Metalloproteins as risk factors for osteoarthritis: improving and understanding causal estimates using Mendelian randomization.

Osteoarthritis (OA) is one of the most prevalent musculoskeletal disorders and a primary cause of pain and disability among the elderly population. Research on the relationship between metalloproteins (MPs) and OA is limited, and causality remains unclear. Our objective is to utilize Mendelian randomization (MR) to explore the possible causal relationship between MPs and OA. The data on MPs were derived from a Genome-Wide Association Study (GWAS) analysis involving 3301 samples. The GWAS data for OA were obtained from an analysis involving 462,933 European individuals. In this study, a variety of two-sample Mendelian randomization methods (two-sample MR) to evaluate the causal effect of MPs on OA, including inverse variance weighted method (IVW), MR-Egger method, weighted median method (WM), simple mode, weight mode, and Wald ratio. The primary MR analysis using the IVW method reveals a significant negative correlation between Metallothionein-1F (MT-1F), zinc finger protein 134 (ZNF134), calcium/calmodulin-dependent protein kinase type 1D (CAMK1D), and EF-hand calcium-binding domain-containing protein 14 (EFCAB14) with the occurrence of osteoarthritis (OA) (p value < 0.05). However, no causal relationship was observed in the opposite direction between these MPs and OA. Notably, even in combined models accounting for confounding factors, the negative association between these four MPs and OA remained significant. Sensitivity analysis demonstrated no evidence of horizontal pleiotropy or heterogeneity, and leave-one-out analysis confirmed the robustness of the results. In this study, we have established a conspicuous association between four distinct MPs and OA. This discovery augments our understanding of potential avenues for the diagnosis and treatment of this condition. Key Points • The MR method was employed to assess the relationship between MPs and OA. • A total of four types of MPs have demonstrated inhibitory effects on the occurrence of OA.

Reconstructing the evolution history of networked complex systems.

The evolution processes of complex systems carry key information in the systems' functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks.

Biomimetic Surface Engineering to Modulate the Coffee-Ring Effect for Amyloid-ß Detection in Rat Brains.

Surface engineering of nanoparticles has been widely used in biosensing and assays, where sensitivity was mainly limited by plasmonic colour change or electrochemical responses. Here, we report a novel biomimetic sensing strategy involving protein-modified gold nanoparticles (AuNPs), where the modulation strategy was inspired by gastropods in inhibition of coffee-ring effects in their trail-followings. The so-called coffee-ring effect presents the molecular behaviour of AuNPs to a macroscopic ring through aggregation, and thus greatly improves sensitivity. The assay relies upon the different assembly patterns of AuNPs against analytes, resulting in the formation or suppression of coffee-ring effects by the different surface engineering of AuNPs by proteins and peptides. The mechanism of the coffee-ring formation process is examined through experimental characterizations and computational simulations. A practical coffee-ring effect assay is developed for a proof-of-concept target, amyloid ß (1-42), which is a typical biomarker of Alzheimer's disease. A novel quasi-titrimetric protocol is constructed for quantitative determination of the target molecule. The assay shows excellent selectivity and sensitivity for the amyloid ß monomer, with a low detection limit of 20 pM. Combined with a fluorescent staining technique, the assay is designed as a smart sensor for amyloid ß detection and fibrillation evaluation in rat cerebrospinal fluids, which is a potential point-of-care test for Alzheimer's disease. Connections between amyloid fibrillation and different courses of brain ischaemia are also studied, with improved sensitivity, lower sample volumes that are required, convenience for rapid detection, and point-of-care testing.

AP003352.1/miR-141-3p axis enhances the proliferation of osteosarcoma by LPAR3.

Osteosarcoma (OS) is a highly malignant tumor with a poor prognosis and a growing incidence. LncRNAs and microRNAs control the occurrence and development process of osteosarcoma through ceRNA patterns. The LPAR3 gene is important in cancer cell proliferation, apoptosis and disease development. However, the regulatory mechanism of the ceRNA network through which LPAR3 participates in osteosarcoma has not been clarified. Herein, our study demonstrated that the AP003352.1/miR-141-3p axis drives LPAR3 expression to induce the malignant progression of osteosarcoma. First, the expression of LPAR3 is regulated by the changes in AP003352.1 and miR-141-3p. Similar to the ceRNA of miR-141-3p, AP003352.1 regulates the expression of LPAR3 through this mechanism. In addition, the regulation of AP003352.1 in malignant osteosarcoma progression depends to a certain degree on miR-141-3p. Importantly, the AP003352.1/miR-141-3p/LPAR3 axis can better serve as a multi-gene diagnostic marker for osteosarcoma. In conclusion, our research reveals a new ceRNA regulatory network, which provides a novel potential target for the diagnosis and treatment of osteosarcoma.

