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Record W2051347675 · doi:10.1117/12.2004998

Intense picosecond THz pulses alter gene expression in human skin tissue<i>in vivo</i>

2013· article· en· W2051347675 on OpenAlexafffund
Lyubov V. Titova, Ayesheshim K. Ayesheshim, Andrey Golubov, Rocio Rodriguez‐Juarez, Anna Kovalchuk, Frank A. Hegmann, Olga Kovalchuk

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of LethbridgeUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Cancer Foundation
KeywordsTerahertz radiationHuman skinCarcinogenesisGene expressionIn vivoDNA damageMaterials scienceGeneOptoelectronicsBiologyDNAGenetics

Abstract

fetched live from OpenAlex

Pulsed terahertz (THz) imaging has been suggested as a novel high resolution, noninvasive medical diagnostic tool. However, little is known about the influence of pulsed THz radiation on human tissue, i.e., its genotoxicity and effects on cell activity and cell integrity. We have carried out a comprehensive investigation of the biological effects of THz radiation on human skin tissue using a high power THz pulse source and an <i>in vivo</i> full-thickness human skin tissue model. We have observed that exposure to intense THz pulses causes DNA damage and changes in the global gene expression profile in the exposed skin tissue. Several of the affected genes are known to play major roles in human cancer. While the changes in the expression levels of some of them suggest possible oncogenic effects of pulsed THz radiation, changes in the expression of the other cancer-related genes might have a protective influence. This study may serve as a roadmap for future investigations aimed at elucidating the exact roles that all the affected genes play in skin carcinogenesis and in response to pulsed THz radiation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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 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

Citations13
Published2013
Admission routes2
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicTerahertz technology and applicationsFrench-language works237,207