Impact of professional identity on learner well-being of undergraduate nursing students of "double tops" universities: Mediating effect of self-regulated learning.

OBJECTIVES: The pressure of internal competition at the college level has increased in recent years in China with an impact on nursing students' learning and well-being. This study aimed to investigate the current situation and factors affecting professional identity, learner well-being and self-regulated learning of undergraduate nursing students in the Neijuan ecology of the "double tops" universities, and to explore the relationships between these three variables. METHODS: A cross-sectional design was adopted to conduct an online survey of 322 Chinese undergraduate nursing students from seven "double tops" universities. The survey included socio-demographics characteristics, students' professional identity, learner well-being, and self-regulated learning. RESULTS: Results of Pearson correlation analysis showed that professional identity was significantly and positively correlated with learner well-being (R = 0.795, p < 0.001); professional identity was significantly and positively correlated with self-regulated learning (R = 0.843, p < 0.001); and, self-regulated learning was significantly and positively correlated with learner well-being (R = 0.852, p < 0.001). After mediation effect testing, self-regulated learning had a mediating effect between professional identity and learner well-being (95 % CI 0.366-0.548, p < 0.001). Professional identity had a positive predictive effect on self-regulated learning (a = 0.570, p < 0.001), and self-regulated learning also had a positive predictive effect on learner well-being (b = 0.798, p < 0.001). The direct effect of professional identity on learner well-being (0.225) and its mediating effect (0.455) account for 33.1 % and 66.9 % of the total effect (0.680), respectively. CONCLUSIONS: The learner well-being of undergraduate Chinese nursing students is in the middle to upper range, and it is crucial to enhance professional identity and develop students' self-regulated learning to improve their learner well-being. This study provides empirical evidence to support the mediating effect of self-regulated learning on the relationship between professional identity and learner well-being among undergraduate nursing students in "double tops" universities. Universities are expected to strengthen career planning guidance and professional competence training for students as early as possible in order to develop quality nursing education programs that produce graduates who enter and remain in the workforce.

Clinical Effects of Ultrasound-Guided Acupotomy in Knee Osteoarthritis Treatment.

The protocol presented here demonstrates the operation method of ultrasound-guided acupotomy for knee osteoarthritis (KOA), including patient recruitment, preoperative preparation, manual operation, and postoperative care. The purpose of this protocol is to relieve pain and improve knee function in patients with KOA. A total of 60 patients with KOA admitted between June 2022 and June 2023 were treated with ultrasound-guided acupotomy. Pathological changes and knee function scores were compared before and after the treatment. After 1 week of treatment, the synovial thickness of the suprapatellar bursae was significantly lesser than before treatment (p < 0.05), the Hospital for Special Surgery Knee Score (HSS) was significantly higher than before treatment (p < 0.05), the Visual analogue scale (VAS) was significantly lower than those of the control group (p < 0.05) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were significantly lower than those of the control group (p < 0.05). Therefore, ultrasound-guided acupotomy for the treatment of KOA can reduce synovial thickness, relieve pain, improve knee joint function, and have a remarkable curative effect.

Effectiveness of a breastfeeding promotion intervention model based on Society ecosystems Theory for maternal women: a study protocol of randomized controlled trial.

