Drug eluting electrospun scaffolds for tissue regeneration

The desired healing response to electrospun scaffolds in tissue engineering is often limited by poor ingrowth due to insufficient porosity, thrombogenicity, lack of vascularisation and/or excessive inflammation. This study aimed at increasing structural porosity and incorporating/delivering anti-thrombotic/angiogenic (heparin) and anti-inflammatory (dexamethasone) agents. Porosity enhancement techniques were explored using two different approaches i) electrospinning of biostable polymer (Pellethane® , Pel) with concomitant electrospraying of soluble microparticles, which were subsequently removed to increase scaffold interconnectivity and ii) electrospinning of biodegradable polymer (DegraPol® , DP) at low collecting temperatures. Dexamethasone (Dex) was incorporated by simple admixture, while heparin (Hep) required chemical modification (heparin tributylammonium, HepTBA) to achieve solubility. Release rates were determined in vitro, followed by thrombogenicity (thromboelastography) and cytotoxicity (cell viability) assessments of modified/unmodified heparin prior to incorporation and after elution. Finally, in vivo responses were evaluated in a subcutaneous model (24 rats) for up to 12 weeks. Porosity was enhanced (P0.1). At 12 weeks of implantation, high-porosity Pel scaffolds allowed for full tissue ingrowth (>98%) while conventional scaffolds were limited (0.3). High-porosity scaffolds produced by combined electrospinning/spraying have the potential to enhance healing. Dex or HepTBA can be incorporated and eluted from degradable electrospun scaffolds, and localised delivery of HepTBA improves implant vascularisation. This study may contribute towards tissue engineered vascular graft development where anti-thrombogenicity and increased vascularisation are desired

Competitive exception learning using fuzzy frequency distributions

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy frequency distribution describing the general, average system's output behavior. Next, we look for a fuzzy partitioning of the input space in such away that the corresponding fuzzy output frequency distributions `deviate at most' from the average one as found in the first step. In this way, the most important `exceptional regions' in the input-output relation are determined. Using the joint input-output fuzzy frequency distributions, the complete input-output function as extracted from the data, can be expressed mathematically. In addition, the exceptions encountered can be collected and described as a set of fuzzy if-then-else-rules. Besides presenting a theoretical description of the new exception learning algorithm, we report on the outcomes of certain practical simulations

Financial Markets Analysis by Probabilistic Fuzzy Modelling

For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models

Relative Distress and Return Distribution Characteristics of Japanese Stocks, a Fuzzy-Probabilistic Approach

In this article, we demonstrate that a direct relation exists between the context of Japanese firms indicating relative distress and conditional return distribution properties. We map cross-sectional vectors with company characteristics on vectors with return feature vectors, using a fuzzy identification technique called Competitive Exception Learning Algorithm (CELA)1. In this study we use company characteristics that follow from capital structure theory and we relate the recognized conditional return properties to this theory. Using the rules identified by this mapping procedure this approach enables us to make conditional predictions regarding the probability of a stock's or a group of stocks' return series for different return distribution classes (actually return indices). Using these findings, one may construct conditional indices that may serve as benchmarks. These would be particularly useful for tracking and portfolio management

Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning

Recently, a Competitive Exception Learning Algorithm (CELA) was introduced [1, 2]. This algorithm establishes an optimal mapping from a (continuous) M-dimensional input sample space to an N-dime

Practices of knowledge intensive process management: quantitative insights

Purpose – In contemporary businesses, the importance of knowledge workers and the knowledge intensive business processes (KIBPs) is ever increasing, yet they seem very hard to control and manage. The purpose of this paper is to grasp the specific characteristics of KIBPs and how they differ from non‐knowledge intensive business processes (non‐KIBP), also to question how organizations are using business process management (BPM) to manage and improve KIBPs. The differences in maturity and effectiveness of both types of processes are also evaluated. Design/methodology/approach – Data for this research were collected through an online survey. The survey was designed based on a previously conducted exploratory study with semi‐structured interviews as well as the literature. The target group was BPM practitioners and the final sample included 98 respondents. Due to non‐normality, the analyses were conducted with non‐parametric tests. The research questions were analysed using Mann‐Whitney U test and Spearman's correlations. Findings – It was found that KIBP and non‐KIBP have clearly different characteristics, such as the level of complexity, repeatability and creativity required. Also it was found that these processes are not managed or improved differently than non‐KIBPs, and suggest that organizations need to take these differences into consideration while managing and improving these processes. Furthermore, the results suggest that applying methodologies that aim to provide operational improvements may not necessarily produce the best results for KIBPs. Originality/value – The paper answers a call for further development of the body of knowledge on knowledge‐intensive business processes, a rapidly emerging field of interest for BPM practitioners, where a clear gap in literature exists

The interrelation between clinical presentation and neurophysiology of posthypoxic myoclonus

ObjectivePosthypoxic myoclonus (PHM) in the first few days after resuscitation can be divided clinically into generalized and focal (uni- and multifocal) subtypes. The former is associated with a subcortical origin and poor prognosis in patients with postanoxic encephalopathy (PAE), and the latter with a cortical origin and better prognosis. However, use of PHM as prognosticator in PAE is hampered by the modest objectivity in its clinical assessment. Therefore, we aimed to obtain the anatomical origin of PHM with use of neurophysiological investigations, and relate these to its clinical presentation. MethodsThis study included 20 patients (56 18 y/o, 68% M, 2 survived, 1 excluded) with EEG-EMG-video recording. Three neurologists classified PHM into generalized or focal PHM. Anatomical origin (cortical/subcortical) was assessed with basic and advanced neurophysiology (Jerk-Locked Back Averaging, coherence analysis). ResultsClinically assessed origin of PHM did not match the result obtained with neurophysiology: cortical PHM was more likely present in generalized than in focal PHM. In addition, some cases demonstrated co-occurrence of cortical and subcortical myoclonus. Patients that recovered from PAE had cortical myoclonus (1 generalized, 1 focal). InterpretationHypoxic damage to variable cortical and subcortical areas in the brain may lead to mixed and varying clinical manifestations of myoclonus that differ of those patients with myoclonus generally encountered in the outpatient clinic. The current clinical classification of PHM is not adequately refined to play a pivotal role in guiding treatment decisions to withdraw care. Our neurophysiological characterization of PHM provides specific parameters to be used in designing future comprehensive studies addressing the potential role of PHM as prognosticator in PAE

Slow recruitment in the HIMALAIA study:lessons for future clinical trials in patients with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on feasibility data

Background : Our randomized clinical trial on induced hypertension in patients with delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) was halted prematurely due to unexpected slow recruitment rates. This raised new questions regarding recruitment feasibility. As our trial can therefore be seen as a feasibility trial, we assessed the reasons for the slow recruitment, aiming to facilitate the design of future randomized trials in aSAH patients with DCI or other critically ill patient categories. Methods : Efficiency of recruitment and factors influencing recruitment were evaluated, based on the patient flow in the two centers that admitted most patients during the study period. We collected numbers of patients who were screened for eligibility, provided informed consent, and developed DCI and who eventually were randomized. Results : Of the 862 aSAH patients admitted in the two centers during the course of the trial, 479 (56%) were eligible for trial participation of whom 404 (84%) were asked for informed consent. Of these, 188 (47%) provided informed consent, of whom 50 (27%) developed DCI. Of these 50 patients, 12 (24%) could not be randomized due to a logistic problem or a contraindication for induced hypertension emerging at the time of randomization, and four (8%) were missed for randomization. Eventually, 34 patients were randomized and received intervention or control treatment. Conclusions : Enrolling patients in a randomized trial on a treatment strategy for DCI proved unfeasible: only 1 out of 25 admitted and 1 out of 14 eligible patients could eventually be randomized. These rates, caused by a large proportion of ineligible patients, a small proportion of patients providing informed consent, and a large proportion of patients with contraindications for treatment, can be used to make sample size calculations for future randomized trials in DCI or otherwise critically ill patients. Facilitating informed consent through improved provision of information on risks, possible benefits, and study procedures may result in improved enrolment

The Diagnostic Value of Near-Infrared Spectroscopy to Predict Delayed Cerebral Ischemia and Unfavorable Outcome After Subarachnoid Hemorrhage

OBJECTIVE: Near-infrared spectroscopy (NIRS) is a non-invasive tool to monitor cerebral regional oxygen saturation. Impairment of microvascular circulation with subsequent cerebral hypoxia during delayed cerebral ischemia (DCI) is associated with poor functional outcome after subarachnoid hemorrhage (SAH). Therefore, NIRS could be useful to predict the risk for DCI and functional outcome. However, only limited data is available on NIRS regional cerebral tissue oxygen saturation (rSO2) distribution in SAH. The aim of this study was to compare the distribution of NIRS rSO2 values in non-traumatic SAH patients with the occurrence of DCI and functional outcome at two months. In addition, the predictive value of NIRS rSO2 was compared with the previously validated SAFIRE grade (derived from Size of the aneurysm, Age, FIsher grade, world federation of neurosurgical societies after REsuscitation).METHODS: In this study, the rSO2 distribution of patient with and without DCI after SAH are compared. The optimal cutoff points to predict DCI and outcome are assessed, and its predictive value is compared to the SAFIRE grade.RESULTS: Out of 41 patients, 12 developed DCI, and 9 had unfavorable outcome at 60 days. Prediction of DCI with NIRS had an area under the curve (AUC) of 0.77 (95%CI 0.62-0.92; p=0.0028) with an optimal cutoff point of 65% (sensitivity 1.00; specificity 0.45). Prediction of favorable outcome with NIRS had an AUC of 0.86 (95%CI 0.74-0.98; p=0.0003) with an optimal cutoff point of 63% (sensitivity 1.00; specificity 0.63). Regression analysis showed that NIRS rSO2 score is complementary to the SAFIRE grade.CONCLUSION: NIRS rSO2 monitoring in patients with SAH may improve prediction of DCI and clinical outcome after SAH.</p