Considerations in Controlling for Urine Concentration for Biomarkers of Kidney Disease Progression After Acute Kidney Injury.

IntroductionBiomarkers of acute kidney injury (AKI) are often indexed to urine creatinine (UCr) or urine osmolarity (UOsm) to control for urine concentration. We evaluated how these approaches affect the biomarker-outcome association in patients with AKI.MethodsThe Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury Study was a cohort of hospitalized patients with and without AKI between 2009 and 2015. Using Cox proportional hazards regression, we assessed the associations and predictions (C-statistics) of urine biomarkers with a composite outcome of incident chronic kidney disease (CKD) and CKD progression. We used 4 approaches to account for urine concentration: indexing and adjusting for UCr and UOsm.ResultsAmong 1538 participants, 769 (50%) had AKI and 300 (19.5%) developed composite CKD outcome at median follow-up of 4.7 years. UCr and UOsm during hospitalization were inversely associated with the composite CKD outcome. The associations and predictions with CKD were significantly strengthened after indexing or adjusting for UCr or UOsm for urine kidney injury molecule-1 (KIM-1), interleukin-18 (IL-18), and monocyte chemoattractant protein-1 (MCP-1) in patients with AKI. There was no significant improvement with indexing or adjusting UCr or UOsm for albumin, neutrophil gelatinase-associated lipocalin (NGAL), and chitinase 3-like 1 (YKL-40). Uromodulin's (UMOD) inverse association with the outcome was significantly blunted after indexing but not adjusting for UCr or UOsm.ConclusionUCr and UOsm during hospitalization are inversely associated with development and progression of CKD. Indexing or adjusting for UCr or UOsm strengthened associations and improved predictions for CKD for only some biomarkers. Incorporating urinary concentration should be individualized for each biomarker in research and clinical applications

Inequalities in availability of National Health Service general dental practitioners in England and Wales.

AIM: To model the inequalities in availability of National Health Service general dental practitioners in England and Wales in relation to key socio-demographic factors. METHODS: Current estimates of the numbers of NHS general dental practitioners for each health authority were related to data from the 1991 census using Poisson regression models, and generalised estimating equations to allow for correlation between results for neighbouring health authorities. RESULTS: An 'average' health authority, without a dental school, would be expected to have 2,138 residents for every NHS dentist. Controlling for relevant factors, health authorities with higher proportions of the following are associated with lower (better) population to dentist ratios by the amounts shown: each 1% higher female population (-11.8%; 95%CI -19.1%, -3.9% P = 0.004); each 1% greater South Asian population (-1.4%; 95%CI -2.1%, -0.7% P <0.001). A health authority with a dental school is associated with a more favourable ratio compared with one without such a facility (-9.2%; 95%CI -16.2%, -1.6% P = 0.019). Each additional 1% of the following are associated with a worse ratio by the amounts shown: children aged 0 to 14 years old (+5.2%; 95% CI +2.4%, +8.1% P < 0.001); adults aged over 65 years old (+2.8%, 95%CI +1.0%, +4.7% P =0.002); households without a car (+0.8%; 95%CI 0.0%, +1.6% P =0.042). CONCLUSIONS: Ensuring access to dental care may be a more complex issue than simply providing adequate numbers of dentists at a national level. Any manpower planning exercise should additionally consider local factors that may act as incentives or disincentives to those professionals who provide care

Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events After Hospitalization: Findings From the ASSESS-AKI and ARID Studies.

RATIONALE &amp; OBJECTIVE: The role of plasma soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 in the prognosis of clinical events after hospitalization with or without acute kidney injury (AKI) is unknown. STUDY DESIGN: Prospective cohort. SETTING &amp; PARTICIPANTS: Hospital survivors from the ASSESS-AKI (Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury) and ARID (AKI Risk in Derby) studies with and without AKI during the index hospitalization who had baseline serum samples for biomarker measurements. PREDICTORS: We measured sTNFR1 and sTNFR2 from plasma samples obtained 3 months after discharge. OUTCOMES: The associations of biomarkers with longitudinal kidney disease incidence and progression, heart failure, and death were evaluated. ANALYTICAL APPROACH: Cox proportional hazard models. RESULTS: Among 1,474 participants with plasma biomarker measurements, 19% had kidney disease progression, 14% had later heart failure, and 21% died during a median follow-up of 4.4 years. For the kidney outcome, the adjusted HRs (AHRs) per doubling in concentration were 2.9 (95% CI, 2.2-3.9) for sTNFR1 and 1.9 (95% CI, 1.5-2.5) for sTNFR2. AKI during the index hospitalization did not modify the association between biomarkers and kidney events. For heart failure, the AHRs per doubling in concentration were 1.9 (95% CI, 1.4-2.5) for sTNFR1 and 1.5 (95% CI, 1.2-2.0) for sTNFR2. For mortality, the AHRs were 3.3 (95% CI, 2.5-4.3) for sTNFR1 and 2.5 (95% CI, 2.0-3.1) for sTNFR2. The findings in ARID were qualitatively similar in terms of the magnitude of association between biomarkers and outcomes. LIMITATIONS: Different biomarker platforms and AKI definitions; limited generalizability to other ethnic groups. CONCLUSIONS: Plasma sTNFR1 and sTNFR2 measured 3 months after hospital discharge were independently associated with clinical events regardless of AKI status during the index admission. sTNFR1 and sTNFR2 may assist with the risk stratification of patients during follow-up

Discovery-based science education: functional genomic dissection in Drosophila by undergraduate researchers.

How can you combine professional-quality research with discovery-based undergraduate education? The UCLA Undergraduate Consortium for Functional Genomics provides the answe

Polar Microalgae: Functional Genomics, Physiology, and the Environment

Microalgae underpin most foodwebs in polar regions as terrestrial primary production is too limited to support these complex and productive ecosystems. The success of microalgae in these extreme and highly variable ecosystems is rooted in their evolution and adaptation. The recent application of omics approaches in addition to biochemical and physiological measurements enabled a step change in our understanding of how these important organisms are adapted to their environment and how they have evolved from non-polar anchestors. This chapter is focused on diatoms and green algae as both groups of microalgae are most prevalent in polar regions. First genomes, transcriptomes, and reverse genetic tools have recently become available for representative species from both groups. They serve as important platforms to advance studies on their ecology, evolution, and adaptation. We highlight some of the key findings from these studies and link them with biochemical and physiological data to give insights into how genes and their products have shaped important microalgae in their diverse polar environments such as oceans, sea ice, permanently frozen lakes, snow and glaciers. Data from these studies will pave the way for understanding how these key organisms and their communities are going to respond to global climate change. They already provide novel genetic resources for various different biotechnological applications