BACKGROUND: Breastfeeding is recognized internationally as the most scientific and effective way to feed infants and young children. According to the World Health Organization in 2022, the exclusive breastfeeding rate within 6 months is 34.1% in China, which is still far from the goal of "more than 60% exclusive breastfeeding rate of infants within 6 months" by 2030 required by China's State Council. It is necessary to promote breastfeeding and provide maternal breastfeeding guidance to increase exclusive breastfeeding. Factors influencing breastfeeding can be explained by the society ecosystems theory, distributed in macro, mezzo and micro systems. The interventions focused on breastfeeding promotion are mainly carried out in the health systems and services, home and family environment, community environment, work environment, policy environment or a combination of these facilities. But there is sparse research on integrating resources in the macro, mezzo and micro systems of maternal breastfeeding processes to promote breastfeeding behavior. A randomized controlled trial will test the effect of a breastfeeding promotion intervention model based on the society ecosystems theory versus usual prenatal and postnatal care on maternal and infant health and the exclusive breastfeeding rate at 6 months. METHODS/DESIGN: The study is a single-blind, parallel design, randomized controlled trial with an intervention group (n = 109) and a control group (n = 109) that compares the effect of a breastfeeding promotion intervention model based on the society ecosystems theory with usual prenatal and postnatal care. The intervention covers macro- (policy, culture), mezzo- (family-hospital-community) and micro- (biological, psychological and social) systems of the maternal breastfeeding process. Infant feeding patterns, neonatal morbidity and physical and mental health of antenatal and postpartum women will be collected at baseline (28 to 35 weeks of gestation), 1-, 4-, and 6-month postpartum. DISCUSSION: This is a multifaceted, multifactorial, and multi-environmental breastfeeding promotion strategy to help mothers and their families learn breastfeeding knowledge and skills. The study provides a new modality for adding breastfeeding interventions to prenatal and postnatal care for healthcare providers in the hospital and the community. TRIAL REGISTRATION: Chinese Clinical Trial Registry at www.chictr.org.cn , ChiCTR2300075795.

Maternal education and support during breastfeeding can increase maternal breastfeeding self-efficacy, promote breastfeeding behaviors, and improve maternal and infant health outcomes. The interventions focused on breastfeeding promotion are mainly carried out in the health systems and services, home and family environment, community environment, work environment, policy environment or a combination of any of these facilities. But there is sparse research on integrating in multifaceted, multifactorial, and multi-environmental resources of maternal breastfeeding processes to help pregnant women and their families learn breastfeeding knowledge and skills. The current study optimizes the existing breastfeeding promotion intervention program and construct a breastfeeding promotion intervention program to correct the public's perception of breastfeeding, increase breastfeeding self-efficacy and improve breastfeeding behavior, thus increasing the breastfeeding duration and improving maternal and infant outcomes. The program includes presenting breastfeeding-related policies and support facilities; prenatal educational sessions combined with theories and skills on breastfeeding, development of lactation, infants feeding and cares for maternal families; postnatal hands-on instruction and WeChat group peer support from hospital; home visits, group counseling and experience sharing from community and one-on-one personalized counseling throughout the intervention. The present study will be conducted to evaluate the effect of breastfeeding promotion intervention including prenatal and postnatal care on the breastfeeding duration, breastfeeding attitudes, knowledge, and self-efficacy, maternal and infant health.

Prediction of lysine HMGylation sites using multiple feature extraction and fuzzy support vector machine.

Protein 3-hydroxyl-3-methylglutarylation (HMGylation) is newly discovered lysine acylation modification in mitochondrion. The accurate identification of HMGylation sites is the premise and key to further explore the molecular mechanisms of HMGylation. In this study, a novel bioinformatics tool named HMGPred is developed to predict HMGylation sites. Multiple effective features, including amino acid composition, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs, are integrated to encode HMGylation sites. And F-score feature ranking with incremental feature selection was used to eliminate redundant features. Moreover, a fuzzy support vector machine algorithm is used to effectively reduce the influence of noise problem by assigning different samples to different fuzzy membership degrees. As illustrated by 10-fold cross-validation, HMGPred achieves a satisfactory performance with an area under receiver operating characteristic curve of 0.9110. Feature analysis indicates that some k-spaced amino acid pair features, such as 'KxxxT' and 'DxxxE', play a critical role in the prediction of HMGylation sites. The results of prediction and analysis might be helpful for investigating the mechanisms of HMGylation. For the convenience of experimental researchers, HMGPred is implemented as a web server at http://123.206.31.171/HMGPred/.

Polynomial time algorithm for minmax scheduling with common due-window and proportional-linear shortening processing times.

