The moderating effects of demographic and individual characteristics on nurses' acceptance of information systems: A canadian study

ObjectivesThe purpose of this study was to educate on the moderating effects of demographic (i.e., educational level and age) and individual characteristics (i.e., years of nursing experience and computer knowledge) on nurses’ acceptance of information systems (IS). The technology acceptance model (TAM) with its constituent variables such as perceived usefulness (PUSS) and perceived ease of use (PEOU) was the theoretical framework used for this study.MethodsA cross-sectional study was conducted in Nova Scotia, Canada. Usable data was collected from 197 registered nurses (RNs). Relevant hypotheses were formulated and the partial least squares (PLS) technique was used for data analysis.ResultsThe results of the hypothesized relationships showed that education and computer knowledge have positive moderating effects on the influences of PEOU and PUSS on nurses’ attitudes toward IS (ATTI). The factors of nurses’ years of nursing experience and age did not yield meaningful results. ATTI impacted behavioral intentions to use IS, which positively impacted nurses’ use of IS. The nurses sampled in the study have positive IS use behaviors.ConclusionsThis study demonstrates that relevant demographic factors and individual characteristics, if incorporated into frameworks used for investigating nurses’ acceptance of IS, could permit the emergence of useful insights for practitioners and researchers. Specifically, this study showed that nurses with higher educational attainments and more basic computer knowledge readily accept implemented IS at work. Hospital administrators benefit from insights such as the one presented in this study.     

A dynamic system for delivering controlled bromine and chlorine vapor exposures to weanling swine skin

Context: Assessing the hazards of accidental exposure to toxic industrial chemical (TIC) vapors and evaluating therapeutic compounds or treatment regimens require the development of appropriate animal models.

Objective: The objective of this project was to develop an exposure system for delivering controlled vapor concentrations of TICs to the skin of anesthetized weanling pigs. Injury levels targeted for study were superficial dermal (SD) and deep dermal (DD) skin lesions as defined histopathologically.

Materials and methods: The exposure system was capable of simultaneously delivering chlorine or bromine vapor to four, 3-cm diameter exposure cups placed over skin between the axillary and inguinal areas of the ventral abdomen. Vapor concentrations were generated by mixing saturated bromine or chlorine vapor with either dried dilution air or nitrogen.

Results: Bromine exposure concentrations ranged from 6.5?×?10^?4 to 1.03?g/L, and exposure durations ranged from 1 to 45?min. A 7-min skin exposure to bromine vapors at 0.59?g/L was sufficient to produce SD injuries, while a 17-min exposure produced a DD injury. Chlorine exposure concentrations ranged from 1.0 to 2.9?g/L (saturated vapor concentration) for exposures ranging from 3 to 90?min. Saturated chlorine vapor challenges for up to 30?min did not induce significant dermal injuries, whereas saturated chlorine vapor with wetted material on the skin surface for 30–60?min induced SD injuries. DD chlorine injuries could not be induced with this system.

Conclusion: The vapor exposure system described in this study provides a means for safely regulating, quantifying and delivering TIC vapors to the skin of weanling swine as a model to evaluate therapeutic treatments.     

Artificial neural network for modeling the elastic modulus of electrospun polycaprolactone/gelatin scaffolds

Scaffolds for tissue engineering (TE) require the consideration of multiple aspects, including polymeric composition and the structure and mechanical properties of the scaffolds, in order to mimic the native extracellular matrix of the tissue. Electrospun fibers are frequently utilized in TE due to their tunable physical, chemical, and mechanical properties and porosity. The mechanical properties of electrospun scaffolds made from specific polymers are highly dependent on the processing parameters, which can therefore be tuned for particular applications. Fiber diameter and orientation along with polymeric composition are the major factors that determine the elastic modulus of electrospun nano- and microfibers. Here we have developed a neural network model to investigate the simultaneous effects of composition, fiber diameter and fiber orientation of electrospun polycaprolactone/gelatin mats on the elastic modulus of the scaffolds under ambient and simulated physiological conditions. The model generated might assist bioengineers to fabricate electrospun scaffolds with defined fiber diameters, orientations and constituents, thereby replicating the mechanical properties of the native target tissue.     

