Predictive value of anti-Mullerian hormone for pregnancy outcomes following assisted reproductive techniques (ART) in Southwest China.

BACKGROUND: Anti-Müllerian hormone (AMH) is secreted by granulosa cells in preantral follicles and small antral follicles. There is limited information about whether serum AMH levels are related to pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET). The aim of this study was to provide a theoretical basis for improving pregnancy outcomes. METHODS: A retrospective cohort study was conducted on infertile women who were treated at the Reproductive Centre of the Affiliated Hospital of Southwest Medical University between September 2018 and September 2019. The sample included 518 participants from Southwest China. The participants were divided into 2 groups according to their AMH level. Their data were retrieved from the medical records: days and dosage of gonadotropin (Gn) (one bottle equals 75 IU), the number of oocytes obtained, the number of oocytes in metaphase II (MII) and the number of high-quality embryos. The pregnancy outcomes were followed up and divided into two groups according to whether they were pregnant or not, with statistical analysis of the parameters related to the in vitro fertilization process performed separately. RESULTS: Compared to a lower AMH level (AMH ≤ 1.1), a higher AMH level (AMH > 1.1) resulted in less total Gn (bottle) (P = 0.00 < 0.05) and a lower starting Gn (IU) (P = 0.00 < 0.05), while the number of oocytes obtained,MII,cleavages and high-quality embryos were higher (P = 0.00 < 0.05). The participants' pregnancy outcomes (ectopic pregnancy, miscarriage, singleton, twin, multiple births) were found to not be predictable by AMH through ROC curves (P = 0.980, 0.093, 0.447, 0.146, 0.526, and 0.868 > 0.05). For participants in the pregnancy group, although AMH was lower in the nonpregnant participants(P = 0.868 > 0.05), the difference was not statistically significant, and the correlation coefficients between the two groups suggested no differences in the IVF process, except for the starting Gn (IU) (P = 0.038 < 0.05). CONCLUSION: AMH has clinical application value in predicting ovarian reserve function, providing guidance and suggestions for the specific formulation of ovulation promotion programs with assisted reproductive technology, but it cannot effectively predict the outcome of clinical pregnancy.

Combining Dual-Light Emissions to Achieve Efficient Broadband Yellowish-Green Luminescence in One-Dimensional Hybrid Lead Halides.

In recent years, low-dimensional lead halides have emerged as some of most attractive photoelectric materials due to their intrinsic broadband emissions with a potential application in white-light emitting diodes. To achieve the desired performance, tremendous research has emphasized the modulation of inorganic components as optical centers; however, less work has paid attention to the direct contribution of the organic components. Herein, we successfully assembled two new hybrid lead halides of [H2BPP]Pb2X6 (X = Br, 1, and Cl, 2) containing one-dimensional double [Pb2X6]2- chains using optically active 1,3-bis(4-pyridyl)-propane (BPP) as an organic cation. Under UV-light excitation, compounds 1 and 2 exhibit broadband yellowish-green emissions, which were verified by promising photoluminescence quantum efficiencies (PLQEs) of 8.10% and 4.84%, respectively. The broadband light emissions are derived from the combination of dual higher-energy blue and lower-energy yellow light spectra, which can be attributed to the individual contributions of the organic and inorganic components, respectively, according to the time-resolved and temperature-dependent emission spectra as well as theoretical calculations. This work proves the great contribution of organic components to the photophysical properties and provides a new design strategy to realize broadband light emission by rationally combining the dual-emitting properties of different assembly blocks.

Tandem Lewis Pair Polymerization and Organocatalytic Ring-Opening Polymerization for Synthesizing Block and Brush Copolymers.

Lewis pair polymerization is a powerful method for preparing soluble polymers bearing pendant active vinyl groups by directly polymerizing dissymmetric divinyl polar monomers. Herein, we present a strategy for synthesizing block and brush copolymers via tandem Lewis pair polymerization of methacrylates, "thiol-ene" click reaction and organocatalytic ring-opening polymerization of lactide.

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: approach from the strategy of discrete wavelet transform feature extraction.

