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Photooxidation of Lens α-Crystallin by Hypericin (Active Ingredient in St. John's Wort)¶

2000· article· en· W1980709752 on OpenAlexaboutno aff
Kevin L. Schey, Severine A. Patat, Colin F. Chignell, M. Datillo, R. H. Wang, Joan E. Roberts

Bibliographic record

VenuePhotochemistry and Photobiology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsnot available
FundersNational Institutes of Health
KeywordsHypericinChemistryPhototoxicitySinglet oxygenCrystallinPhotosensitivityPhotochemistryBiophysicsBiochemistryOxygenOrganic chemistryIn vitroMaterials scienceBiology

Abstract

fetched live from OpenAlex

Hypericin is the active ingredient in the over the counter antidepressant medication St. John's Wort. Hypericin produces singlet oxygen and other excited state intermediates that indicate it should be a very efficient phototoxic agent in the eye. Furthermore it absorbs in the UV and visible range, which means it can potentially damage both the lens and the retina. Lens alpha-crystallin, isolated from calf lenses, was irradiated in the presence of hypericin (5 x 10(-5) M, 10 mM ammonium bicarbonate, pH 7.0) and in the presence and absence of light (> 300 nm, 24 mW/cm2). Hypericin-induced photosensitized photopolymerization as assessed by sodium dodecylsulfate-polyacrylamide gel electrophoresis. Further analysis of the oxidative changes occurring in alpha-crystallin using mass spectrometry showed specific oxidation of methionine, tryptophan and histidine residues, which increased with irradiation time. Hypericin did not damage the lens protein in the dark. Damage to alpha-crystallin could undermine the integrity of the lens directly by protein denaturation and indirectly by disturbing chaperone function. Therefore, in the presence of light, hypericin can induce changes in lens protein that could lead to the formation of cataracts. Appropriate precautions should be taken to protect the eye from intense sunlight while on this antidepressant medication.

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 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 score0.113
Threshold uncertainty score0.905

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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

Citations62
Published2000
Admission routes1
Has abstractyes

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