MétaCan
Menu
Back to cohort

Microbial Interventions to Improve Neonatal Gut Health

2023· review· en· W4377021629 on OpenAlexafffund
Ranga Nakandalage, Le Luo Guan, Nilusha Malmuthuge

Bibliographic record

VenueMicroorganisms · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
FundersAgriculture Funding Consortium
KeywordsDysbiosisPsychological interventionImmune systemGut floraBiologyGut microfloraImmunologyIntensive care medicineMedicineBacteria

Abstract

fetched live from OpenAlex

The diverse pioneer microbial community colonizing the mammalian gastrointestinal tract is critical for the developing immune system. Gut microbial communities of neonates can be affected by various internal and external factors, resulting in microbial dysbiosis. Microbial dysbiosis during early life affects gut homeostasis by changing metabolic, physiological, and immunological status, which increases susceptibility to neonatal infections and long-term pathologies. Early life is crucial for the establishment of microbiota and the development of the host immune system. Therefore, it provides a window of opportunity to reverse microbial dysbiosis with a positive impact on host health. Recent attempts to use microbial interventions during early life have successfully reversed dysbiotic gut microbial communities in neonates. However, interventions with persistent effects on microbiota and host health are still limited. This review will critically discuss microbial interventions, modulatory mechanisms, their limitations, and gaps in knowledge to understand their roles in improving neonatal gut health.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.047
GPT teacher head0.377
Teacher spread0.331 · 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.

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

Citations12
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueMicroorganismsSame topicGut microbiota and healthFrench-language works237,207