Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 7 de 7
Mais filtros

Base de dados
Tipo de estudo
Intervalo de ano de publicação
Gen Psychiatr ; 32(3): e100077, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31360910


Postpartum psychosis is a condition characterised by rapid onset of psychotic symptoms several weeks after childbirth. Outside of its timing and descriptions of psychotic features, minimal research exists due to its relative rarity (1 to 2 per 1000 births in the USA), with greater emphasis on postpartum sadness and depression. With the existing literature, cultural differences and language barriers previously have not been taken into consideration as there are no documented cases of postpartum psychosis in a non-English-speaking patient. Correctly differentiating postpartum psychosis from other postpartum psychiatric disorders requires adeptly evaluating for the presence of psychotic symptoms with in-depth history taking. Here, we present a case of postpartum psychosis in a Japanese-speaking only patient, with an associated clinical course and culturally appropriate approach to treatment. A review of postpartum psychosis and language/cultural considerations are also discussed, with attention on the Japanese concept of 'Satogaeri bunben'.

Hum Mutat ; 40(9): 1215-1224, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31301154


Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

Processamento Alternativo , Biologia Computacional/métodos , Mutação , Proteínas/genética , Animais , Congressos como Assunto , Aptidão Genética , Humanos , Modelos Genéticos , Homologia de Sequência do Ácido Nucleico
Hum Mutat ; 40(9): 1261-1269, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31090248


Single nucleotide mutations in exonic regions can significantly affect gene function through a disruption of splicing, and various computational methods have been developed to predict the splicing-related effects of a single nucleotide mutation. We implemented a new method using ensemble learning that combines two types of predictive models: (a) base sequence-based deep neural networks (DNNs) and (b) machine learning models based on genomic attributes. This method was applied to the Massively Parallel Splicing Assay challenge of the Fifth Critical Assessment of Genome Interpretation, in which challenge participants predicted various experimentally-defined exonic splicing mutations, and achieved a promising result. We successfully revealed that combining different predictive models based upon the stacked generalization method led to significant improvement in prediction performance. In addition, whereas most of the genomic features adopted in constructing machine learning models were previously reported, feature values generated with DSSP, a DNN-based splice site prediction tool, were novel and helpful for the prediction. Learning the sequence patterns associated with normal splicing and the change in splicing site probabilities caused by a mutation was presumed to be helpful in predicting splicing disruption.

Biologia Computacional/métodos , Polimorfismo de Nucleotídeo Único , Processamento de RNA , Aprendizado Profundo , Éxons , Genômica , Humanos , Modelos Genéticos
J Comput Biol ; 25(8): 954-961, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-29668310


Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site. The length of input is 140 nucleotides, with the consensus sequence (i.e., "GT" and "AG" for the donor and acceptor sites, respectively) in the middle. Each input sequence model is applied to the pretrained DNN model that determines the probability that an input is a splice site. The model consists of convolutional layers and bidirectional long short-term memory network layers. The pretraining and validation were conducted using the data set tested in previously reported methods. The performance evaluation results showed that the proposed method can outperform the previous methods. In addition, the pattern learned by the DNNs was visualized as position frequency matrices (PFMs). Some of PFMs were very similar to the consensus sequence. The trained DNN model and the brief source code for the prediction system are uploaded. Further improvement will be achieved following the further development of DNNs.

