Molecular characterization of lugdunin inactivation mechanisms and their association with Staphylococcus lugdunensis genetic types

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.

High Incidence of Multiple-Drug-Resistant Pheromone-Responsive Plasmids and Transmissions of VanA-Type Vancomycin-Resistant Enterococcus faecalis between Livestock and Humans in Taiwan

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.

The Clinical and Genetic Characteristics of Streptococcus agalactiae Meningitis in Neonates

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.

A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.

Lugdunin production and activity in Staphylococcus lugdunensis isolates are associated with its genotypes

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.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data

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.

Characterization of oxacillin-resistant Staphylococcus lugdunensis isolated from sterile body fluids in a medical center in Taiwan: A 12-year longitudinal epidemiological study

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 MLS_B 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.

Clonal Spreading of ST42 Staphylococcus haemolyticus Strains Occurs Possibly Due to fusB and tetK Resistant Genes and Capsule-Related Genes

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.

[Association between anti-tissue transglutaminase antibody titers and duodenal histopathology among adults with celiac disease]

To evaluate the association between serum anti-tissue transglutaminase antibody (anti-tTG) titers and the severity of histological damage to the duodenal mucosa and to predict a possible anti-tTG cutoff value for diagnosing celiac disease (CD) and villous atrophy in the domestic population. Clinical and pathological data from 76 adult CD patients with positive anti-tTG titers and duodenal biopsy results who were treated at the People's Hospital of Xinjiang Uygur Autonomous Region from July 2017 to January 2022 were retrospectively analyzed. The correlation between anti-tTG titers and the severity of duodenal mucosal damage was statistically assessed to predict the optimal anti-tTG titer cut-off value for diagnosing CD and villous atrophy. Of the 76 patients, 10 had underlying CD, and of the 66 patients with duodenal histopathology, four were Marsh Ⅰ, six were Marsh Ⅱ, and 56 were Marsh Ⅲa-c grade. In adults with CD, anti-tTG titers were shown to be associated with the severity of histological damage to the duodenal mucosa. When the anti-tTG level was ≥5 times the upper limit of normal (ULN), the sensitivity and specificity for diagnosing CD were 83.9% and 92.9%, respectively. When the anti-tTG titer was ≥8 times the ULN, the sensitivity and specificity for diagnosing villous atrophy were 67.9% and 90.0%, respectively. Anti-tTG levels had a strong predictive value for diagnosing CD in adults when titers exceeded 10 times the ULN. Thus, the anti-tTG cut-off value can be combined with clinical judgment to diagnose CD, limiting the use of invasive endoscopy.

Enhanced Intrinsic Anomalous Valley Hall Effect Induced by Spin-Orbit Coupling in MXene Monolayer M3N2O2 (M = Y, La)

The limitation of suitable anomalous valley Hall effect (AVHE) materials has seriously hindered the booming development and the widespread application of valleytronics. Here, through the first-principles calculations, we propose a MXene monolayer Y₃N₂O₂ with spontaneous valley polarization (VP) of 21.3 meV, which induces intrinsic AVHE. The VP can be modulated linearly, which provides a route of effective control of the valley signals. Importantly, VP can be enhanced by adjusting up the spin-orbit coupling (SOC) based on a SOC Hamiltonian model and the first-principles calculations. From this physics underlying, we substitute the Y atom with the La atom and further propose the monolayer La₃N₂O₂, in which the heavy atom La will provide stronger SOC than Y atom. The spontaneous VP in La₃N₂O₂ is enhanced to 100.4 meV, so AVHE can be easily achieved. Our work not only provides compelling candidates for AVHE materials but also offers a novel mindset for finding suitable valleytronic devices.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

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.

Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms

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.

