A self-regulation perspective on avoidance and persistence behaviour in chronic pain: new theories, new challenges?

Objective: Behavioral factors such as avoidance and persistence have received massive theoretical and empirical attention in the attempts to explain chronic pain and disability. The determinants of these pain behaviors remain, however, poorly understood. We propose a self-regulation perspective to increase our understanding of pain-related avoidance and persistence. Methods: A narrative review. Results: We identified several theoretical views that may help explaining avoidance and persistence behavior, and organized these views around 4 concepts central in self-regulation theories: (1) identity, (2) affective-motivational orientation, (3) goal cognitions, and (4) coping. The review shows that each of these self-regulation perspectives allows for a broadened view in which pain behaviors are not simply considered passive consequences of fear, but proactive strategies to regulate the self when challenged by pain. Discussion: Several implications and challenges arising from this review are discussed. In particular, a self-regulation perspective does not consider avoidance and persistence behavior to be intrinsically adaptive or maladaptive, but argues that their effects on disability and well-being rather depend on the goals underlying these behaviors. Such view would require a shift in how avoidance and persistence behavior are assessed and approached in clinical interventions

SchNet - a deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning in general and deep learning in particular is ideally suited for representing quantum-mechanical interactions, enabling to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for \emph{molecules and materials} where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study of the quantum-mechanical properties of C$_{20}$-fullerene that would have been infeasible with regular ab initio molecular dynamics

A cost-effectiveness analysis of provider and community interventions to improve the treatment of uncomplicated malaria in Nigeria: study protocol for a randomized controlled trial.

BACKGROUND: There is mounting evidence of poor adherence by health service personnel to clinical guidelines for malaria following a symptomatic diagnosis. In response to this, the World Health Organization (WHO) recommends that in all settings clinical suspicion of malaria should be confirmed by parasitological diagnosis using microscopy or Rapid Diagnostic Test (RDT). The Government of Nigeria plans to introduce RDTs in public health facilities over the coming year. In this context, we will evaluate the effectiveness and cost-effectiveness of two interventions designed to support the roll-out of RDTs and improve the rational use of ACTs. It is feared that without supporting interventions, non-adherence will remain a serious impediment to implementing malaria treatment guidelines. METHODS/DESIGN: A three-arm stratified cluster randomized trial is used to compare the effectiveness and cost-effectiveness of: (1) provider malaria training intervention versus expected standard practice in malaria diagnosis and treatment; (2) provider malaria training intervention plus school-based intervention versus expected standard practice; and (3) the combined provider plus school-based intervention versus provider intervention alone. RDTs will be introduced in all arms of the trial. The primary outcome is the proportion of patients attending facilities that report a fever or suspected malaria and receive treatment according to malaria guidelines. This will be measured by surveying patients (or caregivers) as they exit primary health centers, pharmacies, and patent medicine dealers. Cost-effectiveness will be presented in terms of the primary outcome and a range of secondary outcomes, including changes in provider and community knowledge. Costs will be estimated from both a societal and provider perspective using standard economic evaluation methodologies. TRIAL REGISTRATION: Clinicaltrials.gov NCT01350752

Characterization of pre-analytical sample handling effects on a panel of Alzheimer's disease–related blood-based biomarkers: Results from the Standardization of Alzheimer's Blood Biomarkers (SABB) working group

Introduction: Pre-analytical sample handling might affect the results of Alzheimer's disease blood-based biomarkers. We empirically tested variations of common blood collection and handling procedures. Methods: We created sample sets that address the effect of blood collection tube type, and of ethylene diamine tetraacetic acid plasma delayed centrifugation, centrifugation temperature, aliquot volume, delayed storage, and freeze–thawing. We measured amyloid beta (Aβ)42 and 40 peptides with six assays, and Aβ oligomerization-tendency (OAβ), amyloid precursor protein (APP)699-711, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), total tau (t-tau), and phosphorylated tau181. Results: Collection tube type resulted in different values of all assessed markers. Delayed plasma centrifugation and storage affected Aβ and t-tau; t-tau was additionally affected by centrifugation temperature. The other markers were resistant to handling variations. Discussion: We constructed a standardized operating procedure for plasma handling, to facilitate introduction of blood-based biomarkers into the research and clinical settings

A review of rapid serial visual presentation-based brain-computer interfaces

International audienceRapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs) measured non-invasively with electroencephalography (EEG) can be associated with infrequent targets amongst a stream of images. Human-machine symbiosis may be augmented by enabling human interaction with a computer, without overt movement, and/or enable optimization of image/information sorting processes involving humans. Features of the human visual system impact on the success of the RSVP paradigm, but pre-attentive processing supports the identification of target information post presentation of the information by assessing the co-occurrence or time-locked EEG potentials. This paper presents a comprehensive review and evaluation of the limited but significant literature on research in RSVP-based brain-computer interfaces (BCIs). Applications that use RSVP-based BCIs are categorized based on display mode and protocol design, whilst a range of factors influencing ERP evocation and detection are analyzed. Guidelines for using the RSVP-based BCI paradigms are recommended, with a view to further standardizing methods and enhancing the inter-relatability of experimental design to support future research and the use of RSVP-based BCIs in practice

A Cluster Randomised Trial Introducing Rapid Diagnostic Tests into Registered Drug Shops in Uganda: Impact on Appropriate Treatment of Malaria

