Hospitalisation of Type 2 diabetes mellitus patients with and without major depressive disorder in a private managed healthcare organisation

Background: The relationship between Type 2 diabetes mellitus (T2DM) and associated co-morbidities, particularly major depressive disorder (MDD), is poorly acknowledged in chronic disease management practices in South Africa. Managed healthcare costs and hospitalisation rates may be influenced by the discrete management of co-morbid conditions. Therefore, the relationship between T2DM and MDD in terms of co-morbidity incidence and hospitalisation resource utilisation was investigated.Method: This retrospective descriptive study analysed the data of 902 adult patients with T2DM from the health system database of a private managed healthcare organisation for 2014.Results: The mean age was 57 ± 15 years and 85% of the identified T2DM patients had at least one recorded co-morbidity. Among this population 17% presented with MDD. A higher percentage of T2DM patients with MDD were admitted to hospital (42%, p = 0.004) compared with those without MDD (30%). The number of overnight admissions was higher among the T2DM with MDD (76%, p = 0.016) compared with T2DM without MDD (66%). The T2DM with MDD group (85%, p = 0.018) had greater non-diabetes related hospital events compared with the T2DM without MDD group (73%). The T2DM patients without MDD were more likely to be hospitalised for diabetes-related events (27%, p = 0.018) at significantly higher admission cost (p = 0.001).Conclusion: Patients with T2DM and MDD present with more co-morbid conditions and had a higher number of hospitalisations than their non-MDD counterparts. However, the hospitalisation costs were significantly higher for diabetes-related admissions in the non-MDD group due to a higher number of macrovascular events. Healthcare organisations need to focus on an integrated approach in the management of chronic conditions with emphasis on active surveillance of T2DM patients, where MDD is identified and treated to lessen the risk of macrovascular complications

Identification of diverse database subsets using property-based and fragment-based molecular descriptions

This paper reports a comparison of calculated molecular properties and of 2D fragment bit-strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity-based selection and k-means cluster-based selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected subsets suggest: that the MaxMin subsets are noticeably superior to the k-means subsets; that the property-based descriptors are marginally superior to the fragment-based descriptors; and that both approaches are noticeably superior to random selection

Clinical inter-professional education activities: Students’ perceptions of their experiences

Background and purposeStudents from different health disciplines should learn together during certain periods of their education to acquire skills necessary for solving the health problems. The Faculty of Health Sciences of University of the Witwatersrand created inter-professional education (IPE) activities for students to assess clinical IPE groups’ perceptions of IPE experiences and to identify lessons learnt during IPE sessions.MethodsThis was a qualitative study with review of the students’ post IPE feedback forms. The students were granted ‘protected time’ of three full days over a period of two months to participate in IPE activities.ResultsStudents felt that knowledge about health team members was gained and that IPE groups should have more than one person from each field with the same level of clinical exposure. The students indicated the need to have regular IPE activities and if possible to incorporate this into clinical practice for them to experience it in daily clinical practice. ConclusionParticipating in the IPE activity made students gain appreciation and respect for other health professionals’ roles and scope. When student groups are big, patient observations can be done as this does not compromise IPE learning outcomes. Group composition should be kept in mind to cater for the learning needs of all students. If it is not possible to meet the needs of all professions, smaller groups with professions applicable to case can be created

Rapid Quantification of Molecular Diversity for Selective Database Acquisition

There is an increasing need to expand the structural diversity of the molecules investigated in lead-discovery programs. One way in which this can be achieved is by acquiring external datasets that will enhance an existing database. This paper describes a rapid procedure for the selection of external datasets using a measure of structural diversity that is calculated from sums of pairwise intermolecular structural similarities

MILEPOST GCC: machine learning based research compiler

International audienceTuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

International audienceTuning compiler optimizations for rapidly evolving hardwaremakes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describeMilepostGCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor.We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilatio

Workplace wellness using online learning tools in a healthcare setting

The aim was to develop and evaluate an online learning tool for use with UK healthcare employees, healthcare educators and healthcare students, to increase knowledge of workplace wellness as an important public health issue. A ‘Workplace Wellness’ e-learning tool was developed and peer-reviewed by 14 topic experts. This focused on six key areas relating to workplace wellness: work-related stress, musculoskeletal disorders, diet and nutrition, physical activity, smoking and alcohol consumption. Each key area provided current evidence-based information on causes and consequences, access to UK government reports and national statistics, and guidance on actions that could be taken to improve health within a workplace setting. 188 users (93.1% female, age 18–60) completed online knowledge questionnaires before (n = 188) and after (n = 88) exposure to the online learning tool. Baseline knowledge of workplace wellness was poor (n = 188; mean accuracy 47.6%, s.d. 11.94). Knowledge significantly improved from baseline to post-intervention (mean accuracy = 77.5%, s.d. 13.71) (t(75) = −14.801, p < 0.0005) with knowledge increases evident for all included topics areas. Usability evaluation showed that participants perceived the tool to be useful (96.4%), engaging (73.8%) and would recommend it to others (86.9%). Healthcare professionals, healthcare educators and pre-registered healthcare students held positive attitudes towards online learning, indicating scope for development of further online packages relating to other important health parameters

Regulation of Coronary Blood Flow

The heart is uniquely responsible for providing its own blood supply through the coronary circulation. Regulation of coronary blood flow is quite complex and, after over 100 years of dedicated research, is understood to be dictated through multiple mechanisms that include extravascular compressive forces (tissue pressure), coronary perfusion pressure, myogenic, local metabolic, endothelial as well as neural and hormonal influences. While each of these determinants can have profound influence over myocardial perfusion, largely through effects on end-effector ion channels, these mechanisms collectively modulate coronary vascular resistance and act to ensure that the myocardial requirements for oxygen and substrates are adequately provided by the coronary circulation. The purpose of this series of Comprehensive Physiology is to highlight current knowledge regarding the physiologic regulation of coronary blood flow, with emphasis on functional anatomy and the interplay between the physical and biological determinants of myocardial oxygen delivery. © 2017 American Physiological Society. Compr Physiol 7:321-382, 2017

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta