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Record W2137596523 · doi:10.5539/jas.v4n9p196

The Anti-brain Ageing Effects of Krill Phosphatidylserine in SAMP10 Mice

2012· article· en· W2137596523 on OpenAlexvenueno aff
Jun Wang, Hongtao Lei, Pengtao Li, Han Li, Yangyang Yan, Haiping Zhao, Tomoko Tsuji

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldMedicine
TopicBiochemical effects in animals
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNissl bodyEndocrinologyOxidative stressInternal medicineGlutathione peroxidaseDownregulation and upregulationNeocortexGlutathioneNeurodegenerationSenescenceChemistryAtrophyAgeingBiologySuperoxide dismutaseMedicinePathologyNeuroscienceBiochemistryStaining

Abstract

fetched live from OpenAlex

We examined the biological effect of krill phosphatidylserine (K-PS) on brain ageing and investigated the mechanisms that how K-PS works on the brain using senescence-accelerated mouse (SAM) model with age-associated neurodegeneration. SAMP10 mice with 5 months of age were treated orally once a day for 75 days with 10 or 100 mg kg?1 d?1 K-PS (low and high dose). The effect of K-PS treatment on the cross-sectional area and Nissl body number of the neocortex at point C, an area prone to atrophy in SAMP10 mice, behavior and oxidative stress were evaluated by Image-Pro Plus software, step-down testing, and malondialdehyde (MDA) and glutathione peroxidase (GSH-Px) assays. Ionized calcium binding adaptor molecule 1 (Iba-1) and insulin-like growth factor 1 (IGF-1) expression was evaluated using immunohistochemistry. Low or high doses of K-PS significantly increased the cross-sectional area and Nissl body number at point C, partly reversed memory impairment, increased the activity of GSH-Px and reduced MDA levels. Moreover, immunohistochemistry indicated that K-PS suppressed Iba-1 expression and upregulate the expression of IGF-1. These findings suggest that K-PS could prevent or slow the progression of brain atrophy and neuronal damage associated with aging by the inhibition of Iba-1 and the upregulation of IGF-1 expression.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.552
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.007
GPT teacher head0.267
Teacher spread0.260 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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