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At least 937 records · Page 52Linked to original sources
PubMed2000

Improvement in the information content of the Glasgow Coma Scale for the prediction of full cognitive recovery after head injury using fuzzy logic.

BACKGROUND: The objective of this study was to modify the existing Glasgow Coma Scale (GCS) into a fuzzy GCS by using fuzzy information representation and fuzzy inferencing. The study compared the information content of the existing GCS with the new fuzzy GCS for prediction of full cognitive recovery in patients with head injury. METHODS: A record-based study was conducted at the Government Medical College and Hospital, a tertiary care facility in Nagpur, India. The study, which covered the period from January 1 to December 31, 1997, included 253 patients with head injuries. Opinions of 17 clinical experts who routinely deal with head injury cases were used for the construction of the fuzzy GCS. RESULTS: By using the max operator for summarization, eye, motor, and verbal stimuli were all significantly associated with the possibility of full cognitive recovery with the fuzzy GCS (P < .001). Nonspecificity of the classical GCS, the min-operated fuzzy GCS, and the max-operated fuzzy GCS was comparable. A reduction in Shannon entropy was maximum with the max-operated fuzzy GCS. Min-operated fuzzy GCS better predicted a lack of full cognitive recovery. CONCLUSIONS: Fuzzy GCS substantially improves the information content for prediction of the possibility of full cognitive recovery after head injury. Eye, motor, and verbal stimuli all uniquely and significantly contribute to prediction of this possibility. We recommend the use of fuzzy GCS for prediction of the possibility of full cognitive recovery in patients with head injuries.

Cognition↗
PubMed1999

A logical analysis of T cell activation and anergy.

Interaction of the antigen-specific receptor of T lymphocytes with its antigenic ligand can lead either to cell activation or to a state of profound unresponsiveness (anergy). Although subtle changes in the nature of the ligand or of the antigen-presenting cell have been shown to affect the outcome of T cell receptor ligation, the mechanism by which the same receptor can induce alternative cellular responses is not completely understood. A model for explaining both positive (cell proliferation and cytokine production) and negative (anergy induction) signaling of T lymphocytes is described herein. This model relies on the autophosphorylative properties of the tyrosine kinases associated with the T cell receptor. One of its basic assumptions is that the kinase activity of these receptor-associated enzymes remains above background level after ligand removal and is responsible for cellular unresponsiveness. Using a simple Boolean formalism, we show how the timing of the binding and intracellular signal-transduction events can affect the properties of receptor signaling and determine the type of cellular response. The present approach integrates into a common framework a large body of experimental observations and allows specification of conditions leading to cellular activation or to anergy.

Animals↗
PubMedDate not supplied

New fuzzy logic strategies for bio-molecular recognition.

The concepts of molecular similarity and molecular complementarity, playing important roles in the broad field of molecular recognition, are chemical problems, in which the eyeball technique used by a human observer is very successful but which are very hard to code into a computer algorithm. Based on the model of molecular surfaces, our new approach defines overlapping surface patches with similar molecular properties. These patches are used to represent local features of the molecule in a way, which is beyond the atomistic resolution but can nevertheless be applied in partial similarity as well as complementarity analyses in a very general sense. It is shown that this molecular description can be used as the first step in a docking algorithm for complexes, where the structures of both molecules are known, as well as for the identification of possible active sites without the knowledge of specific molecules binding to this site.

Algorithms↗
PubMed2005

The logical basis of phylogenetic taxonomy.

Phylogenetic taxonomy, like modern Linnean taxonomy, was modeled on a phylogenetic tree rather than a cladogram and, like its predecessor, perpetuates the use of morphology as a means of recognizing clades. Both practices have generated confusion in graphical representation, operational terminology, and definitional rationale in phylogenetic taxonomy, the history of which is traced. The following points are made: (1) cladograms, rather than trees or hybrid cladogram-trees, provide the framework for the simplest graphical depiction of phylogenetic definitions; (2) a complete notational scheme for phylogenetic definitions is presented that distinguishes symbolic notation from shorthand and longhand versions; (3) phylogenetic definitions are composed of three components (paradigm, specifier, qualifier) arranged in two fundamental patterns-node and stem; (4) apomorphies do not constitute a fundamental definitional pattern but rather serve to qualify a stem-based definition (as do time and geographic range); (5) formulation of phylogenetic definitions involves three heuristic criteria (stability, simplicity, prior use); (6) reasoned definitional revision is encouraged and better defined (textual substitution, first-and second-order revision); and (7) a database, TaxonSearch, allows rapid recall of taxonomic and definitional information.

Classification↗
PubMed1999

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic.

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2ET), AEP and ISOFl were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2ET, DAI or ISOFl) that had the highest causal relation with the output variables (ISOFl and MAP). The variables with highest causal relation constitute the ISOFl and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.

Anesthetics, Inhalation↗