Factors associated with the experience of patients presenting in pain to the emergency department.

AIMS AND OBJECTIVES: This study aims to examine the association between person, environment, health and illness factors, pain care and the patient experience in the emergency department, guided by symptom management theory. BACKGROUND: Current outcome measures of pain care in the emergency department focus on process measures such as the time taken to deliver analgesic medication. Patient-reported outcomes of pain care are rare in emergency department literature and predominantly focus on patient satisfaction. Measuring overall patient experience is common, with extensive surveys undertaken in the United Kingdom, United States of America and Australia; however, these are not used as an outcome of pain care. DESIGN: Prospective cohort study. METHODS: One hundred and ninety patients arriving at a large, inner-city adults-only emergency department in moderate to severe pain were recruited to answer a modified version of the emergency department patient experience of care survey. RESULTS: Fifteen factors were identified as influencing the patient experience of care when presenting in pain. These influences of patient experience included the emergency department environment, time to first analgesic medication and the provision of analgesic medication. CONCLUSIONS: In addition to pain care factors, there is a significant association between the emergency department environment-especially workload, throughput and patient placement-and the experience of patients who present in pain to the emergency department. RELEVANCE TO CLINICAL PRACTICE: This study demonstrated an association between time to first analgesic medication and the patient experience of care. Providing timely care, including pain care, in emergency departments is difficult, but necessary to improve the patient experience of care.

Climatic and evolutionary contexts are required to infer plant life history strategies from functional traits at a global scale.

Life history strategies are fundamental to the ecology and evolution of organisms and are important for understanding extinction risk and responses to global change. Using global datasets and a multiple response modelling framework we show that trait-climate interactions are associated with life history strategies for a diverse range of plant species at the global scale. Our modelling framework informs our understanding of trade-offs and positive correlations between elements of life history after accounting for environmental context and evolutionary and trait-based constraints. Interactions between plant traits and climatic context were needed to explain variation in age at maturity, distribution of mortality across the lifespan and generation times of species. Mean age at maturity and the distribution of mortality across plants' lifespan were under evolutionary constraints. These findings provide empirical support for the theoretical expectation that climatic context is key to understanding trait to life history relationships globally.

Harnessing landscape genomics to identify future climate resilient genotypes in a desert annual.

Local adaptation features critically in shaping species responses to changing environments, complicating efforts to revegetate degraded areas. Rapid climate change poses an additional challenge that could reduce fitness of even locally sourced seeds in restoration. Predictive restoration strategies that apply seeds with favourable adaptations to future climate may promote long-term resilience. Landscape genomics is increasingly used to assess spatial patterns in local adaption and may represent a cost-efficient approach for identifying future-adapted genotypes. To demonstrate such an approach, we genotyped 760 plants from 64 Mojave Desert populations of the desert annual Plantago ovata. Genome scans on 5,960 SNPs identified 184 potentially adaptive loci related to climate and satellite vegetation metrics. Causal modelling indicated that variation in potentially adaptive loci was not confounded by isolation by distance or isolation by habitat resistance. A generalized dissimilarity model (GDM) attributed spatial turnover in potentially adaptive loci to temperature, precipitation and NDVI amplitude, a measure of vegetation green-up potential. By integrating a species distribution model (SDM), we find evidence that summer maximum temperature may both constrain the range of P. ovata and drive adaptive divergence in populations exposed to higher temperatures. Within the species' current range, warm-adapted genotypes are predicted to experience a fivefold expansion in climate niche by midcentury and could harbour key adaptations to cope with future climate. We recommend eight seed transfer zones and project each zone into its relative position in future climate. Prioritizing seed collection efforts on genotypes with expanding future habitat represents a promising strategy for restoration practitioners to address rapidly changing climates.

Factors associated with time to first analgesic medication in the emergency department.

AIM AND OBJECTIVE: To examine the factors associated with time to first analgesic medication in the emergency department. BACKGROUND: Pain is the most common symptom presenting to the emergency department, and the time taken to deliver analgesic medication is a common outcome measure. Factors associated with time to first analgesic medication are likely to be multifaceted, but currently poorly described. DESIGN: Retrospective cohort study. METHODS: Cox proportional hazards regression modelling was undertaken to evaluate the associations between person, environment, health and illness variables within Symptom Management Theory and time to first analgesic medication in a sample of adult patients presenting with moderate-to-severe pain to an emergency department over twelve months. This study was completed in line with the STROBE statement. RESULTS: 383 patients were included in the study, 290 (75.92%) of these patients received an analgesic medication in a median time of 45 minutes (interquartile range, 70 minutes). A model containing nine explanatory variables associated with time to first analgesic medication was identified. These nine variables (employment status, discharge location, triage score, Charlson score, arrival pain score, socio-economic status, first location, daily total treatment time and patient time to be seen) represent all of the domains of the Symptom Management Theory. CONCLUSIONS: Person, environment, health and illness factors are associated with the time taken to deliver analgesic medication to those in pain in the emergency department. This study demonstrates the complexity of factors associated with pain care and the applicability of Symptom Management Theory to pain care in the emergency department. RELEVANCE TO CLINICAL PRACTICE: Identifying a model of factors that are associated with the time in which the most common symptom presenting to the emergency department is treated allows for targeted interventions to groups likely to receive poor care and a framework for its evaluation.

