System Architecture of a European Platform for Health Policy Decision Making: MIDAS.

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

Interferon Lambda in the Pathogenesis of Inflammatory Bowel Diseases.

Interferon λ (IFN-λ) is critical for host viral defense at mucosal surfaces and stimulates immunomodulatory signals, acting on epithelial cells and few other cell types due to restricted IFN-λ receptor expression. Epithelial cells of the intestine play a critical role in the pathogenesis of Inflammatory Bowel Disease (IBD), and the related type II interferons (IFN-γ) have been extensively studied in the context of IBD. However, a role for IFN-λ in IBD onset and progression remains unclear. Recent investigations of IFN-λ in IBD are beginning to uncover complex and sometimes opposing actions, including pro-healing roles in colonic epithelial tissues and potentiation of epithelial cell death in the small intestine. Additionally, IFN-λ has been shown to act through non-epithelial cell types, such as neutrophils, to protect against excessive inflammation. In most cases IFN-λ demonstrates an ability to coordinate the host antiviral response without inducing collateral hyperinflammation, suggesting that IFN-λ signaling pathways could be a therapeutic target in IBD. This mini review discusses existing data on the role of IFN-λ in the pathogenesis of inflammatory bowel disease, current gaps in the research, and therapeutic potential of modulating the IFN-λ-stimulated response.

Discovering and comparing types of general practitioner practices using geolocational features and prescribing behaviours by means of K-means clustering.

Traditionally General Practitioner (GP) practices have been labelled as being in Rural, Urban or Semi-Rural areas with no statistical method of identifying which practices fall into each category. The main aim of this study is to investigate whether location and other characteristics can provide a tautology to identify different types of GP practice and compare the prescribing behaviours associated with the different practice types. To achieve this monthly open source prescription data were analysed by practice considering location, practice size, population density and deprivation rankings. One year's data was subjected to k-means clustering with the results showing that only two different types of GP practice can be classified that are dependent on location characteristics in Northern Ireland. Traditional labels did not describe the two classifications fully and new classifications of Metropolitan and Non-Metropolitan were used. Whilst prescribing patterns were generally similar, it was found that Metropolitan practices generally had higher prescribing rates than Non-Metropolitan practices. Examining prescribing behaviours in accordance with British National Formulary (BNF) categories (known as chapters) showed that Chapter 4 (Central Nervous System) was responsible for most of the difference in prescribing levels. Within Chapter 4 higher prescribing levels were attributable to Analgesic and Antidepressant prescribing. The clusters were finally examined regarding the level of deprivation experienced in the area in which the practice was located. This showed that the Metropolitan cluster, having higher prescription rates, also had a higher proportion of practices located in highly deprived areas making deprivation a contributing factor.

A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.

AIM: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). MATERIALS & METHODS: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. RESULTS: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73-0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67-0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79-0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77-0.78; p < 0.0001. CONCLUSION: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

NewsMeSH: A new classifier designed to annotate health news with MeSH headings.

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.

The 777 Planner: improving the resuscitation call experience at North Shore Hospital.

AIM: To ensure that staff at North Shore Hospital are competent and confident in the roles that they are performing during a 777 call, and to improve leadership and teamwork within the resuscitation team. METHODS: We introduced two 777 Planner meetings each day at 4pm and 10pm at North Shore Hospital, with a 777 Planner template to guide the meeting. The 777 Planner enabled members of the team to meet, introduce themselves and allocate roles in preparation for resuscitative events prior to later calls. We conducted pre- and post-implementation surveys to evaluate the experience of 777 calls prior to and after implementation of the 777 Planner. RESULTS: 68% of respondents felt that the 777 Planner improved their experience of 777 calls, and 78% found it a useful part of the handover. 50% of pre-implementation survey respondents were not clear what other team members roles were in emergency calls, and 53% were not aware who was leading the emergency call. Following the implementation of the intervention, this improved to 74% reporting clarity on roles and 79% stating they knew who was leading the 777 call. CONCLUSION: The 777 Planner ultimately improved members of the resuscitation teams experience of 777 calls at North Shore Hospital, particularly concerning leadership, communication and clarity of roles.