[Imaging study on the safety of axial pedicle screw placement by the position of the screw trajectory tip on the anteroposterior and lateral radiographs]

Objective: To propose a method to judge the safety of axial pedicle screw placement based on the position of the tip of the screw trajectory on the anteroposterior and lateral X-ray radiographs. Methods: The cervical CT data of 40 patients admitted to the Second Affiliated Hospital of Wenzhou Medical University from December 2020 to December 2021 were selected, including 24 males and 16 females, with a mean age of (47.6±13.2) years. Based on the three-dimensional model reconstruction of Mimics software and its function of X-ray, the transmission of the axial pedicle screw and its anteroposterior and lateral films was simulated. The position of the tip of the simulated screw trajectory was divided into 5 regions (regions Ⅰ-Ⅴ) from the inside to the outside on the anteroposterior virtual radiographs, and the upper and lower regions (regions a, b) on the lateral virtual radiographs. By adjusting the direction of the screw, the tip of the screw was located in the corresponding 10 regions (80 screws in each area) on the virtual projections of the anteroposterior and lateral virtual radiographs respectively, and its accuracy was analyzed by CT to determine whether each screw penetrated the medial wall of the pedicle or vertebral artery foramen. The anteroposterior and lateral X-rays and postoperative CT data of 34 patients who underwent axial pedicle screw placement (67 axial pedicle screws were placed in total) from January 2014 to December 2021 were collected, including 18 males and 16 females, with a mean age of (45.8±14.1) years. The position of the tip of the screw trajectory on the anteroposterior and lateral films was divided in the same way. The number of screws in the corresponding 10 positions was counted, and CT analysis was used to determine whether each screw penetrated the medial wall of the axial pedicle or the vertebral artery foreman. Results: The results of the imaging simulation screw placement study showed that the perforation rate of the vertebral artery foramen in region Ⅳ and Ⅴ was 75.0% (120/160) and 100% (160/160), respectively, while the perforation rate of the medial wall of the axial pedicle in the region Ⅰ was 85.6%(137/160). The failure rate in regions Ⅱ and Ⅲ was relatively lower, and the performance of simulated screws located in the region a was better than those in region b. The perforation rates of the medial wall in regions (a-Ⅱ) and (a-Ⅲ) was 7.5% (6/80) and 0 (0/80), respectively, and the perforation rates of the vertebral foramen was 0 (0/80) and 21.3% (17/80), respectively. The retrospective imaging study also showed a higher rate of placement failure in regions Ⅰ, Ⅳ and Ⅴ, and relatively lower in regions Ⅱ and Ⅲ. There were total of 15 screws in region a-Ⅱ and a-Ⅲ, and no destruction of the medial wall of the axial pedicle and the vertebral artery foreman occurred there. Conclusions: Regions a-Ⅱ and a-Ⅲ are the "safety areas" of the tip of the pedicle screw trajectory in the axial vertebra. By analyzing the tip of the pedicle screw trajectory on the anteroposterior and lateral radiographs, the operator can determine the reasonable trajectory of axial pedicle screw placement, prevent the injury of the cervical spinal cord and vertebral artery, and reduce the risk of operation.

Amelioration of Maternal Immune Activation-Induced Autism Relevant Behaviors by Gut Commensal Parabacteroides goldsteinii

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.

Clinical manifestations and antimicrobial susceptibility of Nocardia species at a tertiary hospital in Taiwan, 2011-2020

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.

CONCLUSIONS

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.

Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach

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.

[Clinical and microbiological characteristics of hypervirulent Klebsiella pneumoniae lung abscess]

Objective: To study the clinical and microbiological characteristics of hypervirulent Klebsiella pneumoniae (hvKP) lung abscess, and to compare with the classic Klebsiella pneumoniae (cKP) lung abscess. Methods: A total of 18 patients with Klebsiella pneumoniae lung abscesses admitted to the First Affiliated Hospital of Zhejiang University School of Medicine from January 2017 to September 2020 enrolled. The strains with positive result of string test were defined as hvKP, and the negative strains were defined as cKP. The patients' basic diseases, symptoms, laboratory data and other clinical characteristics were collected. The microbiological characteristics of the strains included as following: VITEK method to determine the in vitro susceptibility of the strains to antibiotics; PCR to detect the capsular serotypes and virulence genes. The differences in clinical characteristics and microbiological characteristics of strains between hvKP group and cKP group were compared. Results: Among the 18 patients with Klebsiella pneumoniae lung abscess, 12 were hvKP infection, mainly male (10 cases), with a median age of 59.0 years; 8 cases in the hvKP group had an onset time of ≤2 weeks, and the median onset time was 10.5 days. There were significantly more diabetes (12 cases) and extrapulmonary abscesses (11 cases) in hvKP group than those in cKP group (both P<0.001). The extrapulmonary abscesses in the hvKP group were mainly liver abscesses (10 cases), and 4 cases were multi-site (≥3) abscesses. The number of indwelling catheters and invasive procedures before infection were higher in cKP group than those in hvKP group (both P=0.025). The imaging of Klebsiella pneumoniae lung abscess was mainly subpleural with the size of less than 10 cm. There were more multiple abscesses cases in hvKP group (9 cases) than cKP group (P=0.009). The median interval time between the detection of a pulmonary abscess and an extrapulmonary abscess was 1.0 day. The resistance rate of common antibiotics was significantly lower in hvKP than cKP. Conclusions: hvKP lung abscesses are more common in patients with diabetes, and the clinical manifestations are nonspecific. The lung imaging manifestations are multiple subpleural abscesses, indicating hematogenous dissemination. Liver abscesses were present in most cases, suggesting the source of infection. The main virulent gene of hypervirulent Klebsiella pneumoniae is aero. For patients with hvKP lung abscess, attention should be paid to finding hidden lesions.

