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Record W4403343031 · doi:10.1002/cmdc.202400611

Adipocyte‐Targeted Nanotechnology and Cell‐Based Therapy for Obesity Treatment

2024· review· en· W4403343031 on OpenAlexaff
Yue Wu, Siqi Deng, Siyu Wei, Wenqi Wei, Yunxiang He, Junling Guo

Bibliographic record

VenueChemMedChem · 2024
Typereview
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsUniversity of British Columbia
FundersNational Key Research and Development Program of ChinaState Key Laboratory of Polymer Materials EngineeringNational Natural Science Foundation of China
KeywordsObesityMedicineAdipose tissueMesenchymal stem cellCell therapyBioinformaticsTargeted therapyAdipocyteCellInternal medicineBiologyPathologyCancer

Abstract

fetched live from OpenAlex

Obesity is a critical risk factor for the development of metabolic diseases and is often associated with dysfunctional adipocytes. Prevalent treatments such as lifestyle intervention, pharmacotherapy, and bariatric surgery are often accompanied by adverse side effects and poor patient compliance. Nanotechnology and cell-based therapy offer innovative approaches for targeted obesity treatments, as they can directly target adipocytes, regulate lipid metabolism, and minimize off-target effects. Here, we provide an overview of the intricate relationship between adipocytes and obesity, highlighting the potential of nanotechnology and cell-based therapy in obesity treatment. Additionally, we discuss the advancements of adipose-derived mesenchymal stem cells (ADMSCs) in obesity progression, including the latest challenges and considerations for developing adipose-targeted treatments for obesity. The objective is to provide a perspective on the design and development of nanotechnology and cell-based therapy for treating obesity and related comorbidities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.965
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.036
GPT teacher head0.280
Teacher spread0.245 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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