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Record W2131096918 · doi:10.5539/jas.v2n3p131

Effect of Mg Deficiency on Antioxidant Enzymes Activities and Lipid Peroxidation

2010· article· en· W2131096918 on OpenAlexvenueno aff
E Esfandiari, Majid Shokrpour, Siamak Alavikia

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsnot available
FundersUniversity of Tabriz
KeywordsAPXMalondialdehydeCatalaseLipid peroxidationChemistryOxidative stressAntioxidantBiochemistryPeroxidaseFood scienceEnzyme

Abstract

fetched live from OpenAlex

The importance of the physiological role of nutrient elements in plant behavior concerning higher and morestable yield, using the durum wheat variety P1252 as model plant, was examined under hydroponic conditions.To investigate the effects of Mg deficiency, the element was eliminated from the media solutions. Resultsshowed that Mg elimination decreased the chlorophyll content. Lack of Mg only affected guaiacol peroxidase(GPX) and catalase (CAT) activities significantly. The SOD/APX+GPX+CAT ratio as an index of assessing thebalance between H2O2-producing and H2O2-scavenging enzymes increased leading to the accumulation of H2O2in cell. The elevation of SOD/APX+GPX+CAT ratio and H2O2 accumulation indicates the occurrence ofoxidative stress in leave cells under Mg deficiency. Lack of magnesium (Mg) resulted in considerable increasein other oxidative stress indices, cell death and Malondialdehyde (MDA). The reason is the occurrence ofHaber-Weiz reaction in absence of Mg and production of hydroxyl radical, a very dangerous radical, leading toincreasing damage of cell biomolecules and their apoptosis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2010
Admission routes1
Has abstractyes

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