Microengineered synthetic cellular microenvironment for stem cells

Stem cells possess the ability of self‐renewal and differentiation into specific cell types. Therefore, stem cells have great potentials in fundamental biology studies and clinical applications. The most urgent desire for stem cell research is to generate appropriate artificial stem cell culture system, which can mimic the dynamic complexity and precise regulation of the in vivo biochemical and biomechanical signals, to regulate and direct stem cell behaviors. Precise control and regulation of the biochemical and biomechanical stimuli to stem cells have been successfully achieved using emerging micro/nanoengineering techniques. This review provides insights into how these micro/nanoengineering approaches, particularly microcontact printing and elastomeric micropost array, are applied to create dynamic and complex environment for stem cells culture. WIREs Nanomed Nanobiotechnol 2012, 4:414–427. doi: 10.1002/wnan.1175 For further resources related to this article, please visit the WIREs website .Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92053/1/1175_ftp.pd

Angiogenic deficiency and adipose tissue dysfunction are associated with macrophage malfunction in SIRT1 \u3csup\u3e-/-\u3c/sup\u3e mice

The histone deacetylase sirtuin 1 (SIRT1) inhibits adipocyte differentiation and suppresses inflammation by targeting the transcription factors peroxisome proliferator-activated receptor γ and nuclear factor κB. Although this suggests that adiposity and inflammation should be enhanced when SIRT1 activity is inactivated in the body, this hypothesis has not been tested in SIRT1 null (SIRT1 -/-) mice. In this study, we addressed this issue by investigating the adipose tissue in SIRT1 -/-mice. Compared with their wild-type littermates, SIRT1 null mice exhibited a significant reduction in body weight. In adipose tissue, the average size of adipocytes was smaller, the content of extracellular matrix was lower, adiponectin and leptin were expressed at 60% of normal level, and adipocyte differentiation was reduced. All of these changes were observed with a 50% reduction in capillary density that was determined using a three-dimensional imaging technique. Except for vascular endothelial growth factor, the expression of several angiogenic factors (Pdgf, Hgf, endothelin, apelin, and Tgf-β)was reduced by about 50%. Macrophage infiltration and inflammatory cytokine expression were 70% less in the adipose tissue of null mice and macrophage differentiation was significantly inhibited in SIRT1 -/- mouse embryonic fibroblasts in vitro. In wild-type mice, macrophage deletion led to a reduction in vascular density. These data suggest that SIRT1 controls adipose tissue function through regulation of angiogenesis, whose deficiency is associated with macrophage malfunction in SIRT1 -/- mice. The study supports the concept that inflammation regulates angiogenesis in the adipose tissue. Copyright © 2012 by The Endocrine Society

Enhanced Boundary Learning for Glass-like Object Segmentation

Glass-like objects such as windows, bottles, and mirrors exist widely in the real world. Sensing these objects has many applications, including robot navigation and grasping. However, this task is very challenging due to the arbitrary scenes behind glass-like objects. This paper aims to solve the glass-like object segmentation problem via enhanced boundary learning. In particular, we first propose a novel refined differential module that outputs finer boundary cues. We then introduce an edge-aware point-based graph convolution network module to model the global shape along the boundary. We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours. Both modules are lightweight and effective: they can be embedded into various segmentation models. In extensive experiments on three recent glass-like object segmentation datasets, including Trans10k, MSD, and GDD, our approach establishes new state-of-the-art results. We also illustrate the strong generalization properties of our method on three generic segmentation datasets, including Cityscapes, BDD, and COCO Stuff. Code and models is available at \url{https://github.com/hehao13/EBLNet}.Comment: ICCV-2021 Code is availabe at https://github.com/hehao13/EBLNe

An interpretable imbalanced semi-supervised deep learning framework for improving differential diagnosis of skin diseases

Dermatological diseases are among the most common disorders worldwide. This paper presents the first study of the interpretability and imbalanced semi-supervised learning of the multiclass intelligent skin diagnosis framework (ISDL) using 58,457 skin images with 10,857 unlabeled samples. Pseudo-labelled samples from minority classes have a higher probability at each iteration of class-rebalancing self-training, thereby promoting the utilization of unlabeled samples to solve the class imbalance problem. Our ISDL achieved a promising performance with an accuracy of 0.979, sensitivity of 0.975, specificity of 0.973, macro-F1 score of 0.974 and area under the receiver operating characteristic curve (AUC) of 0.999 for multi-label skin disease classification. The Shapley Additive explanation (SHAP) method is combined with our ISDL to explain how the deep learning model makes predictions. This finding is consistent with the clinical diagnosis. We also proposed a sampling distribution optimisation strategy to select pseudo-labelled samples in a more effective manner using ISDLplus. Furthermore, it has the potential to relieve the pressure placed on professional doctors, as well as help with practical issues associated with a shortage of such doctors in rural areas

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two critical issues: The one is an extremely foreground-background imbalanced distribution, and the other is multiple small objects along with the complex background. Such problems make the recent dense affinity context modeling perform poorly even compared with baselines due to over-introduced background context. To handle these problems, we propose a point-wise affinity propagation module based on the Feature Pyramid Network (FPN) framework, named PointFlow. Rather than dense affinity learning, a sparse affinity map is generated upon selected points between the adjacent features, which reduces the noise introduced by the background while keeping efficiency. In particular, we design a dual point matcher to select points from the salient area and object boundaries, respectively. Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods. Especially, our methods achieve the best speed and accuracy trade-off on three aerial benchmarks. Further experiments on three general semantic segmentation datasets prove the generality of our method. Code will be provided in (https: //github.com/lxtGH/PFSegNets).Comment: accepted by CVPR202