This article deals with common due-window assignment and single-machine scheduling with proportional-linear shortening processing times. Objective cost is a type of minmax, that is, the maximal cost among all processed jobs is minimized. Our goal is to determine an optimal schedule, the optimal starting time, and size of due-window that minimize the worst cost, which consist of four parts: earliness, tardiness, starting time and length of the due-window. Optimal properties of the problem are given, and then an optimal polynomial algorithm is proposed to solve the problem.

Extensive germline genome engineering in pigs.

The clinical applicability of porcine xenotransplantation-a long-investigated alternative to the scarce availability of human organs for patients with organ failure-is limited by molecular incompatibilities between the immune systems of pigs and humans as well as by the risk of transmitting porcine endogenous retroviruses (PERVs). We recently showed the production of pigs with genomically inactivated PERVs. Here, using a combination of CRISPR-Cas9 and transposon technologies, we show that pigs with all PERVs inactivated can also be genetically engineered to eliminate three xenoantigens and to express nine human transgenes that enhance the pigs' immunological compatibility and blood-coagulation compatibility with humans. The engineered pigs exhibit normal physiology, fertility and germline transmission of the 13 genes and 42 alleles edited. Using in vitro assays, we show that cells from the engineered pigs are resistant to human humoral rejection, cell-mediated damage and pathogenesis associated with dysregulated coagulation. The extensive genome engineering of pigs for greater compatibility with the human immune system may eventually enable safe and effective porcine xenotransplantation.

Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

BACKGROUND: As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. METHODS: In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. RESULTS: Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. CONCLUSION: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

Prediction of 2-hydroxyisobutyrylation sites by integrating multiple sequence features with ensemble support vector machine.

Lysine 2-hydroxyisobutyrylation (Khib) is a new type of histone mark, which has been found to affect the association between histone and DNA. To better understand the molecular mechanism of Khib, it is important to identify 2-hydroxyisobutyrylated substrates and their corresponding Khib sites accurately. In this study, a novel bioinformatics tool named KhibPred is proposed to predict Khib sites in human HeLa cells. Three kinds of effective features, the composition of k-spaced amino acid pairs, binary encoding and amino acid factors, are incorporated to encode Khib sites. Moreover, an ensemble support vector machine is employed to overcome the imbalanced problem in the prediction. As illustrated by 10-fold cross-validation, the performance of KhibPred achieves a satisfactory performance with an area under receiver operating characteristic curve of 0.7937. Therefore, KhibPred can be a useful tool for predicting protein Khib sites. Feature analysis shows that the polarity factor features play significant roles in the prediction of Khib sites. The conclusions derived from this study might provide useful insights for in-depth investigation into the molecular mechanisms of Khib.

Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components.

Lysine formylation is a newly discovered post-translational modification in histones, which plays a crucial role in epigenetics of chromatin function and DNA binding. In this study, a novel bioinformatics tool named CKSAAP_FormSite is proposed to predict lysine formylation sites. An effective feature extraction method, the composition of k-spaced amino acid pairs, is employed to encode formylation sites. Moreover, a biased support vector machine algorithm is proposed to solve the class imbalance problem in the prediction of formylation sites. As illustrated by 10-fold cross-validation, CKSAAP_FormSite achieves an satisfactory performance with an AUC of 0.8234. Therefore, CKSAAP_FormSite can be a useful bioinformatics tool for the prediction of formylation sites. Feature analysis shows that some amino acid pairs, such as 'KA', 'SxxxxK' and 'SxxxA' around formylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of formylation.

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components.

