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  • 1
    Language: English
    In: PLoS One, CA: Public Library of Science
    Description: This article explores the interrelationship between the urinary microbiota and host antimicrobial peptides as mechanisms for urinary tract infection risk.
    Keywords: Resident Bacterial Communities ; Host Antimicrobial Peptides ; Urinary Tract Infection
    ISSN: 19326203
    E-ISSN: 19326203
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  • 2
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article discussing the microbial communities in male first catch urine and how these are highly similar to those paired in urethral swab specimens.
    Keywords: Microbials ; Bacteria ; Urine
    ISSN: 19326203
    E-ISSN: 19326203
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  • 3
    In: PLoS ONE, 2018, Vol.13(7)
    Description: Objective To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. Methods This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. Results A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.88) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86–0.87), 0.87 for XGBoost (95% CI 0.87–0.87) and 0.87 for DNN (95% CI 0.87–0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91–0.91), 0.92 for XGBoost (95% CI 0.92–0.93) and 0.92 for DNN (95% CI 0.92–0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91–0.91). Conclusion Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
    Keywords: Research Article ; Medicine And Health Sciences ; Medicine And Health Sciences ; Research And Analysis Methods ; Physical Sciences ; Medicine And Health Sciences ; Computer And Information Sciences ; Biology And Life Sciences ; Physical Sciences ; Research And Analysis Methods ; Medicine And Health Sciences ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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  • 4
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article discussing the bacterial composition of subgingival plaque among diabetic and non-diabetic subjects to determine the effect that diabetes mellitus has on dental health.
    Keywords: Periodontiitis ; Bacteria ; Diabetes ; Pyrosequencing
    ISSN: 19326203
    E-ISSN: 19326203
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  • 5
    Language: English
    In: PLoS One, San Francisco: Public Library of Science
    Description: Article discussing a study that was conducted to understand the basis of a bacterial infection that is common among dairy cows.
    Keywords: Dairy ; Cows ; Pyrosequencing ; Amplicons ; Bacteria ; Microbiology
    ISSN: 19326203
    E-ISSN: 19326203
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  • 6
    Language: English
    In: PLoS ONE, 01 January 2014, Vol.9(10), p.e109677
    Description: Relationships between host and microbial diversity have important ecological and applied implications. Theory predicts that these relationships will depend on the spatio-temporal scale of the analysis and the niche breadth of the organisms in question, but representative data on host-microbial...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 7
    In: PLoS ONE, 2017, Vol.12(8)
    Description: Several ecological hypotheses ( e . g ., specific plaque, non-specific plaque and keystone pathogen) regarding the etiology of periodontitis have been proposed since the 1990s, most of which have been centered on the concept of dysbiosis associated with periodontitis. Nevertheless, none of the existing hypotheses have presented mechanistic interpretations on how and why dysbiosis actually occurs. Hubbell’s neutral theory of biodiversity offers a powerful null model to test hypothesis regarding the mechanism of community assembly and diversity maintenance from the metagenomic sequencing data, which can help to understand the forces that shape the community dynamics such as dysbiosis. Here we reanalyze the dataset from Abusleme et al .’s comparative study of the oral microbial communities from periodontitis patients and healthy individuals. Our study demonstrates that 14 out of 61 communities (23%) passed the neutrality test, a percentage significantly higher than the previous reported neutrality rate of 1% in human microbiome (Li & Ma 2016, Scientific Reports ). This suggests that, while the niche selection may play a predominant role in the assembly and diversity maintenance in oral microbiome, the effect of neutral dynamics may not be ignored. However, no statistically significant differences in the neutrality passing rates were detected between the periodontitis and healthy treatments with Fisher’s exact probability test and multiple testing corrections, suggesting that the mechanism of community assembly is robust against disturbances such as periodontitis. In addition, our study confirmed previous finding that periodontitis patients exhibited higher biodiversity. These findings suggest that while periodontitis may significantly change the community composition measured by diversity ( i . e ., the exhibition or ‘phenotype’ of community assembly), it does not seem to cause the ‘mutation’ of the ‘genotype” (mechanism) of community assembly. We argue that the ‘phenotypic’ changes explain the observed link (not necessarily causal) between periodontitis and community dysbiosis, which is certainly worthy of further investigation.
