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Record W2726396965 · doi:10.1080/13510002.2017.1343223

Phytochemical treatments target kynurenine pathway induced oxidative stress

2017· review· en· W2726396965 on OpenAlex
Kathyani Parasram

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRedox Report · 2017
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsUniversity of Windsor
FundersDivision of Graduate EducationUniversity of Windsor
KeywordsOxidative stressKynurenine pathwayPhytochemicalKynurenineChemistryOxidative phosphorylationBiologyPharmacologyTraditional medicineBiochemistryMedicineTryptophan

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this paper was to link the phytochemical and metabolic research treating quinolinic acid induced oxidative stress in neurodegenerative disorders. METHODS: Quinolinic acid, a metabolite of the kynurenine pathway of tryptophan catabolism, plays a role in the oxidative stress associated with many neurological disorders and is used to simulate disorders such as Parkinson's disease. RESULTS: In these models, phytochemicals have been shown to reduce striatal lesion size, reduce inflammation and prevent lipid peroxidation caused by quinolinic acid. CONCLUSION: These results suggest that phenolic compounds, a class of phytochemicals, including flavonoids and diarylheptanoids, should be further studied to develop new treatments for oxidative stress related neurological disorders.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.139
GPT teacher head0.386
Teacher spread0.248 · 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