Rapid Antibiotic Resistance Serial Prediction in Staphylococcus aureus Based on Large-Scale MALDI-TOF Data by Applying XGBoost in Multi-Label Learning

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.

Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods

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.

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study

Background

The 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.

Objective

The 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.

Methods

We 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.

Results

The 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.

Conclusions

The 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.

MDRSA: A Web Based-Tool for Rapid Identification of Multidrug Resistant Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

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/.

Comparative Genomic Analyses Reveal Potential Factors Responsible for the ST6 Oxacillin-Resistant Staphylococcus lugdunensis Endemic in a Hospital

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 (SlRI_fusB-118) was found near φSPbeta, which was highly homologous to type III SeRI_fusB-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.

Molecular Epidemiological Survey of Staphylococcus lugdunensis Isolates With Variable Number of Repeats in the von Willebrand Factor-Binding Protein Gene

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 V_t 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 18^th 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.

Genomic Characterization of Serotype III/ST-17 Group B Streptococcus Strains with Antimicrobial Resistance Using Whole Genome Sequencing

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.

Characterization of New Staphylococcus haemolyticus ST42 Populations in Northern Taiwan

Introduction: Staphylococcus haemolyticus is an acquired opportunistic pathogen causing nosocomial infections. Our previous studies of S. haemolyticus showed a group of isolates that produced a significantly higher disease severity than the others. Further molecular typing showed that the sequence type (ST) 42 was the major clone among the isolates. The main aim of this study was to characterize ST42. Materials and Methods: Sixty-one and 36 isolates were collected from burn and nonburn patients, respectively. Molecular typing, antibiotic susceptibility assays, and phenotypic characterizations were performed. Results: Thirteen STs, including seven new STs, were established (ST42 to ST48). ST42 was prevalent in burn and nonburn patients, and all the pulsotype C isolates were ST42. Four of the novel STs originated from ST3, suggesting that these clonal lineages evolved locally. ST3 and ST42 showed a significant difference in clindamycin susceptibility; molecular typing showed only one MLST locus variation among seven loci in SH1431, which has been reportedly involved in the regulation of biofilm formation through Zn ²⁺ binding affinities. Conclusions: Seven novel S. haemolyticus STs were identified; phylogenetic analysis suggested the presence of locally evolved clonal lineages. The predominant ST42 showed weak biofilm formation abilities; other factors that cause the clonal lineage change still need further investigation.

Serum Trace Element Levels and Their Correlation with Picky Eating Behavior, Development, and Physical Activity in Early Childhood

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.

A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra

Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

Alterations of Gut Microbiota in Patients With Graves' Disease

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.

Accurate detection of oxacillin-resistant Staphylococcus lugdunensis by use of agar dilution

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.

Development of a two-step nucleic acid amplification test for accurate diagnosis of the Mycobacterium tuberculosis complex

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.

Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning

BACKGROUND

Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to inaccuracies and inefficiencies, causing alarm fatigue and compromised patient safety.

OBJECTIVES

This study shows how machine learning (ML) models can provide auxiliary information for more accurate identification of critical thrombocytopenic patients when compared with the traditional notification system.

RESEARCH DESIGN

A total of 50,505 patients' platelet count and other 26 additional laboratory datasets of each thrombocytopenic event were used to build prediction models. Conventional logistic regression and ML methods, including random forest (RF), artificial neural network, stochastic gradient descent (SGD), naive Bayes, support vector machine, and decision tree, were applied to build different models and evaluated.

