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Record W2058727950 · doi:10.1159/000366281

Psychobiotics and Their Involvement in Mental Health

2014· editorial· en· W2058727950 on OpenAlexaboutno aff
Fengyi Tang, Bhaskara L. Reddy, Milton H. Saier

Bibliographic record

VenueMicrobial Physiology · 2014
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiomeGut bacteriaBacteriaProbioticBiologyChemistryBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Editorial J Mol Microbiol Biotechnol 2014;24:211–214 DOI: 10.1159/000366281 Published online: August 30, 2014 Psychobiotics and Their Involvement in Mental Health Fengyi Tang a Bhaskara L. Reddy a, b Milton H. Saier Jr. a a Ingested prebiotic organic compounds stimulate the growth of intestinal probiotic bacteria [Saier and Man- sour, 2005]. Pre- and probiotics represent important components in chains of complex biosynthetic and cata- bolic reactions that provide tremendous health benefits to the human or animal host organism [Singh et al., 2013; Vitetta et al., 2014]. These bacteria, which in part com- prise the intestinal microbiome, do so by supplying nu- trients such as short-chain fatty acids [Ohashi and Ush- ida, 2009] and precursors of enzyme cofactors including vitamin B 12 [Capozzi et al., 2012]. They also successfully compete with potential pathogens [Corr et al., 2009] and stimulate host immune responses [Jirillo et al., 2012; Vi- taliti et al., 2014]. They strongly influence either posi- tively or negatively, depending on the bacterial types present, symptoms of numerous metabolic disorders in- cluding those responsible, in part, for the current obesity epidemic [Kotzampassi et al., 2014; Vitetta et al., 2014]. Therefore, not surprisingly, malnutrition in children has been shown to be associated with a lack of certain crucial gut bacteria [Subramanian et al., 2014]. It is now clear that the intestinal microbiome influences innumerable processes essential for the physical fitness of animals and humans. © 2014 S. Karger AG, Basel E-Mail karger@karger.com www.karger.com/mmb The contribution of beneficial gut bacteria to human health is now scientifically well established [Shanahan et al., 2012]. The predominant well-studied probiotic bacte- ria are Firmicutes such as Lactobacillus species, Actino- bacteria such as Bifidobacterium species and Bacteroide- tes such as Bacteroides species. Several others, including Proteobacteria such as certain Escherichia coli strains, have also been shown to exhibit probiotic qualities. In fact, all of these microbes have beneficial consequences to the host organism. In only a few cases have the probiotic bacterial mechanisms of action been elucidated [Butel, Since these bacteria influence so many aspects of hu- man physiology, it should not be considered surprising that recent studies have revealed that they also have pro- nounced effects on brain function. Indeed, these bacteria produce tryptophan, a precursor of serotonin (5-hy- droxytryptamine), tyrosine, a precursor of L-3,4-dihy- droxyphenylalanine (DOPA) and dopamine, and other amino acids such as γ-amino butyric acid (GABA) and glycine, both of which serve as neurotransmitters in ani- mals [Clarke et al., 2014b]. In fact, recent research has shown that the microbiota strongly influences brain ac- tivity and consequently behavior. It exerts effects on our moods, cognition and sensitivities to pain [Borre et al., Milton H. Saier Jr. Department of Molecular Biology Division of Biological Sciences, University of California at San Diego La Jolla, CA 92093-0116 (USA) E-Mail msaier @ ucsd.edu Downloaded by: Univ. of California San Diego 132.239.144.87 - 2/15/2017 1:46:22 AM Department of Molecular Biology, Division of Biological Sciences, University of California at San Diego, La Jolla, Calif. , and b Department of Mathematics and Natural Sciences, College of Letters and Sciences, National University, Ontario, Calif. , USA

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0150.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.258
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2014
Admission routes1
Has abstractyes

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