Persistent inflammation and endothelial dysfunction in patients with treated acromegaly.

OBJECTIVE: Acromegaly is characterized by an excess of growth hormone (GH) and insulin like growth-factor 1 (IGF1). Cardiovascular disease (CVD) risk factors are common in acromegaly and often persist after treatment. Both acute and long-lasting pro-inflammatory effects have been attributed to IGF1. Therefore, we hypothesized that inflammation persists in treated acromegaly and may contribute to CVD risk. METHODS: In this cross-sectional study, we assessed cardiovascular structure and function, and inflammatory parameters in treated acromegaly patients. Immune cell populations and inflammatory markers were assessed in peripheral blood from 71 treated acromegaly patients (with controlled or uncontrolled disease) and 41 matched controls. Whole blood (WB) was stimulated with Toll-like receptor ligands. In a subgroup of 21 controls and 33 patients with controlled disease, vascular ultrasound measurements were performed. RESULTS: Leukocyte counts were lower in patients with controlled acromegaly compared to patients with uncontrolled acromegaly and controls. Circulating IL-18 concentrations were lower in patients; concentrations of other inflammatory mediators were comparable with controls. In stimulated WB, cytokine production was skewed towards inflammation in patients, most pronounced in those with uncontrolled disease. Vascular measurements in controlled patients showed endothelial dysfunction as indicated by a lower flow-mediated dilatation/nitroglycerine-mediated dilatation ratio. Surprisingly, pulse wave analysis and pulse wave velocity, both markers of endothelial dysfunction, were lower in patients, whereas intima-media thickness did not differ. CONCLUSIONS: Despite treatment, acromegaly patients display persistent inflammatory changes and endothelial dysfunction, which may contribute to CVD risk and development of CVD

Non-invasive mechanical ventilation for diagnostic bronchoscopy using a new face mask: an observational feasibility study

Contains fulltext : 89809.pdf (publisher's version ) (Closed access)PURPOSE: Bronchoscopy is an indispensable tool for invasive pulmonary evaluation with high diagnostic yield and low incidence of major complications. However, hypoxemia increases the risk of complications, in particular after bronchoalveolar lavage. Non-invasive positive pressure ventilation may prevent hypoxemia associated with bronchoalveolar lavage. The purpose of this study is to present a modified total face mask to aid bronchoscopy during non-invasive positive pressure ventilation. METHODS: A commercially available full face mask was modified to allow introduction of the bronchoscope without interfering with the ventilator circuit. Bronchoscopy with bronchoalveolar lavage was performed in 12 hypoxemic non-ICU patients during non-invasive positive pressure ventilation in the ICU. Results : Patients had severely impaired oxygen uptake as indicated by PaO(2)/FiO(2) ratio 192 +/- 23 mmHg before bronchoscopy. Oxygenation improved after initiation of non-invasive positive pressure ventilation. In all patients the procedure could be completed without subsequent complications, although in one patient SpO(2) decreased until 86% during bronchoscopy. A microbiological diagnosis could be established in 8 of 12 patients with suspected for infection. CONCLUSIONS: Our modified face mask for non-invasive positive pressure ventilation is a valuable tool to aid diagnostic bronchoscopy in hypoxemic patients.1 januari 201

Dealing with Missing Data and Uncertainty in the Context of Data Mining

Missing data is an issue in many real-world datasets yet robust methods for dealing with missing data appropriately still need development. In this paper we conduct an investigation of how some methods for handling missing data perform when the uncertainty increases. Using benchmark datasets from the UCI Machine Learning repository we generate datasets for our experimentation with increasing amounts of data Missing Completely At Random (MCAR) both at the attribute level and at the record level. We then apply four classification algorithms: C4.5, Random Forest, Naïve Bayes and Support Vector Machines (SVMs). We measure the performance of each classifiers on the basis of complete case analysis, simple imputation and then we study the performance of the algorithms that can handle missing data. We find that complete case analysis has a detrimental effect because it renders many datasets infeasible when missing data increases, particularly for high dimensional data. We find that increasing missing data does have a negative effect on the performance of all the algorithms tested but the different algorithms tested either using preprocessing in the form of simple imputation or handling the missing data do not show a significant difference in performance

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes

Treatment guidelines recommend the UK Prospective Diabetes Study (UKPDS) risk engine for predicting cardiovascular risk in patients with type 2 diabetes, although validation studies showed moderate performance. The methods used in these validation studies were diverse, however, and sometimes insufficient. Hence, we assessed the discrimination and calibration of the UKPDS risk engine to predict 4, 5, 6 and 8 year cardiovascular risk in patients with type 2 diabetes. The cohort included 1,622 patients with type 2 diabetes. During a mean follow-up of 8 years, patients were followed for incidence of CHD and cardiovascular disease (CVD). Discrimination and calibration were assessed for 4, 5, 6 and 8 year risk. Discrimination was examined using the c-statistic and calibration by visually inspecting calibration plots and calculating the Hosmer-Lemeshow χ(2) statistic. The UKPDS risk engine showed moderate to poor discrimination for both CHD and CVD (c-statistic of 0.66 for both 5 year CHD and CVD risks), and an overestimation of the risk (224% and 112%). The calibration of the UKPDS risk engine was slightly better for patients with type 2 diabetes who had been diagnosed with diabetes more than 10 years ago compared with patients diagnosed more recently, particularly for 4 and 5 year predicted CVD and CHD risks. Discrimination for these periods was still moderate to poor. We observed that the UKPDS risk engine overestimates CHD and CVD risk. The discriminative ability of this model is moderate, irrespective of various subgroup analyses. To enhance the prediction of CVD in patients with type 2 diabetes, this model should be update