Algoritmos , Biologia Computacional/métodos , Aprendizado Profundo , Redes Neurais de Computação , Sítios de Splice de RNA/genética , Gráficos por Computador , Humanos , Matrizes de Pontuação de Posição Específica , Software
J Neurosci Methods ; 291: 141-149, 2017 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-28837816


BACKGROUND: The morphometric analysis of myelinated nerve fibers of peripheral nerves in cross-sectional optical microscopic images is valuable. Several automated methods for nerve fiber identification and segmentation have been reported. This paper presents a new method that uses a deep learning model of a convolutional neural network (CNN). We tested it for human sural nerve biopsy images. METHODS: The method comprises four steps: normalization, clustering segmentation, myelinated nerve fiber identification, and clump splitting. A normalized sample image was separated into individual objects with clustering segmentation. Each object was applied to a CNN deep learning model that labeled myelinated nerve fibers as positive and other structures as negative. Only positives proceeded to the next step. For pretraining the model, 70,000 positive and negative data each from 39 samples were used. The accuracy of the proposed algorithm was evaluated using 10 samples that were not part of the training set. A P-value of <0.05 was considered statistically significant. RESULTS: The total true-positive rate (TPR) for the detection of myelinated fibers was 0.982, and the total false-positive rate was 0.016. The defined total area similarity (AS) and area overlap error of segmented myelin sheaths were 0.967 and 0.068, respectively. In all but one sample, there were no significant differences in estimated morphometric parameters obtained from our method and manual segmentation. COMPARISON WITH EXISTING METHODS: The TPR and AS were higher than those obtained using previous methods. CONCLUSIONS: High-performance automated identification and segmentation of myelinated nerve fibers were achieved using a deep learning model.

Processamento de Imagem Assistida por Computador/métodos , Aprendizado de Máquina , Microscopia/métodos , Fibras Nervosas Mielinizadas , Reconhecimento Automatizado de Padrão/métodos , Adolescente , Adulto , Idoso , Biópsia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fibras Nervosas Mielinizadas/patologia , Nervo Sural/citologia , Nervo Sural/patologia
J Stroke Cerebrovasc Dis ; 24(12): 2754-8, 2015 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26460245


BACKGROUND: The accuracy of the Alberta Stroke Program Early CT Score (ASPECTS) as a prognostic indicator in the treatment of cerebral infarction with thrombolysis remains controversial. We hypothesized that ASPECTS can more accurately predict treatment outcomes by excluding isolated cortical swelling (ICS) from ASPECTS and retrospectively tested patients treated with thrombolysis. METHODS: This retrospective cohort study included 106 patients treated with intravenous thrombolysis for cerebral infarction in our hospital. We included only patients with middle cerebral artery infarction. For the modification of ASPECTS, we removed each ICS from the ASPECTS system. We compared the correlation coefficients between the ASPECTS and modified ASPECTS with regard to treatment outcome, and performed a multivariate logistic regression analysis to evaluate the association between modified ASPECTS and outcomes. The primary outcome was a modified Rankin Scale score equal to or less than 2 on discharge and the secondary outcomes included an improvement of National Institutes of Health Stroke Scale (NIHSS) score of 4 or greater within 24 hours. RESULTS: Seventy-two patients were included in this study. The correlation coefficient of modified ASPECTS was significantly higher than that of ASPECTS in the primary outcome (r = .249 versus r = .363, P < .001) and in the improvement of NIHSS score (r = .303 versus r = .443, P < .001). Multivariate analysis revealed that a modified ASPECTS greater than 7 was significantly associated with the primary outcome (odds ratio [OR] = 1.334, 95% confidence interval [CI] = 1.071-1.661, P = .012) and the improvement of the NIHSS score (OR = 1.555, 95% CI = 1.208-2.003, P = .001). CONCLUSIONS: The present study reveals that ASPECTS may be more strongly associated with outcome by excluding ICS.

Isquemia Encefálica/tratamento farmacológico , Córtex Cerebral/diagnóstico por imagem , Fibrinolíticos/uso terapêutico , Acidente Vascular Cerebral/tratamento farmacológico , Terapia Trombolítica , Ativador de Plasminogênio Tecidual/uso terapêutico , Idoso , Idoso de 80 Anos ou mais , Alberta , Isquemia Encefálica/diagnóstico por imagem , Angiografia Cerebral , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Estudos Retrospectivos , Acidente Vascular Cerebral/diagnóstico por imagem , Resultado do Tratamento