Macular microcirculation changes after repair of rhegmatogenous retinal detachment assessed with optical coherence tomography angiography: A systematic review and meta-analysis

Purpose: The aim of the study was to investigate microcirculation changes in the macula evaluated by optical coherence tomography angiography (OCTA)in patients receiving anatomical repair after surgery for rhegmatogenous retinal detachment (RRD).Methods: A literature search was conducted in PubMed, EMBASE, Web of Science and the Cochrane Library. Studies including patients with macula-on or macula-off RRD and repaired successfully through primary surgery were selected. Foveal avascular zone (FAZ) area and macular vascular density (VD) in both the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were analyzed using RevMan 5.4 software.Results: Twelve studies including 430 RRD eyes and 430 control eyes were selected. In eyes with macula-on RRD, FAZ area, VD in the foveal SCP and DCP, and VD in the parafoveal SCP and DCP were not altered compared with control eyes, after the retina was reattached. In eyes with macula-off RRD that was repaired successfully through surgery, FAZ area in the DCP (0.13 mm2, 95% CI: 0.02 to 0.25, p = 0.02) remained enlarged compared with control eyes. Meanwhile, VD in the foveal DCP was also significantly reduced (−3.12%, 95% CI: −6.15 to −0.09%, p = 0.04), even though retinal reattachment was achieved by surgery in eyes with macula-off RRD.Conclusion: In patients with macula-off rhegmatogenous retinal detachment, foveal avascular zone area in the deep capillary plexuses was enlarged and vascular density in the foveal deep capillary plexus was reduced, even after the retina was successfully reattached through a primary surgery

Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

Abstract The interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics, and targeted therapeutics. Significance: Analysis of matched primary and metastatic tumor samples supports a unidirectional, linear cancer evolution process and sheds light on longstanding issues regarding the origins of molecular subtypes and their progression relationships. </jats:sec

Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

AbstractThe interpretation of accumulating genomic data with respect to tumor evolution and cancer progression requires integrated models. We developed a computational approach that enables the construction of disease progression models using static sample data. Application to breast cancer data revealed a linear, branching evolutionary model with two distinct trajectories for malignant progression. Here, we used the progression model as a foundation to investigate the relationships between matched primary and metastasis breast tumor samples. Mapping paired data onto the model confirmed that molecular breast cancer subtypes can shift during progression, and supported directional tumor evolution through luminal subtypes to increasingly malignant states. Cancer progression modeling through the analysis of available static samples represents a promising breakthrough. Further refinement of a roadmap of breast cancer progression will facilitate the development of improved cancer diagnostics, prognostics and targeted therapeutics.</jats:p

Deep Learning Approach to Identifying Breast Cancer Subtypes Using High-Dimensional Genomic Data

AbstractMotivationCancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.ResultsTo address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.Availability and implementationAn open-source software package for the proposed method is freely available atwww.acsu.buffalo.edu/~yijunsun/lab/DeepType.html.</jats:sec

An information diffusion-based recommendation framework for micro-blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches

Supplementary Data from Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

Supplementary data: Detailed descriptions of bioinformatics methods used in the study</p

Supplementary Table 1 from Molecular Profiles of Matched Primary and Metastatic Tumor Samples Support a Linear Evolutionary Model of Breast Cancer

Table S1: Patient clinical information</p