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RELEASE OF SILVER IN BLOOD AND TISSUES FROM SILVER COATED CATHETERS

2005· article· en· W2019751710 on OpenAlexaff
Mrinal K. Dasgupta, Patrick N. Nation

Bibliographic record

VenueASAIO Journal · 2005
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug Solubulity and Delivery Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSilver nanoparticleCatheterMedicineSilver stainSurgerySilver NanoChemistryPathologyMaterials science

Abstract

fetched live from OpenAlex

Silver-coated catheters are clinically used for antibacterial properties of the metal. However, risk of silver toxicity on long term use is not proven. Thus, our objective was to study of release of silver in blood and pericatheter tissues after long term placement of silver-coated catheters in a rabbit model.We implanted surgically silver-coated peritoneal catheter segments (5cm/2) in each rabbit accross the spine of 9 NZW rabbits at Day 0 and divided in 3 groups of 3 rabbits each. The rabbits were followed for a total period of 60 days and sacrificed at Day 14 (n=3), Day 30(n=3) and Day 60(n=3).Blood and pericatheter tissue samples were collected at sacrifice and were analyzed for silver. Histological examination of the tissues were also done.Control experiments were done by similar procedures in rabbits with non-coated catheters. Silver levels(ug/L) in blood of rabbits with silver catheters were low (2.5±0.4, 2.1± 0, 1.8± 0.5 at Day 14,30, 60 respectively) compared to gradual rise in silver levels (ug/L) from pericatheter tissue segments (2.5 ±0.6, 24.33±3.3, 27.5± 8.8) at Day14, 30 and 60 respectively. Silver like particles in corresponding tissues were also noted by histological examination and confirmed to be silver particles by EM energy dispersion X-ray analysis.Silver was absent in tissues and insignificant amount in blood samples (0 -7ug/L) of control rabbits.We conclude that higher levels of silver are released in pericatheter tissues compared to blood; however, the levels are not in the toxic range.Further refinement in silver-coating technology is needed.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.064
GPT teacher head0.375
Teacher spread0.311 · 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 designObservational
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
Published2005
Admission routes1
Has abstractyes

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