibiotic resistance after acquisition of ICESag37 highlights the urgent need for continuous surveillance of GBS infections.

Group B Streptococcus (GBS) is an important pathogen of neonatal infections, and the clonal complex (CC)-17/serotype III GBS strain has emerged as the dominant strain. The clinical manifestations of CC17/III GBS sepsis may vary greatly but have not been well-investigated. A total of 103 CC17/III GBS isolates that caused neonatal invasive diseases were studied using a new approach based on clustered regularly interspaced short palindromic repeats (CRISPR) loci and restriction fragment length polymorphism (RFLP) analyses. All spacers of CRISPR loci were sequenced and analyzed with the clinical presentations. After CRISPR-RFLP analyses, a total of 11 different patterns were observed among the 103 CRISPR-positive GBS isolates. GBS isolates with the same RFLP patterns were found to have highly comparable spacer contents. Comparative sequence analysis of the CRISPR1 spacer content revealed that it is highly diverse and consistent with the dynamics of this system. A total of 29 of 43 (67.4%) spacers displayed homology to reported phage and plasmid DNA sequences. In addition, all CC17/III GBS isolates could be categorized into three subgroups based on the CRISPR-RFLP patterns and eBURST analysis. The CC17/III GBS isolates with a specific CRISPR-RFLP pattern were more significantly associated with occurrences of severe sepsis (57.1% vs. 29.3%, p = 0.012) and meningitis (50.0% vs. 20.8%, p = 0.009) than GBS isolates with RFLP lengths between 1000 and 1300 bp. Whole-genome sequencing was also performed to verify the differences between CC17/III GBS isolates with different CRISPR-RFLP patterns. We concluded that the CRISPR-RFLP analysis is potentially applicable to categorizing CC17/III GBS isolates, and a specific CRISPR-RFLP pattern could be used as a new biomarker to predict meningitis and illness severity after further verification.

Trace elements are vital components for healthy growth, development, and physical activity. The aim of this study was to investigate the relationship between trace element (iron, zinc, copper) deficiencies and picky eating behavior, development level, and physical activity level. This cross-sectional study involved 203 children aged 4–7 years; picky eating behavior, development level, and physical activity level were assessed through questionnaires. Zinc deficiency has the highest prevalence (37.4%); 67.5% of the children were assessed as picky eaters. Children with picky eating behaviors, poor development level, or poor physical activity level have significantly lower zinc levels, and higher prevalence of zinc deficiency. Pearson’s correlation coefficient indicated a positive correlation between serum zinc level and development scores (r = 0.221, p = 0.002) and physical activity scores (r = 0.469, p < 0.001). In multivariate analysis, zinc deficiency independently related to picky eating (OR = 2.124, p = 0.037, CI = 1.042–4.312), developmental level (OR = 0.893, p = 0.022, CI = 0.810–0.984), and physical activity level (OR = 0.785, p < 0.001, CI = 0.700–0.879). In conclusion, the prevalence of zinc deficiency in children aged 4–7 was high, especially in picky eaters. Zinc deficiency was significantly associated with low development and poor physical activity in early childhood.

Background: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used in microbial identification. This study evaluated the performance of MALDI-TOF MS and investigated the economic and medical impact of MALDI-TOF MS implementation. Methods: A total of 12,202 clinical isolates collected from April to September 2013 were identified using MALDI-TOF MS, and the success rates in identifying isolates were analyzed. The differences in the processing time, cost of consumables, weight of waste, and clinical impact between MALDI-TOF MS and biochemical reaction were compared. Results: MALDI-TOF MS successfully identified 96% of 12,202 isolates, including 96.8% of 10,502 aerobes, 90.5% of 1481 anaerobes, 93.8% of 81 yeasts, and 90.6% of 138 nontuberculous mycobacteria at the genus level. By using MALDI-TOF MS, the processing time for aerobes decreased from 32.5 hours to 4.1 hours, and that for anaerobes decreased from 71.5 hours to 46 hours. For detection of aerobes and anaerobes, the cost of consumables was estimated to decrease by US$0.9 per isolate, thus saving US$94,500 in total annual isolation. Furthermore, the weight of waste decreased six-fold, resulting in a reduction of 350 kg/month or 4.2 tons/year. MALDI-TOF MS also increased the percentage of correct antibiotics treatment for Escherichia coli and Klebsiella pneumonia from 56.1% to 75% and shortened the initiation time of the correct antibiotic action from 3.3 hours to 2.5 hours. Conclusions: MALDI-TOF MS is a rapid, reliable, economical, and environmentally friendly method for routine microbial identification and may contribute to early appropriate antibiotic treatment in clinical settings.

Background: Pulsed-field gel electrophoresis (PFGE) is the “gold standard” for epidemiological investigation of methicillin-resistant Staphylococcus aureus (MRSA), but several DNA sequence-based methods have been developed in MRSA typing because of the unambiguous results. Methods: Ninety-one MRSA isolates were collected from the blood cultures of different patients from July 2008 to December 2008 in central Taiwan. The molecular characteristics of each isolate, including double locus sequence typing (DLST; spa and clfB typing), Staphylococcus cassette chromosome mec (SCCmec), and PFGE were determined for comparison. Results: Five major clfB types (types A–E), 18 spa types, 33 DLST genotypes, five SCCmec types, 17 pulsotypes have been observed. Three major DLST genotypes (A1-t002, C0-t037, and B1-t437) and two major pulsotypes (6 and 8) were identified. Most clfB type A isolates (97.1%) were SCCmec type II and all clfB type C isolates (100%) were SCCmec type III. Most clfB type B isolates (88.9%) were SCCmec type IV (59.3%) and VT (29.6%). All (100%) clfB subtypes A1, A2, and C isolates and 70.4% of clfB type B isolates belonged to healthcare-associated-MRSA. The average congruence was 57.7% between DLST and PFGE, and 96.6% between clfB and SCCmec type. The index of discrimination of SCCmec, clfB, spa, PFGE, and DLST was 0.72, 0.79, 0.80, 0.81, and 0.87, respectively. Conclusion: ClfB type has high congruence with SCCmec type. The DLST method in this study yielded a higher discriminatory power than PFGE in local investigation of molecular epidemiology of MRSA and a promising alternative to PFGE.

Abstract We aimed to determine the clinical and microbiological characteristics of Candida bloodstream infections in children and the impact of therapeutic strategies on outcomes. All pediatric patients with candidemia from a medical center in Taiwan over a 13-year period (2003–2015) were included and a total of 262 patients with 319 episodes of candidemia were analyzed. Overall susceptibility to fluconazole was 86.1%. Cumulative mortality at 7 and 30 days after the first episode of candidemia was 13.4% and 25.2%, respectively. The overall in-hospital mortality rate was 35.1%. The treatment outcomes did not change over the study period. Multivariate analysis showed that delayed catheter removal (odds ratio [OR], 5.52; 95% confidence interval [CI]: 2.97–10.25), septic shock (OR, 5.49; 95% CI: 2.85–10.57), and breakthrough candidemia (OR, 3.66; 95% CI: 1.43–9.35) were independently associated with clinical treatment failure. In children with candidemia, underlying renal insufficiency and hematological/oncological malignancy, delayed catheter removal, and septic shock at onset were independently associated final in-hospital mortality. Analyzing the subgroup of non-neonatal children did not change the findings. We concluded overall mortality of pediatric candidemia remains high during the past decade. Prompt early catheter removal and aggressive treatment strategy in patients with septic shock would be critical to improve outcomes.