Use of machine learning to shorten observation-based screening and diagnosis of autism.

The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60 min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization-in particular those focused on assessment of short home videos of children--that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.

Empowering adolescents with asthma to take control through adaptation.

Clinical nurse specialists, family nurse practitioners, pediatric nurse practitioners, and pediatric nurses in schools, primary practice settings, and the emergency department are at the front line of caring for adolescents with asthma. By empowering adolescents, these health care professionals can reduce the cost of health care and morbidity for these patients. The purposes of this article are (a) to discuss adolescence and powerlessness and (b) to identify nursing interventions that can empower adolescents to adapt behaviors that will enhance the prevention of acute attacks and encourage preventive management of the disease process. The article will also discuss current nursing strategies used with adolescents to promote feelings of power and control of medications and treatment based on current national guidelines and the Roy adaptation model.

Regulation of neurokinin-1 receptor expression by GABA(B) receptor agonists.

Activation of GABA(B) receptors produces analgesia in acute and chronic pain models. Data indicate that a possible mechanism for this effect is a GABA(B) receptor-induced blockade of neurokinin-1 (NK-1) receptor gene expression in the spinal cord. While much more potent GABA(B) receptor agonists (CGP 44532) have been developed, there is no information on their antinociceptive properties or their ability to influence NK-1 receptors. To address these issues, rats were treated with baclofen or CGP 44532 and tested for sedation, ataxia, and pain-related behaviors in a chronic pain model (formalin hindpaw injection). In a separate group of experiments the analgesic response to a single dose of CGP 44532 was tested prior, and subsequent to, its chronic administration. The results indicate that CGP 44532 is a substantially more potent analgesic than baclofen. In addition, after chronic administration baclofen was no longer capable of inducing analgesia or of inhibiting the increased expression of NK-1R mRNA and CGP 44532 was still fully effective in both regards. The results suggest that GABA(B) agonists could be clinically useful analgesics.