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Record W4400213536 · doi:10.3746/jkfn.2024.53.6.545

Inhibitory Effect of Herb Extract-Amino Acid Mixtures on UV-Induced Photoaging in HaCaT Cell

2024· article· en· W4400213536 on OpenAlexaff
Ye-Lim You, Ha-Jun Byun, Minha Kim, Namgil Kang, Hyeon‐Son Choi

Bibliographic record

VenueJournal of the Korean Society of Food Science and Nutrition · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Quality and Safety Studies
Canadian institutionsNutrasource
Fundersnot available
KeywordsPhotoagingHaCaTHerbChemistryAmino acidCellInhibitory postsynaptic potentialBiochemistryTraditional medicinePharmacologyDermatologyMedicinal herbsBiologyMedicineIn vitroInternal medicine

Abstract

fetched live from OpenAlex

This study examined the optimal ratio of herb extract to amino acids, effectively protecting skin cells from UVB-induced photoaging.The response surface methodology program suggested seven mixture ratios of herb extracts, including ginger and star anise extracts.Among them, the H7 mixture showed the highest antioxidant activity.Arginine and glutamate were added to H7, producing HA1, HA2, and HA3 mixtures to determine their effects on UV-induced photoaging.The HA3 mixture had the highest protective effect against UV-induced cell death.HA3 significantly increased the mRNA expression of collagen 1, hyaluronic acid, and matrix metalloproteinase (1/2) while reducing the activity of hyaluronic acid lyase.HA3 significantly increased the expression of skin barrier genes, including filaggrin, loricrin, and involucrin.Overall, the HA3 mixture effectively protects skin cells from UV-induced photodamage.This study suggested an optimized herb extract/amino acid mixture ratio for developing skin-protective functional foods.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.013
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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