Airway smooth muscle dynamics: a common pathway of airway obstruction in asthma.

Excessive airway obstruction is the cause of symptoms and abnormal lung function in asthma. As airway smooth muscle (ASM) is the effecter controlling airway calibre, it is suspected that dysfunction of ASM contributes to the pathophysiology of asthma. However, the precise role of ASM in the series of events leading to asthmatic symptoms is not clear. It is not certain whether, in asthma, there is a change in the intrinsic properties of ASM, a change in the structure and mechanical properties of the noncontractile components of the airway wall, or a change in the interdependence of the airway wall with the surrounding lung parenchyma. All these potential changes could result from acute or chronic airway inflammation and associated tissue repair and remodelling. Anti-inflammatory therapy, however, does not "cure" asthma, and airway hyperresponsiveness can persist in asthmatics, even in the absence of airway inflammation. This is perhaps because the therapy does not directly address a fundamental abnormality of asthma, that of exaggerated airway narrowing due to excessive shortening of ASM. In the present study, a central role for airway smooth muscle in the pathogenesis of airway hyperresponsiveness in asthma is explored.

Fuzzy expert system in the prediction of neonatal resuscitation

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5 percent and specificity of 94.8 percent in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Fuzzy expert system in the prediction of neonatal resuscitation.

In view of the importance of anticipating the occurrence of critical situations in medicine, we propose the use of a fuzzy expert system to predict the need for advanced neonatal resuscitation efforts in the delivery room. This system relates the maternal medical, obstetric and neonatal characteristics to the clinical conditions of the newborn, providing a risk measurement of need of advanced neonatal resuscitation measures. It is structured as a fuzzy composition developed on the basis of the subjective perception of danger of nine neonatologists facing 61 antenatal and intrapartum clinical situations which provide a degree of association with the risk of occurrence of perinatal asphyxia. The resulting relational matrix describes the association between clinical factors and risk of perinatal asphyxia. Analyzing the inputs of the presence or absence of all 61 clinical factors, the system returns the rate of risk of perinatal asphyxia as output. A prospectively collected series of 304 cases of perinatal care was analyzed to ascertain system performance. The fuzzy expert system presented a sensitivity of 76.5% and specificity of 94.8% in the identification of the need for advanced neonatal resuscitation measures, considering a cut-off value of 5 on a scale ranging from 0 to 10. The area under the receiver operating characteristic curve was 0.93. The identification of risk situations plays an important role in the planning of health care. These preliminary results encourage us to develop further studies and to refine this model, which is intended to implement an auxiliary system able to help health care staff to make decisions in perinatal care.

Telemedicine model for training non-medical persons in the early recognition of melanoma.

A Web-based educational model, called JUTE, was developed for the early diagnosis of melanoma. It was compared with a control Website composed of information available on the Internet for teaching undergraduate medical students. The JUTE model was designed to allow the student linear navigation of the main topics that were assumed to be important in learning to make a diagnosis. The rate of success in correctly deciding to refer pigmented lesions to a dermatologist was compared among 34 new medical students who were randomly divided into two groups. There was no significant difference between the JUTE and control groups in the pre-test. When comparing the pre- and post-tests, the number of correct decisions increased significantly only in the JUTE group. In the JUTE group there was a slight but significant improvement when comparing decisions about thin melanoma before and after the training. The educational approach chosen for the JUTE Website appears to be useful for teaching the early recognition of melanoma and could be used for larger educational campaigns of skin cancer prevention.