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Record W4361011433 · doi:10.3389/fcimb.2023.1188092

Editorial: Interplay between gut microbiota and the immune system in liver surgery and liver diseases

2023· editorial· en· W4361011433 on OpenAlexaff
Julio Plaza‐Díaz, Luis Fontana, Ana I. Álvarez‐Mercado

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2023
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersUniversidad de Granada
KeywordsImmune systemMicrobiomeGut floraGut microbiomeLiver diseaseImmunologyMedicineBiologyBioinformaticsGastroenterology

Abstract

fetched live from OpenAlex

Interplay between gut microbiota and the immune system in liver surgery and liver diseases Gut microbiota and liver diseases Microbiota refers to the assemblage of microbes present in the environment (as first defined by Lederberg and McCray (Lederberg and Mccray, 2001).There is no conclusive evidence that individuals, or even different body locations, harbor a "core" set of these microorganisms.In this regard, every person harbors a symbiotic community of microbes in their digestive tract, the gut microbiota.A wide range of factors influence microbiota regulation, including host characteristics, dietary patterns, and environmental and microbiological factors.A dynamic equilibrium exists between the microbiota and the host, in which the former plays a role both locally and remotely in fundamental physiological processes such as inflammation and immunity.The microbes that live in our gut are capable of producing metabolites that protect the host from pathogens, but they can also produce molecules that are detrimental to the host when the relationship between the host and gut microbes is disturbed (a state known as dysbiosis) (Alvarez-Mercado et al., 2023).A wide range of evidence has shown that the gut microbiota plays a dual role in maintaining the health of the host and in the development of diseases such as liver disease (Zheng et al., 2020)."Liver disease" refers to a variety of conditions that impair the ability of the liver to function normally.The progression of liver disease may result in scarring and more serious complications in the future.Nowadays, liver disease is a global burden that causes a tremendous socioeconomic cost (Giuffre et al., 2020).Advanced liver damage can manifest itself in the form of steatosis, fibrosis, and cirrhosis.These lesions can have alcoholic, viral, genetic, and metabolic origins.In fact, the increasing incidence of obesity and diabetes is causing non-alcoholic steatohepatitis to keep rising (Levicǎr et al., 2007).

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)0.005
metaresearch head score (Gemma)0.015
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 score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.001
Science and technology studies0.0030.002
Scholarly communication0.0060.004
Open science0.0030.002
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0370.026

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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