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Record W2546389260 · doi:10.1128/mbio.01184-16

The Origin of Mucosal Immunity: Lessons from the Holobiont <i>Hydra</i>

2016· review· en· W2546389260 on OpenAlexfundno aff
Katja Schröder, Thomas C. G. Bosch

Bibliographic record

VenuemBio · 2016
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
FundersDeutsche ForschungsgemeinschaftCanadian Institute for Advanced Research
KeywordsLernaean HydraBiologyInnate immune systemHolobiontImmunityMucosal immunityCnidariaImmune systemMicrobiomeVertebrateOrganismImmunologyEvolutionary biologyEcologyCell biologyBacteriaSymbiosisCoralBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Historically, mucosal immunity-i.e., the portion of the immune system that protects an organism's various mucous membranes from invasion by potentially pathogenic microbes-has been studied in single-cell epithelia in the gastrointestinal and upper respiratory tracts of vertebrates. Phylogenetically, mucosal surfaces appeared for the first time about 560 million years ago in members of the phylum Cnidaria. There are remarkable similarities and shared functions of mucosal immunity in vertebrates and innate immunity in cnidarians, such as Hydra species. Here, we propose a common origin for both systems and review observations that indicate that the ultimately simple holobiont Hydra provides both a new perspective on the relationship between bacteria and animal cells and a new prism for viewing the emergence and evolution of epithelial tissue-based innate immunity. In addition, recent breakthroughs in our understanding of immune responses in Hydra polyps reared under defined short-term gnotobiotic conditions open up the potential of Hydra as an animal research model for the study of common mucosal disorders.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.050
GPT teacher head0.291
Teacher spread0.240 · 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
GenreReview

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

Citations60
Published2016
Admission routes1
Has abstractyes

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