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/.

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.

Foxa2 and Cdx2 cooperate with Nkx2-1 to inhibit lung adenocarcinoma metastasis.

Despite the fact that the majority of lung cancer deaths are due to metastasis, the molecular mechanisms driving metastatic progression are poorly understood. Here, we present evidence that loss of Foxa2 and Cdx2 synergizes with loss of Nkx2-1 to fully activate the metastatic program. These three lineage-specific transcription factors are consistently down-regulated in metastatic cells compared with nonmetastatic cells. Knockdown of these three factors acts synergistically and is sufficient to promote the metastatic potential of nonmetastatic cells to that of naturally arising metastatic cells in vivo. Furthermore, silencing of these three transcription factors is sufficient to account for a significant fraction of the gene expression differences between the nonmetastatic and metastatic states in lung adenocarcinoma, including up-regulated expression of the invadopodia component Tks5long, the embryonal proto-oncogene Hmga2, and the epithelial-to-mesenchymal mediator Snail. Finally, analyses of tumors from a genetically engineered mouse model and patients show that low expression of Nkx2-1, Foxa2, and Cdx2 strongly correlates with more advanced tumors and worse survival. Our findings reveal that a large part of the complex transcriptional network in metastasis can be controlled by a small number of regulatory nodes that function redundantly, and loss of multiple nodes is required to fully activate the metastatic program.

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.

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/.

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.

A common glycan structure on immunoglobulin G for enhancement of effector functions.

Antibodies have been developed as therapeutic agents for the treatment of cancer, infection, and inflammation. In addition to binding activity toward the target, antibodies also exhibit effector-mediated activities through the interaction of the Fc glycan and the Fc receptors on immune cells. To identify the optimal glycan structures for individual antibodies with desired activity, we have developed an effective method to modify the Fc-glycan structures to a homogeneous glycoform. In this study, it was found that the biantennary N-glycan structure with two terminal alpha-2,6-linked sialic acids is a common and optimized structure for the enhancement of antibody-dependent cell-mediated cytotoxicity, complement-dependent cytotoxicity, and antiinflammatory activities.

Cell-permeable probe for identification and imaging of sialidases.

Alkyne-hinged 3-fluorosialyl fluoride (DFSA) containing an alkyne group was shown to be a mechanism-based target-specific irreversible inhibitor of sialidases. The ester-protected analog DFSA (PDFSA) is a membrane-permeable precursor of DFSA designed to be used in living cells, and it was shown to form covalent adducts with virus, bacteria, and human sialidases. The fluorosialyl-enzyme adduct can be ligated with an azide-annexed biotin via click reaction and detected by the streptavidin-specific reporting signals. Liquid chromatography-mass spectrometry/mass spectrometry analysis on the tryptic peptide fragments indicates that the 3-fluorosialyl moiety modifies tyrosine residues of the sialidases. DFSA was used to demonstrate influenza infection and the diagnosis of the viral susceptibility to the anti-influenza drug oseltamivir acid, whereas PDFSA was used for in situ imaging of the changes of sialidase activity in live cells.

The transcriptional repression activity of STAF65γ is facilitated by promoter tethering and nuclear import of class IIa histone deacetylases.

Aberrant expression levels of transcriptional regulators result in alterations in transcriptional control. STAF65γ is a structural subunit of the GCN5 transcriptional co-activator complex. Reports showed that STAF65γ is highly expressed in several human cancer cells, but the consequences of this aberrant expression pattern remain elusive. Here, we show that the STAF65γ protein is highly expressed in lung adenocarcinoma patients and high levels of STAF65γ correlate with poor prognosis. High levels of STAF65γ cause repression of the c-Myc oncogene through physical association with transcription factor YY1 and co-repressors HDACs. Physical interactions between STAF65γ and class IIa HDACs facilitate nuclear enrichment and regulate the assembly of HDAC complexes. Moreover, SUMOylation of STAF65γ is necessary for maintaining the co-repressor complex containing YY1 and class IIa HDACs at the promoter. Our findings reveal a distinct role of STAF65γ in nuclear import, transcriptional repression, and cell cycle regulation at high levels of expression, which is associated with poor clinical outcomes of lung adenocarcinoma.

Oseltamivir hydroxamate and acyl sulfonamide derivatives as influenza neuraminidase inhibitors.

