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Record W3111968574 · doi:10.1155/2020/8837893

Mitochondria-Targeted Antioxidants: A Step towards Disease Treatment

2020· review· en· W3111968574 on OpenAlexfundno aff
Qian Jiang, Jie Yin, Jiashun Chen, Xiaokang Ma, Miaomiao Wu, Gang Liu, Yao Kang, Bie Tan, Yulong Yin

Bibliographic record

VenueOxidative Medicine and Cellular Longevity · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsnot available
FundersCAST Innovation FoundationMinistry of Agriculture and Rural Affairs of the People's Republic of ChinaUniversity of Manitoba
KeywordsMitochondrionDiseaseComputational biologyChemistryBiologyMedicineCell biologyPathology

Abstract

fetched live from OpenAlex

Mitochondria are the main organelles that produce adenosine 5'-triphosphate (ATP) and reactive oxygen species (ROS) in eukaryotic cells and meanwhile susceptible to oxidative damage. The irreversible oxidative damage in mitochondria has been implicated in various human diseases. Increasing evidence indicates the therapeutic potential of mitochondria-targeted antioxidants (MTAs) for oxidative damage-associated diseases. In this article, we introduce the advantageous properties of MTAs compared with the conventional (nontargeted) ones, review different mitochondria-targeted delivery systems and antioxidants, and summarize their experimental results for various disease treatments in different animal models and clinical trials. The combined evidence demonstrates that mitochondrial redox homeostasis is a potential target for disease treatment. Meanwhile, the limitations and prospects for exploiting MTAs are discussed, which might pave ways for further trial design and drug development.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.002

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 head0.047
GPT teacher head0.315
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations187
Published2020
Admission routes1
Has abstractyes

Explore more

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