Detecting autonomic dysfunction in patients with glaucoma using dynamic pupillometry.

Autonomic dysfunction is a feature of glaucoma patients, which are reported to be related to glaucoma progression. We investigated pupil responses to a light flash using dynamic pupillometry in glaucoma patients to assess autonomic nervous system status. In total, 97 glaucoma patients, including 21 eyes of 21 glaucoma patients with cardiac autonomic dysfunction, were enrolled. Pupil reactions were assessed using 1 flash of white light after 2 minutes of dark adaptation and recorded using dynamic pupillometry. Changes in the radius of the pupil were evaluated as a function of several time-dependent and pupil/iris (P/I) diameter ratio parameters. Autonomic function was assessed using a cardiac heart-rate-variability test which performs 5 autonomic function tests and classifies patients with cardiac autonomic neuropathy (CAN). Comparison of pupil parameters between eyes with and without disc hemorrhage indicated larger P/I ratios in darkness, greater changes in the P/I ratio during examination, shorter latency to plateau, and shorter duration of constriction in eyes with disc hemorrhages. A comparison of pupil parameters between eyes with and without CAN showed larger P/I ratios in darkness, larger P/I ratios at maximum constriction, and prolonged latency to maximum constriction. The presence of CAN was significantly related to the P/I ratio in darkness and the latency of maximum constriction. Using dynamic pupillometry, we found that glaucoma patients with CAN dysfunction have larger baseline pupils in darkness and different constriction responses to light. Assessing the pupils might be a good method of identifying patients with autonomic dysfunction.

A computational model for predicting protein interactions based on multidomain collaboration.

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.

Protein complex prediction based on simultaneous protein interaction network.

MOTIVATION: The increase in the amount of available protein-protein interaction (PPI) data enables us to develop computational methods for protein complex predictions. A protein complex is a group of proteins that interact with each other at the same time and place. The protein complex generally corresponds to a cluster in PPI network (PPIN). However, clusters correspond not only to protein complexes but also to sets of proteins that interact dynamically with each other. As a result, conventional graph-theoretic clustering methods that disregard interaction dynamics show high false positive rates in protein complex predictions. RESULTS: In this article, a method of refining PPIN is proposed that uses the structural interface data of protein pairs for protein complex predictions. A simultaneous protein interaction network (SPIN) is introduced to specify mutually exclusive interactions (MEIs) as indicated from the overlapping interfaces and to exclude competition from MEIs that arise during the detection of protein complexes. After constructing SPINs, naive clustering algorithms are applied to the SPINs for protein complex predictions. The evaluation results show that the proposed method outperforms the simple PPIN-based method in terms of removing false positive proteins in the formation of complexes. This shows that excluding competition between MEIs can be effective for improving prediction accuracy in general computational approaches involving protein interactions. AVAILABILITY: http://code.google.com/p/simultaneous-pin/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein complex prediction based on mutually exclusive interactions in protein interaction network.

The increasing amount of available Protein-Protein Interaction (PPI) data enables scalable methods for the protein complex prediction. A protein complex is a group of two or more proteins formed by interactions that are stable over time, and it generally corresponds to a dense sub-graph in PPI Network (PPIN). However, dense sub-graphs correspond not only to stable protein complexes but also to sets of proteins including dynamic interactions. As a result, conventional simple PPIN based graph-theoretic clustering methods have high false positive rates in protein complex prediction. In this paper, we propose an approach to predict protein complexes based on the integration of PPI data and mutually exclusive interaction information drawn from structural interface data of protein domains. The extraction of Simultaneous Protein Interaction Cluster (SPIC) is the essence of our approach, which excludes interaction conflicts in network clusters by achieving mutually exclusion among interactions. The concept of SPIC was applied to conventional graph-theoretic clustering algorithms, MCODE and LCMA, to evaluate the density of clusters for protein complex prediction. The comparison with original graph-theoretic clustering algorithms verified the effectiveness of our approach; SPIC based methods refined false positives of original methods to be true positive complexes, without any loss of true positive predictions yielded by original methods.

Weighted feature value based Drug Target Protein prediction.

Drug discovery is a long process in which only a few successful new therapeutic discoveries are made and identification of drug target candidate proteins requires considerable time and efforts. However, the accumulation of information on drugs has made it possible to devise new computational methods for classifying drug target candidates. In this paper, we devise a Drug Target Protein (DT-P) classification method by the summation of weighted features which is extracted from known DT-P. The method is validated using Bayesian decision theory and SVM, and it was revealed to achieve high specificity of 89.5% with 88% accuracy.

No association between 5-HTTLPR and harm avoidance in Korean college students.

There have been numerous studies on the association between 5-HTTLPR (polymorphisms in the promoter region of the serotonin transporter gene) and anxiety-related personality traits, with conflicting results. In this study, we administered Korean version of the Temperament and Character Inventory (K-TCI) to a sample of 158 Korean college students and genotyped for the 5-HTTLPR in order to compare the TCI dimensional scores including harm avoidance according to the 5-HTTLPR genotype and sex. We could not find the association between 5-HTTLPR and harm avoidance and other TCI measures. Considering known allele frequencies differences of 5-HTTLPR among different ethnic groups, further cross-cultural studies with a larger sample would be needed.

[Clinical Characteristics of Extended-spectrum beta-lactamase Producing Shigella sonnei Infection Outbreaked in Chungju Area.].

BACKGROUND: An outbreak of extended-spectrum beta-lactamase (ESBL)-producing Shigella sonnei enteritis, especially in pediatric populations, was unprecedented not only in Korea, but also throughout the world in the past. This study was intended to devise a management guideline for shigellosis caused by an ESBL-producing strain based on analysis of the clinical manifestations and response to therapy. METHODS: We examined 24 strains of S. sonnei isolated from stool cultures of patients with acute enteritis, between November 2004 and February 2005, for antimicrobial susceptibility and ESBL production, and we also performed DNA sequencing with PCR for the typing of ESBL genes. In addition, we retrospectively analyzed the clinical characteristics, laboratory results, and therapeutic responses to antibiotics of the 103 patients who grew S. sonnei on stool cultures. RESULTS: All 24 isolates showed a very similar antibiotic sensitivity pattern and were ESBL gene type of CTX-M-14. The most frequent clinical symptom in the 103 patients was a fever, followed by diarrhea, abdominal pain, headache, vomiting, and nausea. Leukocytosis and CRP were positive in 53.4% and 78.6% of the patients, respectively. On stool direct smears, 11.7% showed more than 50 WBCs per HPF and 71% were positive on stool occult blood. Microbiological eradication rates were as follows: azithromycin and ciprofloxacin, 100%; imipenem-cilastatin, 68.8%; ampicillin-sulbactam, 42.9%; amoxicillin-clavulanic acid, 20%; ceftizoxime, 12.5%; cefdinir, 6.9%; and ceftriaxone and trimethoprim-sulfamethoxazole, 0%. CONCLUSIONS: We presumed that, given its cost-effectiveness and safety, azithromycin can be an attractive option for the treatment of ESBL-producing S. sonnei enteritis in pediatric populations. Although ciprofloxacin is another cost-effective agent, its use in pediatric populations is not recommended.