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At least 613 records · Page 34Linked to original sources
PubMed1988

On working through: a model from artificial intelligence.

Working through is centrally important to clinical psychoanalysis. It is inadequately explained in analytic theory. An artificial intelligence model of the process is proposed. Models of problem solving show that the complexity of necessary computation is an important determinant of how a problem is solved. Not optimal, but only good enough solutions are usually found. The quality of solutions depends on the time and resources available. Generally it is far easier to use existing methods than to develop new approaches. When problems must be solved in an emergency fashion, as in trauma, poor solutions are likely to emerge. In studying the annealing of metals and other complex optimization problems, a process, the Boltzman algorithm, was discovered, which continues the search for better solutions while gradually developing a coherent structure of the overall solution. The algorithm provides a model both for psychoanalytic working through and for the normally ongoing process of psychological development and reworking whose deficiency is characteristic of much psychopathology. Working through in the analytic situation is the reactivation of this normal process, and a good analytic outcome is achieved when the process can continue without the analyst. Properties of the Boltzman algorithm clarify such concepts as "optimal" frustration and anxiety which correspond to working in the area where the stable but not rigid structures emerge in the algorithms operation. These studies are an example of how computer science and artificial intelligence are a potentially rich source for psychoanalytic theory.

Algorithms↗
PubMed2005

Environmental cognitive remediation in schizophrenia: ethical implications of "smart home" technology.

OBJECTIVE: In light of the advent of new technologies, we proposed to reexamine certain challenges posed by cognitive remediation and social reintegration (that is, deinstitutionalization) of patients with severe and persistent mental disorders. METHOD: We reviewed literature on cognition, remediation, smart homes, as well as on objects and utilities, using medical and computer science electronic library and Internet searches. RESULTS: These technologies provide solutions for disabled persons with respect to care delivery, workload reduction, and socialization. Examples include home support, video conferencing, remote monitoring of medical parameters through sensors, teledetection of critical situations (for example, a fall or malaise), measures of daily living activities, and help with tasks of daily living. One of the key concepts unifying all these technologies is the health-smart home. We present the notion of the health-smart home in general and then examine it more specifically in relation to schizophrenia. CONCLUSION: Management of people with schizophrenia with cognitive deficits who are being rehabilitated in the community can be improved with the use of technology; however, such technology has ethical ramifications.

Cognitive Behavioral Therapy↗
PubMed2006

A traveling salesman approach for predicting protein functions.

BACKGROUND: Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways. RESULTS: Here we present a new approach utilizing the classic Traveling Salesman Problem to study the protein-protein interactions and to predict protein functions in budding yeast Saccharomyces cerevisiae. We apply the global optimization tool from combinatorial optimization algorithms to cluster the yeast proteins based on the global protein interaction information. We then use this clustering information to help us predict protein functions. We use our algorithm together with the direct neighbor algorithm 1 on characterized proteins and compare the prediction accuracy of the two methods. We show our algorithm can produce better predictions than the direct neighbor algorithm, which only considers the immediate neighbors of the query protein. CONCLUSION: Our method is a promising one to be used as a general tool to predict functions of uncharacterized proteins and a successful sample of using computer science knowledge and algorithms to study biological problems.

Journal Article↗
PubMed2006

Alarm algorithms in critical care monitoring.

The alarms of medical devices are a matter of concern in critical and perioperative care. The frequent false alarms not only are a nuisance for patients and caregivers but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements of alarm algorithms are urgently needed. We give an overview of the current clinical situation and the underlying problems and discuss different methods from statistics and computational science and their potential for clinical application.

Algorithms↗
PubMed2003

Estimating physical activity using the CSA accelerometer and a physical activity log.

PURPOSE: To compare two methods for measuring time spent in physical activity of differing absolute intensities. METHODS: Over a 7-d period, 59 women wore Computer Science and Applications, Inc. (CSA) accelerometers and recorded their activity in physical activity logs (PAL) at 15-min intervals. Three published cut points were used to classify CSA data into resting/light, moderate, and vigorous intensity categories. Data were analyzed using descriptive statistics, Spearman rank-order correlations, and Bland-Altman plots. RESULTS: The CSA estimates of total (moderate plus vigorous) physical activity using the three cut points ranged from a mean (+/- SD) of 38.1 (+/-26.8) min.d-1 to 312.6 (+/- 101.1) min.d-1. Using the PAL, women self-reported a mean (+/- SD) of 75.1 (+/- 51.7) min.d-1 of total activity. There was fair to modest rank-order agreement between each of the three CSA measures and the PAL measure of total activity, with correlations ranging from r = 0.15 to 0.24. Correlations between CSA and PAL estimates of total activity were higher in women with body mass index values (BMI) below 25 kg.m-2 (r = 0.23-0.38) compared with women with BMI > or = 25 kg.m-2 (r = 0.06-0.08) but did not differ according to age. Correlations between the three CSA cut points ranged from r = 0.45 to 0.86. CONCLUSIONS: Three published cut points designed to classify CSA output by intensity level produced different estimates of physical activity participation. Correlations between CSA and PAL measures of activity intensity were fair overall but higher among leaner women.

