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Record W4406085612 · doi:10.3390/nu17010197

The Novel Effect and Potential Mechanism of Lactoferrin on Organ Fibrosis Prevention

2025· review· en· W4406085612 on OpenAlexaff
Yonghui Yu, Juan Fang, Yingying Li, Xueli Wang, Jingjie Zhang, Jing Wang, Baoguo Sun

Bibliographic record

VenueNutrients · 2025
Typereview
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaBeijing Nova ProgramNational Natural Science Foundation of China
KeywordsFibrosisLactoferrinMedicinePathologicalMechanism (biology)DiseaseDrugPathogenesisBioinformaticsImmunologyPathologyPharmacologyBiology

Abstract

fetched live from OpenAlex

Organ fibrosis is gradually becoming a human health and safety problem, and various organs of the body are likely to develop fibrosis. The ultimate pathological feature of numerous chronic diseases is fibrosis, and few interventions are currently available to specifically target the pathogenesis of fibrosis. The medical detection of organ fibrosis has gradually matured. However, there is currently no effective treatment method for these diseases. Therefore, we need to strive for developing effective and reliable drugs or substances to treat and prevent fibrotic diseases. Lactoferrin (LF) is a multifunctional glycoprotein with many pathological and physiologically active effects, such as antioxidant, anti-inflammatory and antimicrobial effects, and it protects against pathological and physiological conditions in various disease models. This review summarizes the effects and underlying mechanisms of LF in preventing organ fibrosis. As a naturally active substance, LF can be used as a promising and effective drug for the prevention and remission of fibrotic diseases.

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)0.000
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.344
Teacher spread0.323 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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