64 research outputs found

    Frailty and medication adherence among older adult patients with hypertension: a moderated mediation model

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    ObjectiveMedication adherence has a critical impact on the well-being of older adult patients with hypertension. As such, the current study aimed to investigate the mediating role of health literacy between frailty and medication adherence and the moderating role of educational level.MethodsThis cross-sectional study included patients admitted to the geriatric unit of a hospital. Participants were interviewed using the four-item Morisky Medication Adherence Scale, the Frailty Phenotype Scale, and the Health Literacy Management Scale. Spearman’s correlation coefficients were used to assess the association between variables. Mediation and moderated mediation analyses were performed using Process version 4.1 via Model 4 and 14, respectively.ResultsData from 388 participants were analyzed. The median (IQR [P25–P75]) score for medication adherence was 4.00 (2.00–4.00). Results revealed that after controlling for age, sex, hypertension complication(s) and body mass index, frailty significantly contributed to medication adherence (βtotal −0.236 [95% confidence interval (CI) −0.333 to −0.140]). Medication adherence was influenced by frailty (βdirect −0.192 [95% CI −0.284 to −0.099]) both directly and indirectly through health literacy (βindirect −0.044 [95% CI −0.077 to −0.014]). Educational level moderated the pathway mediated by health literacy; more specifically, the conditional indirect effect between frailty and medication adherence was significant among older adult hypertensive patients with low, intermediate, and high educational levels (effect −0.052 [95% CI −0.092 to −0.106]; effect −0.041 [95% CI −0.071 to −0.012]; effect −0.026 [95% CI −0.051 to −0.006]). The relationship between frailty and medication adherence in older adult patients with hypertension was found to have mediating and moderating effects.ConclusionA moderated mediation model was proposed to investigate the effect of frailty on medication adherence. It was effective in strengthening medication adherence by improving health literacy and reducing frailty. More attention needs to be devoted to older adult patients with hypertension and low educational levels

    Down-regulation of Stargazin Inhibits the Enhanced Surface Delivery of α-Amino-3-hydroxy-5-methyl-4-isoxazole Propionate Receptor GluR1 Subunit in Rat Dorsal Horn and Ameliorates Postoperative Pain

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    Stargazin is the first transmembrane protein known to regulate synaptic targeting of α-amino-3-hydroxy-5-methyl-4-isoxazole propionate (AMPA) receptors. Yet, it is unclear whether regulation of the surface delivery of spinal AMPA receptor subunits by stargazin contributes to postoperative pain development

    Accelerated surgery versus standard care in hip fracture (HIP ATTACK): an international, randomised, controlled trial

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    Precis of Lengyan Sutra

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    Precis of Lengyan Sutra. Detail

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    Precis of Lengyan Sutra. Detail

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    Case study: A simple optical inverse problem from a geometrical optics point of view

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    In this paper, we revisit the simple problem of reflection from a dielectric sphere for light rays and define a form of optical inverse problem in the sense of geometrical optics (GO). A general analytic formula is derived to obtain the refraction index of the sphere for any incidence light to emerge in a deflected angle. Numerical wave simulation and ray tracing are performed to verify the inverse formulae derived

    Endothelial Glycocalyx Layer: A Possible Therapeutic Target for Acute Lung Injury during Lung Resection

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    Background. Shedding of the endothelial glycocalyx layer (EGL) is known to occur during major surgery, but its degradation associated with minimally invasive video-assisted thoracoscopy (VATS) remains unclear. We investigated if serum biomarkers of EGL disruption were elevated during VATS lobectomy, and whether the urinary trypsin inhibitor (UTI) ulinastatin exerted a protective effect during this procedure. Materials and Methods. Sixty ASA II-III lung cancer patients undergoing elective VATS lobectomy were divided equally into UTI and control groups. UTI group patients received intravenous UTI during surgery. Serum levels of syndecan-1 and heparan sulfate were examined before (T0) and at the end of surgery (T1). Serum albumin and hemoglobin were measured before surgery (BOD) and on the first postoperative day (POD1). Results. In control group, syndecan-1 levels were significantly elevated at T1 compared with T0 (3.77±3.15 versus 4.28±3.30, P=0.022⁎) and increased even more significantly in patients whose surgery lasted >3 h (3.28±2.84 versus 4.31±3.39, P=0.003⁎⁎). Serum albumin levels on POD1 were significantly lower in control group compared with UTI group (32.63±4.57 versus 35.76±2.99, P=0.031⁎). Conclusion. EGL degradation occurs following VATS lobectomy. UTI can alleviate this shedding, thus helping preserve normal vascular permeability. Trail Registration. This trial is registered with ChiCTR-IOC-17010416 (January 13, 2017)

    Intelligent Fault Diagnosis of Rolling Bearings Based on Markov Transition Field and Mixed Attention Residual Network

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    To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively
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