A Current Review of Cypermethrin-Induced Neurotoxicity and Nigrostriatal Dopaminergic Neurodegeneration

Cypermethrin, a class II pyrethroid pesticide, is used to control insects in the household and agricultural fields. Despite beneficial roles, its uncontrolled and repetitive applications lead to unintended effects in non-target organisms. Cypermethrin crosses the blood-brain barrier and induces neurotoxicity and motor deficits. Cypermethrin prolongs the opening of sodium channel, a major site of its action, leading to hyper-excitation of the central nervous system. In addition to sodium channel, cypermethrin modulates chloride, voltage-gated calcium and potassium channels, alters the activity of glutamate and acetylcholine receptors and adenosine triphosphatases and induces DNA damage and oxidative stress in the neuronal cells. Cypermethrin also modulates the level of neurotransmitters, including gamma-aminobutyric acid and dopamine. It is one of the most commonly used pesticides in neurotoxicology research not only because of its variable responses depending upon the doses, time and routes of exposure and strain, age, gender and species of animals used across multiple studies but also owing to its ability to induce the nigrostriatal dopaminergic neurodegeneration. This article describes the effect of acute, chronic, developmental and adulthood exposures to cypermethrin in experimental animals. The article sheds light on cypermethrin-induced changes in the central nervous system, including its contribution in the onset of specific features, which are associated with the nigrostriatal dopaminergic neurodegeneration. Resemblances and dissimilarities of cypermethrin-induced nigrostriatal dopaminergic neurodegeneration with sporadic and chemicals-induced disease models along with its advantages and pitfalls are also discussed.     

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end‐point target, indicating a vision‐based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision‐based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after‐effects for rhythmic reversal (out‐and‐back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision‐based predictor and suggests that adaptation occurred in the forward vision‐based predictor, and concurrently in the state‐based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain–machine interfaces.     

Achieving Best Practice: A Review of Evidence Linked to Occupation-Focused Practice Models

ABSTRACT

The aim of this paper is to identify and synthesize evidence concerning the most widely used contemporary occupation-focused models including Ecology of human performance; Model of human occupation; Occupational adaptation; and Person–environment–occupational performance. The paper characterizes the amount and type of evidence. Evidence found includes illustrations of how the models can be applied to understand and address a particular problem or population, basic studies that test or expand theoretical concepts, psychometric studies, studies that document therapy outcomes, and studies that examine clinical reasoning and/or practice based on the models. The four models differed widely in the amount and type of evidence available.     

Preclinical models of multiple sclerosis in nonhuman primates

Biotechnology has enabled the development of specifically acting therapies for immune-mediated inflammatory disorders (IMIDs) based on biological molecules. The high species specificity precludes safety and effectivity testing in lower species (mice and rats), thus creating a need for valid experimental models in nonhuman primates (NHPs). Here, we review the creation of relevant NHP model(s) for multiple sclerosis (MS), an IMID of the human CNS. We will also discuss how the model(s) can help in the translation of a scientific principle developed in lower species into a therapy for MS.     

Modeling cumulative incidence function for competing risks data

A frequent occurrence in medical research is that a patient is subject to different causes of failure, where each cause is known as a competing risk. The cumulative incidence curve is a proper summary curve, showing the cumulative failure rates over time due to a particular cause. A common question in medical research is to assess the covariate effects on a cumulative incidence function. The standard approach is to construct regression models for all cause-specific hazard rate functions and then model a covariate-adjusted cumulative incidence curve as a function of all cause-specific hazards for a given set of covariates. New methods have been proposed in recent years, emphasizing direct assessment of covariate effects on cumulative incidence function. Fine and Gray proposed modeling the effects of covariates on a subdistribution hazard function. A different approach is to directly model a covariate-adjusted cumulative incidence function, including a pseudovalue approach by Andersen and Klein and a direct binomial regression by Scheike, Zhang and Gerds. In this paper, we review the standard and new regression methods for modeling a cumulative incidence function, and give the sources of computer packages/programs that implement these regression models. A real bone marrow transplant data set is analyzed to illustrate various regression methods.