BackgroundAnimal and human studies suggest that inflammation and malnutrition are common in acute kidney injury (AKI) patients. However, only a few studies reported CRP, a marker of inflammation, albumin, prealbumin and cholesterol, markers of nutritional status were associated with the prognosis of AKI patients. No study examined whether the combination of inflammatory and nutritional markers could predict the mortality of AKI patients.Methods155 patients with hospital-acquired AKI were recruited to this prospective cohort study according to RIFLE (Risk, Injury, Failure, Lost or End Stage Kidney) criteria. C-reactive protein (CRP), and the nutritional markers (albumin, prealbumin and cholesterol) measured at nephrology consultation were analyzed in relation to all cause mortality of these patients. In addition, CRP and prealbumin were also measured in healthy controls (n = 45), maintenance hemodialysis (n = 70) and peritoneal dialysis patients (n = 50) and then compared with AKI patients.ResultsCompared with healthy controls and end-stage renal disease patients on maintenance hemodialysis or peritoneal dialysis, patients with AKI had significantly higher levels of CRP/prealbumin (p < 0.001). Higher level of serum CRP and lower levels of albumin, prealbumin and cholesterol were found to be significant in the patients with AKI who died within 28 days than those who survived >28 days. Similarly, the combined factors including the ratio of CRP to albumin (CRP/albumin), CRP/prealbumin and CRP/cholesterol were also significantly higher in the former group (p < 0.001 for all). Multivariate analysis (Cox regression) revealed that CRP/prealbumin was independently associated with mortality after adjustment for age, gender, sepsis and sequential organ failure assessment (SOFA, p = 0.027) while the others (CRP, albumin, prealbumin, cholesterol, CRP/albumin and CRP/cholesterol) became non-significantly associated. The hazard ratio was 1.00 (reference), 1.85, 2.25 and 3.89 for CRP/prealbumin increasing according to quartiles (p = 0.01 for the trend).ConclusionsInflammation and malnutrition were common in patients with AKI. Higher level of the ratio of CRP to prealbumin was associated with mortality of AKI patients independent of the severity of illness and it may be a valuable addition to SOFA score to independent of the severity of illness and it may be a valuable addition to SOFA score to predict the prognosis of AKI patients.

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BackgroundDiabetes-associated cognition decline is one of central nervous system complications in diabetic mellitus, while its pathogenic mechanism remains unclear. In this study, 1H nuclear magnetic resonance-based metabonomics and immunohistochemistry was used to explore key metabolic alterations in hippocampus of type 2 diabetic db/db mice with cognition decline in order to advance understanding of mechanisms underlying the pathogenesis of the disease.ResultsMetabonomics reveals that lactate level was significantly increased in hippocampus of db/db mice with cognition decline compared with age-matched wild-type mice. Several tricarboxylic acid cycle intermediates including succinate and citrate were reduced in hippocampus of db/db mice with cognition decline. Moreover, an increase in glutamine level and a decrease in glutamate and γ-aminobutyric acid levels were observed in db/db mice. Results from immunohistochemistry analysis show that glutamine synthetase was increased and glutaminase and glutamate decarboxylase were decreased in db/db mice.ConclusionsOur results suggest that the development of diabetes-associated cognition decline in db/db mice is most likely implicated in a reduction in energy metabolism and a disturbance of glutamate-glutamine shuttling between neurons and astrocytes in hippocampus.Electronic supplementary materialThe online version of this article (doi:10.1186/s13041-016-0223-5) contains supplementary material, which is available to authorized users.

Highlights Brain network dysfunction is the core mechanism of Parkinson’s disease. Fixel-based analysis is a novel method to detect fiber-specific white matter alterations. Degeneration of corpus callosum fiber density was related to the motor symptoms. Increased cortical spinal tract fiber density may compensate the motor impairments in Parkinson’s disease. Fiber alterations were different across disease stages of Parkinson’s disease.

