Patients with cervical cancer show minimal clinical response to the tyrosine kinase inhibitor gefitinib, which targets the epidermal growth factor receptor (EGFR). The molecular mechanisms underlying sensitivity to gefitinib are unknown. The purpose of this study was to investigate the possible mechanism by which microRNA-221 (miR-221) affects sensitivity to gefitinib. We showed that miR-221 expression was significantly increased in cervical cancer tissues compared with adjacent normal tissues. Upregulation of miR-221 expression in cervical cancer cells decreased PTEN expression levels, resulting in increased pAkt and BCL-2 expression. Importantly, gefitinib sensitivity was decreased by the upregulation of miR-221, which was blocked by pcDNA-PTEN co-transfection or by the phosphatidylinositol-3 kinase (PI3K) inhibitor LY294002. These data suggest that miR-221 can reduce the sensitivity of cervical cancer cells to gefitinib through the PTEN/PI3K/Akt signaling pathway. miR-221 represents a potential target to increase the sensitivity to gefitinib in cervical cancer treatment.

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Autism spectrum disorder (ASD) is a severe neurodevelopment disorder. This study tests the hypothesis that children with ASD show atypical intrinsic complexity of brain activity. Electroencephalogram data were collected from boys with ASD and matching normal typically developing children while performing an observation and an imitation task. The multiscale entropy was estimated within the 0.5–30 Hz frequency band over 30 time scales using a coarse-grained procedure. A decreased electroencephalogram complexity was observed in the ASD children both during the observation and during the imitation tasks. On comparing the two tasks, significant differences were observed between groups in the right hemisphere, and also the central cortex for the observation task. Multiscale entropy could provide further evidence of the relationship between ASD and cerebral dysfunction.

GoalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.MethodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.ResultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.ConclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD.SignificanceThese findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.