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Journal of Zhejiang University. Science. B (1)
PLoS Computational Biology (1)
Chen, Shu-jie (1)
Fan, Yu-jing (1)
Friston, Karl (1)
Hartemink, Alexander J (1)
Jarvis, Erich D (1)
Liu, Bin (1)
Si, Jian-min (1)
Smith, V. Anne (1)
Smulders, Tom V (1)
Yu, Jing (1)
Yu, Ying-cong (1)
Year of Publication
A probiotic treatment containing Lactobacillus, Bifidobacterium and Enterococcus improves IBS symptoms in an open label trial
Journal of Zhejiang University. Science. B
Objective: To evaluate the efficacy and safety of live combined Bifidobacterium, Lactobacillus and Enterococcus capsules in treatment of irritable bowel syndrome. Methods: Eighty-five patients [male 32, female 53; age (45.31±11.72) years] were given live combined Bifidobacterium, Lactobacillus and Enterococcus capsules 1260 mg/d t.i.d.×4 weeks. Syndrome scales were used to evaluate the efficacy in gastrointestinal syndrome. Fecal flora was also measured before and after the treatment. Six bacteria were cultured and the colony forming units were counted in stool. SPSS was used for data analysis. Results: Seventy-four patients finished the follow-up. No side-effect was found. For treatment of irritable bowel syndrome, the effective rate of live combined Bifidobacterium, Lactobacillus and Enterococcus capsules was 56.8% in the second week, 74.3% in the fourth week and 73.0% in the sixth week. Single symptom was improved, especially in abdominal pain and stool character. The probiotica containing live combined Bifidobacterium, Lactobacillus and Enterococcus could increase bifidobacterium count (P<0.01) and lactobacillus count (P<0.05); decrease bacteroides count (P<0.05) and enterococci count (P<0.01); No obvious changes were observed in clostridium difficile colonitis and enterobacteriaceae (P>0.05). Conclusion: The result of the study indicated that the administration of live combined Bifidobacterium, Lactobacillus and Enterococcus improved the symptom of irritable bowel syndrome and that there was a gradual increase of this effect. Thereafter conditions remained stable for 2 weeks. That improvement may be associated with alterations in gastrointestinal flora.
Irritable bowel syndrome (IBS); Intestinal flora; Probiotic agents
Computational Inference of Neural Information Flow Networks
Smith, V. Anne
Smulders, Tom V
Hartemink, Alexander J
Jarvis, Erich D
PLoS Computational Biology
Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.
One of the challenges in the area of brain research is to decipher networks describing the flow of information among communicating neurons in the form of electrophysiological signals. These networks are thought to be responsible for perceiving and learning about the environment, as well as producing behavior. Monitoring these networks is limited by the number of electrodes that can be placed in the brain of an awake animal, while inferring and reasoning about these networks is limited by the availability of appropriate computational tools. Here, Smith and Yu and colleagues begin to address these issues by implanting microelectrode arrays in the auditory pathway of freely moving songbirds and by analyzing the data using new computational tools they have designed for deciphering networks. The authors find that a dynamic Bayesian network algorithm they developed to decipher gene regulatory networks from gene expression data effectively infers putative information flow networks in the brain from microelectrode array data. The networks they infer conform to known anatomy and other biological properties of the auditory system and offer new insight into how the auditory system processes natural and synthetic sound. The authors believe that their results represent the first validated study of the inference of information flow networks in the brain.
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