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study.

BACKGROUND: Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE: This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. METHODS: Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers-decision trees, Naïve Bayes, and random forests-to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. RESULTS: In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual's score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant's family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. CONCLUSIONS: The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing.

BACKGROUND: The exploitation of synthetic data in health care is at an early stage. Synthetic data could unlock the potential within health care datasets that are too sensitive for release. Several synthetic data generators have been developed to date; however, studies evaluating their efficacy and generalizability are scarce. OBJECTIVE: This work sets out to understand the difference in performance of supervised machine learning models trained on synthetic data compared with those trained on real data. METHODS: A total of 19 open health datasets were selected for experimental work. Synthetic data were generated using three synthetic data generators that apply classification and regression trees, parametric, and Bayesian network approaches. Real and synthetic data were used (separately) to train five supervised machine learning models: stochastic gradient descent, decision tree, k-nearest neighbors, random forest, and support vector machine. Models were tested only on real data to determine whether a model developed by training on synthetic data can used to accurately classify new, real examples. The impact of statistical disclosure control on model performance was also assessed. RESULTS: A total of 92% of models trained on synthetic data have lower accuracy than those trained on real data. Tree-based models trained on synthetic data have deviations in accuracy from models trained on real data of 0.177 (18%) to 0.193 (19%), while other models have lower deviations of 0.058 (6%) to 0.072 (7%). The winning classifier when trained and tested on real data versus models trained on synthetic data and tested on real data is the same in 26% (5/19) of cases for classification and regression tree and parametric synthetic data and in 21% (4/19) of cases for Bayesian network-generated synthetic data. Tree-based models perform best with real data and are the winning classifier in 95% (18/19) of cases. This is not the case for models trained on synthetic data. When tree-based models are not considered, the winning classifier for real and synthetic data is matched in 74% (14/19), 53% (10/19), and 68% (13/19) of cases for classification and regression tree, parametric, and Bayesian network synthetic data, respectively. Statistical disclosure control methods did not have a notable impact on data utility. CONCLUSIONS: The results of this study are promising with small decreases in accuracy observed in models trained with synthetic data compared with models trained with real data, where both are tested on real data. Such deviations are expected and manageable. Tree-based classifiers have some sensitivity to synthetic data, and the underlying cause requires further investigation. This study highlights the potential of synthetic data and the need for further evaluation of their robustness. Synthetic data must ensure individual privacy and data utility are preserved in order to instill confidence in health care departments when using such data to inform policy decision-making.

Healthcare pathway discovery and probabilistic machine learning.

BACKGROUND AND PURPOSE: Healthcare pathways define the execution sequence of clinical activities as patients move through a treatment process, and they are critical for maintaining quality of care. The aim of this study is to combine healthcare pathway discovery with predictive models of individualized recovery times. The pathway discovery has a particular emphasis on producing pathway models that are easy to interpret for clinicians without a sufficient background in process mining. The predictive model takes the stochastic volatility of pathway performance indicators into account. METHOD: This study utilizes the business process-mining software ProM to design a process mining pipeline for healthcare pathway discovery and enrichment using hospital records. The efficacy of combining learned healthcare pathways with probabilistic machine learning models is demonstrated via a case study that applies the proposed process mining pipeline to discover appendicitis pathways from hospital records. Machine learning methodologies based on probabilistic programming are utilized to explore pathway features that influence patient recovery time. RESULTS: The produced appendicitis pathway models are easy for clinical interpretation and provide an unbiased overview of patient movements through the treatment process. Analysis of the discovered pathway model enables reasons for longer than usual treatment times to be explored and deviations from standard treatment pathways to be identified. A probabilistic regression model that estimates patient recovery time based on the information extracted by the process mining pipeline is developed and has the potential to be very useful for hospital scheduling purposes. CONCLUSION: This study establishes the application of the business process modelling tool ProM for the improvement of healthcare pathway mining methods. The proposed pipeline for healthcare pathway discovery has the potential to support the development of probabilistic machine learning models to further relate healthcare pathways to performance indicators such as patient recovery time.

