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Record W2617326069 · doi:10.3390/ijms18061126

The Peroxisome-Mitochondria Connection: How and Why?

2017· review· en· W2617326069 on OpenAlex

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.

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsWestern University
FundersVlaamse regeringKU Leuven
KeywordsPeroxisomeOrganelleMitochondrionCell biologyBiologyFunction (biology)BiochemistryReceptor

Abstract

fetched live from OpenAlex

Over the past decades, peroxisomes have emerged as key regulators in overall cellular lipid and reactive oxygen species metabolism. In mammals, these organelles have also been recognized as important hubs in redox-, lipid-, inflammatory-, and innate immune-signaling networks. To exert these activities, peroxisomes must interact both functionally and physically with other cell organelles. This review provides a comprehensive look of what is currently known about the interconnectivity between peroxisomes and mitochondria within mammalian cells. We first outline how peroxisomal and mitochondrial abundance are controlled by common sets of cis- and trans-acting factors. Next, we discuss how peroxisomes and mitochondria may communicate with each other at the molecular level. In addition, we reflect on how these organelles cooperate in various metabolic and signaling pathways. Finally, we address why peroxisomes and mitochondria have to maintain a healthy relationship and why defects in one organelle may cause dysfunction in the other. Gaining a better insight into these issues is pivotal to understanding how these organelles function in their environment, both in health and disease.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0020.000
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 head0.048
GPT teacher head0.359
Teacher spread0.312 · 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