Hierarchical community structure in networks.

Modular and hierarchical community structures are pervasive in real-world complex systems. A great deal of effort has gone into trying to detect and study these structures. Important theoretical advances in the detection of modular have included identifying fundamental limits of detectability by formally defining community structure using probabilistic generative models. Detecting hierarchical community structure introduces additional challenges alongside those inherited from community detection. Here we present a theoretical study on hierarchical community structure in networks, which has thus far not received the same rigorous attention. We address the following questions. (1) How should we define a hierarchy of communities? (2) How do we determine if there is sufficient evidence of a hierarchical structure in a network? (3) How can we detect hierarchical structure efficiently? We approach these questions by introducing a definition of hierarchy based on the concept of stochastic externally equitable partitions and their relation to probabilistic models, such as the popular stochastic block model. We enumerate the challenges involved in detecting hierarchies and, by studying the spectral properties of hierarchical structure, present an efficient and principled method for detecting them.

Study on Mechanical Properties and Micro Characterization of Fibre Reinforced Ecological Cementitious Coal Gangue Materials.

Eco-gelled coal gangue materials (EGCGMs) are usually produced using coal gangue, slag, and fly ash in a highly alkaline environment. Herein, to improve the mechanical properties of such materials, polypropylene fibers were uniformly mixed with them. An unconfined compressive strength test and a three-point bending test of the fiber-reinforced EGCGMs under different conditions were conducted. Based on the performance degradation control technology of the fiber structure, the interface mechanism of the composite materials was analyzed from the micro level using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). In the mechanical test, the 28 d UCS and flexural properties of the fiber-reinforced EGCGMs were analyzed using the Box-Behnken design response surface design method and orthogonal design method, respectively. The order of significance was as follows: sodium hydroxide, fiber length, and fiber content. Within the scope of the experimental study, when the NaOH content is 3, the fiber content is 5 ‱, and the fiber length is 9 mm, the mechanical properties are the best. Based on the microscopic equipment, it was discovered that the amorphous ecological glue condensation product formed by the reaction of slag and fly ash in the alkaline environment was filled between the coal gangue particles and the fibers, and several polymerization products accumulated to form a honeycomb network topology. The distribution of fibers in the EGCGM matrix could be primarily divided into single embedded and network occurrences. The fiber inhibits the crack initiation and development of the matrix through the crack resistance effect, and improves the brittleness characteristics through the bridging effect during the failure process, which promotes the ductility of the ecological cementitious coal gangue matrix.The results presented herein can provide a theoretical basis for improving the mechanical properties of alkali-activated geopolymers.

A Bayesian deep learning method for freeway incident detection with uncertainty quantification.

Incident detection is fundamental for freeway management to reduce non-recurrent congestions and secondary incidents. Recently, machine learning technologies have made considerable progress in the incident detection field, but many still face challenges in uncertainty quantification due to the aleatoric uncertainty of traffic data and the epistemic uncertainty of model deviations. In this study, a Bayesian deep learning method was proposed for freeway incident detection with uncertainty quantification. A convolutional neural network variant was designed on a Bayesian framework, and mechanisms of Bayes by backpropagation and local reparameterization technics were used to update the weight of the proposed model. The predictive uncertainty of the proposed method was modeled jointly by integrating the aleatoric and epistemic uncertainty. The proposed model was tested on the PORTAL dataset and compared with four benchmark models: standard normal deviate, wavelet neural network, long-short term memory neural network, and convolutional neural network. The results show that the proposed model outperforms the baseline methods in terms of accuracy, detection rate and false alarm rate. Perturbation experiments were used to test the robustness of the model against noise. The results indicated that the aleatoric uncertainty of the model remained almost constant under different noise levels. The proposed method may benefit future studies on uncertainty quantification while using machine learning method in incident management and other fields in intelligent transportation systems.

Employees' Entrepreneurial Dreams and Turnover Intention to Start-Up: The Moderating Role of Job Embeddedness.