The relationship between time to analgesic administration and emergency department length of stay: A retrospective review.

AIM: To determine the association between time to first analgesic medication and emergency department length of stay (ED LOS). DESIGN: Retrospective cohort study. METHOD: We conducted this study in a large, inner-city emergency department and included adult patients who presented with pain as a symptom and received analgesic medication(s). Study participants were identified from a random selection of 2,000 adult patients who presented between August-October 2018. The relationship between ED LOS and time to first analgesic medication was described using bivariate and multivariate linear regression. RESULTS: Of the 2,000 randomly selected patients presenting between August and October 2018, 727 (36.35%) had pain as a symptom on arrival, 423 (21.15%) had analgesic medication administered. The median time to first analgesic medication was in 62 (interquartile range: 36-105) minutes and median ED LOS was 218 (interquartile range: 160-317.5) minutes. After adjusting for the effects of sex, urgency of the presentation, emergency department location first seen by clinician, departure destination and workload metrics (average daily time to be seen and daily access block). Time to first analgesic medication was independently associated with ED LOS, contributing to 6.96% of the variance in the multivariate model. CONCLUSION: Providing analgesic medication faster to patients presenting in pain, in addition to previously demonstrated positive patient outcomes, may decrease their ED LOS. IMPACT: Reducing ED LOS through faster pain care, benefits the patient through faster pain relief and can benefit the department by reducing the total amount of care delivered in the emergency department. Reducing total care delivery frees up resources to improve the care to all emergency department patients.

Computational multivariate modelling of electrical activity of the porcine uterus during spontaneous and hormone-induced oestrus.

NEW FINDINGS: What is the central question of this study? Does oestrous cycle synchronization influence myoelectrical activity of porcine myometrium? What is the main finding and its importance? Exogenous hormones used to synchronize oestrus in pigs altered myoelectrical activity, which was effectively modelled. Higher-order multivariate statistic modelling provided evidence of similar activity in both types of oestrus, but a larger order of EMG signals during induced oestrus. Higher-order statistical analysis of the probabilistic model suggests the beginning of the early follicular phase and the mid-luteal phase to be most important in evaluation of the natural patterns of myoelectrical activity. Higher-order multivariate cumulants are more informative than classical statistics in characterization of myoelectrical activity changes in porcine myometrium. ABSTRACT: In pig production units, control of the oestrous cycle and synchronization of ovulation have become routine herd management procedures. During the oestrous cycle, in both induced and spontaneous conditions, the ovaries and the uterus undergo hormone-dominated physiological changes, which are consistent with the hypothesis that there is a functional role of uterine contractions in promoting fertilization. We have used electromyography to determine whether the use of exogenous hormones, such as equine chorionic gonadotrophin and human chorionic gonadotrophin, which have the potential to control the timing of ovulation in female pigs, changes the multivariate relationships between parameters of electrical bursts and modulates the patterns of myoelectrical activity. We used the mathematical approach of higher-order multivariate cumulants in complex modelling of the myometrial electrical activity. The experiment was conducted on 12 mature Polish Landrace sows, and uterine activity was recorded during both spontaneous and induced oestrous cycles. The burst parameters were determined using six features in the time domain and, after Fast Fourier transformation, in the frequency domain. Evaluation of myoelectrical activity patterns was conducted based on classical univariate statistical methods and multivariate probabilistic modelling. The classical statistical approach indicated weaker myoelectrical activity after hormonal stimulation, whereas the higher-order multivariate statistical model showed evidence of similar status of activity and a larger order of signals during induced oestrus. Routine oestrous cycle synchronization affects the multivariate probabilistic model of myometrial electrical activity.

Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort.

AIMS/HYPOTHESIS: The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction. METHODS: We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index. RESULTS: We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors. CONCLUSIONS/INTERPRETATION: Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise.

