Medically unexplained pain complaints are associated with underlying unrecognized mood disorders in primary care

<p>Abstract</p> <p>Background</p> <p>Patients with chronic pain frequently display comorbid depression, but the impact of this concurrence is often underestimated and mistreated. The aim of this study was to determine the prevalence of unrecognized major depression and other mood disorders and comorbid unexplained chronic pain in primary care settings and to explore the associated factors.</p> <p>Also, to compare the use of health services by patients with unexplained chronic pain, both with and without mood disorder comorbidity.</p> <p>Methods</p> <p>A cross-sectional study was carried out in a sample of primary care centers. 3189 patients consulting for "unexplained chronic pain" were assessed by the Visual Analogue Scales (VAS) and the Primary Care Evaluation of Mental Disorders (PRIME-MD) questionnaire.</p> <p>Results</p> <p>We report: a) a high prevalence of unrecognized mood disorders in patients suffering from unexplained chronic pain complaints (80.4%: CI 95%: 79.0%; 81.8%); b) a greater susceptibility of women to mood disorders (OR adjusted = 1.48; CI 95%:1.22; 1.81); c) a direct relationship between the prevalence of mood disorders and the duration of pain (OR adjusted = 1.01; CI 95%: 1.01; 1.02) d) a higher comorbidity with depression if the pain etiology was unknown (OR adjusted = 1.74; CI 95%: 1.45; 2.10) and, e) an increased use of health care services in patients with such a comorbidity (p < 0.0001).</p> <p>Conclusions</p> <p>The prevalence of undiagnosed mood disorders in patients with unexplained chronic pain in primary care is very high, leading to dissatisfaction with treatment processes and poorer outcomes. Consequently, it seems necessary to explore this condition more regularly in general practice in order to reach accurate diagnoses and to select the appropriate treatment.</p

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Assessment of foot health and animal welfare: clinical findings in 229 dairy Mediterranean Buffaloes (Bubalus bubalis) affected by foot disorders.

BACKGROUND Lameness represents the third most important health-related cause of economic loss in the dairy industry after fertility and mastitis. Although, dairy Mediterranean Buffaloes (MB) and dairy cows share similar breeding systems predisposing to similar herd problems, published studies exploring its relevance and role in these ruminants are still rare and incomplete. The aims of this study were to describe the clinical findings of foot disorders (FDs) in dairy MB and their influence on animal welfare, determined by assessment of locomotion score (LS), body condition score (BCS) and cleanliness score (CS). RESULTS Of 1297 multiparous MB submitted to routine trimming procedures, 229 buffaloes showed at least one FD. The prevalence of buffaloes affected by FDs was 17.7 %, while motility and lameness indexes were 84.1 % (1091/1297) and 15.9 % (206/1297), respectively. Overgrowth was present in 17.0 % (220/1297), corkscrew claw in 15.8 % (205/1297), interdigital phlegmon in 0.9 % (12/1297), white line abscess in 0.8 % (11/1297), digital dermatitis in 0.1 % (1/1297) and interdigital hyperplasia in 0.1 % (1/1297). Simultaneous presence of FDs was recorded in 17.0 % of MB (221/1297): overgrowth and corkscrew claw occurred together in 15.8 % of cases (205/1297), overgrowth and interdigital phlegmon in 0.3 % (4/1297), overgrowth and white line abscess in 0.8 % (11/1297), digital dermatitis and interdigital hyperplasia in 0.1 % (1/1297). The presence of FDs was always associated with lameness (LS > 2), except from 23 MB with simultaneous overgrowth and interdigital phlegmon occurrence. The majority of MB within the under-conditioned group (95.5 %, 43/45) and all those with CS > 2 (122/122) had a locomotion score above the threshold of normality (LS > 2). Furthermore, foot diseases such as interdigital hyperplasia, white line abscess and digital dermatitis or interdigital hyperplasia seemed to occur more frequently associated with decreased BCS and increased CS scores. CONCLUSIONS This study describes for the first time the involvement of white line disease, interdigital phlegmona, digital dermatitis and interdigital hyperplasia in foot disorders of dairy Mediterranean buffalo and shows their association with an impairment of animal welfare