Case study of the prediction of elective surgery durations in a New Zealand teaching hospital.

We present an elective surgery redesign project involving several New Zealand hospitals that is primarily data-driven. One of the project objectives is to improve the predictions of surgery durations. We address this task by considering two approaches: (a) linear regression modelling, and (b) improvement of the data quality. For (a) we evaluate the accuracy of predictions using two performance measures. These predictions are compared to the surgeons' estimates that may subsequently be adjusted. We demonstrate using the historical surgical lists that the estimates from our prediction techniques improve the scheduling of elective surgeries by minimising the occurrences of list under- and over-runs. For (b), we discuss how the surgical data motivates a review of the surgery procedure classification which takes into account the design of the electronic booking form. The proposed hierarchical classification streamlines the specification of surgery types and therefore retains the potential for improved predictions.

Exploring patient information needs in type 2 diabetes: A cross sectional study of questions.

This study set out to analyze questions about type 2 diabetes mellitus (T2DM) from patients and the public. The aim was to better understand people's information needs by starting with what they do not know, discovered through their own questions, rather than starting with what we know about T2DM and subsequently finding ways to communicate that information to people affected by or at risk of the disease. One hundred and sixty-four questions were collected from 120 patients attending outpatient diabetes clinics and 300 questions from 100 members of the public through the Amazon Mechanical Turk crowdsourcing platform. Twenty-three general and diabetes-specific topics and five phases of disease progression were identified; these were used to manually categorize the questions. Analyses were performed to determine which topics, if any, were significant predictors of a question's being asked by a patient or the public, and similarly for questions from a woman or a man. Further analysis identified the individual topics that were assigned significantly more often to the crowdsourced or clinic questions. These were Causes (CI: [-0.07, -0.03], p < .001), Risk Factors ([-0.08, -0.03], p < .001), Prevention ([-0.06, -0.02], p < .001), Diagnosis ([-0.05, -0.02], p < .001), and Distribution of a Disease in a Population ([-0.05,-0.01], p = .0016) for the crowdsourced questions and Treatment ([0.03, 0.01], p = .0019), Disease Complications ([0.02, 0.07], p < .001), and Psychosocial ([0.05, 0.1], p < .001) for the clinic questions. No highly significant gender-specific topics emerged in our study, but questions about Weight were more likely to come from women and Psychosocial questions from men. There were significantly more crowdsourced questions about the time Prior to any Diagnosis ([(-0.11, -0.04], p = .0013) and significantly more clinic questions about Health Maintenance and Prevention after diagnosis ([0.07. 0.17], p < .001). A descriptive analysis pointed to the value provided by the specificity of questions, their potential to disclose emotions behind questions, and the as-yet unrecognized information needs they can reveal. Large-scale collection of questions from patients across the spectrum of T2DM progression and from the public-a significant percentage of whom are likely to be as yet undiagnosed-is expected to yield further valuable insights.

Vestibular-Evoked Responses Indicate a Functional Role for Intrinsic Foot Muscles During Standing Balance.

