Predicting brain structural network using functional connectivity

Uncovering the non-trivial brain structure–function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and function, e.g., the relations between individual brain structural connectivity (SC) and functional connectivity (FC). Brain structure–function displays a distributed and heterogeneous pattern, that is, many functional relationships arise from non-overlapping sets of anatomical connections. This complex relation can be interwoven with widely existed individual structural and functional variations. Motivated by the advances of generative adversarial network (GAN) and graph convolutional network (GCN) in the deep learning field, in this work, we proposed a multi-GCN based GAN (MGCN-GAN) to infer individual SC based on corresponding FC by automatically learning the complex associations between individual brain structural and functional networks. The generator of MGCN-GAN is composed of multiple multi-layer GCNs which are designed to model complex indirect connections in brain network. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish the predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designed a new structure-preserving (SP) loss function to guide the generator to learn the intrinsic SC patterns more effectively. Using Human Connectome Project (HCP) dataset and Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset as test beds, our MGCN-GAN model can generate reliable individual SC from FC. This result implies that there may exist a common regulation between specific brain structural and functional architectures across different individuals.     

Molecular Imaging Cellular SPIO Uptake with Nonlinear Optical Microscopy

We report a novel application to demonstrate and visualize the selective binding of lipids in cells of the reticuloendothelial system to super paramagnetic iron oxide (SPIO) nanoparticles. Ten New Zealand White rabbits that were experimentally injected intravenously with SPIO and five controls were investigated with vibrational microspectroscopy based on surface-enhanced coherent anti-Stokes Raman scattering (SECARS) microscopy. Marked cellular intensity enhancements in hepatic Kupffer cells and melanomacrophages of spleen have been observed in the range of 2850–2875 cm^?1 in SPIO-injected animals but not in controls. The enhancements are related to the selective association of lipid molecules in cells of the reticuloendothelial system to uptaken SPIO, which can uniquely be visualized with SECARS microscopy.     

Segmental aneuploidies with 1 Mb resolution in human preimplantation blastocysts

PurposeThis study aimed to investigate the spectrum and characteristics of segmental aneuploidies (SAs) of <10 megabase (Mb) length in human preimplantation blastocysts.MethodsPreimplantation genetic testing for aneuploidy was performed in 15,411 blastocysts from 5171 patients using a validated 1 Mb resolution platform. The characteristics and spectrum of SAs, including the incidence, sizes, type, inheritance pattern, clinical significance, and embryo distribution, were studied.ResultsIn total, 6.4% of the 15,411 blastocysts carried SAs of >10 Mb, 4.9% of embryos had SAs ranging between 1 to 10 Mb, and 84.3% of 1 to 10 Mb SAs were <5 Mb in size. Inheritance pattern analysis indicated that approximately 63.8% of 1 to 10 Mb SAs were inherited and were predominantly 1 to 3 Mb in size. Furthermore, 18.4% of inherited SAs and 51.9% de novo 1 to 10 Mb SAs were pathogenic or likely pathogenic (P/LP). Different from whole-chromosome aneuploidies, reanalysis indicated that 50% of the de novo 1 to 10 Mb SAs and 70% of the >10 Mb SAs arose from mitotic errors.ConclusionBased on the established platform, 1 to 10 Mb SAs are common in blastocysts and include a subset of P/LP SAs. Inheritance pattern analysis and clinical interpretation based on the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines contributed to determine the P/LP SAs.     

Nutritional assessment and risk factors associated to malnutrition in patients with esophageal cancer