Antarctica is the world's largest fresh-water reservoir, with the potential to raise sea levels by about 60 m. An ice sheet contributes to sea-level rise (SLR) when its rate of ice discharge and/or surface melting exceeds accumulation through snowfall. Constraining the contribution of the ice sheets to present-day SLR is vital both for coastal development and planning, and climate projections. Information on various ice sheet processes is available from several remote sensing data sets, as well as in situ data such as global positioning system data. These data have differing coverage, spatial support, temporal sampling and sensing characteristics, and thus, it is advantageous to combine them all in a single framework for estimation of the SLR contribution and the assessment of processes controlling mass exchange with the ocean. In this paper, we predict the rate of height change due to salient geophysical processes in Antarctica and use these to provide estimates of SLR contribution with associated uncertainties. We employ a multivariate spatio-temporal model, approximated as a Gaussian Markov random field, to take advantage of differing spatio-temporal properties of the processes to separate the causes of the observed change. The process parameters are estimated from geophysical models, while the remaining parameters are estimated using a Markov chain Monte Carlo scheme, designed to operate in a high-performance computing environment across multiple nodes. We validate our methods against a separate data set and compare the results to those from studies that invariably employ numerical model outputs directly. We conclude that it is possible, and insightful, to assess Antarctica's contribution without explicit use of numerical models. Further, the results obtained here can be used to test the geophysical numerical models for which in situ data are hard to obtain. © 2015 The Authors. Environmetrics published by John Wiley & Sons Ltd.

Prediction of the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient-elution conditions.

In this paper, a multilayer artificial neural network is used to model simultaneously the effect of solute structure and eluent concentration profile on the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient elution. The retention data of 24 triazines, including common herbicides and their metabolites, are collected under 13 different elution modes, covering the following experimental domain: starting acetonitrile volume fraction ranging between 40 and 60% and gradient slope ranging between 0 and 1% acetonitrile/min. The gradient parameters together with five selected molecular descriptors, identified by quantitative structure-retention relationship modelling applied to individual separation conditions, are the network inputs. Predictive performance of this model is evaluated on six external triazines and four unseen separation conditions. For comparison, retention of triazines is modelled by both quantitative structure-retention relationships and response surface methodology, which describe separately the effect of molecular structure and gradient parameters on the retention. Although applied to a wider variable domain, the network provides a performance comparable to that of the above "local" models and retention times of triazines are modelled with accuracy generally better than 7%.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study.

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Applying generalized linear models as an explanatory tool of sex steroids, thyroid hormones and their relationships with environmental and physiologic factors in immature East Pacific green sea turtles (Chelonia mydas).

Generalized linear models were fitted to evaluate the relationship between 17ß-estradiol (E2), testosterone (T) and thyroxine (T4) levels in immature East Pacific green sea turtles (Chelonia mydas) and their body condition, size, mass, blood biochemistry parameters, handling time, year, season and site of capture. According to external (tail size) and morphological (<77.3 straight carapace length) characteristics, 95% of the individuals were juveniles. Hormone levels, assessed on sea turtles subjected to a capture stress protocol, were <34.7nmolTL(-1), <532.3pmolE2 L(-1) and <43.8nmolT4L(-1). The statistical model explained biologically plausible metabolic relationships between hormone concentrations and blood biochemistry parameters (e.g. glucose, cholesterol) and the potential effect of environmental variables (season and study site). The variables handling time and year did not contribute significantly to explain hormone levels. Differences in sex steroids between season and study sites found by the models coincided with specific nutritional, physiological and body condition differences related to the specific habitat conditions. The models correctly predicted the median levels of the measured hormones in green sea turtles, which confirms the fitted model's utility. It is suggested that quantitative predictions could be possible when the model is tested with additional data.

Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods.

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.

Directional sensitivity of dynamic cerebral autoregulation during spontaneous fluctuations in arterial blood pressure at rest.

Directional sensitivity, the more efficient response of cerebral autoregulation to increases, compared to decreases, in mean arterial pressure (MAP), has been demonstrated with repeated squat-stand maneuvers (SSM). In 43 healthy subjects (26 male, 23.1 ± 4.2 years old), five min. recordings of cerebral blood velocity (bilateral Doppler ultrasound), MAP (Finometer), end-tidal CO2 (capnograph), and heart rate (ECG) were obtained during sitting (SIT), standing (STA) and SSM. A new analytical procedure, based on autoregressive-moving average models, allowed distinct estimates of the autoregulation index (ARI) by separating the MAP signal into its positive (MAP+D) and negative (MAP-D) derivatives. ARI+D was higher than ARI-D (p < 0.0001), SIT: 5.61 ± 1.58 vs 4.31 ± 2.16; STA: 5.70 ± 1.24 vs 4.63 ± 1.92; SSM: 4.70 ± 1.11 vs 3.31 ± 1.53, but the difference ARI+D-ARI-D was not influenced by the condition. A bootstrap procedure determined the critical number of subjects needed to identify a significant difference between ARI+D and ARI-D, corresponding to 24, 37 and 38 subjects, respectively, for SSM, STA and SIT. Further investigations are needed on the influences of sex, aging and other phenotypical characteristics on the phenomenon of directional sensitivity of dynamic autoregulation.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

Assessment of the Most Impactful Combination of Factors Associated with Nocturia and to Define Nocturnal Polyuria by Multivariate Modelling.

