Pubmed du 24/01/22
1. Knoedler JR, Inoue S, Bayless DW, Yang T, Tantry A, Davis CH, Leung NY, Parthasarathy S, Wang G, Alvarado M, Rizvi AH, Fenno LE, Ramakrishnan C, Deisseroth K, Shah NM. A functional cellular framework for sex and estrous cycle-dependent gene expression and behavior. Cell. 2022; 185(4): 654-71.e22.
Sex hormones exert a profound influence on gendered behaviors. How individual sex hormone-responsive neuronal populations regulate diverse sex-typical behaviors is unclear. We performed orthogonal, genetically targeted sequencing of four estrogen receptor 1-expressing (Esr1(+)) populations and identified 1,415 genes expressed differentially between sexes or estrous states. Unique subsets of these genes were distributed across all 137 transcriptomically defined Esr1(+) cell types, including estrous stage-specific ones, that comprise the four populations. We used differentially expressed genes labeling single Esr1(+) cell types as entry points to functionally characterize two such cell types, BNSTpr(Tac1/Esr1) and VMHvl(Cckar/Esr1). We observed that these two cell types, but not the other Esr1(+) cell types in these populations, are essential for sex recognition in males and mating in females, respectively. Furthermore, VMHvl(Cckar/Esr1) cell type projections are distinct from those of other VMHvl(Esr1) cell types. Together, projection and functional specialization of dimorphic cell types enables sex hormone-responsive populations to regulate diverse social behaviors.
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2. Scavarda A, Ariel Cascio M. Embracing and rejecting the medicalization of autism in Italy. Social science & medicine (1982). 2022; 294: 114728.
Medicalization is increasingly recognized as a bidirectional process, with patients and their families as agents. The paper considers the specific case of the medicalization of autism in Italy, from the point of view of parents of autistic people with different levels of support needs. Through reporting and comparing results of two independently conducted qualitative studies, this paper aims to analyze how parents embrace and resist the medicalization of autism in their everyday lives and in healthcare contexts. Both studies involved participant-observation with services that targeted autistic people and interviews with parents, professionals, and autistic people. Results show that parents of autistic people both embrace and resist medicalization. While parents (sometimes ambivalently) accept the responsibilization inherent in their engagement with interventions (a sort of « therapeutization » of life) and reject lay expertise by deferring to experts’ knowledge, they also resist the application of medical labels, language and practices in various ways in their everyday lives. Both embracing and resisting medicalization can be useful for achieving overarching social goals of being a good parent, helping their children, and pursuing respect and social harmony. Medicalization derives not only from the cultural dominance of medical discourses, which seems to incorporate resistance to medicalization stances, but also from the absence of continuity and coordination of services, particularly in the Italian context of public (but increasingly privatizing) health and welfare services.
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3. Wen G, Cao P, Bao H, Yang W, Zheng T, Zaiane O. MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis. Computers in biology and medicine. 2022; 142: 105239.
PURPOSE: Recently, functional brain networks (FBN) have been used for the classification of neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder diagnosis with FBN is a challenging task due to the high heterogeneity in subjects and the noise correlations in brain networks. Meanwhile, it is challenging for the existing deep learning models to provide interpretable insights into the brain network. We propose a machine learning approach for the classification of neurological disorders while providing an interpretable framework. METHOD: In this paper, we build upon graph neural network in order to learn effective representations for brain networks in an end-to-end fashion. Specifically, we present a prior brain structure learning-guided multi-view graph convolutional neural network (MVS-GCN), which collaborates the graph structure learning and multi-task graph embedding learning to improve the classification performance and identify the potential functional subnetworks. RESULTS: To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental results indicate that our MVS-GCN can achieve enhanced performance compared with state-of-the-art methods. Notably, MVS-GCN achieves an average accuracy/AUC of 69.38%/69.01% on the ABIDE dataset. Moreover, the obtained results from our model show high consistency with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed MVS-GCN model. CONCLUSION: The proposed MVS-GCN method performs a graph embedding learning from the multi-views graph embedding learning perspective while considering eliminating the heterogeneity in brain networks and enhancing the feature representation of functional subnetworks, which can capture the essential embeddings to improve the classification performance of brain disorder diagnosis. The code is available at https://github.com/GuangqiWen/MVS-GCN.