Adult↗
PubMed2005

Fundamental movement skills and habitual physical activity in young children.

PURPOSE: To test for relationships between objectively measured habitual physical activity and fundamental movement skills in a relatively large and representative sample of preschool children. METHODS: Physical activity was measured over 6 d using the Computer Science and Applications (CSA) accelerometer in 394 boys and girls (mean age 4.2, SD 0.5 yr). Children were scored on 15 fundamental movement skills, based on the Movement Assessment Battery, by a single observer. RESULTS: Total physical activity (r=0.10, P<0.05) and percent time spent in moderate to vigorous physical activity (MVPA) (r=0.18, P<0.001) were significantly correlated with total movement skills score. Time spent in light-intensity physical activity was not significantly correlated with motor skills score (r=0.02, P>0.05). CONCLUSIONS: In this sample and setting, fundamental movement skills were significantly associated with habitual physical activity, but the association between the two variables was weak. The present study questions whether the widely assumed relationships between motor skills and habitual physical activity actually exist in young children.

Anthropometry↗
PubMed2005

Predicting walking METs and energy expenditure from speed or accelerometry.

PURPOSE: a) Compare the predictive potential of speed and CSA(hip) (Computer Science Applications accelerometer positioned on the hip) for level terrain walking METs (1 MET = VO2 of 3.5 mL.kg(-1).min(-1)) and energy expenditure (kcal.min(-1)); b) cross-validate previously published CSA(hip)- and speed-based MET and energy expenditure prediction equations; c) measure self-paced walking speed, exercise intensity (METs) and energy expenditure in the middle aged population. METHODS: Seventy-two 35- to 45-yr-old volunteers walked around a level, paved quadrangle at what they perceived to be a moderate pace. Oxygen consumption was measured using the criterion Douglas bag technique. Speed, CSA(hip), heart rate, and Borg rating of perceived exertion were also monitored. RESULTS: Speed explained 10% more variance of walking METs than CSA(hip). Speed and mass explained 8% more variance of walking energy expenditure (kcal.min) than CSA(hip) and mass. The best previously published regression equations predict our walking METs and energy expenditures within 95% prediction limits of +/- 0.7 METs and +/- 1.0 kcal.min(-1), respectively. Women paced themselves at a significantly higher mean speed (5.5 km.h(-1)) and intensity (4.1 METs) than their male counterparts (5.2 km.h(-1) and 3.8 METs). Both genders expended approximately 0.75 kcal.kg(-1) for every kilometer of level terrain walked. CONCLUSION: Speed-based MET and energy expenditure predictions during level terrain walking were more accurate than those utilizing CSA(hip).

Acceleration↗
PubMed1998

13th meeting of the Scientific Group on Methodologies for the Safety Evaluation of Chemicals (SGOMSEC): alternative testing methodologies and conceptual issues.

Substantial world-wide resources are being committed to develop improved toxicological testing methods that will contribute to better protection of human health and the environment. The development of new methods is intrinsically driven by new knowledge emanating from fundamental research in toxicology, carcinogenesis, molecular biology, biochemistry, computer sciences, and a host of other disciplines. Critical evaluations and strong scientific consensus are essential to facilitate adoption of alternative methods for use in the safety assessment of drugs, chemicals, and other environmental factors. Recommendations to hasten the development of new alternative methods included increasing emphasis on the development of mechanism-based methods, increasing fundamental toxicological research, increasing training on the use of alternative methods, integrating accepted alternative methods into toxicity assessment, internationally harmonizating chemical toxicity classification schemes, and increasing international cooperation to develop, validate, and gain acceptance of alternative methods.

Animal Testing Alternatives↗
PubMed2006

Preanalytical variability: the dark side of the moon in laboratory testing.

Remarkable advances in instrument technology, automation and computer science have greatly simplified many aspects of previously tedious tasks in laboratory diagnostics, creating a greater volume of routine work, and significantly improving the quality of results of laboratory testing. Following the development and successful implementation of high-quality analytical standards, analytical errors are no longer the main factor influencing the reliability and clinical utilization of laboratory diagnostics. Therefore, additional sources of variation in the entire laboratory testing process should become the focus for further and necessary quality improvements. Errors occurring within the extra-analytical phases are still the prevailing source of concern. Accordingly, lack of standardized procedures for sample collection, including patient preparation, specimen acquisition, handling and storage, account for up to 93% of the errors currently encountered within the entire diagnostic process. The profound awareness that complete elimination of laboratory testing errors is unrealistic, especially those relating to extra-analytical phases that are harder to control, highlights the importance of good laboratory practice and compliance with the new accreditation standards, which encompass the adoption of suitable strategies for error prevention, tracking and reduction, including process redesign, the use of extra-analytical specifications and improved communication among caregivers.

