The Application of the GP Model to Manage Controllable Risk Factors in Stroke Patients with Diabetes Can Effectively Improve the Prognosis and Reduce the Recurrence Rate.

Objective: The aim of this study is to examine the impacts of general practice model (GP) on prognosis and recurrence of stroke patients with diabetes. Methods: Ninety patients with stroke combined with diabetes mellitus admitted to our hospital from June 2019 to June 2020 were selected for the study and were randomly and equally divided into 45 cases each in the control and experimental groups for the prospective trial. The patients in the control group received routine treatment while those in the experimental group were treated with GP model. Comparison in treatment effects, patients satisfaction, psychological status, quality of life, glycosylated hemoglobin level, and stroke recurrence was carried out between the two groups. Results: The experimental group showed markedly better treatment effects (P < 0.05), higher satisfaction degree (P < 0.05), higher HAD (P < 0.05), GQOLI-74 score (P < 0.05), and BI index (P < 0.05), lower level of glycosylated hemoglobin (P < 0.05), and much lower recurrence rate (P < 0.05), as compared to the control group. Conclusion: The application of the GP model to manage controllable risk factors in stroke patients with diabetes can effectively improve the prognosis and reduce the recurrence rate, which is worthy of clinical application and promotion.

Identification of herbal categories active in pain disorder subtypes by machine learning help reveal novel molecular mechanisms of algesia.

Chronic pain is highly prevalent and poorly controlled, of which the accurate underlying mechanisms need be further elucidated. Herbal drugs have been widely used for controlling various pain disorders. The systematic integration of pain herbal data resources might be promising to help investigate the molecular mechanisms of pain phenotypes. Here, we integrated large-scale bibliographic literatures and well-established data sources to obtain high-quality pain relevant herbal data (i.e. 426 pain related herbs with their targets). We used machine learning method to identify three distinct herb categories with their specific indications of symptoms, targets and enriched pathways, which were characterized by the efficacy of treatment to the chronic cough related neuropathic pain, the reproduction and autoimmune related pain, and the cancer pain, respectively. We further detected the novel pathophysiological mechanisms of the pain subtypes by network medicine approach to evaluate the interactions between herb targets and the pain disease modules. This work increased the understanding of the underlying molecular mechanisms of pain subtypes that herbal drugs are participating and with the ultimate aim of developing novel personalized drugs for pain disorders.

Identifying four DNA methylation gene sites signature for predicting prognosis of osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is a common malignant bone tumor in children and adolescents. DNA methylation plays a crucial role in the prognosis prediction of cancer. Identification of novel DNA methylation sites biomarkers could be beneficial for the prognosis of OS patients. In this study, we aim to find an efficient methylated site model for predicting survival in OS. METHODS: DNA methylation data were downloaded from the Cancer Genome Atlas database (TCGA) and the GEO database. Cox proportional hazard regression and random survival forest algorithm (RSFVH) were applied to identify DNA methylated site signature in the samples randomly assigned to the training subset and the other samples as the test subset. By randomizing 71 clinical samples into two individual groups and a series of statistical analyses between the two groups, a DNA methylation signature is verified. RESULTS: This signature comprises four methylation sites (cg04533248, cg12401425, cg13997435, and cg15075357) associated with the patient training group from the univariate Cox proportional hazards regression analysis, RSFVH, and multivariate Cox regression analysis. Kaplan-Meier survival curves showed the OS patients in the high-risk group have a poor 5-year overall survival compared with the low-risk group, and this finding was identified in the test data set. A ROC analysis was performed in the current research. The results revealed that this signature was an independent predictor of patient survival by investigating the AUC of the four methylation sites signature in the training data set (AUC =0.861) and test data set, respectively (AUC =0.920). The nomogram described in the current study placed a great guiding value for predicting 1-, 2-, 3-year survival of the OS by combining age, gender, grade, and TNM stage as covariates with the RS of patients' methylation related signatures. CONCLUSIONS: Our study proved that this signature might be a powerful prognostic tool for survival rate evaluation and guide tailored therapy for OS patients.

Link between community-acquired pneumonia and vitamin D levels in older patients.

PURPOSE: To investigate the correlation between the level of 25-hydroxyvitamin D [25-(OH)D] and community-acquired pneumonia (CAP) in elderly patients. METHODS: A total of 163 older patients were chosen from those hospitalized between October 2011 and October 2012. Patients were divided into pneumonia and non-pneumonia groups, and the concentrations of 25-(OH)D (nmol/L) in serum were measured. Clinical data were then compared between the two groups and pneumonia-related risk factors were analyzed using logistic regression. RESULTS: Among the 163 older hospitalized patients, 49 suffered from pneumonia. Levels of 25-(OH)D, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) were lower (P < 0.05) in the pneumonia group, while severe vitamin D (VitD) deficiency was higher in the pneumonia group (71.4 vs. 19.3%; P < 0.0001). Age and number of hospitalization days were higher in the pneumonia group (P < 0.05). Multivariate logistic regression showed that age and VitD levels were independent risk factors for pneumonia. No significant difference was observed in terms of gender, onset season, body mass index, or smoking and drinking history between the two groups (P > 0.05). CONCLUSION: The older patients with CAP had a severe VitD deficiency, indicating that low levels of VitD might play an important role in the occurrence and development of CAP.