Maintaining standing balance involves multisensory processing and integration to produce dynamic motor responses. Electrical vestibular stimulation (EVS) delivered over the mastoid processes can be used to explore the vestibular control of balance. The purpose of this study was to determine whether intrinsic foot muscles exhibit vestibular-evoked balance responses and to characterize the traits associated with these responses. Electromyography (EMG) of the abductor hallucis (AH), abductor digiti minimi (ADM) and medial gastrocnemius (MG) and anterior-posterior (AP) forces were sampled while quietly standing participants were subjected to a random continuous EVS signal (peak-to-peak amplitude = ±3 mA). The relationship between EVS input and motor output was characterized in both the frequency (coherence) and time (cumulant density) domains. When head orientation was rotated in yaw from left to right, the biphasic cumulant density function was inverted for all muscle (EVS-EMG) and whole-body (EVS-AP forces) balance responses. When vision was occluded, the EVS-EMG and EVS-AP forces coherence function amplitude increased at low frequencies (<2 Hz) and was accompanied by a heightened medium-latency peak amplitude for all muscles as well as the whole-body balance response (AP forces) compared to when static visual cues were present. The enhanced coherence amplitudes at lower frequencies may highlight a mechanism for the increase in postural sway from vision to occluded vision. The current findings indicate that the vestibular control of standing balance can be represented by the intrinsic foot muscles and implicate a postural role for these muscles in modulating quiet standing.

Soliciting and Responding to Patients' Questions about Diabetes Through Online Sources.

When patients cannot get answers from health professionals or retain the information given, increasingly they search online for answers, with limited success. Researchers from the United States, Ireland, and the United Kingdom explored this problem for patients with type 2 diabetes mellitus (T2DM). In 2014, patients attending an outpatient clinic (UK) were asked to submit questions about diabetes. Ten questions judged representative of different types of patient concerns were selected by the researchers and submitted to search engines within trusted and vetted websites in the United States, Ireland, and the United Kingdom. Two researchers independently assessed if answers could be found in the three top-ranked documents returned at each website. The 2014 search was repeated in June, 2016, examining the two top-ranked documents returned. One hundred and sixty-four questions were collected from 120 patients during 12 outpatient clinics. Most patients had T2DM (95%). Most questions were about diabetes (N = 155) with the remainder related to clinic operation (N = 9). Of the questions on diabetes, 152 were about T2DM. The 2014 assessment found no adequate answers to the questions in 90 documents (10 questions, 3 websites, 3 top documents). In the 2016 assessment, 1 document out of 60 (10 questions, 3 websites, 2 top documents) provided an adequate answer relating to 1 of the 10 questions. Available online sources of information do not provide answers to questions from patients with diabetes. Our results highlight the urgent need to develop novel ways of providing answers to patient questions about T2DM.

Time-dependent neuromuscular parameters in the plantar flexors support greater fatigability of old compared with younger males.

Older adults are more fatigable than young during dynamic tasks, especially those that involve moderate to fast unconstrained velocity shortening contractions. Rate of torque development (RTD), rate of velocity development (RVD) and rate of neuromuscular activation are time-dependent neuromuscular parameters which have not been explored in relation to age-related differences in fatigability. The purpose was to determine whether these time-dependent measures affect the greater age-related fatigability in peak power during moderately fast and maximal effort shortening plantar flexions. Neuromuscular properties were recorded from 10 old (~ 78 years) and 10 young (~ 24 years) men during 50 maximal-effort unconstrained velocity shortening plantar flexions against a resistance equivalent to 20% maximal voluntary isometric contraction torque. At task termination, peak power, and angular velocity, and torque at peak power were decreased by 30, 18, and 16%, respectively, for the young (p < 0.05), and 46, 28, 30% for the old (p < 0.05) compared to pre-fatigue values with the old exhibiting greater reductions across all measures (p<0.05). Voluntary RVD and RTD decreased, respectively, by 24 and 26% in the young and by 47 and 40% in the old at task termination, with greater decrements in the old (p < 0.05). Rate of neuromuscular activation of the soleus decreased over time for both age groups (~ 47%; p < 0.05), but for the medial gastrocnemius (MG) only the old experienced significant decrements (46%) by task termination. All parameters were correlated strongly with the fatigue-related reduction in peak power (r = 0.81-0.94, p < 0.05), except for MG and soleus rates of neuromuscular activation (r = 0.25-0.30, p > 0.10). Fatigue-related declines in voluntary RTD and RVD were both moderately correlated with MG rate of neuromuscular activation (r = 0.51-0.52, p < 0.05), but exhibited a trend with soleus (r = 0.39-0.41, p = 0.07-0.09). Thus, time-dependent factors, RVD and RTD, are likely important indicators of intrinsic muscle properties leading to the greater age-related decline in peak power when performing a repetitive dynamic fatigue task, which may be due to greater fatigue-related central impairments for the older men than young.

