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Record W4303579109 · doi:10.3390/antiox11101956

Dose–Response Effects of Glutathione Supplement in Parenteral Nutrition on Pulmonary Oxidative Stress and Alveolarization in Newborn Guinea Pig

2022· article· en· W4303579109 on OpenAlexafffund
Jean‐Claude Lavoie, Ibrahim Mohamed, Vitor Teixeira

Bibliographic record

VenueAntioxidants · 2022
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersCanadian Institutes of Health Research
KeywordsGlutathioneOxidative stressBronchopulmonary dysplasiaGlutathione disulfideParenteral nutritionInternal medicineMedicineOxidative phosphorylationEndocrinologyPharmacologyChemistryBiochemistryBiologyEnzyme

Abstract

fetched live from OpenAlex

metabolism and those contaminating the parenteral nutrition (PN) leading to increased oxidative stress, which is a major contributor to bronchopulmonary dysplasia (BPD) development. In animals, the supplementation of PN with glutathione prevented the induction of pulmonary oxidative stress and hypoalveolarization (characteristic of BPD). Hypothesis: the dose of glutathione that corrects the plasma glutathione deficiency is sufficient to prevent oxidative stress and preserve pulmonary integrity. Three-day-old guinea pigs received a PN, supplemented or not with GSSG (up to 1300 µg/kg/d), the stable form of glutathione in PN. Animals with no handling other than being orally fed constituted the control group. After 4 days, lungs were removed to determine the GSH, GSSG, redox potential and the alveolarization index. Total plasma glutathione was quantified. The effective dose to improve pulmonary GSH and prevent the loss of alveoli was 330 µg/kg/d. A 750 µg/kg/d dose corrected the low-plasma glutathione, high-pulmonary GSSG and oxidized redox potential. Therefore, the results suggest that, in a clinical setting, the dose that improves low-plasma glutathione could be effective in preventing BPD development.

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 score0.512
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.334
Teacher spread0.314 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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