Clinical Laboratory Techniques↗
PubMed2005

Recursive partitioning analysis of complex disease pharmacogenetic studies. I. Motivation and overview.

Identifying genetic variation predictive of important phenotypes, including disease susceptibility, drug efficacy, and adverse events, is a challenging task, and theory and computer science work is being carried out in an attempt to tackle this issue. For many important diseases, such as diabetes, schizophrenia, and depression, the etiology is complex; either the disease is a result of several multiple mechanisms or is caused by an interaction among multiple genes or gene-environment interactions, or both. There is a need for statistical methods to deal with the large, complex data sets that will be used to disentangle these diseases. Each putative genetic polymorphism can be tested for association sequentially. The most difficult problem, however, is the identification of combinations of polymorphisms or genetic markers with increased predictive characteristics. Data from clinical trials, where patients with a particular disease are treated with certain drugs, can be retrospectively assembled using a case-control design. Such data will typically include treatment assignment, demographics, medical history, and genotypes for a large number of genetic markers. The number of variables in such data is expected to be much larger than the number of subjects. This report focuses on some of the methods being employed to deal with this complex data and covers, in some detail, a data-mining method--recursive partitioning--to analyze such data. The methods are demonstrated using a complex simulated data set, as there are few available public data sets. This explication of recursive partitioning should provide researchers with a better idea of the current available analysis techniques, in order to allow them to plan their experiments more effectively.

Biomedical Research↗
PubMed2003

Analyzing microarray data using cluster analysis.

As pharmacogenetics researchers gather more detailed and complex data on gene polymorphisms that effect drug metabolizing enzymes, drug target receptors and drug transporters, they will need access to advanced statistical tools to mine that data. These tools include approaches from classical biostatistics, such as logistic regression or linear discriminant analysis, and supervised learning methods from computer science, such as support vector machines and artificial neural networks. In this review, we present an overview of another class of models, cluster analysis, which will likely be less familiar to pharmacogenetics researchers. Cluster analysis is used to analyze data that is not a priori known to contain any specific subgroups. The goal is to use the data itself to identify meaningful or informative subgroups. Specifically, we will focus on demonstrating the use of distance-based methods of hierarchical clustering to analyze gene expression data.

Cluster Analysis↗
PubMed2001

Heart rate variability analysis.

The expansion of heart rate variability analysis has been facilitated by the remarkable development of computer sciences and digital signal processing during the last thirty years. The beat-to-beat fluctuation of the heart rate originates from the momentary summing of sympathetic and parasympathetic influences on the sinus node. According to the extensive associations of the autonomic nervous system, several factors affect heart rate and its variability such as posture, respiration frequency, age, gender, physical or mental load, pain, numerous disease conditions, and different drugs. Heart rate variability can be quantitatively measured by time domain and frequency domain methods that are detailed in the paper. Non-linear methods have not spread in the clinical practice yet. Various cardiovascular and other pathologies as well as different forms of mental and physical load are associated with altered heart rate variability offering the possibility of predicting disease outcome and assessing stress.

Autonomic Nervous System↗
PubMed2002

[The informatics process in health: subjects discussed in articles published from 1978 to 1998].

The principal themes are described approached in goods of indexed newspapers, in two bases of available data in Internet, in the period from 1978 to 1998. Consultations were accomplished BIREME, through Internet, being used the descriptors in several orders. A total of 54 goods was obtained that were codified and tabulated. It was ended that, in the studied sample, the thematic of the researches developed on computer science in health had a profile change that passed of theoretical studies for applications in the work atmosphere.

Medical Informatics↗
PubMed2001

Protein folding: a perspective for biology, medicine and biotechnology.

At the present time, protein folding is an extremely active field of research including aspects of biology, chemistry, biochemistry, computer science and physics. The fundamental principles have practical applications in the exploitation of the advances in genome research, in the understanding of different pathologies and in the design of novel proteins with special functions. Although the detailed mechanisms of folding are not completely known, significant advances have been made in the understanding of this complex process through both experimental and theoretical approaches. In this review, the evolution of concepts from Anfinsen's postulate to the "new view" emphasizing the concept of the energy landscape of folding is presented. The main rules of protein folding have been established from in vitro experiments. It has been long accepted that the in vitro refolding process is a good model for understanding the mechanisms by which a nascent polypeptide chain reaches its native conformation in the cellular environment. Indeed, many denatured proteins, even those whose disulfide bridges have been disrupted, are able to refold spontaneously. Although this assumption was challenged by the discovery of molecular chaperones, from the amount of both structural and functional information now available, it has been clearly established that the main rules of protein folding deduced from in vitro experiments are also valid in the cellular environment. This modern view of protein folding permits a better understanding of the aggregation processes that play a role in several pathologies, including those induced by prions and Alzheimer's disease. Drug design and de novo protein design with the aim of creating proteins with novel functions by application of protein folding rules are making significant progress and offer perspectives for practical applications in the development of pharmaceuticals and medical diagnostics.