BACKGROUND Despite advances in the treatment of poor-grade aneurysmal subarachnoid hemorrhage (aSAH), predicting the long-term outcome of aSAH remains challenging, although essential. OBJECTIVE To predict long-term outcomes after poor-grade aSAH using decision tree modeling. METHODS This was a retrospective analysis of a prospective multicenter observational registry of patients with poor-grade aSAH with a World Federation of Neurosurgical Societies (WFNS) grade IV or V. Outcome was assessed by the modified Rankin Scale (mRS) at 12 mo, and an unfavorable outcome was defined as an mRS of 4 or 5 or death. Long-term prognostic models were developed using multivariate logistic regression and decision tree algorithms. An additional independent testing dataset was collected for external validation. Overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curves (AUC) were used to assess model performance. RESULTS Of the 266 patients, 139 (52.3%) had an unfavorable outcome. Older age, absence of pupillary reactivity, lower Glasgow coma score (GCS), and higher modified Fisher grade were independent predictors of unfavorable outcome. Modified Fisher grade, pupillary reactivity, GCS, and age were used in the decision tree model, which achieved an overall accuracy of 0.833, sensitivity of 0.821, specificity of 0.846, and AUC of 0.88 in the internal test. There was similar predictive performance between the logistic regression and decision tree models. Both models achieved a high overall accuracy of 0.895 in the external test. CONCLUSION Decision tree model is a simple tool for predicting long-term outcomes after poor-grade aSAH and may be considered for treatment decision-making.

Background: The dilation of perivascular space (PVS) has been widely used to reflect brain degeneration in clinical brain imaging studies. However, PVS characteristics exhibit large differences in healthy subjects. Such variations need to be better addressed before PVS can be used to reflect pathological changes. In the present study, we aim to investigate the potential influence of several related factors on PVS dilation in healthy elderly subjects.Methods: One-hundred and three subjects (mean age = 59.5) were retrospectively included from a prospectively collected community cohort. Multi-modal high-resolution magnetic resonance imaging and cognitive assessments were performed on each subject. Machine-learning based segmentation methods were employed to quantify PVS volume and white matter hyperintensity (WMH) volume. Multiple regression analysis was performed to reveal the influence of demographic factors, vascular risk factors, intracranial volume (ICV), major brain artery diameters, and brain atrophy on PVS dilation.Results: Multiple regression analysis showed that age was positively associated with the basal ganglia (BG) (standardized beta = 0.227, p = 0.027) and deep white matter (standardized beta = 0.220, p = 0.029) PVS volume. Hypertension was positively associated with deep white matter PVS volume (standardized beta = 0.234, p = 0.017). Furthermore, we found that ICV was strongly associated with the deep white matter PVS volume (standardized beta = 0.354, p &lt; 0.001) while the intracranial artery diameter was negatively associated with the deep white matter PVS volume (standardized beta = −0.213, p = 0.032).Conclusions: Intracranial volume has significant influence on deep white matter PVS volume. Future studies on PVS dilation should include ICV as an important covariate.

Diabetes mellitus is a typical heterogeneous metabolic disorder characterized by abnormal metabolism of carbohydrates, lipids, and proteins. Investigating the changes in metabolic pathways during the evolution of diabetes mellitus may contribute to the understanding of its metabolic features and pathogenesis. In this study, serum samples were collected from diabetic rats and age-matched controls at different time points: 1 and 9 weeks after streptozotocin (STZ) treatment. (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with quantitative analysis was performed to study the metabolic changes. The serum samples were also subjected to clinical chemistry analysis to verify the metabolic changes observed by metabonomics. Partial least squares discriminant analysis (PLS-DA) demonstrated that the levels of serum metabolites in diabetic rats are different from those in control rats. These findings indicate that the metabolic characteristics of the two groups are markedly different at 1 and 9 weeks. Quantitative analysis showed that the levels of some metabolites, such as pyruvate, lactate, citrate, acetone, acetoacetate, acetate, glycerol, and valine, varied in a time-dependent manner in diabetic rats. These results suggest that serum metabolites related to glycolysis, the tricarboxylic acid cycle, gluconeogenesis, fatty acid β-oxidation, branched-chain amino acid metabolism, and the tyrosine metabolic pathways are involved in the evolution of diabetes. The metabolic changes represent potential features and promote a better understanding of the mechanisms involved in the development of diabetes mellitus. This work further suggests that (1)H NMR metabonomics is a valuable approach for providing novel insights into the pathogenesis of diabetes mellitus and its complications.

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