Many people have entrepreneurial dreams in mind, yet existing research has neglected to focus on this phenomenon. This paper introduces the concept of entrepreneurial dreams, constructs a model of the relationship between entrepreneurial dreams and turnover intention to start-up, based on identity theory and prospect theory, and empirically analyses the mechanism of the effect of entrepreneurial dreams on turnover intention to start-up. Through the analysis of data from two multi-provincial and multi-wave employee studies (Study 1 N = 198, Study 2 N = 227), the findings show that: (1) employees' entrepreneurial dreams positively influence turnover intention to start-up; (2) employees' entrepreneurial dreams can stimulate employees' sense of entrepreneurial self-efficacy, thus positively influencing turnover intention to start-up; (3) job embeddedness plays a moderating role in the relationship between entrepreneurial self-efficacy and turnover intention to start-up, specifically, the higher the degree of job embeddedness, the weaker the effect of entrepreneurial self-efficacy on turnover intention to start-up; (4) job embeddedness moderates the indirect effect of entrepreneurial dreams on turnover intention to start-up through entrepreneurial self-efficacy, specifically, the higher the degree of job embeddedness, the weaker the indirect effect of entrepreneurial dreams on turnover intention to start-up through entrepreneurial self-efficacy. This study reveals the mediating role of employees' entrepreneurial self-efficacy and the moderating role of job embeddedness in the influence of entrepreneurial dreams on employees' turnover intention to start-up, which provides theoretical and practical references for relevant organizations.

Research on the Pollutant Migration Law Based on Large-Scale Three-Dimensional Similar Simulation Experiments of Underground Coal Gasification.

The potential pollution risk of underground coal gasification (UCG) has become a key factor restricting the development of UCG industrialization. Therefore, studying the migration and diffusion behavior of harmful pollutants is of great significance for preventing UCG pollution. In this paper, a large-scale three-dimensional similar simulation experimental device for UCG is used to simulate the gasification of Tianjin fat coal under actual working conditions. The rock layer around the simulated coal seam was sampled after the gasification was completed, the contaminants in the samples were examined by XRD, and the changes in the relative content of the contaminants at different sampling points were studied by FTIR. The results showed that benzene, phenols, aldehydes, aromatic hydrocarbons, and aromatic heterocyclic compounds remained after the gasification of No. 7 sampling point in Qianjiang, Tianjin, and that the main pollutants were aromatic hydrocarbons. The migration and enrichment of phenol and aldehyde pollutants were about the same on the east and west sides of the gasification center, while benzene pollutants were more easily migrated and enriched than aromatic heterocyclic compounds. The migration distance of phenolic pollutants on the south side of the gasification area is smaller than that of other pollutants and their maximum vertical distance from the gasification reaction area to the south is about 0.7 m. The results can provide a scientific basis for pollutant risk identification and prevention and control in the later UCG field test.

Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data.

Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects-dimension, object, and situation-for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.

The Role of Grit on Students' Academic Success in Experiential Learning Context.

Students' success as a cognitive issue in learning is prejudiced by proper learning approaches which improve their comprehension and achievement. In an attempt to scrutinize supplementary or alternate variables that envisage students' success, the researcher inspected a non-cognitive factor, namely grit, theorized as passion and perseverance due to its long-term quality, on the one hand, and its popularity among scholars in preceding decades on the other hand. Moreover, experiential learning (EL) is a momentous instructional approach used in the educational process to accelerate "do it and learn." The proposed review aims to gauge the EL approach as well as grit to regulate learners' educational success. Consequently, some pedagogical implications are presented for teachers, students, and syllabus designers.

First derivative synchronous fluorometric method to continuously measure monophenolase activity.

Tyrosinase plays an essential role in melanin biosynthesis and inherently exhibits both monophenolase and diphenolase activity. A first derivative synchronous fluorometric assay was established for directly monitoring monophenolase activity. The zero-crossing point at 322 nm for the first-derivative under synchronous fluorescence with Δλ = 67 nm was utilized to selectively quantify tyrosine in the presence of the reaction product dihydroxyphenylalanine (DOPA). The limit of detection (LOD) for tyrosine was 0.54 µM. The fluorescence intensity of tyrosine was monitored at intervals of 30 s to establish the time course of tyrosine consumption. The LOD for the monophenolase activity was 0.0706 U⋅ mL-1. The Michaelis-Menten e constant and maximum speed were 21.83 µM and 1.12 µM min-1, respectively. Zinc ions competitively inhibited the monophenolase activity, with an IC50 value of 14.36 µM. This assay is easily and rapidly executed and is of great significance for analyzing the kinetics of enzymatic reactions and in fundamental research on monophenolase. This approach has potential applications in the discovery of tyrosinase inhibitors for medicine and cosmetics, as well as in the industrial synthesis of substituted o-diphenol intermediates.

