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ShareScore release 0.9.0
Dataset results
20 results for “Microbial contamination”
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.25% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.25% contaminant spike-in.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 1% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 1% contaminant spike-in.</p>
Raw Data for the article: Efficacy of Three Commercial Disinfectants in Reducing Microbial Surfaces' Contaminations of Pharmaceuticals Hospital Facilities
<p>To evaluate and validate the efficacy of disinfectants used in our cleaning procedure, in order to reduce pharmaceutical hospital surfaces' contaminations, we tested the action of three commercial disinfectants on small representative samples of the surfaces present in our hospital cleanrooms. These samples (or coupons) were contaminated with selected microorganisms for the validation of the disinfectants. The coupons were sampled before and after disinfection and the microbial load was assessed to calculate the Log<sub>10</sub> reduction index. Subsequently, we developed and validated a disinfection procedure on real surfaces inside the cleanrooms intentionally contaminated with microorganisms, using approximately 10<sup>7</sup>-10<sup>8</sup> total colony forming units per coupon. Our results showed a bactericidal, fungicidal, and sporicidal efficacy coherent to the acceptance criteria suggested by United States Pharmacopeia 35 <1072>. The correct implementation of our cleaning and disinfection procedure, respecting stipulated concentrations and contact times, led to a reduction of at least 6 Log<sub>10</sub> for all microorganisms used. The proposed disinfection procedure reduced the pharmaceutical hospital surfaces' contaminations, limited the propagation of microorganisms in points adjacent to the disinfected area, and ensured high disinfection and safety levels for operators, patients, and treated surfaces.</p>
Ease of Use and Microbial Contamination of Tobramycin Inhalation Powder (TIP) Versus Nebulised Tobramycin Inhalation Solution (TIS) and Nebulised Colistimethate (COLI)
ClinicalTrials.gov study NCT01844778. IPD Sharing: Not stated. Countries: 5. Publications: 1.
Data from: Ecological selection of siderophore-producing microbial taxa in response to heavy metal contamination
Some microbial public goods can provide both individual and community-wide benefits, and are open to exploitation by non-producing species. One such example is the production of metal-detoxifying siderophores. Here, we investigate whether conflicting selection pressures on siderophore production by heavy metals – a detoxifying effect of siderophores, and exploitation of this detoxifying effect – results in a net increase or decrease. We show that the proportion of siderophore-producing taxa increases along a natural heavy metal gradient. A causal link between metal contamination and siderophore production was subsequently demonstrated in a microcosm experiment in compost, in which we observed changes in community composition towards taxa that produce relatively more siderophores following copper contamination. We confirmed the selective benefit of siderophores by showing that taxa producing large amount of siderophores suffered less growth inhibition in toxic copper. Our results suggest that ecological selection will favour siderophore-mediated decontamination, with important consequences for potential remediation strategies.
Soil fauna-microbial interactions complexity triggers shifts in both fungal and bacterial communities under a contamination disturbance
<p>meta.otu.june2020.txt : Willow morphological data, data related to qPCR of PAH-RHD genes and phenanthrene amounts found by GC-MS in soil, associated to the paper entitled: Soil fauna-microbial interactions complexity triggers shifts in both fungal and bacterial communities under a contamination disturbance.</p> <p>Files starting by 16s, its, gn and gp are data tables of bioinformatically processed amplicon sequencing data containing filtered and rarefied counts corresponding to 4 set of genes (16S rRNA gene, fungal ITS, PAH-RHD Gram Negative and Gram Positive bacteria) and corresponding taxonomy. </p>
Hand Hygiene Practices and Microbial Contamination on Feeding Tubes and Other Components of Feeding Systems
ClinicalTrials.gov study NCT04240132. IPD Sharing: NO. Countries: 1. Publications: 4.
Intraoperative Microbial Contamination
ClinicalTrials.gov study NCT03139539. IPD Sharing: YES. Countries: 1. Publications: 12.
Data from: Ecological selection of siderophore-producing microbial taxa in response to heavy metal contamination
Open the record for dataset details and reuse information.
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes
<p> </p> <p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.5% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.5% contaminant spike-in. </p>
Intraperitoneal microbial contamination drives post-surgical peritoneal adhesions by mesothelial EGFR-signaling.
GEO Series GSE186658. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
The influence of long-term copper contaminated agricultural soil at different pH levels on microbial communities and springtail transcriptional regulation
GEO Series GSE29644. Folsomia candida. 64 samples. Type: Expression profiling by array.
Effects of the anti-microbial contaminant triclocarban on the reproductive function and ovarian transcriptome of the fathead minnow (Pimephales promelas)
GEO Series GSE64291. Pimephales promelas. 24 samples. Type: Expression profiling by array.
Effectiveness of Povidone-Iodine Versus Chlorhexidine Gluconate Solutions in Reducing Microbial Contamination in Spinal Surgery Wounds During Intraoperative Soaking.
ClinicalTrials.gov study NCT06284174. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluation of Microbial Colonisation and Contamination Caused by the Transvaginal and Transabdominal Access for Cholecystectomy
ClinicalTrials.gov study NCT01078025. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Compression of the Lid Margin Increase Microbial Contamination Risk of Patients Undergoing Cataract Surgery
ClinicalTrials.gov study NCT01859910. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of the microbial diversity of an uncontaminated and a 2,4,6-trinitrotoluene contaminated soil sample
GEO Series GSE3499. unidentified. 6 samples. Type: Other.
Microbiota as Early Diagnostic and predictivE Factor for Osteoarthritic Degeneration and Microbial Contamination
ClinicalTrials.gov study NCT06944288. IPD Sharing: YES. Countries: 0. Publications: 0.
Microbial diversity of an uncontaminated and a 2,4,6-TNT contaminated soil sample with CodeLink microarrays
GEO Series GSE3525. Pseudomonas putida; unidentified. 12 samples. Type: Other.
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International Brain Laboratory public data
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OpenNeuro
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