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Activation of Dopamine Receptor D1 and Downstream Cellular Functions by Polydopamine

2023· article· en· W4390168225 on OpenAlexaff
Xin Sun, Chao Wu, Xiaowen Tian, Peng Wang, Junling Guo, Zhenhua Shao, Qiang Wei

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsDopamineReceptorDopamine receptorNeurotransmitterRHOAD1-like receptorAgonistChemistryCatecholBiophysicsDopamine receptor D1Cell biologyNeuroscienceNanotechnologyBiologyMaterials scienceSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

Polydopamine is a remarkable molecule that has gained considerable attention for its role in material surface modification, leading to an abundance of research in the biomaterial domain. While its widespread use is well documented, the molecule's potential cellular interactions have been less explored. In particular, dopamine serves as a neurotransmitter and a hormone that interacts with dopamine receptors in cells. Our study sheds light on the previously unexamined interaction between polydopamine and dopamine receptor D1 (DRD1). We discovered that polydopamine, along with its derivatives, such as levodopa and catechol, can activate DRD1─a function previously attributed solely to dopamine. Moreover, we found that polydopamine has the ability to influence cell behavior through the cAMP/PKA pathway, thereby affecting RhoA activity and stress fiber formation. These observations invite further consideration regarding the biological safety of polydopamine in biomedical contexts and also open avenues for new research directions in designing bioactive functional materials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations4
Published2023
Admission routes1
Has abstractyes

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Same venueACS Biomaterials Science & EngineeringSame topicPolymer Surface Interaction StudiesFrench-language works237,207