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Record W4386258606 · doi:10.1016/j.fhfh.2023.100154

Dual and triple encapsulated iron gluconate speed up anemia recovery in an animal model

2023· article· en· W4386258606 on OpenAlexafffund
Alberto Baldelli, Yigong Guo, Anubhav Pratap‐Singh

Bibliographic record

VenueFood Hydrocolloids for Health · 2023
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsMaltodextrinVitamin B12BioavailabilityAnemiaFood scienceChemistryMicronutrientIron-deficiency anemiaVitaminSpray dryingBiochemistryInternal medicineMedicinePharmacologyChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Food fortification can be a solution to anemia in developing countries. A previous study determined that the combination of spray drying, hydroxypropyl-methylcellulose as wall material, and maltodextrin as bulk material, encapsulated iron gluconate achieved the highest bioavailability. However, the addition of vitamin B12 to the hydroxypropyl-methylcellulose/maltodextrin capsules increased the iron cell uptake over the previously reported results. The cell viability, the number of live, healthy cells in a sample, of HepG2, human liver cancer cells, increases by about 17 % for dual-encapsulated iron gluconate and vitamin b12. The cell uptake in Caco2, human colorectal adenocarcinoma cells, is higher by 25 % when using encapsulated iron and vitamin b12 compared to encapsulated iron. The strength of dual-encapsulated iron and vitamin b12 is also confirmed in in-vivo studies. Once fully anemic, young female rats eating food with encapsulated iron gluconate and vitamin b12, show the fastest recovery with respect to rats eating food with encapsulated iron and pure iron. The first needed only five days for their hemoglobulin values to return to normal. The second and the third needed 15 and 21 days, respectively.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.327
Teacher spread0.294 · 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 designSimulation or modeling
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

Citations5
Published2023
Admission routes2
Has abstractyes

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