Respiratory syncytial virus-associated hospital admissions by deprivation levels among children and adults in Scotland

Background: Socioeconomic deprivation may predispose individuals to respiratory tract infections. We estimated RSV-associated hospitalizations by socioeconomic deprivation in Scotland. Methods: Using national routine health care records and virological surveillance from 2010 to 2016, we used a time-series linear regression model and a direct measurement based on ICD-10 coded diagnoses to estimate RSV-associated hospitalizations by Scottish Index of Multiple Deprivation (SIMD) quintile and age in comparison to influenza-associated hospitalizations. Results: We estimated an annual average rate per 1000 people of 0.76 (95% CI: 0.43–0.90) in the least deprived group to 1.51 (1.03–1.79) for the most deprived group using model-based approach. The rate ratio (RR) was 1.96 (1.23–3.25), 1.60 (1.0–2.66), 1.35 (0.85–2.25), and 1.12 (0.7–1.85) in the 1st to 4th quintile versus the least deprived group. The pattern of RSV-associated hospitalization rates variation with SIMD was most pronounced in children 0-2y. The ICD-10 approach provided much lower rates than the model-based approach but yielded similar RR estimates between SIMD. Influenza-associated hospitalization rate generally increased with higher deprivation levels among individuals 1y+. Conclusions: Higher RSV and influenza hospitalization rates are related to higher deprivation levels. Differences between deprivation levels are most pronounced in infants and young children for RSV, and are more apparent among individuals 1y+ for influenza

Remnant cholesterol is associated with cardiovascular mortality

Background: Genetic, observational, and clinical intervention studies indicate that circulating levels of remnant cholesterol (RC) are associated with cardiovascular diseases. However, the predictive value of RC for cardiovascular mortality in the general population remains unclear. Methods: Our study population comprised 19,650 adults in the United States from the National Health and Nutrition Examination Survey (NHANES) (1999–2014). RC was calculated from non-high-density lipoprotein cholesterol (non-HDL-C) minus low-density lipoprotein cholesterol (LDL-C) determined by the Sampson formula. Multivariate Cox regression, restricted cubic spline analysis, and subgroup analysis were applied to explore the relationship of RC with cardiovascular mortality. Results: The mean age of the study cohort was 46.4 ± 19.2 years, and 48.7% of participants were male. During a median follow-up of 93 months, 382 (1.9%) cardiovascular deaths occurred. In a fully adjusted Cox regression model, log RC was significantly associated with cardiovascular mortality [hazard ratio (HR) 2.82; 95% confidence interval (CI) 1.17–6.81]. The restricted cubic spline curve indicated that log RC had a linear association with cardiovascular mortality (p for non-linearity = 0.899). People with higher LDL-C (≥130 mg/dL), higher RC [≥25.7/23.7 mg/dL in males/females corresponding to the LDL-C clinical cutoff point (130 mg/dL)] and abnormal HDL-C (<40/50 mg/dL in males/females) levels had a higher risk of cardiovascular mortality (HR 2.18; 95% CI 1.13–4.21 in males and HR 2.19; 95% CI 1.24–3.88 in females) than the reference group (lower LDL-C, lower RC and normal HDL-C levels). Conclusions: Elevated RC levels were associated with cardiovascular mortality independent of traditional risk factors

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value &lt; 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value &lt; 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values &lt; 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value &lt; 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

Combinational Growth Factor and Gas Delivery for Thrombosis Prevention

Cardiovascular stents enable the rapid re-endothelialization of endothelial cells (ECs), and the constant suppression of smooth muscle cell (SMC) proliferation has been proved to effectively prevent thrombosis. However, the development and application of such stents are still insufficient due the delayed re-endothelialization progress, as well as the poor durability of the SMC inhibition. In this paper, we developed a mussel-inspired coating with the ability for the dual delivery of both growth factor (e.g., platelet-derived growth factor, PDGF) and therapeutic gas (e.g., nitric oxide, NO) for thrombosis prevention. We firstly synthesized the mussel-inspired co-polymer (DMHM) of dopamine methacrylamide (DMA) and hydroxyethyl methacrylate (HEMA) and then coated the DMHM on 316L SS stents combined with CuII. Afterwards, we immobilized the PDGF on the DMHM-coated stent and found that the PDGF could be released in the first 3 days to enhance the recruitment, proliferation, and migration of human umbilical vein endothelial cells (HUVECs) to promote re-endothelialization. The CuII could be “sealed” in the DMHM coating, with extended durability (2 months), with the capacity for catalyzed NO generation for up to 2 months to suppress the proliferation of SMCs. Such a stent surface modification strategy could enhance the development of the cardiovascular stents for thrombosis prevention

Biological carbon fixation: From natural to synthetic

The looming energy crisis and greenhouse effect are two of the greatest problems facing the sustainable development of humanity. Conversion of carbon dioxide (CO2) into fuels and chemicals by organisms is a promising way to solve these problems. However, since the natural biological carbon fixation rate cannot meet the industrial demand, more efficient carbon fixation processes are urgently needed. With the rapid development of biotechnology and life sciences, more and more information about the natural carbon fixation processes have been revealed. The unrelenting efforts have been practiced for improving the carbon fixation efficiency by redesigning carbon fixation pathways and even introducing novel energy supply patterns. In this review, we summarized the recent achievements and discussed the future prospects on biological carbon fixation

DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression

With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity

Respiratory syncytial virus–associated hospital admissions by deprivation levels among children and adults in Scotland

Background: Socioeconomic deprivation may predispose individuals to respiratory tract infections. We estimated RSV-associated hospitalizations by socioeconomic deprivation in Scotland. Methods: Using national routine health care records and virological surveillance from 2010 to 2016, we used a time-series linear regression model and a direct measurement based on ICD-10 coded diagnoses to estimate RSV-associated hospitalizations by Scottish Index of Multiple Deprivation (SIMD) quintile and age in comparison to influenza-associated hospitalizations. Results: We estimated an annual average rate per 1000 people of 0.76 (95% CI: 0.43–0.90) in the least deprived group to 1.51 (1.03–1.79) for the most deprived group using model-based approach. The rate ratio (RR) was 1.96 (1.23–3.25), 1.60 (1.0–2.66), 1.35 (0.85–2.25), and 1.12 (0.7–1.85) in the 1st to 4th quintile versus the least deprived group. The pattern of RSV-associated hospitalization rates variation with SIMD was most pronounced in children 0-2y. The ICD-10 approach provided much lower rates than the model-based approach but yielded similar RR estimates between SIMD. Influenza-associated hospitalization rate generally increased with higher deprivation levels among individuals 1y+. Conclusions: Higher RSV and influenza hospitalization rates are related to higher deprivation levels. Differences between deprivation levels are most pronounced in infants and young children for RSV, and are more apparent among individuals 1y+ for influenza

Biological carbon fixation: From natural to synthetic

The looming energy crisis and greenhouse effect are two of the greatest problems facing the sustainable development of humanity. Conversion of carbon dioxide (CO2) into fuels and chemicals by organisms is a promising way to solve these problems. However, since the natural biological carbon fixation rate cannot meet the industrial demand, more efficient carbon fixation processes are urgently needed. With the rapid development of biotechnology and life sciences, more and more information about the natural carbon fixation processes have been revealed. The unrelenting efforts have been practiced for improving the carbon fixation efficiency by redesigning carbon fixation pathways and even introducing novel energy supply patterns. In this review, we summarized the recent achievements and discussed the future prospects on biological carbon fixation.</p