Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Early and correct diagnosis of Parkinson's disease (PD) is critical for patient counseling and therapeutic management. The diagnostic accuracy of transcranial sonography of substantia nigra (SN-TCS) for early stage PD patients remains controversial. Dopamine transporter (DAT) imaging is sensitive to detect presynaptic dopamine neuronal dysfunction, and has been studied as a diagnostic tool for degenerative Parkinsonism. To investigate the predictive value of SN-TCS for the DAT PET scans in clinically diagnosed early stage PD patients, we performed the SN-TCS and DAT Positron Emission Computed Tomography (PET) imaging examinations on 53 patients. Using the DAT PET results as clinical gold standard, the sensitivity and specificity of TCS was 68.75% and 40% respectively. The positive predictive value (PPV) of an abnormal TCS for an abnormal PET scan was 91.67%. However, the negative predictive value (NPV) for a normal PET scan was only 11.76%. The false negative rate was 31.25%. In 35 patients, the result of the SN-TCD was in accordance with the result of the DAT PET scan (Kappa=0.042, P>0.05). The consistency between SN-TCS and PET scans was poor. We conclude that SN-TCS would not be used as a diagnostic tool for early stage PD patients. Negative result of TCS could not exclude the diagnosis of PD. Further tests like DAT-PET is needed for validation. On the other hand, positive SN-TCS will reduce the added diagnostic value of a presynaptic neuronimaging scan.
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PMID:The predictive value of transcranial sonography in clinically diagnosed patients with early stage Parkinson's disease: comparison with DAT PET scans. 2521 16

Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict GTP binding sites in Rab proteins, which is one of the most vital molecular functions in life science. A functional loss of GTP binding sites in Rab proteins has been implicated in a variety of human diseases (choroideremia, intellectual disability, cancer, Parkinson's disease). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases and designing the drug targets. Our deep learning model with two-dimensional convolutional neural network and position-specific scoring matrix profiles could identify GTP binding residues with achieved sensitivity of 92.3%, specificity of 99.8%, accuracy of 99.5%, and MCC of 0.92 for independent dataset. Compared with other published works, this approach achieved a significant improvement. Throughout the proposed study, we provide an effective model for predicting GTP binding sites in Rab proteins and a basis for further research that can apply deep learning in bioinformatics, especially in nucleotide binding site prediction.
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PMID:Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins. 3086 34