Prescribing for nausea in palliative care : A cross-sectional national survey of Australian palliative medicine doctors

Background: Nausea can be a debilitating symptom for patients with a life-limiting illness. While addressing reversible components, nonpharmacological strategies and antiemetics are the main therapeutic option. The choice of medication, dose, and route of administration remain highly variable. Objective: The aim of this study was to codify the current clinical approaches and quantify any variation found nationally. Methods: A cross-sectional study utilizing a survey of palliative medicine clinicians examined prescribing preferences for nausea using a clinical vignette. Respondent characteristics, the use of nonpharmacological interventions, first- and second-line antiemetic choices, commencing and maximal dose, and time to review were collected. Results: Responding clinicians were predominantly working in palliative medicine across a range of settings with a 49% response rate (105/213). The main nonpharmacological recommendation was “small, frequent snacks.” Metoclopramide was the predominant first-line agent (69%), followed by haloperidol (26%), while second-line haloperidol was the predominant agent (47%), with wide variation in other nominated agents. Respondents favoring metoclopramide as first-line tended to use haloperidol second-line (65%), but not vice versa. Maximal doses for an individual antiemetic varied up to tenfold. Conclusion: For nausea, a commonly encountered symptom in palliative care, clinicians' favored metoclopramide and haloperidol; however, after these choices, there was large variation in antiemetic selection. While most clinicians recommended modifying meal size and frequency, use of other nonpharmacological therapies was limited.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Information and communication technologies transform the practice of medicine

Information and Communication Technologies are dramatically transforming Allopathic medicine. Technological developments including Tele-medicine, Electronic health records, Standards to ensure computer systems inter-operate, Data mining, Simulation, Decision Support and easy access to medical information each contribute to empowering patients in new ways and change the practice of medicine. To date, informatics has had little impact on Ayurvedic medicine. This tutorial provides an introduction to key informatics initiatives in Allopothic medicine using real examples and suggests how applications can be applied to Ayurvedic medicine.

The association between the measurement of adherence to anti-diabetes medicine and the HbA1c

BACKGROUND: Adherence to medicines is important in subjects with diabetes, as nonadherence is associated with an increased risk of morbidity and mortality. However, it is not clear whether there is an association between adherence to medicines and glycaemic control, as not all studies have shown this. One of the reasons for this discrepancy may be that, although there is a standard measure of glycaemic control i.e. HbA1c, there is no standard measure of adherence to medicines. Adherence to medicines can be measured either qualitatively by Morisky or non-Morisky methods or quantitatively using the medicines possession ratio (MPR). AIMS OF THE REVIEW: The aims of this literature review are (1) to determine whether there is an association between adherence to anti-diabetes medicines and glycaemic control, and (2) whether any such association is dependent on how adherence is measured. Methods A literature search of Medline, CINAHL and the Internet (Google) was undertaken with search terms; 'diabetes' with 'adherence' (or compliance, concordance, persistence, continuation) with 'HbA1c' (or glycaemic control). RESULTS: Twenty-three studies were included; 10 qualitative and 12 quantitative studies, and one study using both methods. For the qualitative methods measurements of adherence to anti-diabetes medicines (non-Morisky and Morisky), eight out of ten studies show an association with HbA1c. Nine of ten studies using the quantitative MPR, and two studies using MPR for insulin only, have also shown an association between adherence to anti-diabetes medicines and HbA1c. However, the one study that used both Morisky and MPR did not show an association. Three of the four studies that did not show a relationship, did not use a range of HbA1c values in their regression analysis. The other study that did not show a relationship was specifically in a low income population. CONCLUSIONS: Most studies show an association between adherence to anti-diabetes medicines and HbA1c levels, and this seems to be independent of method used to measure adherence. However, to show an association it is necessary to have a range of HbA1c values. Also, the association is not always apparent in low income populations.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

Job satisfaction of preventive medicine workers in northern Vietnam : a multi-method approach

In the health care industry, Job Satisfaction (JS) is linked with work performance, psychological well-being and employee turnover. Although research into JS among health professionals has a long history worldwide, there has been very little analysis in Vietnam. No study has addressed JS of preventive medicine workers in Vietnam, and there is no reliable and valid instrument in Vietnamese language and context for evaluation of JS in this group. This project was conducted to fill these gaps. The findings contribute evidence regarding factors that influence JS in this sector of the health industry that should be applied to personnel management policies and practices in Vietnam.

Complexity and high-end computing in biology and medicine

Biomedical systems involve a large number of entities and intricate interactions between these. Their direct analysis is, therefore, difficult, and it is often necessary to rely on computational models. These models require significant resources and parallel computing solutions. These approaches are particularly suited, given parallel aspects in the nature of biomedical systems. Model hybridisation also permits the integration and simultaneous study of multiple aspects and scales of these systems, thus providing an efficient platform for multidisciplinary research.

Investigating the potential of Shikonin, a Chinese herbal medicine, as a novel scar remediation therapy

This project investigates for the first time the biological mechanisms underlying the anecdotal use of Shikonin, an active component extracted from the Chinese herbal medicine "Zi Cao", as a treatment for hypertrophic scars. Compelling molecular and cellular evidence was generated supporting the therapeutic value of Shikonin as a scar treatment, suggesting that further development of this agent is warranted.

Constructing a traditional Chinese medicine data warehouse application

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to enhance the effectiveness and durability in TCM is paramount. To showcase the role of data warehousing in the improvement of TCM, this paper presents a practical model for data warehousing with detailed explanation, which is based on the structured electronic records, for TCM clinical researches and medical knowledge discovery.

In vivo non-invasive high resolution MR-based method for the determination of the elastic modulus of arterial vessels

The mechanical properties of arterial walls have long been recognized to play an essential role in the development and progression of cardiovascular disease (CVD). Early detection of variations in the elastic modulus of arteries would help in monitoring patients at high cardiovascular risk stratifying them according to risk. An in vivo, non-invasive, high resolution MR-phase-contrast based method for the estimation of the time-dependent elastic modulus of healthy arteries was developed, validated in vitro by means of a thin walled silicon rubber tube integrated into an existing MR-compatible flow simulator and used on healthy volunteers. A comparison of the elastic modulus of the silicon tube measured from the MRI-based technique with direct measurements confirmed the method's capability. The repeatability of the method was assessed. Viscoelastic and inertial effects characterizing the dynamic response of arteries in vivo emerged from the comparison of the pressure waveform and the area variation curve over a period. For all the volunteers who took part in the study the elastic modulus was found to be in the range 50-250 kPa, to increase during the rising part of the cycle, and to decrease with decreasing pressure during the downstroke of systole and subsequent diastole.