Background: Inappropriate treatment of malaria is widely reported particularly in areas where there is poor access to health facilities and self-treatment of fevers with anti-malarial drugs bought in shops is the most common form of care-seeking. The main objective of the study was to examine the impact of introducing rapid diagnostic tests for malaria (mRDTs) in registered drug shops in Uganda, with the aim to increase appropriate treatment of malaria with artemisinin-based combination therapy (ACT) in patients seeking treatment for fever in drug shops. Methods: A cluster-randomized trial of introducing mRDTs in registered drug shops was implemented in 20 geographical clusters of drug shops in Mukono district, central Uganda. Ten clusters were randomly allocated to the intervention (diagnostic confirmation of malaria by mRDT followed by ACT) and ten clusters to the control arm (presumptive treatment of fevers with ACT). Treatment decisions by providers were validated by microscopy on a reference blood slide collected at the time of consultation. The primary outcome was the proportion of febrile patients receiving appropriate treatment with ACT defined as: malaria patients with microscopically-confirmed presence of parasites in a peripheral blood smear receiving ACT or rectal artesunate, and patients with no malaria parasites not given ACT. Findings: A total of 15,517 eligible patients (8672 intervention and 6845 control) received treatment for fever between January-December 2011. The proportion of febrile patients who received appropriate ACT treatment was 72·9% versus 33·7% in the control arm; a difference of 36·1% (95% CI: 21·3 – 50·9), p<0·001. The majority of patients with fever in the intervention arm accepted to purchase an mRDT (97·8%), of whom 58·5% tested mRDT-positive. Drug shop vendors adhered to the mRDT results, reducing over-treatment of malaria by 72·6% (95% CI: 46·7– 98·4), p<0·001) compared to drug shop vendors using presumptive diagnosis (control arm). Conclusion: Diagnostic testing with mRDTs compared to presumptive treatment of fevers implemented in registered drug shops substantially improved appropriate treatment of malaria with ACT

The costs of introducing artemisinin-based combination therapy: evidence from district-wide implementation in rural Tanzania

BACKGROUND\ud \ud The development of antimalarial drug resistance has led to increasing calls for the introduction of artemisinin-based combination therapy (ACT). However, little evidence is available on the full costs associated with changing national malaria treatment policy. This paper presents findings on the actual drug and non-drug costs associated with deploying ACT in one district in Tanzania, and uses these data to estimate the nationwide costs of implementation in a setting where identification of malaria cases is primarily dependant on clinical diagnosis.\ud \ud METHODS\ud \ud Detailed data were collected over a three year period on the financial costs of providing ACT in Rufiji District as part of a large scale effectiveness evaluation, including costs of drugs, distribution, training, treatment guidelines and other information, education and communication (IEC) materials and publicity. The district-level costs were scaled up to estimate the costs of nationwide implementation, using four scenarios to extrapolate variable costs.\ud \ud RESULTS\ud \ud The total district costs of implementing ACT over the three year period were slightly over one million USD, with drug purchases accounting for 72.8% of this total. The composite (best) estimate of nationwide costs for the first three years of ACT implementation was 48.3 million USD (1.29 USD per capita), which varied between 21 and 67.1 million USD in the sensitivity analysis (2003 USD). In all estimates drug costs constituted the majority of total costs. However, non-drug costs such as IEC materials, drug distribution, communication, and health worker training were also substantial, accounting for 31.4% of overall ACT implementation costs in the best estimate scenario. Annual implementation costs are equivalent to 9.5% of Tanzania's recurrent health sector budget, and 28.7% of annual expenditure on medical supplies, implying a 6-fold increase in the national budget for malaria treatment.\ud \ud CONCLUSION\ud \ud The costs of implementing ACT are substantial. Although drug purchases constituted a majority of total costs, non-drug costs were also considerable. It is clear that substantial external resources will be required to facilitate and sustain effective ACT delivery across Tanzania and other malaria-endemic countries

Self-discrepancies in work-related upper extremity pain: relation to emotions and flexible goal adjustment.

Recurrent pain not only has an impact on disability, but on the long term it may become a threat to one's sense of self. This paper presents a cross-sectional study of patients with work-related upper extremity pain and focuses on: (1) the role of self-discrepancies in this group, (2) the associations between self-discrepancies, pain, emotions and (3) the interaction between self-discrepancies and flexible-goal adjustment. Eighty-nine participants completed standardized self-report measures of pain intensity, pain duration, anxiety, depression and flexible-goal adjustment. A Selves Questionnaire was used to generate self-discrepancies. A series of hierarchical regression analyses showed relationships between actual-ought other, actual-ought self, actual-feared self-discrepancies and depression as well as a significant association between actual-ought other self-discrepancy and anxiety. Furthermore, significant interactions were found between actual-ought other self-discrepancies and flexibility, indicating that less flexible participants with large self-discrepancies score higher on depression. This study showed that self-discrepancies are related to negative emotions and that flexible-goal adjustment served as a moderator in this relationship. The view of self in pain and flexible-goal adjustment should be considered as important variables in the process of chronic pain. (C) 2009 European Federation of International Association for the Study of Pain Chapters. Published by Elsevier Ltd. All rights reserved

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum- chemical principles. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work