Folding of the SARS coronavirus spike glycoprotein immunological fragment (SARS_S1b): thermodynamic and kinetic investigation correlating with three-dimensional structural modeling.

Spike glycoprotein of SARS coronavirus (S protein) plays a pivotal role in SARS coronavirus (SARS_CoV) infection. The immunological fragment of the S protein (Ala251-His641, SARS_S1b) is believed to be essential for SARS_CoV entering the host cell through S protein-ACE-2 interaction. We have quantitatively characterized the thermally induced and GuHCl-induced unfolding features of SARS_S1b using circular dichroism (CD), tryptophan fluorescence, and stopped-flow spectral techniques. For the thermally induced unfolding at pH 7.4, the apparent activation energy (E(app)) and transition midpoint temperature (Tm) were determined to be 16.3 +/- 0.2 kcal/mol and 52.5 +/- 0.4 degrees C, respectively. The CD spectra are not dependent on temperature, suggesting that the secondary structure of SARS_S1b has a relatively high thermal stability. GuHCl strongly affected SARS_S1b structure. Both the CD and fluorescent spectra resulted in consistent values of the transition middle concentration of the denaturant (Cm, ranging from 2.30 to 2.45 M) and the standard free energy change (deltaG(o), ranging from 2.1 to 2.5 kcal/mol) for the SARS_S1b unfolding reaction. Moreover, the kinetic features of the chemical unfolding and refolding of SARS_S1b were also characterized using a stopped-flow CD spectral technique. The obvious unfolding reaction rates and relaxation times were determined at various GuHCl concentrations, and the Cm value was obtained, which is very close to the data that resulted from CD and fluorescent spectral determinations. Secondary and three-dimensional structural predictions by homology modeling indicated that SARS_S1b folded as a globular-like structure by beta-sheets and loops; two of the four tryptophans are located on the protein surface, which is in agreement with the tryptophan fluorescence result. The three-dimensional model was also used to explain the recently published experimental results of S1-ACE-2 binding and immunizations.

Nucleocapsid protein of SARS coronavirus tightly binds to human cyclophilin A.

Severe acute respiratory syndrome coronavirus (SARS-CoV) is responsible for SARS infection. Nucleocapsid protein (NP) of SARS-CoV (SARS_NP) functions in enveloping the entire genomic RNA and interacts with viron structural proteins, thus playing important roles in the process of virus particle assembly and release. Protein-protein interaction analysis using bioinformatics tools indicated that SARS_NP may bind to human cyclophilin A (hCypA), and surface plasmon resonance (SPR) technology revealed this binding with the equilibrium dissociation constant ranging from 6 to 160nM. The probable binding sites of these two proteins were detected by modeling the three-dimensional structure of the SARS_NP-hCypA complex, from which the important interaction residue pairs between the proteins were deduced. Mutagenesis experiments were carried out for validating the binding model, whose correctness was assessed by the observed effects on the binding affinities between the proteins. The reliability of the binding sites derived by the molecular modeling was confirmed by the fact that the computationally predicted values of the relative free energies of the binding for SARS_NP (or hCypA) mutants to the wild-type hCypA (or SARS_NP) are in good agreement with the data determined by SPR. Such presently observed SARS_NP-hCypA interaction model might provide a new hint for facilitating the understanding of another possible SARS-CoV infection pathway against human cell.

Binding analyses between Human PPARgamma-LBD and ligands.

The binding characteristics of a series of PPARgamma ligands (GW9662, GI 262570, cis-parinaric acid, 15-deoxy-Delta(12,14)-prostaglandin J(2), LY171883, indomethacin, linoleic acid, palmitic acid and troglitazone) to human PPARgamma ligand binding domain have been investigated for the first time by using surface plasmon resonance biosensor technology, CD spectroscopy and molecular docking simulation. The surface plasmon resonance biosensor determined equilibrium dissociation constants (KD values) are in agreement with the results reported in the literature measured by other methods, indicating that the surface plasmon resonance biosensor can assume a direct assay method in screening new PPARgamma agonists or antagonists. Conformational changes of PPARgamma caused by the ligand binding were detected by CD determination. It is interesting that the thermal stability of the receptor, reflected by the increase of the transition temperature (T(m)), was enhanced by the binding of the ligands. The increment of the transition temperature (DeltaT(m)) of PPARgamma owing to ligand binding correlated well with the binding affinity. This finding implies that CD could possibly be a complementary technology with which to determine the binding affinities of ligands to PPARgamma. Molecular docking simulation provided reasonable and reliable binding models of the ligands to PPARgamma at the atomic level, which gave a good explanation of the structure-binding affinity relationship for the ligands interacting with PPARgamma. Moreover, the predicted binding free energies for the ligands correlated well with the binding constants measured by the surface plasmon resonance biosensor, indicating that the docking paradigm used in this study could possibly be employed in virtual screening to discover new PPARgamma ligands, although the docking program cannot accurately predict the absolute ligand-PPARgamma binding affinity.