RESULTS

Models using logistic regression [area under the curve (AUC)=0.842], RF (AUC=0.859), artificial neural network (AUC=0.867), or SGD (AUC=0.826) achieved the desired average AUC>0.80. The highest positive predictive value was obtained by the SGD model in the testing data (72.2%), whereas overall, the RF model showed higher sensitivity and total positive predictions in both the training and testing data and outperformed other models. The positive 2-day mortality predictive rate of RF methods is as high as 46.1%-significantly higher than using the traditional notification system at only 14.8% [χ2(1)=81.66, P<0.001].

CONCLUSIONS

This study demonstrates a data-driven ML approach showing a significantly more accurate 2-day mortality prediction after a critical thrombocytopenic event, which can reinforce the accuracy of the traditional notification system.

Molecular Epidemiology of Cytomegalovirus UL97 and UL54 variants in Taiwan

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.

The Use of MALDI-TOF Mass Spectrometry to Analyze Commensal Oral Yeasts in Nursing Home Residents

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a rapid and accurate method to identify microorganisms in clinical laboratories. This study isolates yeast-like microorganisms in the oral washes that are collected from non-bedridden nursing home residents, using CHROMagar Candida plates, and identifies them using Bruker MALDI-TOF MS. The ribosomal DNA sequences of the isolates are then examined. Three hundred and twenty yeast isolates are isolated from the oral washes. Candida species form the majority (78.1%), followed by Trichosporon/Cutaneotrichosporon species (8.8%). Bruker MALDI-TOF MS gives a high-level confidence, with a log(score) value of ≥1.8, and identifies 96.9% of the isolates. There are six inconclusive results (1.9%), and those sequences are verified as rare clinical species, including Candida ethanolica, Cutaneotrichosporon jirovecii, Exophiala dermatitidis, and Fereydounia khargensis. Almost all of the isolates have a regular color on the CHROMagar Candida plates. If the colonies are grouped by color on the plates, a specific dominant yeast species is present in each color group, except for purple or orange isolates. In conclusion, MALDI-TOF MS is verified as a fast, accurate and practical method to analyze oral yeasts in elderly subjects.

Large-scale mass spectrometry data combined with demographics analysis rapidly predicts methicillin resistance in Staphylococcus aureus

BACKGROUND

A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibility testing, methicillin-resistant S. aureus remains a serious threat to human health.

RESULTS

Here, linking a 7 years of longitudinal study from two cohorts in the Taiwan area of over 20 000 individually resolved methicillin susceptibility testing results, we identify associations of methicillin resistance with the demographics and mass spectrometry data. When combined together, these connections allow for machine-learning-based predictions of methicillin resistance, with an area under the receiver operating characteristic curve of >0.85 in both the discovery [95% confidence interval (CI) 0.88-0.90] and replication (95% CI 0.84-0.86) populations.

CONCLUSIONS

Our predictive model facilitates early detection for methicillin resistance of patients with S. aureus infection. The large-scale antibiotic resistance study has unbiasedly highlighted putative candidates that could improve trials of treatment efficiency and inform on prescriptions.

Association between CAI microsatellite, multilocus sequence typing, and clinical significance within Candida albicans isolates

Candida albicans bloodstream infection (BSI) is epidemiologically important because of its increasing frequency and serious outcome. Strain typing and delineation of the species are essential for understanding the phylogenetic relationship and clinical significance. Microsatellite CAI genotyping and multilocus sequence typing (MLST) were performed on 285 C. albicans bloodstream isolates from patients in Chang Gung Memorial Hospital at Linkou (CGMHL), Taiwan from 2003 to 2011. Data regarding demographics, comorbidities, risk factors, and clinical outcomes were recorded within adult patients with C. albicans BSI. Both CAI genotyping and MLST yielded comparable discriminatory power for C. albicans characterization. Besides, the distribution of CAI repetition showed a satisfactory phylogenetic association, which could be a good alternative method in the molecular phylogenetics of C. albicans and epidemiological studies. As for the clinical scenario, clade 17 isolates with CAI alleles either possessing 29 or more repetitions were related to higher 14-day and 30-day mortality, and shorter median survival days.

