A longitudinal study on the effects of social support on self-stigma, psychiatric symptoms, and personal and social functioning in community patients with severe mental illnesses in China.

BACKGROUND: Few studies have examined whether social support contributes to better consequences among chronic patients with severe mental illnesses (SMI) in their community recovery stage and whether self-stigma would be a mechanism through which social support impacts psychiatric symptoms and personal and social functioning. AIMS: This study aimed to examine prospective associations of social support with long-term self-stigma, psychiatric symptoms, and personal and social functioning, and to investigate whether self-stigma would mediate the associations of social support with psychiatric symptoms and personal and social functioning among patients with SMI. METHODS: A total of 312 persons with SMI (schizophrenia and bipolar disorder) in their community recovery stage participated in the study. Social support, self-stigma, psychiatric symptoms, and personal and social functioning were evaluated at baseline. The follow-up assessment was conducted at 6 months with the baseline measures except for social support. Hierarchical linear regression and mediation analysis were performed. RESULTS: The results showed that baseline social support predicted decreases in stigma (ß = -.115, p = .029) and psychiatric symptoms (ß = -.193, p < .001), and increases in personal and social functioning (ß = .134, p = .008) over 6 months, after adjusting for relevant covariates. Stigma at 6 months partially mediated the association between baseline social support and 6-month psychiatric symptoms (indirect effect: ß = -.043, CI [-0.074, -0.018]). Stigma and psychiatric symptoms at 6 months together mediated the association between baseline social support and 6-month personal and social functioning (indirect effect: ß = .084, 95% CI [0.029, 0.143]). CONCLUSION: It is necessary to provide comprehensive social support services and stigma reduction interventions at the community level to improve the prognosis of SMI.

A set of new amino acid descriptors applied in prediction of MHC class I binding peptides.

A set of new amino acid descriptors, namely factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha and turn propensities, bulky properties, compositional characteristics, local flexibility and electronic properties, was proposed to resolve the representation of peptide structures. FASGAI vectors were then used to represent the structures of 152 HLA-A(*)0201 restrictive T-cell epitopes with 9 amino acid residues. The features that are closely related to binding affinities were selected by genetic arithmetic, and the model based on partial least squares was developed to predict binding affinities. The model revealed promising predictive power, giving relatively high predictions for training and test samples. Further, the PreMHCbinding program at significantly lower computational complexity was exploited to predict MHC class I binding peptides. Quantitative structure-affinity relationship analyses demonstrated the bulky properties and hydrophobicity of the 3rd residue, bulky properties of the 2nd residue, hydrophobicity of the 9th residue that provided high positive contribution to the binding affinities, and that the hydrophobicity of the 4th residue and local flexibility of the 3rd residue were negative to binding affinities. The results showed that FASGAI vectors can be further utilized to represent the structures of other functional peptides; moreover, it has thus showed us further direction into the potential applications on relationship between structures and functions of proteins.

Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis.

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (R ia) for training samples is 99.8% and R ia by leave one out cross validation is 99.5%. Both R ia of 99.8% for training samples and R ia of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external R ia for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED).

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.