Tamiflu, the ethyl ester form of oseltamivir carboxylic acid (OC), is the first orally available anti-influenza drug for the front-line therapeutic option. In this study, the OC-hydroxamates, OC-sulfonamides and their guanidino congeners (GOC) were synthesized. Among them, an OC-hydroxamate 7d bearing an O-(2-indolyl)propyl substituent showed potent NA inhibition (IC50 = 6.4 nM) and good anti-influenza activity (EC50 = 60.1 nM) against the wild-type H1N1 virus. Two GOC-hydroxamates (9b and 9d) and one GOC-sulfonamide (12a) were active to the tamiflu-resistant H275Y virus (EC50 = 2.3-6.9 µM).

Serum uric acid levels are associated with polymorphisms in the SLC2A9, SF1, and GCKR genes in a Chinese population.

AIM: Genome-wide association studies have identified several novel loci associated with serum uric acid concentrations in individuals of European descent. In the current study, we aimed to evaluate the associations between these loci and serum uric acid concentrations in a Chinese population. METHODS: Fourteen single nucleotide polymorphisms (SNPs) mapped in or near 11 loci (PDZK1, GCKR, LRP2, SLC2A9, ABCG2, LRRC16A, SLC17A1, SLC17A3, SLC22A11, SLC22A12 and SF1) were genotyped in 2329 Chinese subjects in Shanghai. Serum biochemical parameters including uric acid concentrations were determined. All the variants were analyzed for gender differences since uric acid metabolism differed between genders. RESULTS: In males after adjustments for age and BMI, GCKR rs780094, SLC2A9 rs11722228 and SF1 rs606458 were associated with the uric acid concentrations, which were statistically significant (P=0.016, 0.001 and 0.03, respectively), whereas SLC2A9 rs3775948 was marginally associated with the uric acid concentrations (P=0.071). In females, SLC22A12 rs506338 was also marginally associated with the uric acid concentrations (P=0.057). The meta-analysis for combined data from both males and females revealed that rs3775948 and rs606458 were associated with the uric acid concentrations (P=0.036 and 0.043, respectively). Furthermore, the gender significantly affected the association of rs11722228 with serum uric acid levels (P=0.012). CONCLUSION: The SLC2A9 rs11722228, SF1 rs606458 and GCKR rs780094 variants modulate uric acid concentrations in Chinese males, while SF1 rs606458 and SLC2A9 rs3775948 are associated with the uric acid concentrations in both Chinese males and females.

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.

High-throughput identification of compounds targeting influenza RNA-dependent RNA polymerase activity.

As influenza viruses have developed resistance towards current drugs, new inhibitors that prevent viral replication through different inhibitory mechanisms are useful. In this study, we developed a screening procedure to search for new antiinfluenza inhibitors from 1,200,000 compounds and identified previously reported as well as new antiinfluenza compounds. Several antiinfluenza compounds were inhibitory to the influenza RNA-dependent RNA polymerase (RdRP), including nucleozin and its analogs. The most potent nucleozin analog, 3061 (FA-2), inhibited the replication of the influenza A/WSN/33 (H1N1) virus in MDCK cells at submicromolar concentrations and protected the lethal H1N1 infection of mice. Influenza variants resistant to 3061 (FA-2) were isolated and shown to have the mutation on nucleoprotein (NP) that is distinct from the recently reported resistant mutation of Y289H [Kao R, et al. (2010) Nat Biotechnol 28:600]. Recombinant influenza carrying the Y52H NP is also resistant to 3061 (FA-2), and NP aggregation induced by 3061 (FA-2) was identified as the most likely cause for inhibition. In addition, we identified another antiinfluenza RdRP inhibitor 367 which targets PB1 protein but not NP. A mutant resistant to 367 has H456P mutation at the PB1 protein and both the recombinant influenza and the RdRP expressing the PB1 H456P mutation have elevated resistance to 367. Our high-throughput screening (HTS) campaign thus resulted in the identification of antiinfluenza compounds targeting RdRP activity.

Two new antioxidant diarylheptanoids from the fruits of Alpinia oxyphylla.

Two new diarylheptanoids, 1-(3',5'-dihydroxy-4'-methoxyphenyl)-7-phenyl-3-heptanone (1) and 1-(2',4'-dihydroxy-3'-methoxyphenyl)-7-(4″-methoxyphenyl)-3-heptanone (2), along with known diarylheptanoid yakuchinone A (3), and five flavanoids, tectochrysin (4), chrysin (5), izalpinin (6), kaempferol 7, 4'-dimethyl ether (7), and kaempferide (8) were isolated from the fruits of Alpinia oxyphylla Miq. Their structures were determined by means of spectroscopic methods. Antioxidant activities of all the isolated compounds were evaluated using a 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay. Compounds 1-3 and 6-8 exhibited potent antioxidant activities in the DPPH assay.