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A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
Background: The number of implantable cardioverter defibrillator (ICD) infections is increasing due to an increased number of ICD implants, higher-risk patients, and more frequent replacement procedures, which carry a higher risk of infection. Reducing the morbidity, mortality, and cost of ICD-related infections requires an understanding of the current rate of this complication and its predictors.
Methods: The Shock Implant Evaluation Trial (SIMPLE) trial randomized 2500 ICD recipients to defibrillation testing or not. Over an average of 3.1 years, patients were seen every 6 months and examined for evidence of ICD infection, which was defined as requiring device removal and/or intravenous antibiotics.
Results: Within 24 months, 21 patients (0.8%) developed infection. Fourteen patients (67%) with infection presented within 30 days, 20 patients by 12 months, and only 1 patient beyond 12 months. Univariate analysis demonstrated that patients with primary electrical disorders (3 patients, P = 0.009) and those with a secondary prevention indication (13 patients, P = 0.0009) were more likely to develop infection. Among the 2.2% of patients who developed an ICD wound hematoma, 10.4% developed an infection. Among the 8.3% of patients requiring an ICD reintervention, 1.9% developed an infection.
Conclusions: This cohort of ICD recipients at high-volume centres have a low risk of device-related infection. However; strategies to reduce wound hematoma and the need for ICD reintervention could further reduce the rate of infection.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.