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Record W2990418048 · doi:10.5539/jmbr.v9n1p172

Effects of Acute Low-Salinity Stress on the Activities of Catalase (CAT), Superoxide Dismutase (SOD) and Glutathiones-Transferase (GST) in Scapharca broughtonii

2019· article· en· W2990418048 on OpenAlexvenueno aff
Biao Wu, Aiguo Yang, Jingkai Yan, Zhihong Liu, Xiujun Sun, Liqing Zhou, Guangming Zhang

Bibliographic record

VenueJournal of Molecular Biology Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquaculture Nutrition and Growth
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSuperoxide dismutaseCatalaseSalinityBiologyEnzymeBiochemistryEcology

Abstract

fetched live from OpenAlex

Salinity is an important environmental factor of osmotic pressure which is closely related to growth and development of aquatic organisms. Some physiological studies regarding to challenge of salinity have been reported in marine shellfish. However, information about the effects of acute low-salinity stress on the enzyme activities in ark shell, Scapharca broughtonii, is still limited now. In this study, the enzyme activities of catalase (CAT), superoxide dismutase (SOD) and glutathiones-transferase (GST) in five tested tissues of S. broughtonii under different salinity (10, 15, 20, 25 and 30‰) for different challenged time (24, 48, 72 and 96h) were investigated. The dynamic change of the three enzyme activities after challenged were further analyzed according to the detected data. Results revealed that low-salinity stress generated great change of CAT, SOD and GST activities in organism, but the change peculiarity in different tissues was different in some degree. Especially, two of the five tissues, hepatopancreas and foot, showed special characteristics responding to the low-salinity stress. It also showed that the variations of the three enzymes activities have tissue specificity and time sequentiality in S. broughtonii.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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