A computationally efficient gradient-enhanced healing model for soft biological tissues.

Soft biological tissues, such as arterial tissue, have the ability to grow and remodel in response to damage. Computational method plays a critical role in understanding the underlying mechanisms of tissue damage and healing. However, the existing healing model often requires huge computation time and it is inconvenient to implement finite element simulation. In this paper, we propose a computationally efficient gradient-enhanced healing model that combines the advantages of the gradient-enhanced damage model, the homeostatic-driven turnover remodeling model, and the damage-induced growth model. In the proposed model, the evolution of healing-related parameters can be solved explicitly. Additionally, an adaptive time increment method is used to further reduce computation time. The proposed model can be easily implemented in Abaqus, requiring only a user subroutine UMAT. The effectiveness of proposed model is verified through a semi-analytical example, and the influence of the variables in the proposed model is investigated using uniaxial tension and open-hole plate tests. Finally, the long-term development of aneurysms is simulated to demonstrate the potential applications of the proposed model in real biomechanical problems.

Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method.

Considering the differences between individuals, in this paper, an uncertainty analysis model for predicting rupture risk of atherosclerotic arteries is established based on a back-propagation artificial neural network. The influence of isotropy and anisotropy on the rupture risk of atherosclerotic arteries is analyzed, and the results demonstrate the effectiveness of the artificial neural network in predicting the rupture risk. Moreover, the rupture risk of atherosclerotic arteries at different inflation sizes are simulated. This study contributes to a better understanding of the underlying mechanisms of atherosclerotic arteries rupture and promotes the advancement of artificial neural networks in atherosclerosis research.

Kawasaki disease: SOCS2-AS1/miR-324-5p/CUEDC2 axis regulates the progression of human umbilical vein endothelial cells.

BACKGROUND: Kawasaki disease (KD) is the most prevailing cause of acquired heart disease in children, due to permanent coronary artery damage. Recently, the role of long noncoding RNAs (lncRNAs) in human diseases has been highlighted. However, the role of lncRNA SOCS2 antisense RNA 1 (SOCS2-AS1) on the function of human umbilical vein endothelial cells (HUVECs) in KD remains elusive. METHODS: SOCS2-AS1 expression was examined via RT-qPCR. CCK-8, EdU, caspase-3 activity, flow cytometry and TUNEL assays were conducted for exploring the function of SOCS2-AS1 in HUVECs of KD. The interaction among RNAs (SOCS2-AS1, miR-324-5p and CUEDC2) was validated via luciferase reporter, RIP and RNA pull-down assays. RESULTS: SOCS2-AS1 was highly expressed in serum and tissues of KD patients. SOCS2-AS1 depletion repressed the proliferation of HUVECs, whereas it facilitated apoptosis. Further, SOCS2-AS1 could bind with miR-324-5p and negatively regulated miR-324-5p expression in HUVECs. Besides, CUE domain containing 2 (CUEDC2) was the downstream target of miR-324-5p, and SOCS2-AS1 could release CUEDC2 expression via sponging miR-324-5p in HUVECs. Furthermore, downregulating miR-324-5p or upregulating CUEDC2 could rescue the progression of HUVECs restrained by SOCS2-AS1 knockdown. CONCLUSIONS: SOCS2-AS1 upregulates CUEDC2 via inhibiting miR-324-5p to promote the progression of HUVECs in KD, providing new insights for KD treatment. IMPACT: SOCS2-AS1 is highly expressed in the serum of KD patients. SOCS2-AS1 contributes to cell proliferation in HUVECs of KD through elevating CUEDC2 expression by sequestering miR-324-5p. SOCS2-AS1/miR-324-5p/CUEDC2 axis exerts a progression-facilitating function in KD. These findings suggest SOCS2-AS1 as a novel potential target for KD treatment.

A meta-analysis of levetiracetam for randomized placebo-controlled trials in patients with refractory epilepsy.

OBJECTIVE: The objective of this study was to investigate the efficacy and safety profile of levetiracetam as add-on therapy in patients with refractory epilepsy. METHODS: Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar were systematically searched to identify potential eligible randomized controlled trials by two reviewers independently. Pooled estimates of risk ratios (RRs) for 50%, 75%, and 100% reduction from baseline were calculated using the fixed-effect model or random-effect model. Quality of included studies was assessed with the Cochrane Collaboration's Risk of Bias tool. Serious adverse events and withdrawals induced by interventions and the most common side effects were analyzed. RESULTS: Seventeen trials with a total of 3,205 participants were included in this meta-analysis, including 14 trials for adulthood and three trials for children. Pooled estimates suggested that levetiracetam was an effective anti-epileptic drug at 1,000-3,000 mg/day (RR =2.00 for 1,000 mg/day, RR =2.68 for 2,000 mg/day, RR =2.18 for 3,000 mg/day) for adults and 60 mg/kg/day (RR =2.00) for children compared to placebo in terms of 50% reduction from baseline. Likewise, as for seizure freedom rate, levetiracetam had an advantage over placebo at 1,000-3,000 mg/day (RR =5.84 for 1,000 mg/day, RR =4.55 for 2,000 mg/day, RR =4.57 for 3,000 mg/day, respectively) for adults and 60 mg/kg/day (RR =4.52) for children. Regarding safety profile, patients treated with levetiracetam had significantly higher occurrence than placebo for somnolence, asthenia, dizziness, infection, nasopharyngitis, anxiety, and irritability; however, most studies reported that these adverse events were mild and transient. CONCLUSION: Levetiracetam is an effective anti-epileptic drug for both adults and children with generalized or partial-onset refractory seizures at 1,000-3,000 or 60 mg/kg/day, with a favorable adverse event profile.