Emotional flexibility and general self-efficacy: A pilot training intervention study with knowledge workers.

Emotional flexibility advancement has been found to be highly effective in clinical settings to treat, for example, depression, anxiety, and chronic pain. Developing these skills in the working context has also shown very encouraging results in public sector settings. Also, a few studies have revealed effectiveness in a private sector setting, but no studies have yet looked at the effectiveness of developing these skills amongst high-paced, high-demanding, and highly-educated knowledge workers. In this pilot training intervention study, we report evidence that emotional flexibility can be developed in this context. We conducted an experiment with treatment and control groups, with only the treatment group receiving an emotional flexibility training. Emotional flexibility improved significantly for the treatment group, whereas the improvements were minimal or negative for the control group. Furthermore, we reveal that General self-efficacy improved amongst treatment group participants (and not for control group participants), and that this is associated with emotional flexibility. Finally, we show that the improvements were higher for participants starting from a lower baseline.

Long-lasting disruption of spatial memory by GABAB receptor antagonist induced seizures.

The objective of this project was to test whether a drug-induced model of temporal lobe seizures, namely seizures induced by a gamma aminobutyric acid (GABAB) receptor antagonist, CGP35348, result in long-term disruption of hippocampal memory function. Seizures were induced in experimental rats by intracerebroventricular (i.c.v.) injection of CGP35348 (0.64 µmol in 3 µL) for three consecutive days; control rats received no injection. Rats were first trained to criterion on an open radial arm maze (RAM) with 4 of the 8 arms baited, then received seizure and control treatment, and tested again on the RAM during the first week (days 1-5) and fourth week (days 22-29) after the last injection. An initial i.c.v. CGP35348 injection induced a mean of 4.4 seizures in the hippocampus, often accompanied with stages 3-5 convulsions, and sometimes with jumping; three daily CGP35348 injections induced 10.4 ±â€¯1.8 (n = 7 rats) seizures in total. In two separate experiments, seizure-treated rats performed worse than control rats in working memory (WM) during both the 1st and 4th weeks after seizures. Reference memory (RM) deficit during the 1st week after seizures was observed in only one experiment in which RM was acquired >2 weeks ago. The memory deficits were not accompanied by gross neuronal loss in the hippocampus. In conclusion, i.c.v. injection of a GABAB receptor antagonist in adult rats induced brief, multiple, focal hippocampal seizures that induced deficits in spatial memory for up to 4 weeks.

Application of probabilistic and fuzzy cognitive approaches in semantic web framework for medical decision support.

This study aimed to focus on medical knowledge representation and reasoning using the probabilistic and fuzzy influence processes, implemented in the semantic web, for decision support tasks. Bayesian belief networks (BBNs) and fuzzy cognitive maps (FCMs), as dynamic influence graphs, were applied to handle the task of medical knowledge formalization for decision support. In order to perform reasoning on these knowledge models, a general purpose reasoning engine, EYE, with the necessary plug-ins was developed in the semantic web. The two formal approaches constitute the proposed decision support system (DSS) aiming to recognize the appropriate guidelines of a medical problem, and to propose easily understandable course of actions to guide the practitioners. The urinary tract infection (UTI) problem was selected as the proof-of-concept example to examine the proposed formalization techniques implemented in the semantic web. The medical guidelines for UTI treatment were formalized into BBN and FCM knowledge models. To assess the formal models' performance, 55 patient cases were extracted from a database and analyzed. The results showed that the suggested approaches formalized medical knowledge efficiently in the semantic web, and gave a front-end decision on antibiotics' suggestion for UTI.

Semantic patient information aggregation and medicinal decision support.

Although the health care sector has already been subjected to a major computerization effort, this effort is often limited to the implementation of standalone systems which do not communicate with each other. Interoperability problems limit health care applications from achieving their full potential. In this paper, we propose the use of Semantic Web technologies to solve interoperability problems between data providers. Through the development of unifying health care ontologies, data from multiple health care providers can be aggregated, which can then be used as input for a decision support system. This way, more data is taken into account than a single health care provider possesses in his local setting. The feasibility of our approach is demonstrated by the creation of an end-to-end proof of concept, focusing on Belgian health care providers and medicinal decision support.

Formalization of treatment guidelines using Fuzzy Cognitive Maps and semantic web tools.

Therapy decision making and support in medicine deals with uncertainty and needs to take into account the patient's clinical parameters, the context of illness and the medical knowledge of the physician and guidelines to recommend a treatment therapy. This research study is focused on the formalization of medical knowledge using a cognitive process, called Fuzzy Cognitive Maps (FCMs) and semantic web approach. The FCM technique is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. Thus, it was selected for the case of modeling and knowledge integration of clinical practice guidelines. The semantic web tools were established to implement the FCM approach. The knowledge base was constructed from the clinical guidelines as the form of if-then fuzzy rules. These fuzzy rules were transferred to FCM modeling technique and, through the semantic web tools, the whole formalization was accomplished. The problem of urinary tract infection (UTI) in adult community was examined for the proposed approach. Forty-seven clinical concepts and eight therapy concepts were identified for the antibiotic treatment therapy problem of UTIs. A preliminary pilot-evaluation study with 55 patient cases showed interesting findings; 91% of the antibiotic treatments proposed by the implemented approach were in fully agreement with the guidelines and physicians' opinions. The results have shown that the suggested approach formalizes medical knowledge efficiently and gives a front-end decision on antibiotics' suggestion for cystitis. Concluding, modeling medical knowledge/therapeutic guidelines using cognitive methods and web semantic tools is both reliable and useful.

The DebugIT core ontology: semantic integration of antibiotics resistance patterns.

Antibiotics resistance development poses a significant problem in today's hospital care. Massive amounts of clinical data are being collected and stored in proprietary and unconnected systems in heterogeneous format. The DebugIT EU project promises to make this data geographically and semantically interoperable for case-based knowledge analysis approaches aiming at the discovery of patterns that help to align antibiotics treatment schemes. The semantic glue for this endeavor is DCO, an application ontology that enables data miners to query distributed clinical information systems in a semantically rich and content driven manner. DCO will hence serve as the core component of the interoperability platform for the DebugIT project. Here we present DCO and an approach thet uses the semantic web query language SPARQL to bind and ontologically query hospital database content using DCO and information model mediators. We provide a query example that indicates that ontological querying over heterogeneous information models is feasible via SPARQL construct- and resource mapping queries.

Role for reelin in the development of granule cell dispersion in temporal lobe epilepsy.

The reelin signaling pathway plays a crucial role during the development of laminated structures in the mammalian brain. Reelin, which is synthesized and secreted by Cajal-Retzius cells in the marginal zone of the neocortex and hippocampus, is proposed to act as a stop signal for migrating neurons. Here we show that a decreased expression of reelin mRNA by hippocampal Cajal-Retzius cells correlates with the extent of migration defects in the dentate gyrus of patients with temporal lobe epilepsy. These results suggest that reelin is required for normal neuronal lamination in humans, and that deficient reelin expression may be involved in migration defects associated with temporal lobe epilepsy.