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Does Imiquimod Histologically Rejuvenate Ultraviolet Radiation–Damaged Skin?

2007· article· en· W2130275301 on OpenAlexaff
Kathleen J. Smith, Sate Hamza, Marguerite Germain, Henry G. Skelton

Bibliographic record

VenueDermatologic Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDermisImmunohistochemistryEpidermis (zoology)Proliferating cell nuclear antigenPathologyPapillary dermisStainingMedicineHistologyHyperkeratosisImiquimodDermatologyAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: Imiquimod (IMI) 5% is believed by some to result in an improved cosmetic appearance of chronically ultraviolet radiation (UV)-damaged skin. OBJECTIVE: The objective was to determine what histologic and immunohistologic changes were present in actinically damaged skin after treatment with IMI. METHODS AND MATERIALS: Pre- and posttherapy biopsies of 12 patients with histories of actinic keratoses were evaluated with routine histology and immunohistochemical stains including p53, p63, proliferating cell nuclear antigen (PCNA), c-kit, and Factor XIIIa. RESULTS: After IMI therapy there was less compact hyperkeratosis, a more uniform rete ridge pattern with a more ordered proliferation of the epidermis, and a decrease in sun-damaged melanocytes. The papillary dermis showed a more uniform cellularity, and there was increased cellularity within the area of solar elastosis. After therapy, staining for p53, p63, and PCNA was decreased within the epidermis; staining for c-kit was decreased but more uniform in the basal cell; and Factor XIIIa expression was increased within the papillary dermis with a more ordered pattern of staining. CONCLUSION: These morphologic and immunohistochemical patterns may explain some of the improvement in overall skin appearance after IMI therapy and may be related to the spectrum of signaling pathways induced by the imidazoquinolines.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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 designObservational
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

Citations12
Published2007
Admission routes1
Has abstractyes

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