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Record W7029709828

Laser Welding of Porcine Skeletal Muscle Tissue Using Near Infra-red Irradiation with Gold Nanorod Biosolders

2024· dissertation· en· W7029709828 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSkeletal muscleWeldingLaserLaser beam weldingNanorodMuscle tissueIrradiationLaser power scaling
DOInot available

Abstract

fetched live from OpenAlex

Laser tissue welding is an efficient and quick technique used to join tissues for wound repair in the event of injury or surgery. Compared to traditional joining methods such as suturing, optimized laser tissue welding offers a simpler procedure, reduced operator skill requirements, and improved healing. With the use of laser light, exogenous chromophores such as gold nanorods can be added in addition to localize thermal energy and streamline the welding process. The focus of this research is to build upon current understandings of laser tissue welding, using near-infrared irradiation and exogenous chromophores, performed on ex vivo porcine skeletal muscle tissue as a model. \nTo accomplish this goal, laser interactions with porcine skeletal muscle tissue was studied using a parametric approach. By manipulating laser power (0-30 W) and lasing duration (0-20 s), the thermal effects of laser irradiation (Ex. coagulation) on biological tissue was modeled. It produced a threshold whereby laser parameters that cause irreversible tissue damage can be estimated with the model. This information guided welding efforts and may facilitate the safe development of laser-muscle treatments for chronic muscle pain and muscle regeneration through non-thermal effects. \nLaser welding of porcine skeletal muscle tissue was performed using a continuous wave, near-infrared (1070 nm) fiber laser. A tensile test revealed a 46.7 % recovery in tensile strength. Optical and scanning electron microscopy revealed gaps at the interface which suggests the potential for procedural or parametric changes to improve the weld quality. \nExogenous chromophores (gold nanorods) were introduced to the laser tissue welding procedure and was delivered to the tissue in two ways, using a biocompatible hydrogel comprised of hyaluronic acid, and a solid collagen disc. The gold nanorod within these biosolders had an absorbance maximum that was attuned to the continuous wave fiber-coupled diode laser (808 nm) used. A complete seal of an artificial incision on porcine skeletal muscle tissue using the hyaluronic acid hydrogel was achieved within 6 minutes of irradiation time with a power density of 4.7 W/cm2. In addition, welds achieved with the GNR-collagen disc (1.9 W/cm2) with an irradiation time of 4 minutes revealed a 48 % recovery in tensile strength. \nThese results highlight the simplicity of the procedure and strength of the bonds achieved using laser tissue welding. It also showcases the advantages of using gold nanorod-infused biosolders. This study demonstrates their ease of application, versatility with different tissues, and speed. This is evidence that laser tissue welding is a strong alternative to traditional joining techniques and may be useful in optimization of this technique for increased use in surgical scenarios.

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 score0.000
Threshold uncertainty score0.002

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)0.0000.000

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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