Soil Carbon Turnover and Changes in Soil Nitrogen under the Agropastoral System in Brazilian Savannas (Cerrados).

Na publicação - Cesar Miranda (National Beef Cattle Research Center, Brazilian Agricultural Research Corporation, Mato Grosso do Sul State, Brazil)

Predictors of Long-Term Care Utilization by Dutch Hospital Patients aged 65+

Background Long-term care is often associated with high health care expenditures. In the Netherlands, an ageing population will likely increase the demand for long-term care within the near future. The development of risk profiles will not only be useful for projecting future demand, but also for providing clues that may prevent or delay long-term care utilization. Here, we report our identification of predictors of long-term care utilization in a cohort of hospital patients aged 65+ following their discharge from hospital discharge and who, prior to hospital admission, were living at home. Methods The data were obtained from three national databases in the Netherlands: the national hospital discharge register, the long-term care expenses register and the population register. Multinomial logistic regression was applied to determine which variables were the best predictors of long-term care utilization. The model included demographic characteristics and several medical diagnoses. The outcome variables were discharge to home with no formal care (reference category), discharge to home with home care, admission to a nursing home and admission to a home for the elderly. Results The study cohort consisted of 262,439 hospitalized patients. A higher age, longer stay in the hospital and absence of

ACC2 Is Expressed at High Levels Human White Adipose and Has an Isoform with a Novel N-Terminus

Acetyl-CoA carboxylases ACC1 and ACC2 catalyze the carboxylation of acetyl-CoA to malonyl-CoA, regulating fatty-acid synthesis and oxidation, and are potential targets for treatment of metabolic syndrome. Expression of ACC1 in rodent lipogenic tissues and ACC2 in rodent oxidative tissues, coupled with the predicted localization of ACC2 to the mitochondrial membrane, have suggested separate functional roles for ACC1 in lipogenesis and ACC2 in fatty acid oxidation. We find, however, that human adipose tissue, unlike rodent adipose, expresses more ACC2 mRNA relative to the oxidative tissues muscle and heart. Human adipose, along with human liver, expresses more ACC2 than ACC1. Using RT-PCR, real-time PCR, and immunoprecipitation we report a novel isoform of ACC2 (ACC2.v2) that is expressed at significant levels in human adipose. The protein generated by this isoform has enzymatic activity, is endogenously expressed in adipose, and lacks the N-terminal sequence. Both ACC2 isoforms are capable of de novo lipogenesis, suggesting that ACC2, in addition to ACC1, may play a role in lipogenesis. The results demonstrate a significant difference in ACC expression between human and rodents, which may introduce difficulties for the use of rodent models for development of ACC inhibitors

The Persisting Burden of Intracerebral Haemorrhage: Can Effective Treatments Be Found?

Colin Josephson, Rustam Al-Shahi Salman, and colleagues discuss the effectiveness of treatments for intracerebral haemorrhage

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

<p>Abstract</p> <p>Background</p> <p>Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.</p> <p>Methods</p> <p>We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.</p> <p>Results</p> <p>The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO₂: FiO₂ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r²was 20.2% across individuals and 44.3% across units.</p> <p>Conclusions</p> <p>A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.</p