Background Preventing chronic disease is important in health policy in countries with significantly ageing populations. This study aims to examine the prevalence of chronic disease multimorbidity and its association with physical activity and sleep duration; and to understand whether physical activity modifies associations between sleep duration and multimorbidity. Methods We utilized longitudinal data of a nationally-representative sample from the China Health and Retirement Longitudinal Study (in year 2011 and 2015; N = 5321; 54.7% female; age ≥ 45 years old). Fourteen chronic diseases were used to measure multimorbidity (ten self-reported, and four by blood test). Participants were grouped into high, moderate, and low level based on self-reported frequencies and durations of physical activity with different intensities for at least 10 min at a time in a usual week. Poor and good sleepers were categorized according to average hours of actual sleep at each night during the past month. Panel data method of random-effects logistic regression model was applied to estimate the association of physical activity and sleep with multimorbidity, adjusting for social-demographic and behavioural confounders. Results From 2011 to 2015, the prevalence of multimorbidity increased from 52.2 to 62.8%. In 2015, the proportion of participants engaging in high, moderate, and low level of physical activity was 30.3, 24.4 and 45.3%, respectively, and 63.6% of adults had good sleep. For both genders, compared with good sleep, poor sleep was associated with higher odds of multimorbidity (OR = 1.527, 95% CI: 1.277, 1.825). Compared to the high-level group, participants with a low level of physical activity were significantly more likely to have multimorbidity (OR = 1.457, 95% CI: 1.277, 1.825), but associations were stronger among women. The relative excess risk due to interaction between poor sleep and moderate or low physical activity was positive but non-significant on multimorbidity. Conclusions The burden of multimorbidity was high in China. Low physical activity and poor sleep was independently and significantly associated with a higher likelihood of multimorbidity in women and both genders, separately. Physical activity could modify the association between sleep and multimorbidity.

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RAB39B is located on the X chromosome and encodes the RAB39B protein that belongs to the RAB family. Mutations in RAB39B are known to be associated with X-linked intellectual disability (XLID), Parkinson’s disease, and autism. However, the patho/physiological functions of RAB39B remain largely unknown. In the present study, we established Rab39b knockout (KO) mice, which exhibited overall normal birth rate and morphologies as wild type mice. However, Rab39b deficiency led to reduced anxiety and impaired learning and memory in 2 months old mice. Deletion of Rab39b resulted in impairments of synaptic structures and functions, with reductions in NMDA receptors in the postsynaptic density (PSD). RAB39B deficiency also compromised autophagic flux at basal level, which could be overridden by rapamycin-induced autophagy activation. Further, treatment with rapamycin partially rescued impaired memory and synaptic plasticity in Rab39b KO mice, without affecting the PSD distribution of NMDA receptors. Together, these results suggest that RAB39B plays an important role in regulating both autophagy and synapse formation, and that targeting autophagy may have potential for treating XLID caused by RAB39B loss-of-function mutations.

A Control SidebarFigure 1: With BLUFF, users interactively visualize how adversarial attacks penetrate a deep neural network to induce incorrect outcomes. Here, a user inspects why INCEPTIONV1 misclassifies adversarial giant panda images, crafted by the Projected Gradient Descent (PGD) attack, as armadillo. PGD successfully perturbed pixels to induce the "brown bird" feature, an appearance more likely shared by an armadillo (small, roundish, brown body) than a panda, activating more features that contribute to the armadillo (mis)classification (e.g., "scales," "bumps," "mesh"). The adversarial pathways, formed by these neurons and their connections, overwhelm the benign panda pathways and lead to the ultimate misclassification. (A) Control Sidebar allows users to specify what data is to be included and highlighted. (B) Graph Summary View visualizes pathways most activated or changed by an attack as a network graph of neurons (each labeled by the channel ID in its layer) and their connections. When hovering over a neuron, (C) Detail View displays its feature visualization, representative dataset examples, and activation patterns over attack strengths.

