SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.     

行为-结构化-关系干预模式对短期住院孤独症谱系障碍儿童的疗效分析

目的 探讨以行为-结构化-关系(BSR)干预模式对短期住院孤独症谱系障碍(ASD)儿童的干预疗效,为改善ASD儿童的预后提供参考依据。方法 选取2015年12月－2016年12月确诊为ASD的2~6岁儿童141例,随机分为治疗组和对照组。治疗组ASD儿童均接受BSR模式的短期课程训练,训练课程包括个别辅导、游戏课、运动课、音乐课等,每天训练时间6 h,持续1个月;对照组ASD儿童处于干预等待,接受随访观察和评估。所有ASD儿童干预前、后均接受儿童心理教育评估(第三版)(PEP-3)来进行各方面能力变化的评估。结果 治疗组、对照组干预前各副测验的原积分差异无统计学意义(P>0.05)。两组ASD儿童干预后认知、语言理解、模仿、情感表达、社会互动、行为特征-非语言、适应行为的原积分比较,差异均有统计学意义(t=2.41、2.02、4.14、3.69、4.42、2.69、2.96,P<0.05);但小肌肉、大肌肉、自理的原积分比较,差异无统计学意义(t=-1.13、-1.05、-0.84,P>0.05)。结论 BSR干预模式能够有效改善短期住院ASD儿童的预后,可推广用于儿童ASD的治疗。     

MRI图像融合技术在肛瘘评估中的初步应用

目的采用图像融合技术获得T2WI与T2WI-FS的融合图像，评估其在肛瘘及肛周结构显示中的优势。 方法2016年6月至2018年6月，前瞻性选择中山大学附属第一医院29例肛瘘患者进行肛管磁共振（MR）检查，采用图像融合技术获取T2WI与T2WI-FS的融合图像T2WI-Fusion，利用Fisher score算法计算瘘管及肛门括约肌的组织间分辨力Fisher值、脂肪与肛门括约肌间的Fisher值，评估融合图像中瘘管及肛周结构的显示情况。采用改进的双刺激连续质量量表（DSCQS）对T2WI-FS、T2WI、增强3D-VIBE和T2WI-Fusion序列图像进行主观图像质量评价。 结果29例患者均成功获得T2WI与T2WI-FS的融合图像T2WI-Fusion。T2WI-Fusion、T2WI瘘管与括约肌间Fisher均值分别为6.46、3.31，T2WI-Fusion图像对瘘管的显示优于T2WI序列图像（P<0.001）。T2WI-Fusion、T2WI-FS脂肪与括约肌间Fisher均值分别为10.61、2.45，T2WI-Fusion图像对括约肌的显示优于T2WI-FS序列图像（P<0.001）。T2WI-Fusion对瘘管与括约肌的图像质量评价总评分均高于T2WI-FS、T2WI、增强3D-VIBE序列（P<0.001）。 结论MRI图像融合技术同时具备T2WI及T2WI-FS的优势，无需增加扫描序列及扫描时间，且操作简单，花费时间短，显著提高病变及肛周解剖结构的对比度和图像质量。     

Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

BackgroundPrediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort.MethodsThe Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model.ResultsOf the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability.ConclusionsThe ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.     

Identification of key genes and pathways in discoid lupus skin via bioinformatics analysis

Discoid lupus erythematosus (DLE) is the most common skin manifestation of lupus; however, the molecular mechanisms underlying DLE remain unknown. Therefore, we aimed to identify key differentially expressed genes (DEGs) in discoid lupus skin and investigate their potential pathways.To identify candidate genes involved in the occurrence and development of the disease, we downloaded the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE52471","term_id":"52471"}}GSE52471 and {"type":"entrez-geo","attrs":{"text":"GSE72535","term_id":"72535"}}GSE72535 from the Gene Expression Database (GEO). DEGs between discoid lupus skin and normal controls were selected using the GEO2R tool and Venn diagram software (http://bioinformatics.psb.ugent.be/webtools/Venn/). The Database for Annotation, Visualization, and Integrated Discovery (DAVID), Enrichr, and Cytoscape ClueGo were used to analyze the Kyoto Encyclopedia of Gene and Genome pathways and gene ontology. Protein-protein interactions (PPIs) of these DEGs were further assessed using the Search Tool for the Retrieval Interacting Genes version 10.0.Seventy three DEGs were co-expressed in both datasets. DEGs were predominantly upregulated in receptor signaling pathways of the immune response. In the PPI network, 69 upregulated genes were selected. Furthermore, 4 genes (CXCL10, ISG15, IFIH1, and IRF7) were found to be significantly upregulated in the RIG-I-like receptor signaling pathway, from analysis of Enrichr and Cytoscape ClueGo.The results of this study may provide new insights into the potential molecular mechanisms of DLE. However, further experimentation is required to confirm these findings.     

Caveolin-1 in skin aging – From innocent bystander to major contributor

Caveolin-1 (Cav-1) appears to be both a pathophysiological contributor and a target in different inflammatory and hyperproliferative skin conditions as well as in skin aging. Skin fibroblasts demonstrate an up-regulation of Cav-1 expression both in chronological and UV-induced aging, and such an up-regulation was observed both in vitro and in vivo. Typical alterations in aging skin involve a reduction of the dermis thickness, a significant expansion of the dermal white adipose tissue as well as modifications of the content and distribution of hyaluronan, impairment of autophagic flux, a reduction of collagen expression and an increase in tissue inflammation. All of these phenomena can be connected with changes in Cav-1 expression in the aging skin. Modified expression of Cav-1 can also significantly influence the mechanical properties of individual skin layers, thus changing the total mechanical stability of the layered composite skin/WAT, leading to typical structural modifications of the skin surface in the aging skin. Selective reduction of Cav-1 expression has the potential to exert anti-aging effects on the skin.     

Key Social Determinants to Narrow the Gap between Health-adjusted Life Expectancy and Life Expectancy in Megacities

ObjectiveImprovement in the quality of life is reflected in the narrowing of the gap between health-adjusted life expectancy (HALE) and life expectancy (LE). The effect of megacity expansion on narrowing the gap is rarely reported. This study aimed to disclose this potential relationship.MethodsAnnual life tables were constructed from identified death records and population counts from multiple administrative sources in Guangzhou, China, from 2010 to 2020. Joinpoint regression was used to evaluate the temporal trend. Generalized principal component analysis and multilevel models were applied to examine the county-level association between the gap and social determinants.ResultsAlthough LE and HALE in megacities are increasing steadily, their gap is widening. Socio-economic and health services are guaranteed to narrow this gap. Increasing personal wealth, a growing number of newborns and healthy immigrants, high urbanization, and healthy aging have helped in narrowing this gap.ConclusionIn megacities, parallel LE and HALE growth should be highly considered to narrow their gap. Multiple social determinants need to be integrated as a whole to formulate public health plans.