Psychological pathway to emotional exhaustion among nurses and midwives who provide perinatal bereavement care in China: a path analysis.

BACKGROUND: A lack of confidence in perinatal bereavement care (PBC) and the psychological trauma experienced by nurses and midwives during bereavement care leads to their strong need for sufficient organisational support. The current study intended to test a hypothesised model of the specific impact paths among organisational support, confidence in PBC, secondary traumatic stress, and emotional exhaustion among nurses and midwives. METHODS: A descriptive, cross-sectional survey was conducted in sixteen maternity hospitals in Zhejiang Province, China, from August to October 2021. The sample (n = 779) consisted of obstetric nurses and midwives. A path analysis was used to test the relationships among study variables and assess model fit. RESULTS: Organisational support directly and positively predicted confidence in PBC and demonstrated a direct, negative, and significant association with secondary traumatic stress and emotional exhaustion. Confidence in PBC had a positive direct effect on secondary traumatic stress and a positive indirect effect on emotional exhaustion via secondary traumatic stress. Secondary traumatic stress exhibited a significant, direct effect on emotional exhaustion. CONCLUSIONS: This study shows that nurses' and midwives' confidence in PBC and mental health were leadingly influenced by organisational support in perinatal bereavement practice. It is worth noting that higher confidence in PBC may lead to more serious psychological trauma symptoms in nurses and midwives. Secondary traumatic stress plays an essential role in contributing to emotional exhaustion. The findings suggest that support from organisations and self-care interventions were required to improve confidence in PBC and reduce negative psychological outcomes among those providing PBC. The development of objective measures for assessing competence in PBC and organizational support are essential.

Computational drug repositioning based on multi-similarities bilinear matrix factorization.

With the development of high-throughput technology and the accumulation of biomedical data, the prior information of biological entity can be calculated from different aspects. Specifically, drug-drug similarities can be measured from target profiles, drug-drug interaction and side effects. Similarly, different methods and data sources to calculate disease ontology can result in multiple measures of pairwise disease similarities. Therefore, in computational drug repositioning, developing a dynamic method to optimize the fusion process of multiple similarities is a crucial and challenging task. In this study, we propose a multi-similarities bilinear matrix factorization (MSBMF) method to predict promising drug-associated indications for existing and novel drugs. Instead of fusing multiple similarities into a single similarity matrix, we concatenate these similarity matrices of drug and disease, respectively. Applying matrix factorization methods, we decompose the drug-disease association matrix into a drug-feature matrix and a disease-feature matrix. At the same time, using these feature matrices as basis, we extract effective latent features representing the drug and disease similarity matrices to infer missing drug-disease associations. Moreover, these two factored matrices are constrained by non-negative factorization to ensure that the completed drug-disease association matrix is biologically interpretable. In addition, we numerically solve the MSBMF model by an efficient alternating direction method of multipliers algorithm. The computational experiment results show that MSBMF obtains higher prediction accuracy than the state-of-the-art drug repositioning methods in cross-validation experiments. Case studies also demonstrate the effectiveness of our proposed method in practical applications. Availability: The data and code of MSBMF are freely available at https://github.com/BioinformaticsCSU/MSBMF. Corresponding author: Jianxin Wang, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China. E-mail: jxwang@mail.csu.edu.cn Supplementary Data: Supplementary data are available online at https://academic.oup.com/bib.

Standardized Skin Flap Warming Effectively Improves Flap Survival without Obstructing Temperature Monitoring after DIEP.

OBJECTIVE: This study aimed to determine whether skin flap warming after an operation interferes with temperature monitoring. The postoperative nursing workflow of subabdominal deep inferior epigastric artery perforator (DIEP) flap breast reconstruction was optimized. METHODS: A retrospective analysis involving 69 patients who received one-stage breast reconstruction at the Huashan Hospital from July 2017 to December 2019 was performed. The postoperative physical care of patients, including flap temperature monitoring and flap warming, was reviewed. RESULTS: All patients had successful operations. After surgery, all flaps were warmed following the standard protocol. Abnormal temperature and compromised circulation of flaps were observed in three of the patients. These patients received re-exploration surgery and all three flaps survived. A postoperative follow-up shows a high level of patient satisfaction in most cases. CONCLUSIONS: The appropriate warming of transplanted flaps did not interfere with temperature monitoring. This helped determine whether there was compromised circulation, leading to increased skin flap survival and improved patient satisfaction.

De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten p-Norm Minimization.

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug-target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.