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Record W4394740012 · doi:10.1631/jzus.b2300070

Impacts of pancreatic exocrine insufficiency on gut microbiota

2024· article· en· W4394740012 on OpenAlexaff
Yulin Guo, Feng Cao, Fei Li

Bibliographic record

VenueJournal of Zhejiang University SCIENCE B · 2024
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsPancreas Centre (Canada)
FundersBeijing Municipal Science and Technology CommissionCapital Medical University
KeywordsPancreatitisDysbiosisGut floraMalabsorptionExocrine pancreatic insufficiencyInternal medicineGastroenterologyBifidobacteriumAcute pancreatitisEndocrinologyBiologyMedicineLactobacillusBiochemistry

Abstract

fetched live from OpenAlex

Pancreatic exocrine insufficiency (PEI) can be induced by various kinds of diseases, including chronic pancreatitis, acute pancreatitis, and post-pancreatectomy. The main pathogenetic mechanism of PEI involves the decline of trypsin synthesis, disorder of pancreatic fluid flow, and imbalance of secretion feedback. Animal studies have shown that PEI could induce gut bacterial overgrowth and dysbiosis, with the abundance of Lactobacillus and Bifidobacterium increasing the most, which could be partially reversed by pancreatic enzyme replacement therapy. Clinical studies have also confirmed the association between PEI and the dysbiosis of gut microbiota. Pancreatic exocrine secretions and changes in duodenal pH as well as bile salt malabsorption brought about by PEI may affect and shape the abundance and composition of gut microbiota. In turn, the gut microbiota may impact the pancreatic exocrine acinus through potential bidirectional crosstalk. Going forward, more and higher-quality studies are needed that focus on the mechanism underlying the impact of PEI on the gut microbiota.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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 designObservational
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

Citations7
Published2024
Admission routes1
Has abstractyes

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