妊娠期阻塞性睡眠呼吸暂停筛查的研究进展

阻塞性睡眠呼吸暂停（OSA）是妊娠人群中常见的睡眠呼吸障碍性疾病,不仅与多种不良妊娠结局相关,也可能对母婴远期健康产生重要影响。目前诊断OSA的金标准多导睡眠监测在妊娠人群中难以大规模开展,导致绝大多数妊娠期OSA未能得到及时诊断。寻求其他筛查策略和筛查工具准确识别具有OSA风险的孕妇并进行及时诊治,对于改善不良妊娠结局具有重要意义。本文针对妊娠期OSA患病率、筛查现状、筛查时机、目标人群、筛查工具的研究进展进行综述,以期为妊娠期OSA的筛查提供参考和理论依据。     

Rubidium-82 Positron Emission Tomography With Intravenous Ergonovine to Diagnose Vasospastic Angina

The gold standard to diagnose vasospastic angina is intracoronary reactivity testing, which is performed selectively at dedicated centres. Noninvasive imaging with single-photon emission computerized tomography (SPECT) or echocardiography does not enable accurate localization of spasm or quantification of change in myocardial perfusion in response to an abnormal vasoreactivity. Rubidium-82 positron emission tomography myocardial perfusion imaging (⁸²Rb PET-MPI) with intravenous ergonovine was used to diagnose refractory vasospastic angina in a patient with a complex ischemic syndrome, recent coronary stenting, and persistent atypical angina despite maximal tolerable doses of guideline-directed medical therapy.     

Polygenic risk scores and risk-stratified breast cancer screening: Familiarity and perspectives of health care professionals

PurposeHealth care professionals are expected to take on an active role in the implementation of risk-based cancer prevention strategies. This study aimed to explore health care professionals’ (1) self-reported familiarity with the concept of polygenic risk score (PRS), (2) perceived level of knowledge regarding risk-stratified breast cancer (BC) screening, and (3) preferences for continuing professional development.MethodsA cross-sectional survey was conducted using a bilingual—English/French—online questionnaire disseminated by health care professional associations across Canada between November 2020 and May 2021.ResultsA total of 593 professionals completed more than 2 items and 453 responded to all questions. A total of 432 (94%) participants were female, 103 (22%) were physicians, and 323 (70%) were nurses. Participants reported to be unfamiliar with (20%), very unfamiliar (32%) with, or did not know (41%) the concept of PRS. Most participants reported not having enough knowledge about risk-stratified BC screening (61%) and that they would require more training (77%). Online courses and webinar conferences were the preferred continuing professional development modalities.ConclusionThe study indicates that health care professionals are currently not familiar with the concept of PRS or a risk-stratified approach for BC screening. Online information and training seem to be an essential knowledge transfer modality.     

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNN^AM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.     

Modeling spatio-temporal patterns of holistic functional brain networks via multi-head guided attention graph neural networks (Multi-Head GAGNNs)

Mounting evidence has demonstrated that complex brain function processes are realized by the interaction of holistic functional brain networks which are spatially distributed across specific brain regions in a temporally dynamic fashion. Therefore, modeling spatio-temporal patterns of holistic functional brain networks plays an important role in understanding brain function. Compared to traditional modeling methods such as principal component analysis, independent component analysis, and sparse coding, superior performance has been achieved by recent deep learning methodologies. However, there are still two limitations of existing deep learning approaches for functional brain network modeling. They either (1) merely modeled a single targeted network and ignored holistic ones at one time, or (2) underutilized both spatial and temporal features of fMRI during network modeling, and the spatial/temporal accuracy was thus not warranted. To address these limitations, we proposed a novel Multi-Head Guided Attention Graph Neural Network (Multi-Head GAGNN) to simultaneously model both spatial and temporal patterns of holistic functional brain networks. Specifically, a spatial Multi-Head Attention Graph U-Net was first adopted to model the spatial patterns of multiple brain networks, and a temporal Multi-Head Guided Attention Network was then introduced to model the corresponding temporal patterns under the guidance of modeled spatial patterns. Based on seven task fMRI datasets from the public Human Connectome Project and resting state fMRI datasets from the public Autism Brain Imaging Data Exchange I of 1448 subjects, the proposed Multi-Head GAGNN showed superior ability and generalizability in modeling both spatial and temporal patterns of holistic functional brain networks in individual brains compared to other state-of-the-art (SOTA) models. Furthermore, the modeled spatio-temporal patterns of functional brain networks via the proposed Multi-Head GAGNN can better predict the individual cognitive behavioral measures compared to the other SOTA models. This study provided a novel and powerful tool for brain function modeling as well as for understanding the brain-cognitive behavior associations.     

The effect of immunosuppressive regimens on the recurrence of primary biliary cirrhosis after liver transplantation

Recurrence of primary biliary cirrhosis (PBC) has been described in liver transplant recipients. Type of immunosuppression has been reported to influence the frequency of recurrence. The aim of this study is to evaluate the occurrence and pattern of recurrent PBC in our liver transplant recipients and determine any association of immunosuppressive agents with its recurrence. Patients who underwent orthotopic liver transplantation (OLT) for PBC were identified from the University of Chicago Liver Transplant Database. Recurrent PBC was diagnosed based on specific pathological criteria. Of 46 patients who underwent OLT for PBC between 1984 and 2000, a total of 7 patients (15%) were diagnosed with recurrent PBC at a median of 78 months (range, 27 to 120 months) after OLT. Forty-three percent of patients were administered cyclosporine, whereas 57% were administered tacrolimus before disease recurrence. Rates of recurrence were not different between patients maintained on cyclosporine therapy (16%) compared with those maintained on tacrolimus therapy (18%; P = 1.0). There also was no difference in frequency of rejection episodes or duration of corticosteroid therapy between those who did and did not have recurrent PBC. In conclusion, recurrent PBC developed in a small number of patients 2 years or longer after OLT. In our population, there was no difference in recurrence rates between those administered cyclosporine or tacrolimus for immunosuppression. (Liver Transpl 2003;9:733-736.)