It is very challenging and complicated to predict protein locations at the sub-subcellular level. The key to enhancing the prediction quality for protein sub-subcellular locations is to grasp the core features of a protein that can discriminate among proteins with different subcompartment locations. In this study, a different formulation of pseudoamino acid composition by the approach of discrete wavelet transform feature extraction was developed to predict submitochondria and subchloroplast locations. As a result of jackknife cross-validation, with our method, it can efficiently distinguish mitochondrial proteins from chloroplast proteins with total accuracy of 98.8% and obtained a promising total accuracy of 93.38% for predicting submitochondria locations. Especially the predictive accuracy for mitochondrial outer membrane and chloroplast thylakoid lumen were 82.93% and 82.22%, respectively, showing an improvement of 4.88% and 27.22% when other existing methods were compared. The results indicated that the proposed method might be employed as a useful assistant technique for identifying sub-subcellular locations. We have implemented our algorithm as an online service called SubIdent (http://bioinfo.ncu.edu.cn/services.aspx).

PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis.

Tyrosine sulfation is a ubiquitous posttranslational modification that regulates extracellular protein-protein interactions, intracellular protein transportation modulation, and protein proteolytic process. However, identifying tyrosine sulfation sites remains a challenge due to the lability of sulfation sequences. In this study, we developed a method called PredSulSite that incorporates protein secondary structure, physicochemical properties of amino acids, and residue sequence order information based on support vector machine to predict sulfotyrosine sites. Three types of encoding algorithms-secondary structure, grouped weight, and autocorrelation function-were applied to mine features from tyrosine sulfation proteins. The prediction model with multiple features achieved an accuracy of 92.89% in 10-fold cross-validation. Feature analysis showed that the coil structure, acidic amino acids, and residue interactions around the tyrosine sulfation sites all contributed to the sulfation site determination. The detailed feature analysis in this work can help us to understand the sulfation mechanism and provide guidance for the related experimental validation. PredSulSite is available as a community resource at http://www.bioinfo.ncu.edu.cn/inquiries_PredSulSite.aspx.

A method to distinguish between lysine acetylation and lysine methylation from protein sequences.

Lysine acetylation and methylation are two major post-translational modifications of lysine residues. They play vital roles in both biological and pathological processes. Specific lysine residues in H3 histone protein tails appear to be targeted for either acetylation or methylation. Hence it is very challenging to distinguish between acetylated and methylated lysine residues using computational methods. This work presents a method that incorporates protein sequence information, secondary structure and amino acid properties to differentiate acetyl-lysine from methyl-lysine. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The proposed method achieves an accuracy of 93.3% using a jackknife test. Feature analysis demonstrates that the prediction model with multiple features can take full advantage of the supplementary information from different features to improve classification performance and prediction robustness. Analysis of the characteristics of lysine residues which can be either methylated or acetylated shows that they are more similar to methyl-lysine than to acetyl-lysine.

Identification and validation of tumor microenvironment-related lncRNA prognostic signature for uveal melanoma.

AIM: To investigate the role of tumor microenvironment (TME)-related long non-coding RNA (lncRNA) in uveal melanoma (UM), probable prognostic signature and potential small molecule drugs using bioinformatics analysis. METHODS: UM expression profile data were downloaded from the Cancer Genome Atlas (TCGA) and bioinformatics methods were used to find prognostic lncRNAs related to UM immune cell infiltration. The gene expression profile data of 80 TCGA specimens were analyzed using the single sample Gene Set Enrichment Analysis (ssGSEA) method, and the immune cell infiltration of a single specimen was evaluated. Finally, the specimens were divided into high and low infiltration groups. The differential expression between the two groups was analyzed using the R package 'edgeR'. Univariate, multivariate and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to explore the prognostic value of TME-related lncRNAs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses were also performed. The Connectivity Map (CMap) data set was used to screen molecular drugs that may treat UM. RESULTS: A total of 2393 differentially expressed genes were identified and met the criteria for the low and high immune cell infiltration groups. Univariate Cox analysis of lncRNA genes with differential expression identified 186 genes associated with prognosis. Eight prognostic markers of TME-included lncRNA genes were established as potentially independent prognostic elements. Among 269 differentially expressed lncRNAs, 69 were up-regulated and 200 were down-regulated. Univariate Cox regression analysis of the risk indicators and clinical characteristics of the 8 lncRNA gene constructs showed that age, TNM stage, tumor base diameter, and low and high risk indices had significant prognostic value. We screened the potential small-molecule drugs for UM, including W-13, AH-6809 and Imatinib. CONCLUSION: The prognostic markers identified in this study are reliable biomarkers of UM. This study expands our current understanding of the role of TME-related lncRNAs in UM genesis, which may lay the foundations for future treatment of this disease.

Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine.

Apoptosis proteins have a central role in the development and homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. As a result of genome and other sequencing projects, the gap between the number of known apoptosis protein sequences and the number of known apoptosis protein structures is widening rapidly. Because of this extremely unbalanced state, it would be worthwhile to develop a fast and reliable method to identify their subcellular locations so as to gain better insight into their biological functions. In view of this, a new method, in which the support vector machine combines with discrete wavelet transform, has been developed to predict the subcellular location of apoptosis proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method can remarkably improve the prediction accuracy of subcellular locations, and might also become a useful high-throughput tool in characterizing other attributes of proteins, such as enzyme class, membrane protein type, and nuclear receptor subfamily according to their sequences.

Relationship Between Anti-Müllerian Hormone and In Vitro Fertilization-Embryo Transfer in Clinical Pregnancy.

Objectives: To retrospectively analyze the correlation between anti-Müllerian hormone (AMH) and the number of oocytes obtained by controlled ovarian hyperstimulation (COH) in women of different ages and explore the factors affecting in vitro fertilization and embryo transfer (IVF-ET) in clinical pregnancy of infertile women to provide evidence for infertile women to choose assisted reproduction strategies. Methods: Infertile women who received IVF-ET or intracytoplasmic sperm injection and embryo transfer (ICSI-ET) treatment in the reproductive center of XX hospital between October 2018 and September 2019 were included. Patient data on medical records, age, body mass index (BMI), years of infertility, basic follicle-stimulating hormone (FSH), basic luteinizing hormone (LH), basic estradiol (E2), anti-Müllerian hormone level (AMH), antral follicle count (AFC), gonadotropins (Gn) medication days, Gn dosage, endometrial thickness on transplantation day, the number of retrieved oocytes, the number of mature oocytes obtained, the number of embryos transferred, clinical pregnancy status, etc., were collected. Results: A total of 314 patients were enrolled in this study, with an average age of 31.0 ± 4.5 years. The infertility period ranged from 0-21 years. The AMH level showed a downward trend with increasing age. Overall, the AMH level of women of all ages was positively correlated with the number of retrieved oocytes (r = 0.335, p < 0.001). The AMH level of women between 22 and 28 years old was positively correlated with the number of retrieved oocytes (r = 0.164, p < 0.061) but it was not statistically significant. Similarly, the AMH level of women aged 29-35 and 36-43 was positively correlated with the number of retrieved oocytes (r = 0.356, p < 0.001; r = 0.461, p < 0.001). The average age of the pregnant group (30.6 ± 4.4 years) was lower than that of the non-pregnant group (32.2 ± 4.6 years) (p < 0.001). The number of oocytes obtained (9.8 ± 4.5) and the number of embryos transferred (1.9 ± 0.4) in the pregnant group was significantly higher than that in the non-pregnant group (9.2 ± 4.5; 1.7 ± 0.5); the difference was statistically significant. The multivariate logistic regression model showed that age (OR = 0.574 95% CI: 0.350-0.940), AMH (OR = 1.430 95% CI: 1.130-1.820) and the number of oocytes obtained (OR = 1.360 95% CI: 1.030-1.790) were factors affecting clinical pregnancy. Conclusion: We found that the level of AMH in infertile women decreased with age and the number of oocytes obtained in infertile women was positively correlated with AMH. Moreover, the number of oocytes and embryo transferred in the pregnant group was significantly higher than those in the non-pregnant group. Furthermore, age, AMH and the number of oocytes affected the clinical pregnancy.

The prediction of palmitoylation site locations using a multiple feature extraction method.

