Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0037315 (sleep apnea)
 8,000 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

We report 24 unrelated individuals with deletions and 17 additional cases with duplications at 10q11.21q21.1 identified by chromosomal microarray analysis. The rearrangements range in size from 0.3 to 12 Mb. Nineteen of the deletions and eight duplications are flanked by large, directly oriented segmental duplications of >98% sequence identity, suggesting that nonallelic homologous recombination (NAHR) caused these genomic rearrangements. Nine individuals with deletions and five with duplications have additional copy number changes. Detailed clinical evaluation of 20 patients with deletions revealed variable clinical features, with developmental delay (DD) and/or intellectual disability (ID) as the only features common to a majority of individuals. We suggest that some of the other features present in more than one patient with deletion, including hypotonia, sleep apnea, chronic constipation, gastroesophageal and vesicoureteral refluxes, epilepsy, ataxia, dysphagia, nystagmus, and ptosis may result from deletion of the CHAT gene, encoding choline acetyltransferase, and the SLC18A3 gene, mapping in the first intron of CHAT and encoding vesicular acetylcholine transporter. The phenotypic diversity and presence of the deletion in apparently normal carrier parents suggest that subjects carrying 10q11.21q11.23 deletions may exhibit variable phenotypic expressivity and incomplete penetrance influenced by additional genetic and nongenetic modifiers.
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PMID:Recurrent deletions and reciprocal duplications of 10q11.21q11.23 including CHAT and SLC18A3 are likely mediated by complex low-copy repeats. 2194 86

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.
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PMID:Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry. 3194 46