Protein Biomarker Discovery for Methicillin-Sensitive, Heterogeneous Vancomycin-Intermediate and Vancomycin-Intermediate Staphylococcus aureus Strains Using Label-Free Data-Independent Acquisition Proteomics

Rapid identification of methicillin-sensitive Staphylococcus aureus (MSSA), heterogeneous vancomycin-intermediate S. aureus (hVISA), and vancomycin-intermediate S. aureus (VISA) is important for accurate treatment, timely intervention, and prevention of outbreaks. Here, 90 S. aureus isolates were analyzed for protein biomarker discovery, including MSSA, vancomycin-susceptible S. aureus (VSSA), hVISA, and VISA strains. Label-free data-independent acquisition proteomics was used to identify protein biomarkers that allow for discrimination among MSSA, hVISA, and VISA strains. There were 8786 nonredundant peptides identified, corresponding to 418 different annotated nonredundant proteins. Two VISA protein biomarkers, two hVISA protein biomarkers, and one MSSA protein biomarker with high sensitivities and specificities were discovered and verified. Data are available via MassIVE with identifier MSV000085776.

Role of gut microbiota in identification of novel TCM-derived active metabolites

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.

Novel Toilet Paper-Based Point-Of-Care Test for the Rapid Detection of Fecal Occult Blood: Instrument Validation Study

BACKGROUND

Colorectal cancer screening by fecal occult blood testing has been an important public health test and shown to reduce colorectal cancer-related mortality. However, the low participation rate in colorectal cancer screening by the general public remains a problematic public health issue. This fact could be attributed to the complex and unpleasant operation of the screening tool.

OBJECTIVE

This study aimed to validate a novel toilet paper-based point-of-care test (ie, JustWipe) as a public health instrument to detect fecal occult blood and provide detailed results from the evaluation of the analytic characteristics in the clinical validation.

METHODS

The mechanism of fecal specimen collection by the toilet-paper device was verified with repeatability and reproducibility tests. We also evaluated the analytical characteristics of the test reagents. For clinical validation, we conducted comparisons between JustWipe and other fecal occult blood tests. The first comparison was between JustWipe and typical fecal occult blood testing in a central laboratory setting with 70 fecal specimens from the hospital. For the second comparison, a total of 58 volunteers were recruited, and JustWipe was compared with the commercially available Hemoccult SENSA in a point-of-care setting.

RESULTS

Adequate amounts of fecal specimens were collected using the toilet-paper device with small day-to-day and person-to-person variations. The limit of detection of the test reagent was evaluated to be 3.75 µg of hemoglobin per milliliter of reagent. Moreover, the test reagent also showed high repeatability (100%) on different days and high reproducibility (>96%) among different users. The overall agreement between JustWipe and a typical fecal occult blood test in a central laboratory setting was 82.9%. In the setting of point-of-care tests, the overall agreement between JustWipe and Hemoccult SENSA was 89.7%. Moreover, the usability questionnaire showed that the novel test tool had high scores in operation friendliness (87.3/100), ease of reading results (97.4/100), and information usefulness (96.1/100).

CONCLUSIONS

We developed and validated a toilet paper-based fecal occult blood test for use as a point-of-care test for the rapid (in 60 seconds) and easy testing of fecal occult blood. These favorable characteristics render it a promising tool for colorectal cancer screening as a public health instrument.

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

IMPORTANCE

A tool for precisely stratifying postoperative patients with advanced oral cancer is crucial for the treatment plan, such as intensifying or deintensifying the regimen to improve their quality of life and prognosis.

OBJECTIVE

To develop and validate a machine learning-based algorithm that can provide survival risk stratification for patients with advanced oral cancer who have comprehensive clinicopathologic and genetic data.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic cohort study, the elastic net penalized Cox proportional hazards regression-based risk stratification model was developed and validated using single-center data collected between January 1, 1996, and December 31, 2011. In total, comprehensive clinicopathologic and genetic data (including clinical, pathologic, and 44 cancer-related gene variant profiles) of 334 patients with stage III or IV oral squamous cell carcinoma were used to develop and validate the algorithm in this 15-year cohort study. Data analysis was conducted between February 1, 2018, and May 6, 2020.

MAIN OUTCOMES AND MEASURES

The main outcomes were cancer-specific survival, distant metastasis-free survival, and locoregional recurrence-free survival. Model performance was compared in terms of the Akaike information criterion and the Harrell concordance index (C index).