Kallistatin limits abdominal aortic aneurysm by attenuating generation of reactive oxygen species and apoptosis.

Inflammation, vascular smooth muscle cell apoptosis and oxidative stress are believed to play important roles in abdominal aortic aneurysm (AAA) pathogenesis. Human kallistatin (KAL; gene SERPINA4) is a serine proteinase inhibitor previously shown to inhibit inflammation, apoptosis and oxidative stress. The aim of this study was to investigate the role of KAL in AAA through studies in experimental mouse models and patients. Serum KAL concentration was negatively associated with the diagnosis and growth of human AAA. Transgenic overexpression of the human KAL gene (KS-Tg) or administration of recombinant human KAL (rhKAL) inhibited AAA in the calcium phosphate (CaPO4) and subcutaneous angiotensin II (AngII) infusion mouse models. Upregulation of KAL in both models resulted in reduction in the severity of aortic elastin degradation, reduced markers of oxidative stress and less vascular smooth muscle apoptosis within the aorta. Administration of rhKAL to vascular smooth muscle cells incubated in the presence of AngII or in human AAA thrombus-conditioned media reduced apoptosis and downregulated markers of oxidative stress. These effects of KAL were associated with upregulation of Sirtuin 1 activity within the aortas of both KS-Tg mice and rodents receiving rhKAL. These results suggest KAL-Sirtuin 1 signalling limits aortic wall remodelling and aneurysm development through reductions in oxidative stress and vascular smooth muscle cell apoptosis. Upregulating KAL may be a novel therapeutic strategy for AAA.

Bioelectrochemical process for simultaneous removal of copper, ammonium and organic matter using an algae-assisted triple-chamber microbial fuel cell.

Considering the adverse effect of heavy metals (such as Cu2+) on biological wastewater treatment processes, an algae-assisted triple-chamber microbial fuel cell (MFC) was established to remove Cu2+, COD and nitrogen sequentially, and also generate electricity. About 86.2% of the Cu2+ was removed in the first cathodic chamber, and the remaining Cu2+ was largely eliminated by algal uptake, contributing to an overall Cu2+ removal rate of 99.9% across the whole system. The nitrogen removal rate reached 79% in the system. The majority of the ammonium was assimilated by algae, and nitrogen oxides formed during the light period were denitrified at the cathode in the dark period. The variation in electrode potentials indicated that the cathode and anode potentials not only depended on the respective substrate concentrations, but also affected each other. The influence of algae on the microbial communities was greater than that of Cu2+ or the system structure. Devosia, Thauera, Pseudomonas, Acinetobacter and Flavobacterium may influence nitrogen removal, while Delftia, Thauera and Pseudomonas may play an important role in power generation. The present study has developed a practical method for removing pollutants from the wastewater containing heavy metals.

Risk Factors and Mouse Models of Abdominal Aortic Aneurysm Rupture.

Abdominal aortic aneurysm (AAA) rupture is an important cause of death in older adults. In clinical practice, the most established predictor of AAA rupture is maximum AAA diameter. Aortic diameter is commonly used to assess AAA severity in mouse models studies. AAA rupture occurs when the stress (force per unit area) on the aneurysm wall exceeds wall strength. Previous research suggests that aortic wall structure and strength, biomechanical forces on the aorta and cellular and proteolytic composition of the AAA wall influence the risk of AAA rupture. Mouse models offer an opportunity to study the association of these factors with AAA rupture in a way not currently possible in patients. Such studies could provide data to support the use of novel surrogate markers of AAA rupture in patients. In this review, the currently available mouse models of AAA and their relevance to the study of AAA rupture are discussed. The review highlights the limitations of mouse models and suggests novel approaches that could be incorporated in future experimental AAA studies to generate clinically relevant results.

Development of a two-stage limb ischemia model to better simulate human peripheral artery disease.

