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Record W4404830129 · doi:10.1002/advs.202408077

Negative Pressure Smart Patch to Sense and Heal the Wound

2024· article· en· W4404830129 on OpenAlexaff
Xing Liu, Peng Zhao, Xiaozhuo Wu, Yawei Zhao, Feifan Zhou, Ying Luo, Xiaoli Jia, Wen Zhong, Malcolm Xing, Guozhong Lyu

Bibliographic record

VenueAdvanced Science · 2024
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNegative-pressure wound therapyWound healingMedicineInflammationSpongeBiomedical engineeringSurgeryImmunologyBiologyPathology

Abstract

fetched live from OpenAlex

Negative pressure wound therapy (NPWT) offers significant advantages in terms of rate and time for healing through generating sub-vacuum to draw out inflammatory exudate and promote wound closure. However, continuous drainage probably leads to healing delay due to the lack of information about the real status of the wound bed and the potential risk of infection. To address this concern, printed Negative Pressure Smart Patch (NPSP) is reported by integrating smart real-time sensing acidity (infection) and glucose, and anti-infection into NPWT systems. In addition, NPSP delivers vancomycin through chitosan porous microspheres under negative pressure to modulate wound healing. Compared with NPWT, NPSP projects a promising approach to removing bacteria, reducing local inflammation, and accelerating healing in a short period of time.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.015
GPT teacher head0.341
Teacher spread0.326 · 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
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

Citations6
Published2024
Admission routes1
Has abstractyes

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