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Record W34363693 · doi:10.1002/bip.23468

Aspectos críticos de la nutrición de cerdas gestantes

2009· article· en· W34363693 on OpenAlexfundno aff
Ioannis Mavromichalis

Bibliographic record

VenueSuis · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood, Nutrition, and Cultural Practices
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesKing Saud UniversityAlberta Innovates - Technology Futures
KeywordsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Development of elastin-like polypeptide (ELP) biomaterials is widespread, but information critical for clinical deployment is limited, with biocompatibility studies focused on a narrow cross-section of ELP sequences. Macrophages can impair biomaterial systems by degrading or isolating the biomaterial and by activating additional immune functions. Their phagocytic response will reveal early immune biocompatibility of ELP nanoparticles (NPs). This study examines that response, induced by the adsorbed protein corona, as a function of ELP guest amino acid, chain length and NP diameter. The breadth of proteins adsorbed to ELP NPs varied, with valine-containing ELP NPs adsorbing fewer types of proteins than leucine-containing constructs. Particle diameter was also a factor, with smaller leucine-containing ELP NPs adsorbing the broadest range of proteins. Macrophage viability was unaffected by the ELP NPs, and their phagocytic capabilities were unimpeded except when incubated with a 500 nm valine-containing 40-mer. This NP significantly decreased the phagocytic capacity of macrophages relative to the control and to a corresponding 500 nm leucine-containing 40-mer. NP size and the proportion of opsonin to dysopsonin proteins likely influenced this outcome. These results suggest that certain combinations of ELP sequence and particle size can result in an adsorbed protein corona, which may hinder macrophage function.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.646

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 head0.009
GPT teacher head0.245
Teacher spread0.236 · 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 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
Published2009
Admission routes1
Has abstractyes

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