An Adaptive Rule-Based Approach for Managing Situation-Awareness

Situation awareness is a powerful paradigm that can efficiently exploit the increasing capabilities of handheld devices, such as smart phones and PDAs. Indeed, accurate understanding of the current situation can allow the device to proactively provide information and propose services to users in mobility. Of course, to recognize the situation is a challenging task, due to such factors as the variety of possible situations, uncertain and imprecise data, and different user.s preferences and behavior. In this framework, we propose a robust and general rule-based approach to manage situation awareness. We adopt semantic web reasoning, fuzzy logic modeling, and genetic algorithms to handle, respectively, situational/contextual inference, uncertain input processing, and adaptation to the user.s behavior. We exploit an agent-oriented architecture so as to provide both functional and structural interoperability in an open environment. The system is evaluated by means of a real-world case study concerning resource recommendation. Experimental results show the effectiveness of the proposed approach

Using Context History to Personalize a Resource Recommender via a Genetic Algorithm

Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a resource recommender more effective. In this paper, we show how this objective can be achieved by collecting data during the interaction of the user with the mobile device and using this context history to personalize the resource recommender by a genetic algorithm. To describe our approach, we adopt a recently proposed resource recommender which exploits fuzzy linguistic variables to manage the inherent vagueness of some contextual parameters. Experimental results on a real business case show that the responsiveness and modeling capabilities of the recommender increase, thus validating the proposed approach

Situation-Aware Mobile Service Recommendation with Fuzzy Logic and Semantic Web

Today's mobile Internet service portals offer thousands of services and mobile devices can host plenty of applications, documents and web URLs. Hence, for average mobile users there is an increasing cognitive burden in finding the most appropriate service among the many available. On the other hand, methodologies such as bookmarks and resource tagging require a great arranging effort to handle increasing resources. To help mobile users in managing and using this personal information space, new levels of granularity should be introduced in the organization of services, together with some degree of self-awareness. This paper proposes a situation-aware service recommender that helps locating services proactively. In the recommender, a semantic layer determines one or more user current situations by using domain knowledge expressed in terms of ontology and semantic rules. A fuzzy inference layer manages the vagueness of some contextual condition of these rules and outputs an uncertainty degree for each situation. Based on this degree, the recommender proposes a set of specific resources

A Situation-Aware Resource Recommender based on Fuzzy and Semantic Web Rules

Nowadays, a huge quantity of resources for mobile users are made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. A tool able to recommend suitable resources at the right time in each situation would be of great help for the mobile users and would make the use of the handheld devices less boring and more attractive. To this aim, new levels of granularity, together with some degree of self-awareness, are needed to assist mobile users in managing and using resources. In this paper, we propose an efficient situation-aware resource recommender (SARR), which helps mobile users to timely locate resources proactively. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in charge of handling the vagueness of some conditions in the semantic rules, computing a certainty degree for each inferred situation. These degrees are used to rank the situations and consequently to assign a priority to the resources associated with the specific situations. The application of SARR to two real business cases is also shown and discussed

Using BPMN and Tracing for Rapid Business Process Prototyping Environments

Business Process (BP) analysis aims to investigate properties of BPs by performing simulation, diagnosis and verification with the goal of supporting BP Management (BPM). In this paper, we propose a framework for BPM that relies on the BP Modeling Notation (BPMN). More specifically, we first introduce a method to deal with the BPM life cycle. Then, we discuss a platform to support this life cycle. The platform comprises three basic modules: a visual BPMN-based designer, a process tracing service, and a BP Manager for, respectively, the design, configuration and execution phases of the BPM life cycle. The proposed framework is particularly useful to perform business simulations such as what-if analysis, and to provide an efficient integration support within the supply-chain. In this study, we also show some practical application of this framework through a real-world experience on a leather firm, offering an environment for process communication as well as for time and cost analysis