Web-Based Remote Monitoring Systems for Self-Managing Type 2 Diabetes: A Systematic Review.

This systematic review aims to evaluate evidence for viability and impact of Web-based telemonitoring for managing type 2 diabetes mellitus. A review protocol included searching Medline, EMBASE, CINAHL, AMED, the Cochrane Library, and PubMed using the following terms: telemonitoring, type 2 diabetes mellitus, self-management, and web-based Internet solutions. The technology used, trial design, quality of life measures, and the glycated hemoglobin (HbA1c) levels were extracted. This review identified 426 publications; of these, 19 met preset inclusion criteria. Ten quasi-experimental research designs were found, of which seven were pre-posttest studies, two were cohort studies, and one was an interrupted time-series study; in addition, there were nine randomized controlled trials. Web-based remote monitoring from home to hospital is a viable approach for healthcare delivery and enhances patients' quality of life. Six of these studies were conducted in South Korea, five in the United States, three in the United Kingdom, two in Taiwan, and one each in Spain, Poland, and India. The duration of the studies varied from 4 weeks to 18 months, and the participants were all adults. Fifteen studies showed positive improvement in HbA1c levels. One study showed high acceptance of the technology among participants. It remains challenging to identify clear evidence of effectiveness in the rapidly changing area of remote monitoring in diabetes care. Both the technology and its implementations are complex. The optimal design of a telemedicine system is still uncertain, and the value of the real-time blood glucose transmissions is still controversial.

Validating continuous digital light processing (cDLP) additive manufacturing accuracy and tissue engineering utility of a dye-initiator package.

This study tested the accuracy of tissue engineering scaffold rendering via the continuous digital light processing (cDLP) light-based additive manufacturing technology. High accuracy (i.e., <50 µm) allows the designed performance of features relevant to three scale spaces: cell-scaffold, scaffold-tissue, and tissue-organ interactions. The biodegradable polymer poly (propylene fumarate) was used to render highly accurate scaffolds through the use of a dye-initiator package, TiO2 and bis (2,4,6-trimethylbenzoyl)phenylphosphine oxide. This dye-initiator package facilitates high accuracy in the Z dimension. Linear, round, and right-angle features were measured to gauge accuracy. Most features showed accuracies between 5.4-15% of the design. However, one feature, an 800 µm diameter circular pore, exhibited a 35.7% average reduction of patency. Light scattered in the x, y directions by the dye may have reduced this feature's accuracy. Our new fine-grained understanding of accuracy could be used to make further improvements by including corrections in the scaffold design software. Successful cell attachment occurred with both canine and human mesenchymal stem cells (MSCs). Highly accurate cDLP scaffold rendering is critical to the design of scaffolds that both guide bone regeneration and that fully resorb. Scaffold resorption must occur for regenerated bone to be remodeled and, thereby, achieve optimal strength.

Participatory research to design a novel telehealth system to support the night-time needs of people with dementia: NOCTURNAL.

Strategies to support people living with dementia are broad in scope, proposing both pharmacological and non-pharmacological interventions as part of the care pathway. Assistive technologies form part of this offering as both stand-alone devices to support particular tasks and the more complex offering of the "smart home" to underpin ambient assisted living. This paper presents a technology-based system, which expands on the smart home architecture, orientated to support people with daily living. The system, NOCTURNAL, was developed by working directly with people who had dementia, and their carers using qualitative research methods. The research focused primarily on the nighttime needs of people living with dementia in real home settings. Eight people with dementia had the final prototype system installed for a three month evaluation at home. Disturbed sleep patterns, night-time wandering were a focus of this research not only in terms of detection by commercially available technology but also exploring if automated music, light and visual personalized photographs would be soothing to participants during the hours of darkness. The NOCTURNAL platform and associated services was informed by strong user engagement of people with dementia and the service providers who care for them. NOCTURNAL emerged as a holistic service offering a personalised therapeutic aspect with interactive capabilities.