    Keywords: Research Article ; Medicine And Health Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences ; Biology And Life Sciences ; Ecology And Environmental Sciences
    E-ISSN: 1932-6203
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  • 8
    In: PLoS ONE, 2015, Vol.10(3)
    Description: Arsenic (As) mobilization in alluvial aquifers is caused by a complex interplay of hydro-geo-microbiological activities. Nevertheless, diversity and biogeochemical significance of indigenous bacteria in Bengal Delta Plain are not well documented. We have deciphered bacterial community compositions and metabolic properties in As contaminated groundwater of West Bengal to define their role in As mobilization. Groundwater samples showed characteristic high As, low organic carbon and reducing property. Culture-independent and -dependent analyses revealed presence of diverse, yet near consistent community composition mostly represented by genera Pseudomonas , Flavobacterium , Brevundimonas , Polaromonas , Rhodococcus , Methyloversatilis and Methylotenera . Along with As-resistance and -reductase activities, abilities to metabolize a wide range carbon substrates including long chain and polyaromatic hydrocarbons and HCO 3 , As 3+ as electron donor and As 5+ /Fe 3+ as terminal electron acceptor during anaerobic growth were frequently observed within the cultivable bacteria. Genes encoding cytosolic As 5+ reductase ( ars C) and As 3+ efflux/transporter [ ars B and acr 3(2)] were found to be more abundant than the dissimilatory As 5+ reductase gene arr A. The observed metabolic characteristics showed a good agreement with the same derived from phylogenetic lineages of constituent populations. Selected bacterial strains incubated anaerobically over 300 days using natural orange sand of Pleistocene aquifer showed release of soluble As mostly as As 3+ along with several other elements (Al, Fe, Mn, K, etc .). Together with the production of oxalic acid within the biotic microcosms, change in sediment composition and mineralogy indicated dissolution of orange sand coupled with As/Fe reduction. Presence of ars C gene, As 5+ reductase activity and oxalic acid production by the bacteria were found to be closely related to their ability to mobilize sediment bound As. Overall observations suggest that indigenous bacteria in oligotrophic groundwater possess adequate catabolic ability to mobilize As by a cascade of reactions, mostly linked to bacterial necessity for essential nutrients and detoxification.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 9
    Language: English
    In: PLOS ONE, 1/2/2018, Vol.13(1), p.e0190266
    Description: Synechococcus is an important photosynthetic picoplankton in the temperate to tropical oceans. As a photosynthetic bacterium, Synechococcus has an efficient mechanism to adapt to the changes in salinity and light intensity. The analysis of the distributions and functions of such microorganisms in the ever changing river mouth environment, where freshwater and seawater mix, should help better understand their roles in the ecosystem. Toward this objective, we have collected and sequenced the ocean microbiome in the river mouth of Kwangyang Bay, Korea, as a function of salinity and temperature. In conjunction with comparative genomics approaches using the sequenced genomes of a wide phylogeny of Synechococcus, the ocean microbiome was analyzed in terms of their composition and clade-specific functions. The results showed significant differences in the compositions of Synechococcus sampled in different seasons. The photosynthetic functions in such enhanced Synechococcus strains were also observed in the microbiomes in summer, which is significantly different from those in other seasons.
    Keywords: Sciences (General);
    ISSN: PLOS ONE
    E-ISSN: 1932-6203
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  • 10
    In: PLoS ONE, 2018, Vol.13(3)
    Description: Background Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24–48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools. Objective Our aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients. Methods Single-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration). Results The machine learning models had an area under the curve ranging from 0.826–0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3–34.3) and 29.6 (28.5–30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1–39.4) and 33.2 (32.5–33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive. Conclusion The best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.
    Keywords: Research Article ; Medicine And Health Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Medicine And Health Sciences ; Research And Analysis Methods ; Physical Sciences ; Medicine And Health Sciences ; Physical Sciences ; Research And Analysis Methods ; Computer And Information Sciences ; Computer And Information Sciences
    E-ISSN: 1932-6203
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