RESUMO
There is an urgent need for expanding the number of brain banks serving psychiatric research. We describe here the Psychiatric Disorders arm of the Brain Bank of the Brazilian Aging Brain Study Group (Psy-BBBABSG), which is focused in bipolar disorder (BD) and obsessive compulsive disorder (OCD). Our protocol was designed to minimize limitations faced by previous initiatives, and to enable design-based neurostereological analyses. The Psy-BBBABSG first milestone is the collection of 10 brains each of BD and OCD patients, and matched controls. The brains are sourced from a population-based autopsy service. The clinical and psychiatric assessments were done by an expert team including psychiatrists, through an informant. One hemisphere was perfused-fixed to render an optimal fixation for conducting neurostereological studies. The other hemisphere was comprehensively dissected and frozen for molecular studies. In 20 months, we collected 36 brains. A final report was completed for 14 cases: 3 BDs, 4 major depressive disorders, 1 substance use disorder, 1 mood disorder NOS, 3 obsessive compulsive spectrum symptoms, 1 OCD and 1 schizophrenia. The majority were male (64%), and the average age at death was 67.2 ± 9.0 years. The average postmortem interval was 16 h. Three matched controls were collected. The pilot stage confirmed that the protocols are well fitted to reach our goals. Our unique autopsy source makes possible to collect a fairly number of high quality cases in a short time. Such a collection offers an additional to the international research community to advance the understanding on neuropsychiatric diseases.
Assuntos
Pesquisa Biomédica , Encéfalo/patologia , Transtornos Mentais/patologia , Bancos de Tecidos , Idoso , Idoso de 80 Anos ou mais , Brasil , Cérebro/patologia , Criopreservação , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Perfusão , Fixação de TecidosRESUMO
This paper presents a novel approach to the problem of splice site prediction, by applying stochastic grammar inference. We used four grammar inference algorithms to infer 1465 grammars, and used 10-fold cross-validation to select the best grammar for each algorithm. The corresponding grammars were embedded into a classifier and used to run splice site prediction and compare the results with those of NNSPLICE, the predictor used by the Genie gene finder. We indicate possible paths to improve this performance by using Sakakibara's windowing technique to find probability thresholds that will lower false-positive predictions.
Assuntos
Algoritmos , Inteligência Artificial , Modelos Moleculares , Splicing de RNA/genética , Humanos , Processos EstocásticosRESUMO
This paper presents a novel approach to the problem of splice site prediction, by applying stochastic grammar inference. We used four grammar inference algorithms to infer 1465 grammars, and used 10-fold cross-validation to select the best grammar for each algorithm. The corresponding grammars were embedded into a classifier and used to run splice site prediction and compare the results with those of NNSPLICE, the predictor used by the Genie gene finder. We indicate possible paths to improve this performance by using Sakakibaras windowing technique to find probability thresholds that will lower false-positive predictions.