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Record W4353016377 · doi:10.1097/dss.0000000000003742

A Randomized Split-Face Study of Photodynamic Therapy With St. John's Wort and Indole-3-Acetic Acid for the Treatment of Acne

2023· article· en· W4353016377 on OpenAlexaboutno aff
Bo Ri Kim, Minjae Kim, Jung‐Im Na, Chang‐Hun Huh, Jung-Won Shin

Bibliographic record

VenueDermatologic Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhotodynamic therapyAcneMedicineHypericinDermatologyErythemaAntimicrobialPhotosensitizerRandomized controlled trialAcne treatmentSurgeryPharmacologyChemistryPhotochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: St. John's wort (SJW) contains hypericin, a powerful photosensitizer with antimicrobial and anti-inflammatory activities. OBJECTIVE: To compare the efficacy and safety of SJW-photodynamic therapy (PDT) with that of indole-3-acetic acid (IAA)-PDT for the treatment of acne and investigate the skin rejuvenating effects of SJW-PDT. MATERIALS AND METHODS: In vitro experiments were conducted to examine the generation of reactive oxygen species and the antimicrobial effects of SJW-PDT. In the prospective, double-blind, split-face, randomized study, 31 patients with facial acne were treated with SJW or IAA with simultaneous illumination of red light and green light. RESULTS: SJW produces free radicals with visible light irradiation, and the growth of Cutibacterium acnes and Staphylococcus aureus is significantly suppressed. One week after the last treatment, the acne lesion counts were significantly decreased in both groups (56.5% reduction in SJW, p < .001 vs 57.0% in IAA, p < .001). Significant reductions in sebum secretion, erythema index, roughness, and wrinkles were observed in both groups after the treatment. No side effects were observed. CONCLUSION: SJW-PDT is a simple, safe, and effective treatment option for acne that is also beneficial for skin rejuvenation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.049
GPT teacher head0.328
Teacher spread0.279 · 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 designRandomized trial
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

Citations8
Published2023
Admission routes1
Has abstractyes

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