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Record W3041921093 · doi:10.1073/pnas.1920469117

Prototypical pacemaker neurons interact with the resident microbiota

2020· article· en· W3041921093 on OpenAlexfundno aff
Alexander Klimovich, Stefania Giacomello, Åsa K. Björklund, Louis Faure, Markéta Kaucká, Christoph Giez, Andrea P. Murillo‐Rincón, Ann-Sophie Matt, Doris Willoweit-Ohl, Gabriele Crupi, Jaime de Anda, Gerard C. L. Wong, Mauro D’Amato, Igor Adameyko, Thomas C. G. Bosch

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2020
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersUppsala Multidisciplinary Center for Advanced Computational ScienceMedizinische Universität WienVetenskapsrådetSvenska Forskningsrådet FormasKnut och Alice Wallenbergs StiftelseDeutsche ForschungsgemeinschaftScience for Life LaboratoryUniversität WienCanadian Institute for Advanced ResearchAlexander von Humboldt-Stiftung
KeywordsBiologyLernaean HydraNeuroscienceIon channelTranscriptomeInnate immune systemNeuronCell biologyImmune systemGeneReceptorGeneticsGene expression

Abstract

fetched live from OpenAlex

Significance Here, we discover prototypical pacemaker neurons in the ancient cnidarian Hydra and provide evidence for a direct interaction of these neurons with the commensal microbiota. We uncover a remarkable gene-expression program conservation between the Hydra pacemaker neurons and pacemaker cells in Caenorhabditis elegans and the mammalian gut. We suggest that prototypical pacemaker cells emerged as neurons using components of innate immunity to interact with the microbial environment and ion channels to generate rhythmic contractions. The communication of pacemaker neurons with the microbiota represents a mechanistic link between the gut microbiota and gut motility. Our discoveries improve the understanding of the archetypical properties of the enteric nervous systems, which are perturbed in human dysmotility-related conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.295
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations66
Published2020
Admission routes1
Has abstractyes

Explore more

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