Introduction: Esophageal cancer is the fourth most common cause of cancer death in China. Patients with esophageal cancer are more likely to suffer from malnutrition. The purpose of this study is to assess nutritional status of patients with esophageal cancer from multiple perspectives and analyze the risk factors. Methods: A total of 1482 esophageal cancer patients were enrolled in the study. We investigated the Scored Patient Generated Subjective Global Assessment (PG-SGA) scores, NRS-2002 scores, Karnofsky performance status scores, anthropometric, and laboratory indicators of patients. Unconditional logistic regression analysis was applied to identify the risk factors of nutritional status. Results: PG-SGA (≥4) and NRS-2002 (≥3) showed the incidence of malnutrition were 76% and 50%, respectively. In the patients with PG-SGA score ≥4, the proportion of patients who did not receive any nutritional support was 60%. The incidence of malnutrition in females was significantly higher than that in males. Besides, abnormality rates of Red blood cell (P < 0.001), MAC (P = 0.037), and MAMC (P < 0.001) in males was significantly higher than that in females, while abnormality rates of TSF (P < 0.001) was lower than that in females. After adjusted with the other potential risk factors listed, unconditional logistic regression analysis indicated smoking (odds ratio: 2.868, 95% confidence interval: 1.660-4.954), drinking (OR: 1.726, 95% CI: 1.099-2.712), family history (OR: 1.840, 95% CI: 1.132-2.992), radiotherapy or chemotherapy (OR: 1.594, 95% CI: 1.065-2.387), and pathological stage (OR: 2.263, 95% CI: 1.084-4.726) might be the risk factors of nutritional status, while nutritional support can reduce the risk of malnutrition. Conclusion: Effective nutritional risk assessment methods and nutritional intervention measures can be adopted according to the research data to improve quality of life of esophageal cancer patients.     

An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders

It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.     

Impact of Physical Activity After Cancer Diagnosis on Survival in Patients With Recurrent Colon Cancer: Findings From CALGB 89803/Alliance

BackgroundThe impact of physical activity on survival outcomes in patients with recurrent colon cancer has not been studied. We tested the association between the level of postdiagnosis physical activity and survival outcomes of patients with recurrent colon cancer.Patients and MethodsWe conducted a prospective observational study of 237 patients with stage III colon cancer who had recurrence of disease. Physical activity was measured approximately 6 months after the completion of therapy (14 months after surgical resection) but before detection of recurrent disease. The primary end point of the study was survival time after recurrence.ResultsThe hazard ratio comparing patients who reported at least 18 metabolic equivalent task (MET) hours per week of physical activity with those engaging in < 3 MET hours per week was 0.71 (95% confidence interval, 0.46-1.11). Increasing total MET hours of physical activity per week was associated with a borderline statistical significance trend for improved survival after recurrence (P = .052). The benefit of physical activity on survival was not significantly modified by sex, body mass index (BMI), number of positive lymph nodes, age, baseline performance status, adjuvant chemotherapy regimen, or recurrence-free survival period.ConclusionTo our knowledge, this is the first study investigating the association of physical activity with survival outcome of patients with recurrent colon cancer. Although the association exceeded our predefined P trend < .05 for statistical significance, these findings warrant further studies of physical activity in patients with recurrent colorectal cancer.     

Systematic review of clinical practice guidelines for kidney disease in China: application of the Appraisal of Guidelines Research and Evaluation-China

Objective To evaluate the quality of clinical practice guidelines for kidney diseases in China and provide reference for selecting suitable high-quality guidelines for primary care and developing standardized guidelines. Methods The China Guideline Clearinghouse, China Biology Medicine disc, VIP Database, Wanfang Database and CNKI, and other resources were searched from January 2013 to July 2018. In accordance with the criteria for inclusion and exclusion, the published guidelines for kidney diseases were screened. The Appraisal of Guidelines Research and Evaluation-China (AGREE-China) was used to systematically assess the current status of domestic guidelines for kidney diseases. Results A total of 18 guidelines for kidney diseases were included, covering different types of kidney disease such as glomerulonephritis, nephrotic syndrome, end-stage renal disease and other diseases. The overall score ranged from 30 to 68, with an average score of 47.3. The average scores of these guidelines were 20.1, 12.8, 0.5, 9.9 and 3.9 in five review fields including scientificity/rigorism, effectiveness/safety, economy, availability/feasibility, and conflicts of interest, respectively. Of these 18 guidelines, 8(44.4%) guidelines were strongly recommended, and 10(55.6%) guidelines were weakly recommended. Conclusions There are still deficiencies in scientificity/rigorism and economy in current guidelines for kidney diseases in China. The AGREE-China can be used as an evaluation tool for guidelines for kidney diseases in accordance with China's situation, while its practicability and feasibility still need further verification and improvement.