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Record W2037708829 · doi:10.1155/2012/304135

Morphological Characterization of the Polyflux 210H Hemodialysis Filter Pores

2012· article· en· W2037708829 on OpenAlexaff
Assem Hedayat, Jerzy A. Szpunar, Nitin Kumar, Rob Peace, Hamdi Elmoselhi, A Shoker

Bibliographic record

VenueInternational Journal of Nephrology · 2012
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Paul's HospitalUniversity of Saskatchewan
Fundersnot available
KeywordsScanning electron microscopeMaterials scienceMembraneCharacterization (materials science)Morphology (biology)NanotechnologyComposite materialChemistry

Abstract

fetched live from OpenAlex

Background. Morphological characterization of hemodialysis membranes is necessary to improve pore design. Aim. To delineate membrane pore structure of a high flux filter, Polyflux 210H. Methods. We used a Joel JSM-6010LV scanning electron microscope (SEM) and a SU6600 Hitachi field emission scanning electron microscope (FESEM) to characterize the pore and fiber morphology. The maximal diameters of selected uremic toxins were calculated using the macromolecular modeling Crystallographic Object-Oriented Toolkit (COOT) software. Results. The mean pore densities on the outermost and innermost surfaces of the membrane were 36.81% and 5.45%, respectively. The membrane exhibited a tortuous structure with poor connection between the inner and outer pores. The aperture's width in the inner surface ranged between 34 and 45 nm, which is 8.76-11.60 times larger than the estimated maximum diameter of β2-microglobulin (3.88 nm). Conclusion. The results suggest that the diameter size of inner pore apertures is not a limiting factor to middle molecules clearance, the extremely diminished density is. Increasing inner pore density and improving channel structure are strategies to improve clearance of middle molecules.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.866

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)0.0010.000

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 head0.017
GPT teacher head0.273
Teacher spread0.257 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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