RESULTS

Complete data were available for 334 patients (315 men; median age at onset, 48 years [interquartile range, 42-56 years]). The predictive models using comprehensive clinicopathologic and genetic data outperformed those using clinicopathologic data alone. In the groups of postoperative patients receiving adjuvant concurrent chemoradiotherapy, the models demonstrated higher classification performance than those using clinicopathologic data alone in cancer-specific survival (mean [SD] C index, 0.689 [0.050] vs 0.673 [0.051]; P = .02) and locoregional recurrence-free survival (mean [SD] C index, 0.693 [0.039] vs 0.678 [0.035]; P = .004). The classification performance in distant metastasis-free survival was not different (mean [SD] C index, 0.702 [0.056] vs 0.688 [0.048]; P = .09).

CONCLUSIONS AND RELEVANCE

A risk stratification model using comprehensive clinicopathologic and genetic data accurately differentiated the high-risk group from the low-risk group in cancer-specific survival and locoregional recurrence-free survival for postoperative patients with advanced oral cancer. This algorithm could be used through an online calculator to provide additional personalized information for postoperative management of patients with advanced oral squamous cell carcinoma.

Improving Multi-Tumor Biomarker Health Check-up Tests with Machine Learning Algorithms

BACKGROUND

Tumor markers are used to screen tens of millions of individuals worldwide at annual health check-ups, especially in East Asia. Machine learning (ML)-based algorithms that improve the diagnostic accuracy and clinical utility of these tests can have substantial impact leading to the early diagnosis of cancer.

METHODS

ML-based algorithms, including a cancer screening algorithm and a secondary organ of origin algorithm, were developed and validated using a large real world dataset (RWD) from asymptomatic individuals undergoing routine cancer screening at a Taiwanese medical center between May 2001 and April 2015. External validation was performed using data from the same period from a separate medical center. The data set included tumor marker values, age, and gender from 27,938 individuals, including 342 subsequently confirmed cancer cases.

RESULTS

Separate gender-specific cancer screening algorithms were developed. For men, a logistic regression-based algorithm outperformed single-marker and other ML-based algorithms, with a mean area under the receiver operating characteristic curve (AUROC) of 0.7654 in internal and 0.8736 in external cross validation. For women, a random forest-based algorithm attained a mean AUROC of 0.6665 in internal and 0.6938 in external cross validation. The median time to cancer diagnosis (TTD) in men was 451.5, 204.5, and 28 days for the mild, moderate, and high-risk groups, respectively; for women, the median TTD was 229, 132, and 125 days for the mild, moderate, and high-risk groups. A second algorithm was developed to predict the most likely affected organ systems for at-risk individuals. The algorithm yielded 0.8120 sensitivity and 0.6490 specificity for men, and 0.8170 sensitivity and 0.6750 specificity for women.

CONCLUSIONS

ML-derived algorithms, trained and validated by using a RWD, can significantly improve tumor marker-based screening for multiple types of early stage cancers, suggest the tissue of origin, and provide guidance for patient follow-up.

Like Cures Like: Pharmacological Activity of Anti-Inflammatory Lipopolysaccharides From Gut Microbiome

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.

Molecular Epidemiological Survey of Prophages in MRSA Isolates in Taiwan

Introduction

The prevalence of methicillin-resistant Staphylococcus aureus (MRSA) type SCCmec IV or V is increasing in Taiwan. It has been suggested that the surface protein SasX is responsible for their transmission. However, the sasX gene was not detected in our SCCmec IV or V isolates. Since sasX was originally found in S. epidermidis and believed to be transferred to S. aureus by a prophage, studies were conducted to detect and type this prophage in our clinical isolates.

Materials and Methods

A total of 1192 MRSA isolates collected from 2006 to 2014 were examined. Multiplex PCRs were performed to determine SCCmec, sasX, and prophage types.

