Expression of Serum Anti-BP180/230 Antibodies in Bullous Pemphigoid Patients Combined with Nervous System Diseases and Relevant Factor Analysis.

OBJECTIVE: To explore the anti-BP230/180 and anti-BP180 antibodies in patients with bullous pemphigoid (BP) combined with neurological diseases, and to analyse the relevant factors. STUDY DESIGN: Analytical study. Place and Duration of the Study: Neurology Department, Cangzhou People's Hospital, Cangzhou, from April 2019 to June 2022. METHODOLOGY: Eighty BP patients were chosen based on associated neurological diseases, they were split into single (n=42) and combined groups (n=38). Expression of anti-BP180/230 antibodies was compared between the two groups. Associations with neurological diseases were analysed and the factors affecting the expression of anti-BP180/230 antibodies were explored. RESULTS: Out of 80 patients, 61 were positive for anti-BP180 antibodies and 58 were positive for anti-BP230 antibodies. The proportion of patients with positive anti-BP230/180 antibodies in the single group was considerably lower than in the combined group (p<0.05). Presence of both nervous system diseases and BP was found to be associated with the presence of anti-BP230/180 antibodies (p<0.001). Univariate analysis showed statistically significant association with age (<70 years, total IgE (>100 IU/ml), and EOS count >0.5 x 109/L (p<0.05). Logistic analysis demonstrated that age, total IgE and EOS count were independent risk factors affecting the expression of anti-BP180 and anti-BP23 antibodies (p<0.05). CONCLUSION: Serum anti-BP230/180 antibodies expression is abnormally high in BP patients having nervous system diseases. Combined nervous system diseases, age, total IgE and EOS count are independent risk factors affecting expression of anti-BP180/230 antibodies. KEY WORDS: Anti-BP180 antibody, Anti-BP230 antibody, Bullous pemphigoid, Nervous system diseases.

Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data.

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis-Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.

Visibility-graphlet approach to the output series of a Hodgkin-Huxley neuron.

The output signals of neurons that are exposed to external stimuli are of great importance for brain functionality. Traditional time-series analysis methods have provided encouraging results; however, the associated patterns and their correlations in the output signals of neurons are masked by statistical procedures. Here, graphlets are employed to extract the local temporal patterns and the transitions between them from the output signals when neurons are exposed to external stimuli with selected stimulating periods. A transition network is defined where the node is the graphlet and the direct link is the transition between two successive graphlets. The transition-network structure is affected by the simulating periods. When the stimulating period moves close to an integer multiple of the neuronal intrinsic period, only the backbone or core survives, while the other linkages disappear. Interestingly, the size of the backbone (number of nodes) equals the multiple. The transition-network structure is conservative within each stimulating region, which is defined as the range between two successive integer multiples. Nevertheless, the backbone or detailed structure is significantly altered between different stimulating regions. This alternation is induced primarily from a total of 12 active linkages. Hence, the transition network shows the structure of cross correlations in the output time-series for a single neuron.