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

Role of radiosynovectomy in the treatment of rheumatoid arthritis and hemophilic arthropathies

Radiosynovectomy is a novel method of treatment for several acute and chronic inflammatory joint disorders. A small amount of a beta-emitting radionuclide is injected into the affected joint delivering a radiation dose of 70 to 100 Gy to the synovia. The proliferative tissue is destroyed, secretion of fluid and accumulation of inflammation causing cellular compounds stops and the joint surfaces become fibrosed, providing long term symptom relief. The radionuclides are injected in colloidal form so that they remain in the synovium and are not transported by lymphatic vessels causing radiation exposure to other organs. Complete reduction of knee joint swelling has been seen in above 40% and pain relief in 88% of patients. Wrist, elbow, shoulder, ankle and hip joints showed significant improvement in 50-60% and restoration of normal function and long term pain relief has been achieved in about 70% of small finger joints. In hemophilic arthropathies complete cessation of bleeding in about 60% and improved mobility in 75% of patients has been reported

Inorganic pyrophosphate in plasma, urine, and synovial fluid of patients with pyrophosphate arthropathy (chondrocalcinosis or pseudogout).

The concentration of inorganic pyrophosphate (I.P.P.) has been determined in the plasma, urine, and synovial fluid of patients with pyrophosphate arthropathy (chondrocalcinosis articularis or pseudogout). The concentrations of I.P.P. in plasma and urine were normal in patients with pyrophosphate arthropathy. In contrast, the concentrations in synovial fluid were higher in patients with pyrophosphate arthropathy (0·41-3·76 μg. I.P.P. per ml.) than in patients with joint effusions due to other causes (0·06-0·32 μg. I.P.P. per ml.). The concentrations of calcium and alkaline phosphatase were lower in the synovial fluid of patients than of controls. The disturbance in I.P.P. metabolism in pyrophosphate arthropathy seems to be a local rather than generalised phenomenon. © 1970

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

Inorganic pyrophosphate in plasma, urine, and synovial fluid of patients with pyrophosphate arthropathy (chondrocalcinosis or pseudogout).

The concentration of inorganic pyrophosphate (I.P.P.) has been determined in the plasma, urine, and synovial fluid of patients with pyrophosphate arthropathy (chondrocalcinosis articularis or pseudogout). The concentrations of I.P.P. in plasma and urine were normal in patients with pyrophosphate arthropathy. In contrast, the concentrations in synovial fluid were higher in patients with pyrophosphate arthropathy (0·41-3·76 μg. I.P.P. per ml.) than in patients with joint effusions due to other causes (0·06-0·32 μg. I.P.P. per ml.). The concentrations of calcium and alkaline phosphatase were lower in the synovial fluid of patients than of controls. The disturbance in I.P.P. metabolism in pyrophosphate arthropathy seems to be a local rather than generalised phenomenon. © 1970

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

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