Results

The prevalence of SCCmec IV and V isolates was increased in recent years (from 2006 to 2014). The sasX gene was present in most SCCmec III isolates but was absent in SCCmec IV or V isolates. The Sa5 prophage was found only in SCCmec IV and SCCmec V (or V_t) isolates, and the Sa6 prophage was mainly present in SCCmec III isolates. MRSA isolates harboring prophage combinations Sa1, Sa2, and Sa3; Sa2 and Sa3; Sa2, Sa3, and Sa7; or Sa2 and Sa7 were mainly of SCCmec II, and those that harbored prophage combinations Sa3 and Sa6; Sa3, Sa6, and Sa7; or Sa3 and Sa7 were mostly of SCCmec III. The numbers of SCCmec II isolates containing prophages Sa2, Sa3, and Sa7 and those of SCCmec III isolates containing prophages Sa3 and Sa6 or Sa3, Sa6, and Sa7 were decreased from 2010 to 2014. The number of SCCmec IV isolates with prophage Sa3 or prophages Sa3 and Sa5 was decreased, but that of those with prophage Sa6 or prophages Sa2 and Sa3 was increased from 2010 to 2014.

Conclusion

The sasX gene was found to play no role in clonal selection of MRSA. The finding that different SCCmec types of MRSA harbored different types of prophages suggests that these prophages may affect the survival and clonal expansion of certain types of MRSA.

Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques

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.

Fungicidal and anti-biofilm activities of trimethylchitosan-stabilized silver nanoparticles against Candida species in zebrafish embryos

Herein, positively surface-charged silver nanoparticles (AgNPs) capped with trimethylchitosan nitrate (TMCN) were synthesized using an environmentally friendly method. Nano-sized TMCN-AgNPs (~80 nm) with high zeta potential (>30 mV) provide sufficient static repulsion to stabilize colloid AgNPs in aqueous solutions without aggregation for >3 months. In in vitro cell cycle assays, TMCN-AgNPs showed low cytotoxicity towards L929 cells. A microdilution inhibition assay demonstrated the antifungal potential of TMCN-AgNPs, with a minimum inhibitory concentration of 0.06 mM against Candida tropicalis ATCC 750, and 0.46 mM against both Candida albicans ATCC 76615 and Candida glabrata ATCC 15545. Moreover, the addition of TMCN-AgNPs at 0.23 mM significantly reduced biofilm formation in 96-well plates with C. albicans and C. tropicalis. Importantly, when zebrafish eggs were infected with Candida cells, 0.23 mM TMCN-AgNPs greatly diminished the amount of biofilm on eggs and rescued the survival of embryos by up to 70%.

Targeted Next Generation Sequencing for Genetic Mutations of Dilated Cardiomyopathy

BACKGROUND

Approximately one-third of cases of dilated cardiomyopathy (DCM) are caused by genetic mutations. With new sequencing technologies, numerous variants have been associated with this inherited cardiomyopathy, however the prevalence and genotype-phenotype correlations in different ethnic cohorts remain unclear. This study aimed to investigate the variants in Chinese DCM patients and correlate them with clinical presentations and prognosis.

METHODS AND RESULTS

From September 2013 to December 2016, 70 index patients underwent DNA sequencing for 12 common disease-causing genes with next generation sequencing. Using a bioinformatics filtering process, 12 rare truncating variants (7 nonsense variants, 4 frameshift variants, and 1 splice site variant) and 29 rare missense variants were identified. Of these, 3 patients were double heterozygotes and 10 patients were compound heterozygotes. Overall, 47.1% (33/70) of the index patients had the seputatively pathogenic variants. The majority (33/41, 80.4%) of these variants were located in titin (TTN). More than 80% of the TTN variants (27/33, 81.8%) were distributed in the A band region of the sarcomere. Patients carrying these variants did not have a different phenotype in disease severity, clinical outcome and reversibility of ventricular function compared with non-carriers.

CONCLUSIONS

Several new rare variants were identified in a Chinese population in this study, indicating that there are ethnic differences in genetic mutations in DCM patients. TTN remains the major disease-causing gene. Our results could be a reference for future genetic tests in Chinese populations. No specific genotype-phenotype correlations were found, however a prospective large cohort study may be needed to confirm our findings.

Diagnosis of Pneumocystis pneumonia by real-time PCR in patients with various underlying diseases

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 C_T 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 C_T 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 C_T value below 30, whereas samples from 24.8% of PCP patients with underlying diseases had a C_T 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.

