Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics.

OBJECTIVE:To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. STUDY DESIGN:Included were 400 women with singleton deliveries in California in 2009-2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15-20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS:Twenty-five serum biomarkers along with maternal age <34 years and poverty status identified >80% of women with PTB ± preeclampsia with best performance in women with preterm preeclampsia (AUC = 0.889, 95% confidence interval (0.822-0.959) training; 0.883 (0.804-0.963) testing). CONCLUSION:Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB ± preeclampsia

Second trimester inflammatory and metabolic markers in women delivering preterm with and without preeclampsia.

ObjectiveInflammatory and metabolic pathways are implicated in preterm birth and preeclampsia. However, studies rarely compare second trimester inflammatory and metabolic markers between women who deliver preterm with and without preeclampsia.Study designA sample of 129 women (43 with preeclampsia) with preterm delivery was obtained from an existing population-based birth cohort. Banked second trimester serum samples were assayed for 267 inflammatory and metabolic markers. Backwards-stepwise logistic regression models were used to calculate odds ratios.ResultsHigher 5-α-pregnan-3β,20α-diol disulfate, and lower 1-linoleoylglycerophosphoethanolamine and octadecanedioate, predicted increased odds of preeclampsia.ConclusionsAmong women with preterm births, those who developed preeclampsia differed with respect metabolic markers. These findings point to potential etiologic underpinnings for preeclampsia as a precursor to preterm birth

Levels and Correlates of Non-Adherence to WHO Recommended Inter-Birth Intervals in Rufiji, Tanzania.

Poorly spaced pregnancies have been documented worldwide to result in adverse maternal and child health outcomes. The World Health Organization (WHO) recommends a minimum inter-birth interval of 33 months between two consecutive live births in order to reduce the risk of adverse maternal and child health outcomes. However, birth spacing practices in many developing countries, including Tanzania, remain scantly addressed. METHODS: Longitudinal data collected in the Rufiji Health and Demographic Surveillance System (HDSS) from January 1999 to December 2010 were analyzed to investigate birth spacing practices among women of childbearing age. The outcome variable, non-adherence to the minimum inter-birth interval, constituted all inter-birth intervals <33 months long. Inter-birth intervals >=33 months long were considered to be adherent to the recommendation. Chi-Square was used as a test of association between non-adherence and each of the explanatory variables. Factors affecting non-adherence were identified using a multilevel logistic model. Data analysis was conducted using STATA (11) statistical software. RESULTS: A total of 15,373 inter-birth intervals were recorded from 8,980 women aged 15--49 years in Rufiji district over the follow-up period of 11 years. The median inter-birth interval was 33.4 months. Of the 15,373 inter-birth intervals, 48.4% were below the WHO recommended minimum length of 33 months between two live births. Non-adherence was associated with younger maternal age, low maternal education, multiple births of the preceding pregnancy, non-health facility delivery of the preceding birth, being an in-migrant resident, multi-parity and being married. CONCLUSION: Generally, one in every two inter-birth intervals among 15--49 year-old women in Rufiji district is poorly spaced, with significant variations by socio-demographic and behavioral characteristics of mothers and newborns. Maternal, newborn and child health services should be improved with a special emphasis on community- and health facility-based optimum birth spacing education in order to enhance health outcomes of mothers and their babies, especially in rural settings

GAMA/WiggleZ: The 1.4GHz radio luminosity functions of high- and low-excitation radio galaxies and their redshift evolution to z=0.75

We present radio Active Galactic Nuclei (AGN) luminosity functions over the redshift range 0.005 < z < 0.75. The sample from which the luminosity functions are constructed is an optical spectroscopic survey of radio galaxies, identified from matched Faint Images of the Radio Sky at Twenty-cm survey (FIRST) sources and Sloan Digital Sky Survey (SDSS) images.The radio AGN are separated into Low Excitation Radio Galaxies (LERGs) and High Excitation Radio Galaxies (HERGs) using the optical spectra. We derive radio luminosity functions for LERGs and HERGs separately in the three redshift bins (0.005 < z < 0.3, 0.3 < z < 0.5 and 0.5 < z <0.75). The radio luminosity functions can be well described by a double power-law. Assuming this double power-law shape the LERG population displays little or no evolution over this redshift range evolving as ~$(1+z)^{0.06}$ assuming pure density evolution or ~ $(1+z)^{0.46}$ assuming pure luminosity evolution. In contrast, the HERG population evolves more rapidly, best fitted by ~$(1+z)^{2.93}$ assuming a double power-law shape and pure density evolution. If a pure luminosity model is assumed the best fitting HERG evolution is parameterised by ~$(1+z)^{7.41}$. The characteristic break in the radio luminosity function occurs at a significantly higher power (~1 dex) for the HERG population in comparison to the LERGs. This is consistent with the two populations representing fundamentally different accretion modes