A 3D model of SARS_CoV 3CL proteinase and its inhibitors design by virtual screening.

AIM: To constructed a three-dimensional (3D) model for the 3C like (3CL) proteinase of SARS coronavirus (SARS-CoV), and to design inhibitors of the 3CL proteinase based on the 3D model. METHODS: Bioinformatics analyses were performed to search the homologous proteins of the SARS-CoV 3CL proteinase from the GenBank and PDB database. A 3D model of the proteinase was constructed by using homology modeling technique. Targeting to the 3D model and its X-ray crystal structure of the main proteinase (Mpro) of transmissible gastroenteritis virus (TGEV), virtual screening was performed employing molecular docking method to identify possible 3CL proteinase inhibitors from small molecular databases. RESULTS: Sequence alignment indicated that the SARS-CoV 3CL proteinase was extremely homologous to TGEV Mpro, especially the substrate-binding pocket (active site). Accordingly, a 3D model for the SARS-CoV 3CL proteinase was constructed based on the crystal structure of TGEV Mpro. The 3D model adopts a similar fold of the TGEV Mpro, its structure and binding pocket feature are almost as same as that of TGEV Mpro. The tested virtual screening indicated that 73 available proteinase inhibitors in the MDDR database might dock into both the binding pockets of the TGEV Mpro and the SARS-CoV 3CL proteinase. CONCLUSIONS: Either the 3D model of the SARS-CoV 3CL proteinase or the X-ray crystal structure of the TGEV Mpro may be used as a starting point for design anti-SARS drugs. Screening the known proteinase inhibitors may be an appreciated shortcut to discover anti-SARS drugs.

Small envelope protein E of SARS: cloning, expression, purification, CD determination, and bioinformatics analysis.

AIM: To obtain the pure sample of SARS small envelope E protein (SARS E protein), study its properties and analyze its possible functions. METHODS: The plasmid of SARS E protein was constructed by the polymerase chain reaction (PCR), and the protein was expressed in the E coli strain. The secondary structure feature of the protein was determined by circular dichroism (CD) technique. The possible functions of this protein were annotated by bioinformatics methods, and its possible three-dimensional model was constructed by molecular modeling. RESULTS: The pure sample of SARS E protein was obtained. The secondary structure feature derived from CD determination is similar to that from the secondary structure prediction. Bioinformatics analysis indicated that the key residues of SARS E protein were much conserved compared to the E proteins of other coronaviruses. In particular, the primary amino acid sequence of SARS E protein is much more similar to that of murine hepatitis virus (MHV) and other mammal coronaviruses. The transmembrane (TM) segment of the SARS E protein is relatively more conserved in the whole protein than other regions. CONCLUSION: The success of expressing the SARS E protein is a good starting point for investigating the structure and functions of this protein and SARS coronavirus itself as well. The SARS E protein may fold in water solution in a similar way as it in membrane-water mixed environment. It is possible that beta-sheet I of the SARS E protein interacts with the membrane surface via hydrogen bonding, this beta-sheet may uncoil to a random structure in water solution.

Molecular cloning, expression, purification, and mass spectrometric characterization of 3C-like protease of SARS coronavirus.

Severe acute respiratory syndrome (SARS) is an acute respiratory illness, which has broken out in China. It has been known that SARS coronavirus (SARS_CoV) is a novel human coronavirus and is responsible for SARS infection. Belonging to one of the major proteins associated with SARS_CoV, SARS 3C-like protease (SARS_3CL(pro)) functions as a cysteine protease engaging in the proteolytic cleavage of the viral precursor polyprotein to a series of functional proteins required for coronavirus replication and is considered as an appealing target for designing anti-SARS agents. To facilitate the studies regarding the functions and structures of SARS_3CL(pro), in this report the synthetic genes encoding 3CL(pro) of SARS_CoV were assembled, and the plasmid was constructed using pQE30 as vector and expressed in Escherichia coli M15 cells. The highly yielded ( approximately 15mg/L) expressed protease was purified by use of NTA-Ni(2+) affinity chromatography and FPLC system, and its sequence was determined by LC/MS with the residue coverage of 46.4%.