The mechanical and material properties of elderly human articular cartilage subject to impact and slow loading

Copyright © 2013 IPEM. Published by Elsevier Ltd. All rights reserved.Peer reviewedPostprin

Horizontal Branch Stars: The Interplay between Observations and Theory, and Insights into the Formation of the Galaxy

We review HB stars in a broad astrophysical context, including both variable and non-variable stars. A reassessment of the Oosterhoff dichotomy is presented, which provides unprecedented detail regarding its origin and systematics. We show that the Oosterhoff dichotomy and the distribution of globular clusters (GCs) in the HB morphology-metallicity plane both exclude, with high statistical significance, the possibility that the Galactic halo may have formed from the accretion of dwarf galaxies resembling present-day Milky Way satellites such as Fornax, Sagittarius, and the LMC. A rediscussion of the second-parameter problem is presented. A technique is proposed to estimate the HB types of extragalactic GCs on the basis of integrated far-UV photometry. The relationship between the absolute V magnitude of the HB at the RR Lyrae level and metallicity, as obtained on the basis of trigonometric parallax measurements for the star RR Lyrae, is also revisited, giving a distance modulus to the LMC of (m-M)_0 = 18.44+/-0.11. RR Lyrae period change rates are studied. Finally, the conductive opacities used in evolutionary calculations of low-mass stars are investigated. [ABRIDGED]Comment: 56 pages, 22 figures. Invited review, to appear in Astrophysics and Space Scienc

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km <sup>2</sup> resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km <sup>2</sup> pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Correction: The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients (PLoS ONE 13: 8 (0200584) DOI: 10.1371/journal.pone.0200584)

Notice of republication An incorrect version of Fig 3 was published in error. This article was republished on August 17, 2018 to correct for this error. Please download this article again to view the correct version

Correction: The ICE-AKI study: Impact analysis of a Clinical prediction rule and Electronic AKI alert in general medical patients (PLoS ONE 13: 8 (0200584) DOI: 10.1371/journal.pone.0200584)

Notice of republication An incorrect version of Fig 3 was published in error. This article was republished on August 17, 2018 to correct for this error. Please download this article again to view the correct version

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations

Objective: Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations. Design Systematic review. Data sources Medline, Embase and Web of Science until November 2016. Eligibility: Studies describing development of a multivariable model for predicting HA-AKI in nonspecialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal. Results: 14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71–0.80 in derivation (reported in 8/11 studies), 0.66–0.80 for internal validation studies (n=7) and 0.65–0.71 in five external validations. For calibration, the Hosmer- Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found. Conclusions: AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations

Objective: Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations. Design Systematic review. Data sources Medline, Embase and Web of Science until November 2016. Eligibility: Studies describing development of a multivariable model for predicting HA-AKI in nonspecialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal. Results: 14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71–0.80 in derivation (reported in 8/11 studies), 0.66–0.80 for internal validation studies (n=7) and 0.65–0.71 in five external validations. For calibration, the Hosmer- Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found. Conclusions: AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI

Improving clinical prediction rules in acute kidney injury with the use of biomarkers of cell cycle arrest: a pilot study

Introduction:Early recognition of patients developing acute kidney injury (AKI) is of considerable interest, we report the first use of a combination of a clinical prediction rule with a biomarker in emergent adult medical patients to improve AKI recognition. Methods:Single-centre prospective pilot study of medical admissions without AKI identified as high risk by a clinical prediction rule. Urine samples were obtained and tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) – biomarkers associated with cell cycle arrest, were measured. Outcome:Creatinine-based KDIGO hospital-acquired AKI (HA-AKI). Results:Of 69 patients recruited, HA-AKI developed in 13% (n = 9), in whom biomarker values were higher (median 0.43 (interquartile range (IQR) 0.21–1.25) vs. 0.07 (0.03–0.16) in cases without (p = 0.008). Peak rise in creatinine was higher in biomarker positive cases (median 30 μmol/L (7–72) vs. 1 μmol/L (0–16), p = 0.002). AUROC was 0.78 (95% CI 0.57–0.98). At the suggested cut-off (0.3) sensitivity for predicting AKI was 78% (95% CI 40–97%), specificity 89% (78–95%), positive predictive value 50% (31–69%) and negative predictive value 96% (89–99%). Discussion:Addition of a urinary biomarker allows exclusion of a significant number of patients identified to be at higher risk of AKI by a clinical prediction rule.</p