6 research outputs found
Inhibition of microbial sulfate reduction in a flow-through column system by (per)chlorate treatment.
Microbial sulfate reduction is a primary cause of oil reservoir souring. Here we show that amendment with chlorate or perchlorate [collectively (per)chlorate] potentially resolves this issue. Triplicate packed columns inoculated with marine sediment were flushed with coastal water amended with yeast extract and one of nitrate, chlorate, or perchlorate. Results showed that although sulfide production was dramatically reduced by all treatments, effluent sulfide was observed in the nitrate (10 mM) treatment after an initial inhibition period. In contrast, no effluent sulfide was observed with (per)chlorate (10 mM). Microbial community analyses indicated temporal community shifts and phylogenetic clustering by treatment. Nitrate addition stimulated Xanthomonadaceae and Rhizobiaceae growth, supporting their role in nitrate metabolism. (Per)chlorate showed distinct effects on microbial community structure compared with nitrate and resulted in a general suppression of the community relative to the untreated control combined with a significant decrease in sulfate reducing species abundance indicating specific toxicity. Furthermore, chlorate stimulated Pseudomonadaceae and Pseudoalteromonadaceae, members of which are known chlorate respirers, suggesting that chlorate may also control sulfidogenesis by biocompetitive exclusion of sulfate-reduction. Perchlorate addition stimulated Desulfobulbaceae and Desulfomonadaceae, which contain sulfide oxidizing and elemental sulfur-reducing species respectively, suggesting that effluent sulfide concentrations may be controlled through sulfur redox cycling in addition to toxicity and biocompetitive exclusion. Sulfur isotope analyses further support sulfur cycling in the columns, even when sulfide is not detected. This study indicates that (per)chlorate show great promise as inhibitors of sulfidogenesis in natural communities and provides insight into which organisms and respiratory processes are involved
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Machine learning based DNA melt curve profiling enables automated novel genotype detection.
Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications
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Impact of a deep learning sepsis prediction model on quality of care and survival
Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance
Impact of a deep learning sepsis prediction model on quality of care and survival
Abstract Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%–3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%–8.0%), and a 4% (95% CI, 1.1%–7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance
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Inhibition of microbial sulfate reduction in a flow-through column system by (per)chlorate treatment.
Microbial sulfate reduction is a primary cause of oil reservoir souring. Here we show that amendment with chlorate or perchlorate [collectively (per)chlorate] potentially resolves this issue. Triplicate packed columns inoculated with marine sediment were flushed with coastal water amended with yeast extract and one of nitrate, chlorate, or perchlorate. Results showed that although sulfide production was dramatically reduced by all treatments, effluent sulfide was observed in the nitrate (10 mM) treatment after an initial inhibition period. In contrast, no effluent sulfide was observed with (per)chlorate (10 mM). Microbial community analyses indicated temporal community shifts and phylogenetic clustering by treatment. Nitrate addition stimulated Xanthomonadaceae and Rhizobiaceae growth, supporting their role in nitrate metabolism. (Per)chlorate showed distinct effects on microbial community structure compared with nitrate and resulted in a general suppression of the community relative to the untreated control combined with a significant decrease in sulfate reducing species abundance indicating specific toxicity. Furthermore, chlorate stimulated Pseudomonadaceae and Pseudoalteromonadaceae, members of which are known chlorate respirers, suggesting that chlorate may also control sulfidogenesis by biocompetitive exclusion of sulfate-reduction. Perchlorate addition stimulated Desulfobulbaceae and Desulfomonadaceae, which contain sulfide oxidizing and elemental sulfur-reducing species respectively, suggesting that effluent sulfide concentrations may be controlled through sulfur redox cycling in addition to toxicity and biocompetitive exclusion. Sulfur isotope analyses further support sulfur cycling in the columns, even when sulfide is not detected. This study indicates that (per)chlorate show great promise as inhibitors of sulfidogenesis in natural communities and provides insight into which organisms and respiratory processes are involved
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Integrated Genomic and Social Network Analyses of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission in the Healthcare Setting
BackgroundInfection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.MethodsWe performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.ResultsAmong 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).ConclusionsIP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission