Incorporation of spiritual care as a component of healthcare and medical education: comparison of viewpoints of healthcare providers from a rural community hospital in Uganda with those of medical students from United Kingdrom

This study addresses cultural differences regarding views on the place for spirituality within healthcare training and delivery. A questionnaire was devised using a 5-point ordinal scale, with additional free text comments assessed by thematic analysis, to compare the views of Ugandan healthcare staff and students with those of (1) visiting international colleagues at the same hospital; (2) medical faculty and students in United Kingdom. Ugandan healthcare personnel were more favourably disposed towards addressing spiritual issues, their incorporation within compulsory healthcare training, and were more willing to contribute themselves to delivery than their European counterparts. Those from a nursing background also attached a greater importance to spiritual health and provision of spiritual care than their medical colleagues. Although those from a medical background recognised that a patient’s religiosity and spirituality can affect their response to their diagnosis and prognosis, they were more reticent to become directly involved in provision of such care, preferring to delegate this to others with greater expertise. Thus, differences in background, culture and healthcare organisation are important, and indicate that the wide range of views expressed in the current literature, the majority of which has originated in North America, are not necessarily transferable between locations; assessment of these issues locally may be the best way to plan such training and incorporation of spiritual care into clinical practice.

Hydrogel-Forming Microneedle Arrays Allow Detection of Drugs and Glucose In Vivo: Potential for Use in Diagnosis and Therapeutic Drug Monitoring

We describe, for the first time the use of hydrogel-forming microneedle (MN) arrays for minimally-invasive extraction and quantification of drug substances and glucose from skin in vitro and in vivo. MN prepared from aqueous blends of hydrolysed poly(methyl-vinylether-co-maleic anhydride) (11.1% w/w) and poly(ethyleneglycol) 10,000 daltons (5.6% w/w) and crosslinked by esterification swelled upon skin insertion by uptake of fluid. Post-removal, theophylline and caffeine were extracted from MN and determined using HPLC, with glucose quantified using a proprietary kit. In vitro studies using excised neonatal porcine skin bathed on the underside by physiologically-relevant analyte concentrations showed rapid (5 min) analyte uptake. For example, mean concentrations of 0.16 μg/mL and 0.85 μg/mL, respectively, were detected for the lowest (5 μg/mL) and highest (35 μg/mL) Franz cell concentrations of theophylline after 5 min insertion. A mean concentration of 0.10 μg/mL was obtained by extraction of MN inserted for 5 min into skin bathed with 5 μg/mL caffeine, while the mean concentration obtained by extraction of MN inserted into skin bathed with 15 μg/mL caffeine was 0.33 μg/mL. The mean detected glucose concentration after 5 min insertion into skin bathed with 4 mmol/L was 19.46 nmol/L. The highest theophylline concentration detected following extraction from a hydrogel-forming MN inserted for 1 h into the skin of a rat dosed orally with 10 mg/kg was of 0.363 μg/mL, whilst a maximum concentration of 0.063 μg/mL was detected following extraction from a MN inserted for 1 h into the skin of a rat dosed with 5 mg/kg theophylline. In human volunteers, the highest mean concentration of caffeine detected using MN was 91.31 μg/mL over the period from 1 to 2 h post-consumption of 100 mg Proplus® tablets. The highest mean blood glucose level was 7.89 nmol/L detected 1 h following ingestion of 75 g of glucose, while the highest mean glucose concentration extracted from MN was 4.29 nmol/L, detected after 3 hours skin insertion in human volunteers. Whilst not directly correlated, concentrations extracted from MN were clearly indicative of trends in blood in both rats and human volunteers. This work strongly illustrates the potential of hydrogel-forming MN in minimally-invasive patient monitoring and diagnosis. Further studies are now ongoing to reduce clinical insertion times and develop mathematical algorithms enabling determination of blood levels directly from MN measurements.

Reliable survival analysis based on the Dirichlet Process

We present a robust Dirichlet process for estimating survival functions from samples with right-censored data. It adopts a prior near-ignorance approach to avoid almost any assumption about the distribution of the population lifetimes, as well as the need of eliciting an infinite dimensional parameter (in case of lack of prior information), as it happens with the usual Dirichlet process prior. We show how such model can be used to derive robust inferences from right-censored lifetime data. Robustness is due to the identification of the decisions that are prior-dependent, and can be interpreted as an analysis of sensitivity with respect to the hypothetical inclusion of fictitious new samples in the data. In particular, we derive a nonparametric estimator of the survival probability and a hypothesis test about the probability that the lifetime of an individual from one population is shorter than the lifetime of an individual from another. We evaluate these ideas on simulated data and on the Australian AIDS survival dataset. The methods are publicly available through an easy-to-use R package.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.

Learning Bayesian Networks with Thousands of Variables

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Approximating Credal Network Inferences by Linear Programming

An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities

Inference in Polytrees with Sets of Probabilities

Inferences in directed acyclic graphs associated with probability intervals and sets of probabilities are NP-hard, even for polytrees. We propose: 1) an improvement on Tessem’s A/R algorithm for inferences on polytrees associated with probability intervals; 2) a new algorithm for approximate inferences based on local search; 3) branch-and-bound algorithms that combine the previous techniques. The first two algorithms produce complementary approximate solutions, while branch-and-bound procedures can generate either exact or approximate solutions. We report improvements on existing techniques for inference with probability sets and intervals, in some cases reducing computational effort by several orders of magnitude.

Unloading or not unloading? Sheep welfare implication of rest stop at control post after a 29h transport

In Europe, maximum journey time for transported sheep is set at 29. h (EC Regulation 1/2005), after which animals must be unloaded, fed and watered in control posts stopping for 24. h, as all other species, before continuing their journey. The industry considers these resting times too general, not taking into account the peculiar differences between species or age classes. Also, loading and unloading have been reported to be detrimental for the animals. Therefore, the industry pushes to reduce the times at control post and avoid unloading the animals from the truck. Since there is little information concerning the effect of resting in a stationary truck after long journeys, the present study aims to evaluate the effect of an 8. h rest stop on the truck for transported ewes compared to being unloaded for resting in a control post for the same amount of time, considering physiological and behavioural measures. Two groups of ewes were transported for 29. h, after which one was unloaded and housed in a pen (P) at the control post while the other was left inside the truck (T). After 8. h stop, a further 6. h travel was headed to the farm of origin. A third group (C) stayed at the farm as control. During the stop, standing, resting, moving and eating behaviour of all groups was recorded. Blood parameters, salivary and faecal cortisol were assessed at different stages. The behaviour of P animals during the resting period was more similar to C than to T ones, where feeding and lying behaviours were restricted by the limited space allowance on the truck. After returning to the farm of origin, both T and P animals showed different parameters' levels as compared to C. P ewes showed a mean loss weight of 2. kg not recorded in group T and showed higher signs of muscular damage compared to C group. It was concluded that, with so short resting times as 8. h, there is no clear advantages in terms of animal welfare for avoiding the unloading and loading of the animals in the control post after long journeys.

Hydrogel-forming microneedle arrays made from light-responsive materials for on-demand transdermal drug delivery

We describe, for the first time, stimuli-responsive hydrogel-forming microneedle (MN) arrays that enable delivery of a clinically-relevant model drug (ibuprofen) upon application of light. MN arrays were prepared using a polymer prepared from 2-hydroxyethyl methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) by micromolding. The obtained MN arrays showed good mechanical properties. The system was loaded with up to 5% (w/w) ibuprofen included in a light-responsive 3,5-dimethoxybenzoin conjugate. Raman spectroscopy confirmed the presence of the conjugate inside the polymeric MN matrix. In vitro, this system was able to deliver up to three doses of 50 mg of ibuprofen upon application of an optical trigger over a prolonged period of time (up to 160 hours). This makes the system appealing as a controlled release device for prolonged periods of time. We believe that this technology has potential for use in ?on-demand? delivery of a wide range of drugs in a variety of applications relevant to enhanced patient care.