The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications

Diabetic retinopathy is one of the most common complications of diabetes mellitus and the leading cause of low vision and blindness worldwide. Mounting evidence demonstrates that inflammation is a key mechanism driving diabetes-associated retinal disturbance, yet the pathophysiological process and molecular mechanisms of inflammation underlying diabetic retinopathy are not fully understood. Cytokines, chemokines, and adhesion molecules interact with each other to form a complex molecular network that propagates the inflammatory and pathological cascade of diabetic retinopathy. Therefore, it is important to understand and elucidate inflammation-related mechanisms behind diabetic retinopathy progression. Here, we review the current understanding of the pathology and pathogenesis of inflammation in diabetic retinopathy. In addition, we also summarize the relevant clinical trials to further suggest inflammation-targeted therapeutics for prevention and management of diabetic retinopathy

COVID-19 symptoms and compliance: The mediating role of fundamental social motives

BackgroundUnderstanding the compliance of infected individuals and the psychological process underlying compliance during pandemics is important for preventing and controlling the spread of pathogens. Our study investigated whether fundamental social motives mediate the relationship between having infectious disease and compliance.MethodsAn online survey was conducted in March 2020, during the severe phase of the COVID-19 outbreak in China to collect data from 15,758 participants. The survey comprised self-report questionnaires with items pertaining to current symptoms (COVID-19 symptoms, other symptoms or no symptoms), the Fundamental Social Motive Inventory, and measures of compliance. Correlation analysis, linear regression analysis, and structural equation model were used for data analysis.ResultsThe participants with COVID-19 symptoms had lower levels of compliance than those without symptoms, and their lower compliance was caused by a decrease in disease avoidance (indirect effect = −0.058, 95% CI = [−0.061, −0.056]) and familial motives (indirect effect = −0.113, 95% CI = [−0.116, −0.062]). Whereas exclusion concern (indirect effect = 0.014, 95% CI = [0.011, 0.017]) suppressed the effects of COVID-19 symptoms on compliance, the effect disappeared in the multiple mediation model, while those of disease avoidance and familial motives remained.ConclusionOur findings emphasize the critical role of disease avoidance and familial motives in promoting compliance with public health norms during pandemics and suggest that enhancing these motives may serve as an effective intervention strategy to mitigate noncompliance among potentially infected individuals

Depression in Chinese patients with type 2 diabetes: associations with hyperglycemia, hypoglycemia, and poor treatment adherence.

BackgroundWe hypothesize that depression in type 2 diabetes might be associated with poor glycemic control, in part due to suboptimal self-care. We tested this hypothesis by examining the associations of depression with clinical and laboratory findings in a multicenter survey of Chinese type 2 diabetic patients.Method2538 patients aged 18-75 years attending hospital-based clinics in four cities in China underwent detailed clinical-psychological-behavioral assessment during a 12-month period between 2011 and 2012. Depression was diagnosed if Patient Health Questionnaire-9 (PHQ-9) score ≥10. Diabetes self-care and medication adherence were assessed using the Summary of Diabetes Self-care Activities and the 4-item Morisky medication adherence scale respectively.ResultsIn this cross-sectional study (mean age: 56.4 ± 10.5[SD] years, 53% men), 6.1% (n = 155) had depression. After controlling for study sites, patients with depression had higher HbA(1c) (7.9 ± 2.0 vs. 7.7 ± 2.0%, P = 0.008) and were less likely to achieve HbA(1c) goal of <7.0% (36.2% vs 45.6%, P = 0.004) than those without depression. They were more likely to report hypoglycemia and to have fewer days of being adherent to their recommended diet, exercise, foot care and medication. In logistic regression, apart from young age, poor education, long disease duration, tobacco use, high body mass index, use of insulin, depression was independently associated with failure to attain HbA(1c) target (Odds Ratio [OR] = 1.56, 95%CI:1.05-2.32, P = 0.028). The association between depression and glycemic control became non-significant after inclusion of adherence to diet, exercise and medication (OR = 1.48, 95% CI 0.99-2.21, P = 0.058).ConclusionDepression in type 2 diabetes was closely associated with hyperglycemia and hypoglycemia, which might be partly mediated through poor treatment adherence

Epigenome-Wide Histone Acetylation Changes in Peripheral Blood Mononuclear Cells in Patients with Type 2 Diabetes and Atherosclerotic Disease

There is emerging evidence of an association between epigenetic modifications, glycemic control and atherosclerosis risk. In this study, we mapped genome-wide epigenetic changes in patients with type 2 diabetes (T2D) and advanced atherosclerotic disease. We performed chromatin immunoprecipitation sequencing (ChIP-seq) using a histone 3 lysine 9 acetylation (H3K9ac) mark in peripheral blood mononuclear cells from patients with atherosclerosis with T2D (n = 8) or without T2D (ND, n = 10). We mapped epigenome changes and identified 23,394 and 13,133 peaks in ND and T2D individuals, respectively. Out of all the peaks, 753 domains near the transcription start site (TSS) were unique to T2D. We found that T2D in atherosclerosis leads to an H3K9ac increase in 118, and loss in 63 genomic regions. Furthermore, we discovered an association between the genomic locations of significant H3K9ac changes with genetic variants identified in previous T2D GWAS. The transcription factor 7-like 2 (TCF7L2) rs7903146, together with several human leukocyte antigen (HLA) variants, were among the domains with the most dramatic changes of H3K9ac enrichments. Pathway analysis revealed multiple activated pathways involved in immunity, including type 1 diabetes. Our results present novel evidence on the interaction between genetics and epigenetics, as well as epigenetic changes related to immunity in patients with T2D and advanced atherosclerotic disease.Peer reviewe