Peripheral arterial disease (PAD) develops due to the narrowing or blockage of arteries supplying blood to the lower limbs. Surgical and endovascular interventions are the main treatments for advanced PAD but alternative and adjunctive medical therapies are needed. Currently the main preclinical experimental model employed in PAD research is based on induction of acute hind limb ischemia (HLI) by a 1-stage procedure. Since there are concerns regarding the ability to translate findings from this animal model to patients, we aimed to develop a novel clinically relevant animal model of PAD. HLI was induced in male Apolipoprotein E (ApoE-/-) deficient mice by a 2-stage procedure of initial gradual femoral artery occlusion by ameroid constrictors for 14 days and subsequent excision of the femoral artery. This 2-stage HLI model was compared to the classical 1-stage HLI model and sham controls. Ischemia severity was assessed using Laser Doppler Perfusion Imaging (LDPI). Ambulatory ability was assessed using an open field test, a treadmill test and using established scoring scales. Molecular markers of angiogenesis and shear stress were assessed within gastrocnemius muscle tissue samples using quantitative polymerase chain reaction. HLI was more severe in mice receiving the 2-stage compared to the 1-stage ischemia induction procedure as assessed by LDPI (p = 0.014), and reflected in a higher ischemic score (p = 0.004) and lower average distance travelled on a treadmill test (p = 0.045). Mice undergoing the 2-stage HLI also had lower expression of angiogenesis markers (vascular endothelial growth factor, p = 0.004; vascular endothelial growth factor- receptor 2, p = 0.008) and shear stress response mechano-transducer transient receptor potential vanilloid 4 (p = 0.041) within gastrocnemius muscle samples, compared to animals having the 1-stage HLI procedure. Mice subjected to the 2-stage HLI receiving an exercise program showed significantly greater improvement in their ambulatory ability on a treadmill test than a sedentary control group. This study describes a novel model of HLI which leads to more severe and sustained ischemia than the conventionally used model. Exercise therapy, which has established efficacy in PAD patients, was also effective in this new model. This new model maybe useful in the evaluation of potential novel PAD therapies.

High-throughput extracellular pH monitoring and antibiotics screening by polymeric fluorescent sensor with LCST property.

Monitoring extracellular pH (pHe) is important for biology understanding, since pHe and its homeostasis are closely relevant to cellular metabolism. Hydrogel-based pHe sensors have attracted significant attention and showed wide application, while they are tedious with significant time-cost operation and reproducibility variations for high-throughput application. Herein, we synthesized two polymers for pHe monitoring which are soluble in water at room temperature with easy operations and high reproducibility among various micro-plate wells for high-throughput analysis. P1 (P(OEGMA-co-MEO2MA-co-pHS)) and P2 (P(OEGMA-co-pHS)) were synthesized via the Reversible Addition Fragmentation Chain Transfer (RAFT) copolymerization of oligo(ethylene glycol) methacrylate (OEGMA), 2-(2'-methoxyethoxy) ethyl methacrylate (MEO2MA) and the pH sensitive fluorescence moiety N-fluoresceinyl methacrylamide (pHS). P1 is soluble in water at room temperature (25 °C) while insoluble at the temperature above 33 °C, indicating its feature of lower critical solution temperature (LCST) at 33 °C. Further P1 showed higher pH sensitivity and photostability than P2 (without LCST property) when used at physiological temperature (37 °C). Thus, P1 was chosen to in-situ monitor the micro-environmental acidification of E. coli, Hela and Ramos cells during their growth, and the metabolism inhibiting activity of a representative antibiotic, ampicillin. Cell concentration-dependent cellular acidification and drug concentration-dependent inhibition of cellular acidification were observed, demonstrating that the LCST polymer (P1) is suitable for real-time cellular acidification monitoring as well as for high-throughput drug screening. This study firstly demonstrated the use of a LCST polymeric sensor for high-throughput screening of antibiotics and investigation of cell metabolism.

Amphiphilic Fluorine-Containing Block Copolymers as Carriers for Hydrophobic PtTFPP for Dissolved Oxygen Sensing, Cell Respiration Monitoring and In Vivo Hypoxia Imaging with High Quantum Efficiency and Long Lifetime.