INTRODUCTION: Neddylation is a highly dynamic and reversible post-translational modification. The abnormality of neddylation has previously been shown to be closely related to some human diseases. The detection of neddylation sites is essential for elucidating the regulation mechanisms of protein neddylation. OBJECTIVE: As the detection of the lysine neddylation sites by the traditional experimental method is often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. METHODS: In this study, a bioinformatics tool named NeddPred is developed to identify underlying protein neddylation sites. A bi-profile bayes feature extraction is used to encode neddylation sites and a fuzzy support vector machine model is utilized to overcome the problem of noise and class imbalance in the prediction. RESULTS: Matthew's correlation coefficient of NeddPred achieved 0.7082 and an area under the receiver operating characteristic curve of 0.9769. Independent tests show that NeddPred significantly outperforms existing lysine neddylation sites predictor NeddyPreddy. CONCLUSION: Therefore, NeddPred can be a complement to the existing tools for the prediction of neddylation sites. A user-friendly webserver for NeddPred is accessible at 123.206.31.171/NeddPred/.

Predicting lysine lipoylation sites using bi-profile bayes feature extraction and fuzzy support vector machine algorithm.

Lipoylation is a highly conserved post-translational modification which has been found to be involved in many biological processes and closely associated with various metabolic diseases. The accurate identification of lipoylation sites is necessary to elucidate the underlying molecular mechanisms of lipoylation. As the traditional experimental methods are time consuming and expensive, it is desired to develop computational methods to predict lipoylation sites. In this study, a novel predictor named LipoPred is proposed to predict lysine lipoylation sites. On the one hand, an effective feature extraction method, bi-profile bayes encoding, is employed to encode lipoylation sites. On the other hand, a fuzzy support vector machine algorithm is proposed to solve the class imbalance and noise problem in the prediction of lipoylation sites. As illustrated by 10-fold cross-validation, LipoPred achieves an excellent performance with a Matthew's correlation coefficient of 0.9930. Therefore, LipoPred can be a useful bioinformatics tool for the prediction of lipoylation sites. Feature analysis shows that some residues around lipoylation sites may play an important role in the prediction. The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of lipoylation. A user-friendly web-server for LipoPred is established at 123.206.31.171/LipoPred/.

Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm.

Cysteine S-sulfenylation is an important protein post-translational modification, which plays a crucial role in transcriptional regulation, cell signaling, and protein functions. To better elucidate the molecular mechanism of S-sulfenylation, it is important to identify S-sulfenylated substrates and their corresponding S-sulfenylation sites accurately. In this study, a novel bioinformatics tool named Sulf_FSVM is proposed to predict S-sulfenylation sites by using multiple feature extraction and fuzzy support vector machine algorithm. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode S-sulfenylation sites. And the maximum relevance minimum redundancy method are adopted to remove the redundant features. On the other hand, a fuzzy support vector machine algorithm is used to handle the class imbalance and noise problem in S-sulfenylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of Sulf_FSVM achieves a satisfactory performance with a Sensitivity of 73.26%, a Specificity of 70.78%, an Accuracy of 71.07% and a Matthew's correlation coefficient of 0.2971. Independent tests also show that Sulf_FSVM significantly outperforms existing S-sulfenylation sites predictors. Therefore, Sulf_FSVM can be a useful tool for accurate prediction of protein S-sulfenylation sites.

Correction: Development of glycosynthases with broad glycan specificity for the efficient glyco-remodeling of antibodies.

Correction for 'Development of glycosynthases with broad glycan specificity for the efficient glyco-remodeling of antibodies' by Sachin S. Shivatare et al., Chem. Commun., 2018, 54, 6161-6164.

Development of glycosynthases with broad glycan specificity for the efficient glyco-remodeling of antibodies.

The first systematic investigation of the effect of high mannose, hybrid, and bi- and tri-antennary complex type glycans on the effector functions of antibodies was achieved by the discovery of novel Endo-S2 mutants generated by site-directed mutagenesis as glycosynthases with broad substrate specificity.

Prediction of citrullination sites by incorporating k-spaced amino acid pairs into Chou's general pseudo amino acid composition.

