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Record W3139562373 · doi:10.4021/jnr59w

Zonisamide Treatment Delays Motor Neuron Degeneration and Astrocyte Proliferation in Wobbler Mice

2011· article· en· W3139562373 on OpenAlexvenueno aff
Hirayama

Bibliographic record

VenueJournal of Neurology Research · 2011
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMotor neuronBicepsLower motor neuronNeuroprotectionAstrocyteNeuronSpinal cordTetraplegiaInternal medicineSpinal cord injuryEndocrinologyAnesthesiaAnatomyDiseaseCentral nervous system

Abstract

fetched live from OpenAlex

Background : Zonisamide (ZNS) had multifunctional effects on several kinds of neurons. Little is known about neuroprotective effects of ZNS on motor neurons. We aimed to study whether this drug can attenuate motor neuron degeneration in wobbler mice. Methods : Wobbler mice were injected daily two doses of ZNS (0.2 mg/kg , 2.0 mg/kg , i.p.) or vehicle from aged 3 - 4 weeks at disease onset for more than 4 weeks. Motor function was evaluated by pull-strength and deformity scale of the forelimbs. Those symptomatic assessment and body weight were measured weekly. Neuropathological changes of the biceps muscle and the cervical cord were analyzed at 4 weeks posttreatment. Results : ZNS treatment (2.0 mg/kg ) significantly delayed progression of forelimb motor dysfunction compared to vehicle (P less than 0.01). Gain of body weight did not differ statistically between three groups. Higher doses of ZNS administration decreased denervation atrophy in the biceps muscle (P less than 0.01), suppressed loss of motor neurons (P less than 0.01) and inhibited astrocyte proliferation (P less than 0.01). Conclusions : The present study indicated that ZNS treatment attenuated motor neuron degeneration and astrocytosis in the wobbler mouse. This drug may have a therapeutic potential for motor neuron disease. J Neurol Res. 2011;1(4):139-144 doi: https://doi.org/10.4021/jnr59w

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 score0.447
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.151
GPT teacher head0.376
Teacher spread0.225 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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