As an extremely important and ubiquitous post-translational lipid modification, palmitoylation plays a significant role in a variety of biological and physiological processes. Unlike other lipid modifications, protein palmitoylation and depalmitoylation are highly dynamic and can regulate both protein function and localization. The dynamic nature of palmitoylation is poorly understood because of the limitations in current assay methods. The in vivo or in vitro experimental identification of palmitoylation sites is both time consuming and expensive. Due to the large volume of protein sequences generated in the post-genomic era, it is extraordinarily important in both basic research and drug discovery to rapidly identify the attributes of a new protein's palmitoylation sites. In this work, a new computational method, WAP-Palm, combining multiple feature extraction, has been developed to predict the palmitoylation sites of proteins. The performance of the WAP-Palm model is measured herein and was found to have a sensitivity of 81.53%, a specificity of 90.45%, an accuracy of 85.99% and a Matthews correlation coefficient of 72.26% in 10-fold cross-validation test. The results obtained from both the cross-validation and independent tests suggest that the WAP-Palm model might facilitate the identification and annotation of protein palmitoylation locations. The online service is available at http://bioinfo.ncu.edu.cn/WAP-Palm.aspx.

PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features.

Post-translational lysine methylation and acetylation are two major modifications of lysine residues. They play critical roles in various biological processes, especially in gene regulation. Identification of protein methylation and acetylation sites would be a foundation for understanding their modification dynamics and molecular mechanism. This work presents a method called PLMLA that incorporates protein sequence information, secondary structure and amino acid properties to predict methylation and acetylation of lysine residues in whole protein sequences. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The prediction accuracy for methyllysine and acetyllysine are 83.02% and 83.08%, respectively. Feature analysis reveals that methyllysine is likely to occur at the coil region and acetyllysine prefers to occur at the helix region of protein. The upstream residues away from the central site may be close to methylated lysine in three-dimensional structure and have a significant influence on methyllysine, while the positively charged residues may have a significant influence on acetyllysine. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PLMLA.aspx.

Position-specific analysis and prediction for protein lysine acetylation based on multiple features.

Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.

A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization.

Knowing the subcellular localization of proteins within the cell is an important step in elucidating its role in biological processes, its function and its potential as a drug target for disease diagnosis. As the number of complete genomes rapidly increases, accurate and efficient methods that automatically predict the subcellular localizations become more urgent. In the current paper, we developed a novel method that coupled the discrete wavelet transform with support vector machine based on the amino acid polarity to predict the subcellular localizations of prokaryotic and eukaryotic proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method remarkably improved the prediction accuracy of subcellular locations, and could be as an effective and promising high-throughput method in the subcellular localization research.

Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform.

In vivo, some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition.

PMeS: prediction of methylation sites based on enhanced feature encoding scheme.

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew's correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew's correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.

OligoPred: a web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou's pseudo amino acid composition.

In vivo, some proteins exist as monomers (single polypeptide chains) and others as oligomers. Not like monomers, oligomers are composed of two or more chains (subunits) that are associated with each other through non-covalent interactions and, occasionally, through disulfide bonds. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. However, with the dramatic increase in the number of protein sequences submitted to the public data bank, it is important for both basic research and drug discovery research to acquire the possible knowledge about homo-oligomeric attributes of their interested proteins in a timely manner. In this paper, a high-throughput method, combined support vector machines with discrete wavelet transform, has been developed to predict the protein homo-oligomers. The total accuracy obtained by the re-substitution test, jackknife test and independent dataset test are 99.94%, 96.17% and 96.18%, respectively, showing that the proposed method of extracting feature from the protein sequences is effective and feasible for predicting homo-oligomers. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx.

Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation.

Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R(2720) was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx . Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate.

Prediction of the types of membrane proteins based on discrete wavelet transform and support vector machines.

Membrane proteins are crucial for many biological functions and have become attractive targets for both basic research and drug discovery. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is both time-consuming and expensive to determine the types of newly found membrane proteins solely with traditional experiment, and so it is highly demanded to develop an automatic method for fast and accurately identifying the type of membrane proteins according to their amino acid sequences. In this study, the discrete wavelet transform (DWT) and support vector machine (SVM) have been used for the prediction of the types of membrane proteins. Maximum accuracy has been obtained using SVM with a wavelet function of bior2.4, a decomposition scale j = 4, and Kyte-Doolittle hydrophobicity scales. The results indicate that the proposed method may play an important complementary role to the existing methods in this area.