BACKGROUND: Nocturia is common and associated with multiple disease states. Many potential mechanisms have been proposed for nocturia, which also remains challenging to manage. PURPOSE: To use multivariate analysis to determine which combinations of factors can accurately discriminate clinically significant nocturia in patients to facilitate clinical management and treatment decisions. PATIENTS AND METHODS: Data analysis was based on frequency volume charts from three randomized controlled trials. There were 1479 patients included, of which 215 patients had no/mild nocturia and 1264 had clinically significant nocturia with at least two voids per night. Factors studied that may influence nocturia were demographics, sleep duration, functional bladder capacity, 24 h urine volume and literature-suggested definitions of nocturnal polyuria. We used univariate analysis and cross-validated multivariate modelling to assess association between factors and nocturia status, redundancy between factors and whether the combined use of factors could explain patients' nocturia status. RESULTS: The multivariate analyses showed that the most useful definitions of nocturia are 'Nocturia Index' (NI) and 'Nocturnal Urine Production per hour' (NUPh) in combination with functional bladder capacity and sleep duration. Published definitions providing binary nocturnal polyuria outcomes had lower performance than continuous indices. These analyses also showed that NI was not specific to nocturnal polyuria as it also captured nocturia due to low functional bladder capacity. By contrast, NUPh was demonstrated to be specific to nocturnal polyuria. CONCLUSION: NUPh has previously been shown among elderly males to be essential in nocturia and a very valid measure of nocturnal polyuria. However, the current, large and independent dataset now confirms that it can be applied in an adult population with a complaint of nocturia covering both males and females.

Geographical identification of Chianti red wine based on ICP-MS element composition.

Chianti is a precious red wine and enjoys a high reputation for its high quality in the world wine market. Despite this, the production region is small and product needs efficient tools to protect its brands and prevent adulterations. In this sense, ICP-MS combined with chemometrics has demonstrated its usefulness in food authentication. In this study, Chianti/Chianti Classico, authentic wines from vineyard of Toscana region (Italy), together samples from 18 different geographical regions, were analyzed with the objective of differentiate them from other Italian wines. Partial Least Squares-Discriminant Analysis (PLS-DA) identified variables to discriminate wine geographical origin. Rare Earth Elements (REE), major and trace elements all contributed to the discrimination of Chianti samples. General model was not suited to distinguish PDO red wines from samples, with similar chemical fingerprints, collected in some regions. Specific classification models enhanced the capability of discrimination, emphasizing the discriminant role of some elements.

Revealing the structure of the associations between housing system, facilities, management and welfare of commercial laying hens using Additive Bayesian Networks.

After the ban of battery cages in 1988, a welfare control programme for laying hens was developed in Sweden. Its goal was to monitor and ensure that animal welfare was not negatively affected by the new housing systems. The present observational study provides an overview of the current welfare status of commercial layer flocks in Sweden and explores the complexity of welfare aspects by investigating and interpreting the inter-relationships between housing system, production type (i.e. organic or conventional), facilities, management and animal welfare indicators. For this purpose, a machine learning procedure referred to as structure discovery was applied to data collected through the welfare programme during 2010-2014 in 397 flocks housed in 193 different farms. Seventeen variables were fitted to an Additive Bayesian Network model. The optimal model was identified by an exhaustive search of the data iterated across incremental parent limits, accounting for prior knowledge about causality, potential over-dispersion and clustering. The resulting Directed Acyclic Graph shows the inter-relationships among the variables. The animal-based welfare indicators included in this study - flock mortality, feather condition and mite infestation - were indirectly associated with each other. Of these, severe mite infestations were rare (4% of inspected flocks) and mortality was below the acceptable threshold (< 0.6%). Feather condition scored unsatisfactory in 21% of the inspected flocks; however, it seemed to be only associated to the age of the flock, ruling out any direct connection with managerial and housing variables. The environment-based welfare indicators - lighting and air quality - were an issue in 5 and 8% of the flocks, respectively, and showed a complex inter-relationship with several managerial and housing variables leaving room for several options for intervention. Additive Bayesian Network modelling outlined graphically the underlying process that generated the observed data. In contrast to ordinary regression, it aimed at accounting for conditional independency among variables, facilitating causal interpretation.

Age-Related Changes in Segmental Body Composition by Ethnicity and History of Weight Change across the Adult Lifespan.

This study assessed age-related changes in body composition (specifically in trunk fat and appendicular lean masses), with consideration of body mass index (BMI) at age 20 years (BMI reference age, "BMIref"), ethnicity and lifetime weight change history. A cross-sectional dual-energy X-ray absorptiometry-based dataset was extracted from the U.S. National Health and Nutrition Examination Survey (NHANES) 1999-2004. Only European-American and African-American subjects were used (2705 men, 2527 women). For each gender and ethnicity, 6 analytic cases were considered, based on three BMIref categories (normal, overweight and obese, being 22, 27 and 30 kg/m², respectively) and two weight contexts (stable weight or weight gain across the lifespan). A nonparametric model was developed to investigate age-related changes in body composition. Then, parametric modelling was developed for assessing BMIref- and ethnicity-specific effects during aging. In the stable weight, both genders' and ethnicities' trunk fat (TF) increased gradually; body fat (BF) remained stable until 40 years and increased thereafter; trunk lean (TL) remained stable, but appendicular lean (APL) and body lean (BL) declined from 20 years. In the weight gain context, TF and BF increased at a constant rate, while APL, TL and BL increased until 40-50 years, and then declined slightly. Compared with European-American subjects of both genders, African-American subjects had lower TF and BF masses. Ethnic differences in body composition were quantified and found to remain constant across the lifespan.

Quantitative analysis of solid samples using modified specular reflectance accessory.

Diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) is a fast, reliable and cost effective analytical method, requiring minimal or no sample preparation. It is commonly used in the course of qualitative and quantitative analysis of pharmaceutical ingredients and food. We demonstrate that simpler and cheaper specular reflectance (SR) accessory working in a DRIFTS like mode (SR-DL) can be an alternative for DIRFTS attachment. An application of a modified SR accessory for quantitative analysis of solids samples is presented. As a case study the concentration of cinnarizine in commercial tablets has been determined from DRIFTS and SR-DL infrared (IR) and near-infrared (NIR) spectra recorded using DTGS (deuterated triglicine sulphate) detector in the IR and NIR regions and InGaAs (indium-gallium arsenide) detector in the NIR range. Based on these spectra Partial Least Squares (PLS) models were constructed and relative standard errors of prediction (RSEP) were calculated for the calibration, validation and analysed data sets. They amounted to 2.4-2.5%, 2.1-2.7% and 2.0-2.6% for the DRIFTS attachment while 2.1-2.2%, 2.0-2.3% and 1.9-2.6%, respectively, for the modified SR accessory. Obtained error values indicate that modified SR accessory can be effectively used for quantification of solid pharmaceutical samples in the mid- and near-infrared regions.

Impact of training methods and patient characteristics on exercise capacity in patients in cardiovascular rehabilitation.

AIM: We aimed to identify patient characteristics and comorbidities that correlate with the initial exercise capacity of cardiac rehabilitation (CR) patients and to study the significance of patient characteristics, comorbidities and training methods for training achievements and final fitness of CR patients. METHODS: We studied 557 consecutive patients (51.7 ± 6.9 years; 87.9% men) admitted to a three-week in-patient CR. Cardiopulmonary exercise testing (CPX) was performed at discharge. Exercise capacity (watts) at entry, gain in training volume and final physical fitness (assessed by peak O2 utilization (VO2peak) were analysed using analysis of covariance (ANCOVA) models. RESULTS: Mean training intensity was 90.7 ± 9.7% of maximum heart rate (81% continuous/19% interval training, 64% additional strength training). A total of 12.2 ± 2.6 bicycle exercise training sessions were performed. Increase of training volume by an average of more than 100% was achieved (difference end/beginning of CR: 784 ± 623 watts × min). In the multivariate model the gain in training volume was significantly associated with smoking, age and exercise capacity at entry of CR. The physical fitness level achieved at discharge from CR as assessed by VO2peak was mainly dependent on age, but also on various factors related to training, namely exercise capacity at entry, increase of training volume and training method. CONCLUSION: CR patients were trained in line with current guidelines with moderate-to-high intensity and reached a considerable increase of their training volume. The physical fitness level achieved at discharge from CR depended on various factors associated with training, which supports the recommendation that CR should be offered to all cardiac patients.