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Record W3184501995 · doi:10.9734/bpi/hmms/v17/2795f

Investigating the Effects of Oxidative Stress Prevention Using DNA Aptamer (Aptamin C®) in Keratinocyte

2021· book-chapter· en· W3184501995 on OpenAlexaff
Sooho Choi, Yoon-Jeong Hwang, Tae‐Jun Kim, Jeong Hoon Kim

Bibliographic record

VenueBook Publisher International (a part of SCIENCEDOMAIN International) · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsOxidative stressAntioxidantVitamin CReactive oxygen speciesChemistryRadicalBiochemistryVitamin EOxidative phosphorylationAscorbic acidDNA damageIn vitroPharmacologyDNABiologyFood science

Abstract

fetched live from OpenAlex

Oxidative stress is one of the leading causes of various diseases. Oxidative stress conditions occur when reactive oxygen species (ROS) levels exceed cell defense mechanisms. Antioxidants are effective against ROS, a major factor in oxidative stress. Antioxidants play important roles in our body by scavenging free radicals, thereby preventing them from damaging biological molecules. Vitamin C is essential for various physiological functions such as the synthesis of collagen and neurotransmitters, and it is a major antioxidant. Although vitamin C is widely used in cosmetic and therapeutic products, it easily undergoes oxidation by air, pH, temperature, and UV light, thereby decreasing its potency as an antioxidant and reducing the shelf-life of products containing vitamin C. To overcome this drawback, we developed Aptamin C®, an innovative single-strand DNA aptamer that maximizes the antioxidant efficacy of vitamin C by binding to its reduced form and delaying its oxidation. To investigate the effect of Aptamin C® and vitamin C complex on human skin, we performed both in vitro and clinical tests. We observed that the Aptamin C® and vitamin C complex had effective ROS-scavenging and anti-inflammatory effects.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.297
Teacher spread0.265 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBook Publisher International (a part of SCIENCEDOMAIN International)Same topicSkin Protection and AgingFrench-language works237,207