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Record W4286001444 · doi:10.1002/jnr.25110

Targeting mitochondria in the regulation of neurodegenerative diseases: A comprehensive review

2022· review· en· W4286001444 on OpenAlexaff
Shashank Kumar Maurya, Suchi Gupta, Amrita Bakshi, Harpreet Kaur, Arushi Jain, Sabyasachi Senapati, Meghraj Singh Baghel

Bibliographic record

VenueJournal of Neuroscience Research · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMitochondrionNeuroscienceComputational biologyMedicineBiologyCell biology

Abstract

fetched live from OpenAlex

Mitochondria are one of the essential cellular organelles. Apart from being considered as the powerhouse of the cell, mitochondria have been widely known to regulate redox reaction, inflammation, cell survival, cell death, metabolism, etc., and are implicated in the progression of numerous disease conditions including neurodegenerative diseases. Since brain is an energy-demanding organ, mitochondria and their functions are important for maintaining normal brain homeostasis. Alterations in mitochondrial gene expression, mutations, and epigenetic modification contribute to inflammation and neurodegeneration. Dysregulation of reactive oxygen species production by mitochondria and aggregation of proteins in neurons leads to alteration in mitochondria functions which further causes neuronal death and progression of neurodegeneration. Pharmacological studies have prioritized mitochondria as a possible drug target in the regulation of neurodegenerative diseases. Therefore, the present review article has been intended to provide a comprehensive understanding of mitochondrial role in the development and progression of neurodegenerative diseases mainly Alzheimer's, Parkinson's, multiple sclerosis, and amyotrophic lateral sclerosis followed by possible intervention and future treatment strategies to combat mitochondrial-mediated neurodegeneration.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.821
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.178
GPT teacher head0.440
Teacher spread0.261 · 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 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

Citations30
Published2022
Admission routes1
Has abstractyes

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