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.

Molecular Characteristics and Virulence Profile of Clinical Listeria monocytogenes Isolates in Northern Taiwan, 2009-2019.

Listeria monocytogenes is a critical foodborne pathogen that causes severe invasive and noninvasive diseases and is associated with high mortality. Information on the prevalence of L. monocytogenes infections in Taiwan is very limited. This study aimed to analyze the molecular epidemiological surveillance and virulence gene distribution of 176 human clinical L. monocytogenes isolates collected between 2009 and 2019 in northern Taiwan. Our results showed that the isolates belonged to 4 serogroups (IIa, IIb, IVb, and IIc), with most isolates in serogroups IIa (81/176, 46%) and IIb (71/176, 40.3%). Multilocus sequence typing analysis revealed 18 sequence types (STs) and 13 clonal complexes (CCs). Eighty-four percent of all isolates belonged to six STs: CC87-ST87 (40/176, 22.7%), CC19-ST378 (36/176, 19.9%), CC155-ST155 (28/176, 15.5%), CC1-ST710 (16/176, 8.8%), CC5-ST5 (16/176, 8.8%), and CC101-ST101 (11/176, 6.1%). Furthermore, our analysis showed the distributions of four Listeria pathogenicity islands (LIPI) among all isolates. LIPI-1 and LIPI-2 existed in all isolates, whereas LIPI-3 and LIPI-4 only existed in specific STs and CCs. LIPI-3 existed in the STs, CC1-ST710, CC3-ST3, CC288-ST295, and CC191-ST1458, whereas LIPI-4 could be found in the STs, CC87-ST87 and CC87-ST1459. Strains containing LIPI-3 and LIPI-4 are potentially hypervirulent; thus, 68/176 isolates (39.1%) collected in this study were potentially hypervirulent. Since L. monocytogenes infections are considered highly correlated with diet, molecular epidemiological surveillance of Listeria in food is important; continued surveillance will provide critical information to prevent foodborne diseases.

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.

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.

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.

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.

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.

An lnu(A)-Carrying Multi-Resistance Plasmid Derived from Sequence Type 3 Methicillin-Resistant Staphylococcus lugdunensis May Contribute to Antimicrobial Resistance in Staphylococci.

Methicillin-resistant Staphylococcus lugdunensis (MRSL) strains showing resistance to several common antibiotics have been reported recently. Sequence type (ST) 3 MRSL carrying SCCmec types IV, V, or Vt is the major lineage associated with health care-associated infections. We aimed to investigate the distribution and dissemination of antimicrobial resistance determinants in this lineage. Two representative ST3-MRSL strains, CGMH-SL131 (SCCmec V) and CGMH-SL138 (SCCmec IV), were subjected to whole-genome sequencing. Detection of antibiotic resistance genes and screening of susceptibility patterns were performed for 30 ST3-MRSL and 16 ST6-MRSL strains via PCR and standard methods. Except for mecA and blaZ, antimicrobial resistance genes were located within two plasmids: a 28.6 kb lnu(A)-carrying plasmid (pCGMH_SL138) in CGMH-SL138 and a 26 kb plasmid carrying non-lnu(A) resistance genes (pCGMH_SL131) in CGMH-SL131. Both plasmids shared common genetic features with multiple copies of IS257 flanked by genes conferring resistance to aminoglycoside (aacA-aphD and aadD), TET (tetk), and cadmium (cadDX) and tolerance to chlorhexidine (qacA/R); however, only pCGMH_SL138 harbored lnu(A) that conferred resistance to lincomycin and rep13 that encodes a replication initiation protein. Unlike ST6-MRSL, none of the ST3-MRSL isolates contained the ermA gene. Instead, most isolates harbored lnu(A) (20/30, 66.7%), and several other resistance genes found on pCGMH_SL138. These isolates and transformants containing pCGMH_SL138 exhibited susceptibility to ERY and higher MICs for lincomycin and aforementioned antibiotics. A novel lnu(A)-carrying plasmid, pCGMH_SL138, that harbored a multiresistance gene cluster, was identified in ST3-MRSL strains and may contribute to the dissemination of antibiotic resistance in staphylococci.

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.

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. CONCLUSION: Considering that trimethoprim-sulfamethoxazole is effective against most Nocardia species, it is the antibiotic of choice in Taiwan. Besides, amikacin, tigecycline, and linezolid showed high activity against Nocardia species and are thus good alternatives or additional therapies to treat nocardiosis, depending on patient's underlying conditions and site of infection.

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.

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.

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.

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.

Clustered Regularly Interspaced Short Palindromic Repeat Analysis of Clonal Complex 17 Serotype III Group B Streptococcus Strains Causing Neonatal Invasive Diseases.

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

NK Cell-Derived IFN-γ Protects against Nontuberculous Mycobacterial Lung Infection.

In developed countries, pulmonary nontuberculous mycobacteria (NTM) infections are more prevalent than Mycobacterium tuberculosis infections. Given the differences in the pathogenesis of NTM and M. tuberculosis infections, separate studies are needed to investigate the pathological effects of NTM pathogens. Our previous study showed that anti-IFN-γ autoantibodies are detected in NTM-infected patients. However, the role of NK cells and especially NK cell-derived IFN-γ in this context has not been studied in detail. In the current study, we show that NK1.1 cell depletion increases bacterial load and mortality in a mouse model of pulmonary NTM infection. NK1.1 cell depletion exacerbates NTM-induced pathogenesis by reducing macrophage phagocytosis, dendritic cell development, cytokine production, and lung granuloma formation. Similar pathological phenomena are observed in IFN-γ-deficient (IFN-γ-/-) mice following NTM infection, and adoptive transfer of wild-type NK cells into IFN-γ-/- mice considerably reduces NTM pathogenesis. Injection of rIFN-γ also prevents NTM-induced pathogenesis in IFN-γ-/- mice. We observed that NK cells represent the main producers of IFN-γ in the lungs and production starts as soon as 1 d postinfection. Accordingly, injection of rIFN-γ into IFN-γ-/- mice 1 d (but not 2 wk) postinfection significantly improves immunity against NTM infection. NK cells also stimulate mycobacterial killing and IL-12 production by macrophages. Our results therefore indicate that IFN-γ production by NK cells plays an important role in activating and enhancing innate and adaptive immune responses at early stages of pulmonary NTM infection.

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.

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.

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 ß-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.

Emerging serotype III sequence type 17 group B streptococcus invasive infection in infants: the clinical characteristics and impacts on outcomes.

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