Chronic renal graft dysfunction (CAD) is caused by multiple factors, including glomerular sclerosis, inflammation, interstitial fibrosis and tubular atrophy (IF/TA). However, the most prominent elements of CAD are IF/TA. Our studies have confirmed that endothelial-mesenchymal transition (EndMT) is an important source to allograft IF/TA. The characteristic of EndMT is the loss of endothelial marker and the acquisition of mesenchymal or fibroblastic phenotypes. Autophagy is an intracellular degradation pathway that is regulated by autophagy-related proteins and plays a vital role in many fibrotic conditions. However, whether or not autophagy contributes to fibrosis of renal allograft and how such mechanism occurs still remains unclear. Autophagy related 16 like gene (ATG16L) is a critical autophagy-related gene (ARG) necessary for autophagosome formation. Here, we first analyzed kidney transplant patient tissues from Gene Expression Omnibus (GEO) datasets and 60 transplant patients from our center. Recipients with stable kidney function were defined as non-CAD group and all patients in CAD group were histopathologically diagnosed with CAD. Results showed that ATG16L, as one significant differential ARG, was less expressed in CAD group compared to the non-CAD group. Furthermore, we found there were less autophagosomes and autolysosomes in transplanted kidneys of CAD patients, and downregulation of autophagy is a poor prognostic factor. In vitro, we found out that the knockdown of ATG16L enhanced the process of EndMT in human renal glomerular endothelial cells (HRGECs). In vivo, the changes of EndMT and autophagic flux were then detected in rat renal transplant models of CAD. We demonstrated the occurrence of EndMT, and indicated that abundance of ATG16L was accompanied by the dynamic autophagic flux change along different stages of kidney transplantation. Mechanistically, knockdown of ATG16L, specifically in endothelial cells, reduced of NF-κB degradation and excreted inflammatory cytokines (IL-1β, IL-6 and TNF-α), which could facilitate EndMT. In conclusion, ATG16L-dependent autophagic flux causing by transplant showed progressive loss increase over time. Inflammatory cytokines from this process promoted EndMT, thereby leading to progression of CAD. ATG16L served as a negative regulator of EndMT and development of renal graft fibrosis, and autophagy can be explored as a potential therapeutic target for chronic renal graft dysfunction.

Background:The efficacy and safety of direct-acting antivirals (DAAs) for treating hepatitis C virus (HCV)-infected renal transplant recipients (RTRs) has not been determined.Methods:We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials and assessed the quality of eligible studies using the Joanna Briggs Institute scale. DAA efficacy and safety were assessed using standard mean difference (SMD) with 95% confidence intervals (95%CIs).Results:Six studies (360 RTRs) were included. Two hundred thirty six RTRs (98.3%) achieved sustained virological response within 12 weeks; HCV infection was cleared in 239 RTRs after 24-week treatment. Liver function differed significantly pre- and posttreatment (alanine aminotransferase, SMD: 0.96, 95%CIs: 0.65, 1.26; aspartate aminotransferase, SMD: 0.89, 95%CIs: 0.60, 1.18); allograft function pre- and posttreatment was not statistically different (serum creatinine, SMD: −0.13, 95%CIs: −0.38, 0.12; estimated glomerular filtration rate, SMD: 0.20, 95%CIs: −0.11, 0.51). General symptoms (fatigue nausea dizziness or headache) were the most common adverse events (AEs) (39.3%). Severe AEs, that is, anemia, portal vein thrombosis, and streptococcus bacteraemia and pneumonia, were present in 1.1%, 0.6%, and 1.1% of RTRs, respectively.Conclusion:Our findings suggest that DAAs are highly efficacious and safe for treating HCV-infected RTRs and without significant AE.

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen‐induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non‐destructive manner by detecting endogenous changes in metabolic co‐enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single‐cell images from six donors, we evaluate classifiers ranging from traditional models that use previously‐extracted image features to convolutional neural networks (CNNs) pre‐trained on general non‐biological images. Adapting pre‐trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.

Uterine sarcomas are rare malignant tumors of the uterus with a high degree of malignancy. Their clinical manifestations, imaging examination findings, and laboratory test results overlap with those of uterine fibroids. No reliable diagnostic criteria can distinguish uterine sarcomas from other uterine tumors, and the final diagnosis is usually only made after surgery based on histopathological evaluation. Conservative or minimally invasive treatment of patients with uterine sarcomas misdiagnosed preoperatively as uterine fibroids will shorten patient survival. Herein, we will summarize recent advances in the preoperative diagnosis of uterine sarcomas, including epidemiology and clinical manifestations, laboratory tests, imaging examinations, radiomics and machine learning-related methods, preoperative biopsy, integrated model and other relevant emerging technologies.