Analytical Modeling of Riveting Squeezing Force Considering Non-Uniform Deformation of Rivets in Aeronautical Structures.

Analytical modeling of the squeezing force for aircraft wings and fuselage panels in the existing literature usually assumes uniform deformation of the rivets, while in reality, the deformation of the rivets is non-uniform. To achieve high-quality squeezing force modeling, this paper introduces Coulomb's friction and four critical adjustments to the original equation: the non-uniform rivet/sheet interference along the sheet's hole axial ordinate; the barreling effect when calculating the driven head's volume; the spring-back of the driven head's dimensions; the modified height of the driven head; and the modified sheet-hole expanded diameter considering the convex structure of the driven head. The calculated values of the proposed new model demonstrate an improved level of accuracy, particularly under squeeze ratios commonly encountered in the aerospace industry.

Clinical predictors of prognosis in stroke patients after endovascular therapy.

Endovascular therapy (EVT) is effective in the treatment of large vascular occlusive stroke. However, many factors are associated with the outcomes of acute ischemic stroke (AIS) after EVT. This study aimed to identify the main factors related to the prognosis of AIS patients after EVT. We analyzed the clinical data of AIS patients in the neurology department of our medical center from June 2017 to August 2021 following treatment with EVT. The data included the patients' blood pressure upon admission, blood glucose concentration, National Institutes of Health Stroke Scale (NIHSS) score, 90-day modified Rankin scale (mRs) score follow-up data, and time from LKN to the successful groin puncture (GP). A good outcome was defined as a 90-day mRs score of 0-2, and a poor outcome was defined as a 90-day mRs score of 3-6. A total of 144 patients were included in the study. Admission, smoking, and LKN-to-GP time, NIHSS score of 6-12 was found to be relevant to the prognosis. The results of multivariate analysis showed that prognosis was significantly influenced by baseline NIHSS (odds ratio = 3.02; 95% confidence interval, 2.878-4.252; P = 0.001), LKN-to-GP time (odds ratio = 2.17; 95% confidence interval, 1.341-2.625; P = 0.003), and time stratification (6-12 h) (odds ratio = 4.22; 95% confidence interval, 2.519-5.561; P = 0.001). Our study indicated that smoking, baseline NIHSS score, and LKN-to-GP time were the risk factors for a poor outcome in stroke patients following an EVT. Quitting smoking and shortening LKN time to GP should improve the outcome of AIS after EVT.

LncRNA MIAT Inhibits MPP+-Induced Neuronal Damage Through Regulating the miR-132/SIRT1 Axis in PC12 Cells.

Parkinson's disease (PD) is an age-related neurodegenerative disease caused by the loss of dopaminergic neurons in the substantia nigra. LncRNA MIAT has been shown to be critical in Alzheimer's disease, but its role and mechanism in PD are still unknown. Differentiated PC12 cells were treated with 1-methyl-4-phenylpyridinium (MPP+) to establish in vitro cell injury model of PD. MTT, Annexin V-PI double staining test and Western blot were used to detect cell viability and apoptosis. Reactive oxygen species (ROS), superoxide dismutase (SOD) and phospholipid hydroperoxide glutathione peroxidase (GSH-PX) kits were used to evaluate oxidative stress in cells. These results showed that LncRNA MIAT was down-regulated in MPP+-induced PC12 cells. Overexpression of LncRNA MIAT remarkably increased cell viability, inhibited cell apoptosis and oxidative stress in MPP+-treated cells. In addition, we proved that miR-132 is a target of LncRNA MIAT. Overexpression of miR-132 could reverse the positive effect of LncRNA MIAT overexpression on MPP+-induced cell oxidative stress injury. SIRT1 is a target of miR-132 and silencing of SIRT1 attunated the positive effect of LncRNA MIAT overexpression on oxidative stress injury in MPP+-induced PC12 cells. In conclusion, this study indicated that LncRNA MIAT suppressed MPP+-induced oxidative stress injury by regulating miR-132/SIRT1 axis in PC12 cells.

Effectiveness of neuromuscular electrical stimulation therapy in patients with urinary incontinence after stroke: A randomized sham controlled trial.

BACKGROUND: This study aimed to evaluate the effectiveness of neuromuscular electrical stimulation (NMES) therapy in patients with urinary incontinence after stroke (UIAS). METHODS: A total of 82 patients with UIAS were randomly assigned to 2 groups that received NMES therapy (NMES group) or sham NMES (sham group) for 10 weeks. The primary efficacy endpoints were measured by urodynamic values, and Overactive Bladder Symptom Score (OABSS). The secondary efficacy endpoints were assessed by International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) score, Barthel Index (BI) scale, and adverse events. All outcomes were evaluated at baseline and at the end of 10 weeks treatment. RESULTS: After 10-week treatment, the patients received NMES therapy showed better efficacy in primary endpoints of urodynamic values (P <.01) and OABSS (P <.01), and secondary endpoints of ICIQ-SF (P <.01) and BI (P <.01), compared with patients who underwent sham NMES. No adverse events were recorded in both groups. CONCLUSIONS: In summary, we demonstrated that 10 weeks of NMES therapy was efficacious in patients with UIAS.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization.

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.