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Record W4402201695 · doi:10.1016/j.phrs.2024.107394

Investigating the interplay between mitophagy and diabetic neuropathy: Uncovering the hidden secrets of the disease pathology

2024· review· en· W4402201695 on OpenAlexaff
Kiavash Hushmandi, Behzad Einollahi, Rachel Aow, Suhana Binte Suhairi, Daniel J. Klionsky, Amir Reza Aref, Rüssel J. Reiter, Pooyan Makvandi, Navid Rabiee, Yi Xu, Noushin Nabavi, Seyed Hassan Saadat, Najma Farahani, Alan Prem Kumar

Bibliographic record

VenuePharmacological Research · 2024
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Victoria
FundersNational Institute of General Medical SciencesNational Institutes of HealthMinistry of Education, IndiaNational University of Singapore
KeywordsMitophagyDiseaseAutophagyMedicineInsulin resistanceDiabetes mellitusOxidative stressMitochondrionBioinformaticsNeuroscienceBiologyCell biologyInternal medicineEndocrinologyApoptosisGenetics

Abstract

fetched live from OpenAlex

Mitophagy, the cellular process of selectively eliminating damaged mitochondria, plays a crucial role in maintaining metabolic balance and preventing insulin resistance, both key factors in type 2 diabetes mellitus (T2DM) development. When mitophagy malfunctions in diabetic neuropathy, it triggers a cascade of metabolic disruptions, including reduced energy production, increased oxidative stress, and cell death, ultimately leading to various complications. Thus, targeting mitophagy to enhance the process may have emerged as a promising therapeutic strategy for T2DM and its complications. Notably, plant-derived compounds with β-cell protective and mitophagy-stimulating properties offer potential as novel therapeutic agents. This review highlights the intricate mechanisms linking mitophagy dysfunction to T2DM and its complications, particularly neuropathy, elucidating potential therapeutic interventions for this debilitating 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.

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)0.000
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.174
GPT teacher head0.502
Teacher spread0.328 · 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

Citations16
Published2024
Admission routes1
Has abstractyes

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