MétaCan
Menu
Back to cohort
Record W4234544929 · doi:10.32920/ryerson.14644308

The Potential of Tantalum-Doped Mesoporous Bioactive Glasses for Hemostasis

2021· preprint· en· W4234544929 on OpenAlexafffund
Andrew Mendonca

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicHemostasis and retained surgical items
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoUniversity of WaterlooSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsTantalumMesoporous materialHemostasisMaterials scienceMesoporous silicaNuclear chemistryChemistryChemical engineeringSurgeryBiochemistryMetallurgyCatalysis

Abstract

fetched live from OpenAlex

Synthesis and characterization of the first mesoporous bioactive glasses (MBGs) containing tantalum (Ta) are reported, alongside their potential as hemostats. A series of silica MBGs with the molar composition of (80-x)% Si, 15% Ca, 5% P and x% Ta, with x = 0 to 10, were synthesized by the sol-gel method. Increasing Ta content from 1 to 10 mol% caused a decrease in the surface area and pore volume of ~20% and ~35%, respectively, due to the increase in non-bridging oxygens and mismatch of thermal expansion coefficient which creates discontinuities in the ordered channel structure. However, the effect is not significant on the amount of ions released from the samples into deionized water, for short durations (<60 min). In a mouse tail cut model, a significant decrease (≥ 50%) in average bleeding time was found for Ta-MBGs compared to controls (commercial hemostat Arista® and a Ta-free MBG). There was no significance in hemolytic activity between plain blood and when MBG is present and cytotoxicity results show that increasing Ta content from 0 to 10% reduced MBG toxicity by more than 15%; this effect was visually confirmed by live/dead cell assays. Further studies are proposed to determine the mechanism of Ta involvement with the hemostatic process.

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 score0.831
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.027
GPT teacher head0.314
Teacher spread0.286 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicHemostasis and retained surgical itemsFrench-language works237,207