Luis F. Callado
Dr. Luis F. Callado has 25 years of experience in neurochemical studies and functional responses of receptors to psychoactive drugs. His work is focused on neurobiology of receptors and transduction systems in depression, drug dependence and other psychiatric disorders.
Current research involves the neurochemical study of α2 adrenoceptors and their role in some neuropsychiatric disorders. For this purpose, Dr. Callado is also interested in the characterization and development of new chemical compounds with high affinity and selectivity for the α2 adrenoceptors. These studies include the use of radioligand binding techniques, biochemical assays as well as in vivo methods. All these techniques and methodologies are currently functioning in his laboratory.
The experience and qualifications of Dr. Callado in the field are clearly represented in the number and level of his publications: to date, he has published 86 papers in high-standard peer-reviewed journals and has presented results at several international conferences. He has on-going research collaborations with several European groups working in neuropsychopharmacology. His current research work is supported by grants funded by the governments of the Basque Country and Spain.
11th June 2015 – 11:00-12:00 Room 1
New computational applications for drug addiction
Drug addiction is a chronically relapsing disorder characterized by compulsion to seek and take the drug, and a loss of control in limiting intake. A fundamental characteristic of all abused drugs is that they alter mental states. Thus, impulsivity often dominates at the early stages of addiction and impulsivity combined with compulsivity dominates at the later stages. In this context, the outcomes for addiction treatment are still limited Moreover, vulnerability to relapse can persist in addicted patients after years of abstinence.
In the last years, an innovative constellation of technologies that incorporate artificial intelligence, continuous biophysical monitoring, wireless connectivity, and smartphone computation has been developed to help in preventing, detecting and treating drug addiction. From machine-learning analyses to assess whether speech characteristics could predict drug condition, to multimedia devices that can detect developing drug craving, these new technologies could change in a future the way to approach drug addiction. Actually, several randomized controlled trials are ongoing to test the potential of these new computational applications to improve addiction treatment outcomes.