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Record W4406206849 · doi:10.1093/ijfood/vvae019

Exploring the antimicrobial potentials and thermal stability of bovine lactoferrin against foodborne pathogens

2025· article· en· W4406206849 on OpenAlexaff
Usman Mir Khan, Manpreet Kaur

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2025
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsUniversity of Manitoba
FundersUniversity of Veterinary and Animal SciencesUniversity of Agriculture, Faisalabad
KeywordsLactoferrinAntimicrobialMicrobiologyHeat stabilityFood scienceChemistryBiologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Abstract The study was conducted to analyze the antimicrobial activity and the sensitivity of bovine lactoferrin against foodborne pathogens and to evaluate the pH range and thermal stability of bovine lactoferrin. The agar well diffusion test was used to check the antimicrobial activity of each pathogen. Bacterial samples were purified and confirmed by biochemical tests. Three concentrations of lactoferrin 0.5, 1, and 10 mg/ml were evaluated for antibacterial activity against Escherichia coli, Salmonella enteritidis, and Staphylococcus aureus. Our results indicated that all three pathogenic bacteria were inhibited, even at 0.5 mg/ml (p < .05). E. coli was the most sensitive, and S. aureus was the least sensitive. Lactoferrin remains effective at 6, 7, 8, and 9 pH, but no inhibition showed at 3, 4, and 5 pH (p < .05). Lactoferrin showed no change at a pasteurization temperature of 60–75 °C (p < .05) but was inactivated at higher temperatures. The results showed that lactoferrin can inhibit E. coli, S. enteritidis, and S. aureus from growth and remain unchanged at pasteurization temperature and pH ranges of 6–9.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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

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 head0.046
GPT teacher head0.308
Teacher spread0.262 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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