Population pharmacokinetics of colistin methanesulfonate and formed colistin in critically ill patients from a multicenter study provide dosing suggestions for various categories of patients

With increasing clinical emergence of multidrug-resistant Gram-negative pathogens and the paucity of new agents to combat these infections, colistin (administered as its inactive prodrug colistin methane-sulfonate [CMS]) has reemerged as a treatment option, especially for critically ill patients. There has been a dearth of pharmacokinetic (PK) data available to guide dosing in critically ill patients, including those on renal replacement therapy. In an ongoing study to develop a population PK model for CMS and colistin, 105 patients have been studied to date; these included 12 patients on hemodialysis and 4 on continuous renal replacement therapy. For patients not on renal replacement, there was a wide variance in creatinine clearance, ranging from 3 to 169 ml/min/1.73 m 2. Each patient was treated with a physician-selected CMS dosage regimen, and 8 blood samples for PK analysis were collected across a dosage interval on day 3 or 4 of therapy. A linear PK model with two compartments for CMS and one compartment for formed colistin best described the data. Covariates included creatinine clearance on the total clearance of CMS and colistin, as well as body weight on the central volume of CMS. Model-fitted parameter estimates were used to derive suggested loading and maintenance dosing regimens for various categories of patients, including those on hemodialysis and continuous renal replacement. Based on our current understanding of colistin PK and pharmacodynamic relationships, colistin may best be used as part of a highly active combination, especially for patients with moderate to good renal function and/or for organisms with MICs of ≥1.0 mg/liter

Acute kidney disease and renal recovery : consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of > 90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p

A cross-sectional study of the nutritional status of community-dwelling people with idiopathic Parkinson's disease

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) patients have an increased risk of under-nutrition, but we are unaware of any population based prevalence studies of under-nutrition in PD. The main objective of this study was to identify the prevalence, and nature, of under-nutrition in a representative population of people with PD.</p> <p>Methods</p> <p>People diagnosed with idiopathic PD from within two PD prevalence study sites in North-East England were asked to participate in this study. Those who participated (n = 136) were assessed using a number of standard rating scales including Hoehn & Yahr stage and Unified Parkinson's Disease Rating Scale (UPDRS). Body mass index (BMI), mid-arm circumference (MAC), triceps skin fold thickness (TSF) and grip strength were recorded together with social and demographic information.</p> <p>Results</p> <p>BMI < 20 identified over 15% of the study group to have under-nutrition. The Malnutritional Universal Screening Tool (MUST) scoring system identified 23.5% of participants at medium or high risk of malnutrition. Low BMI, indicating under-nutrition, was associated with greater age and disease duration, lower MAC, TSF, mid-arm muscle circumference (MAMC), reduced grip strength and a report of unintentional weight loss. Problems increased with increasing age and disease duration and were greater in females.</p> <p>Conclusions</p> <p>Under-nutrition is a problem for around 15% of community dwelling people with PD. All PD patients should be screened for under-nutrition; the MUST score is a useful early screening tool.</p

Expression of the proteolysis-inducing factor core peptide mRNA is upregulated in both tumour and adjacent normal tissue in gastro-oesophageal malignancy

Gastro-oesophageal cancer is associated with a high incidence of cachexia. Proteolysis-inducing factor (PIF) has been identified as a possible cachectic factor and studies suggest that PIF is produced exclusively by tumour cells. We investigated PIF core peptide (PIF-CP) mRNA expression in tumour and benign tissue from patients with gastro-oesophageal cancer and in gastro-oesophageal biopsies for healthy volunteers. Tumour tissue and adjacent benign tissue were collected from patients with gastric and oesophageal cancer (n=46) and from benign tissue only in healthy controls (n=11). Expression of PIF-CP mRNA was quantified by real-time PCR. Clinical and pathological information along with nutritional status was collected prospectively. In the cancer patients, PIF-CP mRNA was detected in 27 (59%) tumour samples and 31 (67%) adjacent benign tissue samples. Four (36%) gastro-oesophageal biopsies from healthy controls also expressed PIF-CP mRNA. Expression was higher in tumour tissue (P=0.031) and benign tissue (P=0.022) from cancer patients compared with healthy controls. In the cancer patients, tumour and adjacent benign tissue PIF-CP mRNA concentrations were correlated with each other (P<0.0001, r=0.73) but did not correlate with weight loss or prognosis. Although PIF-CP mRNA expression is upregulated in both tumour and adjacent normal tissue in gastro-oesophageal malignancy, expression does not relate to prognosis or cachexia. Post-translational modification of PIF may be a key step in determining the biological role of PIF in the patient with advanced cancer and cachexia