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Record W1992882694 · doi:10.3791/3663

Human Internal Mammary Artery (IMA) Transplantation and Stenting: A Human Model to Study the Development of In-Stent Restenosis

2012· article· en· W1992882694 on OpenAlexaff
Xiaoqin Hua, T. Deuse, Evangelos D. Michelakis, Alois Haromy, Philip S. Tsao, Lars Mäegdefessel, Reinhold G. Erben, Claudia Bergow, Boris Behnisch, Hermann Reichenspurner, Robert C. Robbins, Sonja Schrepfer

Bibliographic record

VenueJournal of Visualized Experiments · 2012
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
FundersDeutsche ForschungsgemeinschaftInternational Society for Heart and Lung Transplantation
KeywordsRestenosisMammary arteryMedicineStentTransplantationCardiologyInternal medicineArtery

Abstract

fetched live from OpenAlex

Preclinical in vivo research models to investigate pathobiological and pathophysiological processes in the development of intimal hyperplasia after vessel stenting are crucial for translational approaches (1,2). The commonly used animal models include mice, rats, rabbits, and pigs (3-5). However, the translation of these models into clinical settings remains difficult, since those biological processes are already studied in animal vessels but never performed before in human research models (6,7). In this video we demonstrate a new humanized model to overcome this translational gap. The shown procedure is reproducible, easy, and fast to perform and is suitable to study the development of intimal hyperplasia and the applicability of diverse stents. This video shows how to perform the stent technique in human vessels followed by transplantation into immunodeficient rats, and identifies the origin of proliferating cells as human.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.129
GPT teacher head0.487
Teacher spread0.359 · 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
GenreMethods

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
Published2012
Admission routes1
Has abstractyes

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