Revealing natural relationships among arbuscular mycorrhizal fungi: culture line BEG47 represents Diversispora epigaea, not Glomus versiforme

Background: Understanding the mechanisms underlying biological phenomena, such as evolutionarily conservative trait inheritance, is predicated on knowledge of the natural relationships among organisms. However, despite their enormous ecological significance, many of the ubiquitous soil inhabiting and plant symbiotic arbuscular mycorrhizal fungi (AMF, phylum Glomeromycota) are incorrectly classified. Methodology/Principal Findings: Here, we focused on a frequently used model AMF registered as culture BEG47. This fungus is a descendent of the ex-type culture-lineage of Glomus epigaeum, which in 1983 was synonymised with Glomus versiforme. It has since then been used as ‘G. versiforme BEG47’. We show by morphological comparisons, based on type material, collected 1860–61, of G. versiforme and on type material and living ex-type cultures of G. epigaeum, that these two AMF species cannot be conspecific, and by molecular phylogenetics that BEG47 is a member of the genus Diversispora. Conclusions: This study highlights that experimental works published during the last >25 years on an AMF named ‘G. versiforme’ or ‘BEG47’ refer to D. epigaea, a species that is actually evolutionarily separated by hundreds of millions of years from all members of the genera in the Glomerales and thus from most other commonly used AMF ‘laboratory strains’. Detailed redescriptions substantiate the renaming of G. epigaeum (BEG47) as D. epigaea, positioning it systematically in the order Diversisporales, thus enabling an evolutionary understanding of genetical, physiological, and ecological traits, relative to those of other AMF. Diversispora epigaea is widely cultured as a laboratory strain of AMF, whereas G. versiforme appears not to have been cultured nor found in the field since its original description

Prediction of persistent shoulder pain in general practice: Comparing clinical consensus from a Delphi procedure with a statistical scoring system

<p>Abstract</p> <p>Background</p> <p>In prognostic research, prediction rules are generally statistically derived. However the composition and performance of these statistical models may strongly depend on the characteristics of the derivation sample. The purpose of this study was to establish consensus among clinicians and experts on key predictors for persistent shoulder pain three months after initial consultation in primary care and assess the predictive performance of a model based on clinical expertise compared to a statistically derived model.</p> <p>Methods</p> <p>A Delphi poll involving 3 rounds of data collection was used to reach consensus among health care professionals involved in the assessment and management of shoulder pain.</p> <p>Results</p> <p>Predictors selected by the expert panel were: symptom duration, pain catastrophizing, symptom history, fear-avoidance beliefs, coexisting neck pain, severity of shoulder disability, multisite pain, age, shoulder pain intensity and illness perceptions. When tested in a sample of 587 primary care patients consulting with shoulder pain the predictive performance of the two prognostic models based on clinical expertise were lower compared to that of a statistically derived model (Area Under the Curve, AUC, expert-based dichotomous predictors 0.656, expert-based continuous predictors 0.679 vs. 0.702 statistical model).</p> <p>Conclusions</p> <p>The three models were different in terms of composition, but all confirmed the prognostic importance of symptom duration, baseline level of shoulder disability and multisite pain. External validation in other populations of shoulder pain patients should confirm whether statistically derived models indeed perform better compared to models based on clinical expertise.</p

Growth Response of Drought-Stressed Pinus sylvestris Seedlings to Single- and Multi-Species Inoculation with Ectomycorrhizal Fungi

Many trees species form symbiotic associations with ectomycorrhizal (ECM) fungi, which improve nutrient and water acquisition of their host. Until now it is unclear whether the species richness of ECM fungi is beneficial for tree seedling performance, be it during moist conditions or drought. We performed a pot experiment using Pinus sylvestris seedlings inoculated with four selected ECM fungi (Cenococcum geophilum, Paxillus involutus, Rhizopogon roseolus and Suillus granulatus) to investigate (i) whether these four ECM fungi, in monoculture or in species mixtures, affect growth of P. sylvestris seedlings, and (ii) whether this effect can be attributed to species number per se or to species identity. Two different watering regimes (moist vs. dry) were applied to examine the context-dependency of the results. Additionally, we assessed the activity of eight extracellular enzymes in the root tips. Shoot growth was enhanced in the presence of S. granulatus, but not by any other ECM fungal species. The positive effect of S. granulatus on shoot growth was more pronounced under moist (threefold increase) than under dry conditions (twofold increase), indicating that the investigated ECM fungi did not provide additional support during drought stress. The activity of secreted extracellular enzymes was higher in S. granulatus than in any other species. In conclusion, our findings suggest that ECM fungal species composition may affect seedling performance in terms of aboveground biomass