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Record W4413883142 · doi:10.1016/j.isci.2025.113466

Current advances in diabetic neuropathy: Proteins as therapeutic targets

2025· review· en· W4413883142 on OpenAlexaff
Gurjeet Kaur, Alberto Santos, Tina Okdahl, Anne‐Marie Wegeberg, Tarunveer S. Ahluwalia, Christian Stevns Hansen, David S. Wishart, Nicolai J. Wewer Albrechtsen, Birgitte Brock, Troels S. Jensen, Peter Rossing, Christina Brock, Cristina Legido‐Quigley, Karolina Sulek

Bibliographic record

VenueiScience · 2025
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of Alberta
FundersNovo Nordisk FondenNovo Nordisk
KeywordsDiabetic neuropathyNeuroscienceMedicineComputational biologyDiabetes mellitusBioinformaticsBiologyEndocrinology

Abstract

fetched live from OpenAlex

Diabetic neuropathy is a debilitating complication of diabetes characterized by nerve damage that may lead to numbness, foot ulcers, and amputations, and is a major cause of morbidity and mortality in people with diabetes. There are no treatments available for diabetic neuropathy aside from pain management. Recent advances in protein research have identified potential targets associated with the disease development and progression. This review explores the latest studies identifying proteins as possible drug targets in diabetic neuropathy and their role in relevant processes such as polyol metabolism, oxidative stress, and cytokine regulation. Additionally, we provide a comprehensive view of current developments and discoveries at single cell and spatial resolution that have revealed deregulated protein profiles in the dorsal root ganglia, sciatic nerve, trigeminal ganglion, or Schwann cells. Finally, we discuss the benefits of proteomics technologies to identify proteins and associated signaling pathways to better understand the source of diabetic neuropathy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.034
GPT teacher head0.375
Teacher spread0.342 · 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 designOther design
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

Citations2
Published2025
Admission routes1
Has abstractyes

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