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Record W4411766116 · doi:10.1007/s00204-025-04116-x

Unravelling the role of nanomedicine in attenuating inflammation, oxidative stress and cellular ageing in chronic obstructive pulmonary disease

2025· review· en· W4411766116 on OpenAlexaff
Lokesh Nagar, Annu Saini, Sukriti Vishwas, Sachin Kumar Singh, Gaurav Gupta, Ronan MacLoughlin, Raimar Löbenberg, Neal M. Davies, Dinesh Kumar Chellappan, Keshav Raj Paudel, Kamal Dua, Harish Dureja

Bibliographic record

VenueArchives of Toxicology · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of Alberta
FundersSydney Partnership for Health, Education, Research and EnterpriseUniversity of Technology Sydney
KeywordsInflammationCOPDOxidative stressMedicineLungSenescencePathogenesisImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease is a chronic lung disease which causes obstruction and inflammation in the airways or other parts of the lung. It is often associated with structural changes in the lung due to persistent inflammation caused by prolong exposure to cigarette smoke. Other factors such as oxidative stress, chronic inflammation and cellular senescence also play a major role in the progression of the disease. Chronic inflammation is responsible for cell cycle dysfunction. Cellular senescence is associated in the pathogenesis of COPD, which can accelerate the lung aging process. Cellular senescence can elevate the level inflammatory mediators, which can comprise lung function and structure. This review explores various pathologic mechanisms which are involved in the progression of COPD. It also explores the application of nanostructure-based drug delivery systems such as solid lipid nanoparticles, polymeric nanoparticles, liposomes, nanoemulsions, dendrimers and other miscellaneous nanostructures in overcoming challenges associated with current conventional treatments for COPD. This review explores recent advancements in the field of nanostructures-based drug delivery systems for COPD treatment.

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 score0.847
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.269
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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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