Animals↗
PubMed2001

[Internet and nursing: development of a site on drug administration].

This study identified existent sites in the internet about Administration of Medications and developed and evaluated a specific site of this thematic. Of the 158 existent and available sites in the database of the Alta Vista search, 17 of these presented some relationship with pharmacology, marketing and information about drugs, technologies and rules of the medication. After that a site was developed and named The process of Administration of Medications in focus which goal was to present investigations conducted by a group about the following topics: errors, technology, complications, study group and the team. The evaluation of this site was made by 2 analyst of systems, 2 computer science technicians and 4 nursing professors and it showed that the quality of the pages, the time of answer, the link, images and content were considered between excellent and satisfactory.

Drug Therapy↗
PubMed2003

Selecting the components for a safe and efficient tuberculosis subunit vaccine--recent progress and post-genomic insights.

Prophylactic vaccination against tuberculosis with BCG gained much of the credit for the decline of TB in Europe. However, with TB resurgent in many parts of the world, better vaccines are urgently needed. To improve on BCG, a rapid, rational approach to vaccine discovery is needed. Fortunately, advances in the fields of molecular biology and computer science have spawned new disciplines: Genomics, Proteomics and Transcriptomics are transforming the ways in which candidate vaccine antigens are discovered. In this review, we discuss how these new approaches have accelerated the pace of antigen discovery and vaccine development, and highlight some of the most promising new candidate vaccines and vaccine targets.

Animals↗
PubMed2002

Impact of recombinant DNA technology and protein engineering on structure-based drug design: case studies of HIV-1 and HCMV proteases.

Structure-based drug design is an organized, multidisciplinary endeavor undertaken by scientists from many different scientific fields. The success of structure-based drug design was only made possible by advances in structure biology that provides the three-dimensional structure of the drug design target with which small molecular chemical ligands interact. Visualization of the conformation and interactions of a small molecule ligand bound to the protein target in the co-crystal structure of the protein:ligand complex enables the design of new chemical compounds with improved binding affinity and specificity. With the advances in molecular biology, lab automation, and computational science, genomic data have now become available for the human genome, as well as various other organisms. The pharmaceutical industry is currently putting forth tremendous effort in the area of functional genomics and structural genomics in attempts to decipher functions and structures of protein encoded by genes, with the ultimate goal of identifying novel targets for drug discovery and development. This chapter discusses the significant impact made by recombinant DNA technology and protein engineering on structural biology and, more specifically, on structure-based drug design.

Biotechnology↗
PubMed2006

The challenge of integrating disparate high-content data: epidemiological, clinical and laboratory data collected during an in-hospital study of chronic fatigue syndrome.

Chronic fatigue syndrome (CFS) is a debilitating illness characterized by multiple unexplained symptoms including fatigue, cognitive impairment and pain. People with CFS have no characteristic physical signs or diagnostic laboratory abnormalities, and the etiology and pathophysiology remain unknown. CFS represents a complex illness that includes alterations in homeostatic systems, involves multiple body systems and results from the combined action of many genes, environmental factors and risk-conferring behavior. In order to achieve understanding of complex illnesses, such as CFS, studies must collect relevant epidemiological, clinical and laboratory data and then integrate, analyze and interpret the information so as to obtain meaningful clinical and biological insight. This issue of Pharmacogenomics represents such an approach to CFS. Data was collected during a 2-day in-hospital study of persons with CFS, other medically and psychiatrically unexplained fatiguing illnesses and nonfatigued controls identified from the general population of Wichita, KS, USA. While in the hospital, the participants' psychiatric status, sleep characteristics and cognitive functioning was evaluated, and biological samples were collected to measure neuroendocrine status, autonomic nervous system function, systemic cytokines and peripheral blood gene expression. The data generated from these assessments was made available to a multidisciplinary group of 20 investigators from around the world who were challenged with revealing new insight and algorithms for integration of this complex, high-content data and, if possible, identifying molecular markers and elucidating pathophysiology of chronic fatigue. The group was divided into four teams with representation from the disciplines of medicine, mathematics, biology, engineering and computer science. The papers in this issue are the culmination of this 6-month challenge, and demonstrate that data integration and multidisciplinary collaboration can indeed yield novel approaches for handling large, complex datasets, and reveal new insight and relevance to a complex illness such as CFS.

Adult↗