As one of the most important and common protein post-translational modifications, citrullination plays a key role in regulating various biological processes and is associated with several human diseases. The accurate identification of citrullination sites is crucial for elucidating the underlying molecular mechanisms of citrullination and designing drugs for related human diseases. In this study, a novel bioinformatics tool named CKSAAP_CitrSite is developed for the prediction of citrullination sites. With the assistance of support vector machine algorithm, the highlight of CKSAAP_CitrSite is to adopt the composition of k-spaced amino acid pairs surrounding a query site as input. As illustrated by 10-fold cross-validation, CKSAAP_CitrSite achieves a satisfactory performance with a Sensitivity of 77.59%, a Specificity of 95.26%, an Accuracy of 89.37% and a Matthew's correlation coefficient of 0.7566, which is much better than those of the existing prediction method. Feature analysis shows that the N-terminal space containing pairs may play an important role in the prediction of citrullination sites, and the arginines close to N-terminus tend to be citrullinated. The conclusions derived from this study could offer useful information for elucidating the molecular mechanisms of citrullination and related experimental validations. A user-friendly web-server for CKSAAP_CitrSite is available at 123.206.31.171/CKSAAP_CitrSite/.

Identification of early ammonium nitrate-responsive genes in rice roots.

Ammonium has long been used as the predominant form of nitrogen source for paddy rice (Oryza sativa). Recently, increasing evidence suggests that nitrate also plays an important role for nitrogen acquisition in the rhizosphere of waterlogged paddy rice. Ammonium and nitrate have a synergistic effect on promoting rice growth. However, the molecular responses induced by simultaneous treatment with ammonium and nitrate have been less studied in rice. Here, we performed transcriptome analysis to identify genes that are rapidly regulated by ammonium nitrate (1.43 mM, 30 min) in rice roots. The combination of ammonium and nitrate preferentially induced the expression of nitrate-responsive genes. Gene ontology enrichment analysis revealed that the early ammonium nitrate-responsive genes were enriched in "regulation of transcription, DNA-dependent" and "protein amino acid phosphorylation" indicating that some of the genes identified in this study may play an important role in nitrogen sensing and signaling. Several defense/stress-responsive genes, including some encoding transcription factors and mitogen-activated protein kinase kinase kinases, were also rapidly induced by ammonium nitrate. These results suggest that nitrogen metabolism, signaling, and defense/stress responses are interconnected. Some of the genes identified here may be involved in the interaction of nitrogen signaling and defense/stress-response pathways in plants.

Peramivir Phosphonate Derivatives as Influenza Neuraminidase Inhibitors.

Peramivir is a potent neuraminidase (NA) inhibitor for treatment of influenza infection by intravenous administration. By replacing the carboxylate group in peramivir with a phosphonate group, phosphono-peramivir (6a), the dehydration and deoxy derivatives (7a and 8a) as well as their corresponding monoalkyl esters are prepared from a pivotal intermediate epoxide 12. Among these phosphonate compounds, the dehydration derivative 7a that has a relatively rigid cyclopentene core structure exhibits the strongest inhibitory activity (IC50 = 0.3-4.1 nM) against several NAs of wild-type human and avian influenza viruses (H1N1, H3N2, H5N1, and H7N9), although the phosphonate congener 6a is unexpectedly less active than peramivir. The inferior binding affinity of 6a is attributable to the deviated orientations of its phosphonic acid and 3-pentyl groups in the NA active site as inferred from the NMR, X-ray diffraction, and molecular modeling analyses. Compound 7a is active to the oseltamivir-resistant H275Y strains of H1N1 and H5N1 viruses (IC50 = 73-86 nM). The phosphonate monoalkyl esters (6b, 6c, 7b, 7c, 8b, and 8c) are better anti-influenza agents (EC50 = 19-89 nM) than their corresponding phosphonic acids (EC50 = 50-343 nM) in protection of cells from the viral infection. The phosphonate monoalkyl esters are stable in buffer solutions (pH 2.0-7.4) and rabbit serum; furthermore, the alkyl group is possibly tuned to attain the desired pharmacokinetic properties.