Assistance for distressed caregivers can indirectly facilitate recovery of the people being cared for, yet how resilience, hope, and social support mediate between caregiving burden and adjustment outcomes is unclear. A structural equation model was constructed based on data from a cross‐sectional survey of 324 caregivers of children and adolescents with schizophrenia using multidimensional caregiver burden inventory, Connor‐Davidson resilience scale, Herth hope index, perceived social support scale, distress management screening measure, and positive aspects of caregiving instruments. On distress, caregiving burden had a relatively large direct effect, and an indirect effect, mainly mediated by resilience. Resilience had a greater effect than social support or hope on distress. On positive aspects of caregiving (PAC), caregiving burden had only an indirect effect, primarily via the processes from social support and resilience to hope. Hope had a significant direct effect, while resilience and support had moderate indirect effects on PAC via hope. Resilience is an important mediator between caregiving burden and distress, with a greatest effect. Resilience, hope, and social support all mediated between caregiving burden and PAC, with hope having a greatest effect. Reducing the care burden may greatly help to relieve caregiver distress. Providing needed social support, encouraging caregivers to proactively utilize the support, and enhancing resilient coping skills will be helpful in developing resilience and mitigating distress. Health professionals should assess and ameliorate burden, be particularly aware of caregiver hopes, provide formal support, and encourage informal support to promote PAC.
The purpose of this study was to evaluate the effects of polydatin (PD) on cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) expressions at protein and transcriptional levels, as well as the production of prostaglandin E2 (PGE2) and nitric oxide (NO) in lipopolysaccharide (LPS)-induced macrophage RAW 264.7 cells. To elucidate the underlying mechanism responsible for these symptoms, we investigated the phosphorylation of mitogen-activated protein kinase (MAPK) pathway and nuclear factor-κB (NF-κB) expression. NO was analyzed with the Griess method. PGE2 was measured by enzyme-linked immunosorbent assay (ELISA). iNOS and COX-2 messenger RNA (mRNA) were identified by qPCR assay. iNOS, COX-2, NF-κB, extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK) and p38 protein expressions were detected with Western blot. The results revealed that PD effectively inhibited NO and PGE2 production, and it is not surprising that PD reduced iNOS and COX-2 expression at protein and transcriptional levels. Additionally, PD significantly ameliorated the activation of NF-κB and the phosphorylation of MAPKs in LPS-induced RAW 264.7 macrophages. These findings suggested that PD exerted potent anti-inflammatory activity in macrophages.
Background As the birth policy has been adjusted from one-child-one-couple to universal two-child-one-couple in China, there is an increasing number of women undergoing a second pregnancy after a previous cesarean section (CS). Undertaking an elective repeat CS (ERCS) has been taken for granted and has thus become a major contributor to the increasing CS rate in China. Promoting trial of labor after CS (TOLAC) can reduce the CS rate without compromising delivery outcomes. This study aimed to investigate Chinese obstetricians’ perspectives regarding TOLAC, and the factors associated with their decision-making regarding recommending TOLAC to pregnant women with a history of CS under the two-child policy. Methods A cross-sectional survey was carried out between May and July 2018. Binary logistic regression was used to determine the factors associated with the obstetricians’ intention to recommend TOLAC to pregnant women with a history of CS. The independent variables included sociodemographic factors and perceptions regarding TOLAC (selection criteria for TOLAC, basis underlying the selection criteria for TOLAC, and perceived challenges regarding promoting TOLAC). Results A total of 426 obstetricians were surveyed, with a response rate of ≥83%. The results showed that 31.0% of the obstetricians had no intention to recommend TOLAC to pregnant women with a history of CS. Their decisions were associated with the perceived lack of confidence regarding undergoing TOLAC among pregnant women with a history of CS and their families (odds ratio [OR] = 2.31; 95% CI: 1.38–1.38); obstetricians’ uncertainty about the safety of TOLAC for pregnant women with a history of CS (OR = 0.49; 95% CI: 0.27–0.96), and worries about medical lawsuits due to adverse delivery outcomes (OR = 0.14; 95% CI: 0.07–0.31). The main reported challenges regarding performing TOLAC were lack of clear guidelines for predicting or avoiding the risks associated with TOLAC (83.4%), obstetricians’ uncertainty about the safety of TOLAC for women with a history of CS (81.2%), pregnant women’s unwillingness to accept the risks associated with TOLAC (81.0%) or demand for ERCS (80.7%), and the perceived lack of confidence (77.5%) or understanding (69.7%) regarding undergoing TOLAC among pregnant women and their families. Conclusion A proportion of Chinese obstetricians did not intend to recommend TOLAC to pregnant women with a history of CS. This phenomenon was closely associated with obstetricians’ concerns about TOLAC safety and perceived attitudes of the pregnant women and their families regarding TOLAC. Effective measures are needed to help obstetricians predict and reduce the risks associated with TOLAC, clearly specify the indications for TOLAC, improve labor management, and popularize TOLAC in China. Additionally, public health education on TOLAC is necessary to improve the understanding of TOLAC among pregnant women with a history of CS and their families, and to improve their interactions with their obstetricians regarding shared decision making.
This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then, a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.
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