Incorporating Statistical Test and Machine Intelligence Into Strain Typing of Staphylococcus haemolyticus Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry

Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing of pathogenic S. haemolyticus is indispensable in modern public health infectious disease control, facilitating the identification of the origin of infections to prevent further infectious outbreak. Rapid identification enables the effective control of pathogenic infections, which is tremendously beneficial to critically ill patients. However, the existing strain typing methods, such as multi-locus sequencing, are of relatively high cost and comparatively time-consuming. A practical method for the rapid strain typing of pathogens, suitable for routine use in clinics and hospitals, is still not available. Matrix-assisted laser desorption ionization-time of flight mass spectrometry combined with machine learning approaches is a promising method to carry out rapid strain typing. In this study, we developed a statistical test-based method to determine the reference spectrum when dealing with alignment of mass spectra datasets, and constructed machine learning-based classifiers for categorizing different strains of S. haemolyticus. The area under the receiver operating characteristic curve and accuracy of multi-class predictions were 0.848 and 0.866, respectively. Additionally, we employed a variety of statistical tests and feature-selection strategies to identify the discriminative peaks that can substantially contribute to strain typing. This study not only incorporates statistical test-based methods to manage the alignment of mass spectra datasets but also provides a practical means to accomplish rapid strain typing of S. haemolyticus.

[Influence of antiretroviral prophylaxis on growth of HIV-exposed uninfected infants]

Objective: To evaluate the influence of antiretroviral prophylaxis on the growth and development of HIV-exposed uninfected infants in Guangzhou. Methods: Data were from the national information system for prevention of mother-to-child transmission of HIV infection, syphilis and hepatitis B. After excluding death and perinatal HIV infection cases, 564 HIV-exposed uninfected infants were included. The infants were divided into three groups, nevirapine (NVP) group, zidovudine (AZT) group and untreated group. The influences of antiretroviral prophylaxis on the body weight and height of the HIV-exposed uninfected infants were analyzed by using generalized estimating equations. Results: The HIV-exposed uninfected infants at 1-month old had lower Z scores of body weight-for-age and body height-for-age than the World Health Organization's reference standard. The prevalence of wasting in AZT group (17.5%) was higher than that in NVP group (6.2%) for 1-month old infants. Taking NVP or AZT was a protective factor for Z score of body length-for-age (P<0.05). Intrauterine exposure to triple antiviral drugs was a risk factor for the Z scores of body weight-for-age and body length-for-age (P<0.05). Conclusion: The physical growth and development of HIV-exposed uninfected infants at 1-month old was not well, and HIV-exposed uninfected infants who taking AZT had a higher incidence of wasting. Attention should be paid to these infants.

Increase Trichomonas vaginalis detection based on urine routine analysis through a machine learning approach

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.

Characterization of a novel, type II staphylococcal cassette chromosome mec element from an endemic oxacillin-resistant Staphylococcus lugdunensis clone in a hospital setting

BACKGROUND

Staphylococcus lugdunensis is a significant pathogen that causes community-acquired and nosocomial infections. The high prevalence of oxacillin-resistant S. lugdunensis (ORSL) is of major concern. Resistance to β-lactams is caused by acquisition of the staphylococcal cassette chromosome mec (SCCmec) element. The cassette is highly diverse, both structurally and genetically, among CoNS. Isolates carrying SCCmec II-ST6 are the major persistent clones in hospitals.

OBJECTIVES

To investigate the structure and evolutionary origin of a novel type II SCCmec element in an endemic ST6 S. lugdunensis clone.

METHODS

The structure of the SCCmec II element carried by ST6 strain CGMH-SL118 was determined by WGS and compared with those reported previously.

RESULTS

A novel 39 kb SCCmec element, SCCmecCGMH-SL118, with a unique mosaic structure comprising 41 ORFs integrated into the 3' end of the rlmH gene, was observed. Some regions of SCCmecCGMH-SL118 were homologous to SCCmec IIa of the prototype MRSA strain N315. The structure of SCCmecCGMH-SL118 was similar to that of SCCmec IIb of the MRSA strain, JCSC3063, mainly lacking the aminoglycoside resistance determinant pUB110 in the J3 region but containing the insertion sequence IS256 in the J2 region. Notably, SCCmecCGMH-SL118 deletions in the J1 region compared with SCCmec types IIa and IIb, and a high homology to SCCmec elements of Staphylococcus aureus JCSC4610 and Staphylococcus haemolyticus strain 621 were found.

CONCLUSIONS

The genetic diversity of the type II SCCmec element in ORSL suggests that CoNS is a potential reservoir for interspecies transfer of SCCmec to S. aureus in hospitals.