New amphiphilic star or multi-arm block copolymers with different structures were synthesized for enabling the use of hydrophobic oxygen probe of platinum (II)-tetrakis (pentafluorophenyl) porphyrin (PtTFPP) for bioanalysis. The amphiphilic star polymers were prepared through the Atom Transfer Radical Polymerization (ATRP) method by using hydrophilic 4-arm polyethylene glycol (4-arm-PEG) as an initiator. Among the five block copolymers, P1 series (P1a, P1b, and P1c) and P3 possess fluorine-containing moieties to improve the oxygen sensitivity with its excellent capacity to dissolve and carry oxygen. A polymer P2 without fluorine units was also synthesized for comparison. The structure-property relationship was investigated. Under nitrogen atmosphere, high quantum efficiency of PtTFPP in fluorine-containing micelles could reach to 22% and long lifetime could reach to 76 µs. One kind of representative PtTFPP-containing micelles was used to detect the respiration of Escherichia coli (E. coli) JM109 and macrophage cell J774A.1 by a high throughput plate reader. In vivo hypoxic imaging of tumor-bearing mice was also achieved successfully. This study demonstrated that using well-designed fluoropolymers to load PtTFPP could achieve high oxygen sensing properties, and long lifetime, showing the great capability for further in vivo sensing and imaging.

Anionic nanoliposomes reduced atherosclerosis progression in Low Density Lipoprotein Receptor (LDLR) deficient mice fed a high fat diet.

Atherosclerosis is a systemic disease characterized by the deposition of cholesterol and inflammatory cells within the arterial wall. Removal of cholesterol from the vessel wall may have an impact on the size and composition of atherosclerotic lesions. Anionic phospholipids or liposome vesicles composed of a lipid bilayer such as nanoliposomes have been suggested as treatments for dyslipidemia. In this study, we investigated the effect of anionic nanoliposomes on atherosclerosis in a mouse model. Low-density lipoprotein receptor knockout mice (Ldlr-/- ) were fed with an atherosclerosis promoting high fat and cholesterol (HFC) diet for 12 weeks. Anionic nanoliposomes including hydrogenated soy phosphatidylcholine (HSPC) and distearoyl phosphatidylglycerol (DSPG) (molar ratio: 1:3) were injected intravenously into HFC-fed Ldlr-/- mice once a week for 4 weeks. Mice receiving nanoliposomes had significantly reduced atherosclerosis within the aortic arch as assessed by Sudan IV staining area (p = 0.007), and reduced intima/media ratio (p = 0.030) and greater collagen deposition within atherosclerosis plaques within the brachiocephalic artery (p = 0.007), compared to control mice. Administration of nanoliposomes enhanced markers of reverse cholesterol transport (RCT) and increased markers of plaque stability in HFC-fed Ldlr-/- mice. Reduced cholesterol accumulation was observed in the liver along with the up-regulation of the major genes involved in the efflux of cholesterol such as hepatic ATP-binding cassette transporters (ABC) including Abc-a1, Abc-g1, Abc-g5, and Abc-g8, Scavenger receptor class B, member 1 (Scarb1), and Liver X receptor alpha (Lxr)-α. Lecithin Cholesterol Acyltransferase activity within the plasma was also increased in mice receiving nanoliposomes. Anionic nanoliposome administration reduced atherosclerosis in HFC-fed Ldlr-/- mice by promoting RCT and upregulating the ABC-A1/ABC-G1 pathway.

Diabetes Reduces Severity of Aortic Aneurysms Depending on the Presence of Cell Division Autoantigen 1 (CDA1).

Diabetes is a negative risk factor for aortic aneurysm, but the underlying explanation for this phenomenon is unknown. We have previously demonstrated that cell division autoantigen 1 (CDA1), which enhances transforming growth factor-ß signaling, is upregulated in diabetes. We hypothesized that CDA1 plays a key role in conferring the protective effect of diabetes against aortic aneurysms. Male wild-type, CDA1 knockout (KO), apolipoprotein E (ApoE) KO, and CDA1/ApoE double-KO (dKO) mice were rendered diabetic. Whereas aneurysms were not observed in diabetic ApoE KO and wild-type mice, 40% of diabetic dKO mice developed aortic aneurysms. These aneurysms were associated with attenuated aortic transforming growth factor-ß signaling, reduced expression of various collagens, and increased aortic macrophage infiltration and matrix metalloproteinase 12 expression. In the well-characterized model of angiotensin II-induced aneurysm formation, concomitant diabetes reduced fatal aortic rupture and attenuated suprarenal aortic expansion, changes not seen in dKO mice. Furthermore, aortic CDA1 expression was downregulated ∼70% within biopsies from human abdominal aortic aneurysms. The identification that diabetes is associated with upregulation of vascular CDA1 and that CDA1 deletion in diabetic mice promotes aneurysm formation provides evidence that CDA1 plays a role in diabetes to reduce susceptibility to aneurysm formation.