The influence of stereolithographic scaffold architecture and composition on osteogenic signal expression with rat bone marrow stromal cells.

Scaffold design parameters, especially physical construction factors such as mechanical stiffness of substrate materials, pore size of 3D porous scaffolds, and channel geometry, are known to influence the osteogenic signal expression and subsequent differentiation of a transplanted cell population. In this study of photocrosslinked poly(propylene fumarate) (PPF) and diethyl fumarate (DEF) scaffolds, the effect of DEF incorporation ratio and pore size on the osteogenic signal expression of rat bone marrow stromal cells (BMSCs) was investigated. Results demonstrated that DEF concentrations and pore sizes that led to increased scaffold mechanical stiffness also upregulated osteogenic signal expression, including bone morphogenic protein-2 (BMP-2), fibroblast growth factors-2 (FGF-2), transforming growth factor-ß1 (TGF-ß1), vascular endothelial growth factor (VEGF), and Runx2 transcriptional factor. Similar scaffold fabrication parameters supported rapid BMSC osteoblastic differentiation, as demonstrated by increased alkaline phosphatase (ALP) and osteocalcin expression. When scaffolds with random architecture, fabricated by porogen leaching, were compared to those with controlled architecture, fabricated by stereolithography (SLA), results showed that SLA scaffolds with the highly permeable and porous channels also have significantly higher expression of FGF-2, TGF-ß1, and VEGF. Subsequent ALP expression and osteopontin secretion were also significantly increased in SLA scaffolds. Based upon these results, we conclude that scaffold properties provided by additive manufacturing techniques such as SLA fabrication, particularly increased mechanical stiffness and high permeability, may stimulate dramatic BMSC responses that promote rapid bone tissue regeneration.

Intraperitoneal aerosolization of bupivacaine is a safe and effective method in controlling postoperative pain in laparoscopic Roux-en-Y gastric bypass.

INTRODUCTION: Obesity is a worldwide problem and has grown in severity in the last few decades making bariatric surgery and, in particular, laparoscopic banding and Roux-en-Y gastric bypass efficacious and cost-effective procedures. The laparoscopic approach has been shown to offer significant healthcare benefits, of particular interests are reports of decreased postoperative pain resulting in a shorter hospital stay and an earlier return to normal activity. However, many patients still experience significant pain, including shoulder tip pain, that require strong analgesia including opiates during their early recovery period. The aims of this study were to establish the safe use of the aerosolization technique in bariatric surgery and to investigate the possible benefits in reducing postoperative pain. METHODS: In this study, fifty patients undergoing laparoscopic gastric bypass were recruited and divided into two groups; control (n = 25) and therapeutic (n = 25). The control group received intraperitoneal aerosolization of 10 mL of 0.9% normal saline while the therapeutic group received 10 mL of 0.5% bupivacaine. All the patients had standard preoperative, intraoperative, and postoperative care. Pain scores were carried out by the nursing staff in recovery and 6 h, 12 h and 24 h postoperatively using a standard 0-10 pain scoring scale. In addition, opiate consumption via patient-controlled analgesia (PCA) was recorded. RESULTS: Aerosolized bupivacaine reduced postoperative pain in comparison to normal saline (p < 0.05). However, PCA usage showed no statistically significant change from the control group. CONCLUSION: The aims of this study were achieved and we were able to establish the safe use of the aerosolization technique in bariatric surgery and its benefits in reducing postoperative pain.

The next generation of mobile medication management solutions.

In this paper, we describe the development of an internet-based system and a novel mobile home based device for the management of medication. We extend these concepts through the descriptions of an enhanced service with the use of mobile phone technology and home based digital TV services.