M. Meizoso

Effect of Desmethyl Tirilazad, Dizocilpine Maleate and Nimodipine on Brain Nitric Oxide Synthase Activity and Cyclic Guanosine Monophosphate during Cerebral Ischemia in Rats

The present study was designed to evaluate the effects of pretreatment with a combination of desmethyl tirilazad (21-aminosteroid) plus dizocilpine maleate (N-methyl-D-aspartate receptor antagonist) and nimodipine (calcium channel antagonist) on constitutive nitric oxide synthase (cNOS) activity and cyclic guanosine monophosphate (cGMP) levels in brain homogenates of rats subjected to global cerebral transient ischemia induced by bilateral clamping of the carotids for 30 min and reduction of arterial pressure (to 50–60 mm Hg) by intravenous infusion of trimethaphan (30 mg/kg). Our results show that cerebral ischemia produced an increase in cNOS activity and cGMP levels in brain homogenates. Pretreatment with desmethyl tirilazad or dizocilpine maleate or nimodipine individually significantly suppressed (p < 0.01) the increase in cNOS activity and cGMP levels induced by cerebral ischemia, which may be related to their neuroprotective effect. Similar results were obtained with pretreatment by a combination of desmethyl tirilazad plus dizocilpine maleate plus nimodipine.

Effect of Tirilazad on Brain Nitric Oxide Synthase Activity during Cerebral Ischemia in Rats

The effect of the 21-aminosteroid tirilazad mesylate (10 mg/kg, i.p.) on nitric oxide synthase (NOS) activity in the brain cortex was studied in male Wistar rats subjected to cerebral global transient ischemia induced by bilateral clamping of the carotids for 10 min and reduction of arterial pressure (to 50 mm Hg) by intravenous infusion of 1.5 ml of a solution of trimethaphan (5 mg/ml). NOS activity was determined by measuring the rate of conversion of [3H]arginine to [3H]citrulline in brain cortex. Our results show for the first time that tirilazad suppresses the increase of NOS activity in brain cortex induced by cerebral ischemia (136 +/- 16 vs. 60 +/- 9 pmol [3H]citrulline/min per mg protein) and also suppresses the increase in K(m) of NOS (5.7 +/- 0.1 vs. 1.2 +/- 0.2 mumol/l). These effects are attributed to the fact that tirilazad acts as a scavenger of oxygen free radicals formed during cerebral ischemia. These results document the neuroprotective efficacy of tirilazad mesylate in cerebral ischemia.

Combining open-source natural language processing tools to parse clinical practice guidelines

Natural language processing NLP has been used to process text pertaining to patient records and narratives. However, most of the methods used were developed for specific systems, so new research is necessary to assess whether such methods can be easily retargeted for new applications and goals, with the same performance. In this paper, open-source tools are reused as building blocks on which a new system is built. The aim of our work is to evaluate the applicability of the current NLP technology to a new domain: automatic knowledge acquisition of diagnostic and therapeutic procedures from clinical practice guideline free-text documents. In order to do this, two publicly available syntactic parsers, several terminology resources and a tool oriented to identify semantic predications were tailored to increase the performance of each tool individually. We apply this new approach to 171 sentences selected by the experts from a clinical guideline, and compare the results with those of the tools applied with no tailoring. The results of this paper show that with some adaptation, open-source NLP tools can be retargeted for new tasks, providing an accuracy that is equivalent to the methods designed for specific tasks.

From natural language descriptions in clinical guidelines to relationships in an ontology

Knowledge Engineering allows to automate entity recognition and relation extraction from clinical texts, which in turn can be used to facilitate clinical practice guideline (CPG) modeling. This paper presents a method to recognize diagnosis and therapy entities, and to identify relationships between these entities from CPG free-text documents. Our approach applies a sequential combination of several basic methods classically used in knowledge engineering (natural language processing techniques, manually authored grammars, lexicons and ontologies), to gradually map sentences describing diagnostic and therapeutic procedures to an ontology. First, using a standardized vocabulary, our method automatically identifies guideline concepts. Next, for each sentence, it determines the patient conditions under which the descriptive knowledge of the sentence is valid. Then, it detects the central information units in the sentence, in order to match the sentence with a small set of predefined relationships. The approach enables automated extraction of relationships about findings that have manifestation in a disease, and procedures that diagnose or treat a disease.

Automated mapping of observation archetypes to SNOMED CT concepts

Integrating clinical data models, such as the European open-EHR Archetypes and standard terminologies, such as SNOMED CT, is important to reduce medical errors and to get interoperability between health information systems. In this study, we propose an automated approach to mapping observation archetypes to SNOMED CT. Our approach applies a sequential combination of several basic matching methods, classically used in ontology matching. First, different lexical techniques identify similar strings between the observation archetypes and SNOMED CT. Second, a structure-based technique traverses two types of SNOMED CT relationships: IS A and interprets, searching for possible mappings not found with lexical techniques. The method was applied to the mapping of 20 observation archetypes. In total, 94% precision and 69% recall of SNOMED CT concepts was reached. Our method has revealed a degree of semantic similarity between some relationships in the tree representing observation archetypes and the relationships IS A and interprets in SNOMED CT.

A Study of Applying Knowledge Modelling to Evidence-Based Guidelines

This paper reports on a case-study of applying the general purpose and widely accepted methodology CommonKADS to a clinical practice guideline. CommonKADS is focussed on obtaining a compact knowledge model. However, guidelines usually contain incomplete and ambiguous knowledge. So, the resulting knowledge model will be incomplete and we will need to detect what parts of the guideline knowledge are missing. A complementary alternative, which we propose in this work, is to reconstruct the process of knowledge model construction, proposed by CommonKADS, in order to force the knowledge engineer to keep the transformation paths during knowledge modeling. That is to say, we propose to establish explicit mappings between original medical texts and the knowledge model, storing these correspondences in a structured way. This alternative will reduce the existing gap between natural language representation and the corresponding knowledge model.

Using lexical, terminological and ontological resources for entity recognition tasks in the medical domain

This paper reports on a case-study of applying various publicly available resources (lexical, terminological and ontological) for medical recognition tasks, that is, for identifying medical entities in the analysis of clinical practice guideline texts. The paper provides a methodological support that systematises the entity recognition task in the medical domain. Preliminary analysis shows that many of the medical linguistic expressions describing goals and intentions in natural language are included in the current terminological resources. So, these resources can be used as a means of disambiguating and structuring this type of expressions, with the final aim of indexing guideline repositories for efficient searching.

A Study of Extracting Knowledge from Guideline Documents

Guideline documents offer a rich repository of information on clinical decisions, actions and prescriptions. However, clinicians do not use them as much as expected since health care organisations started to develop them. One alternative to promote the use of guidelines is to automatically select the relevant information at the point of care. But, extracting knowledge from a guideline document is an arduous and complex task. In this paper, we propose to apply the methodology CommonKADS in the analysis phase of a clinical practice guideline, with the aim of systematizing knowledge acquisition, providing a methodological support that helps to detect and document all the transformations from natural language to the structured representation of a knowledge model. When forcing to the knowledge engineer to keep these transformations, the knowledge modelling becomes more gradual.

In memoriam R.J. Domenjoz

On March 7 this year, Prof. Robert J. Domenjoz died at the age of 89 years. Pharmacology is extremely indebted to him. He founded the journal in 1959, which was named Medicina Experimentalis and some years later renamed Medicina et Pharmacologia Experimentalis still in the tradition that Latin was the medical language.