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Record W3120709504

Structural changes in dendritic spine morphology in early co-morbid behaviours in a mouse model of MS

2017· article· en· W3120709504 on OpenAlexaff
Molly Frizzell, Adrienne M. Benediktsson

Bibliographic record

VenueURSCA Proceedings · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsDendritic spineSPINE (molecular biology)NeuroscienceCognitionPsychologyAmygdalaAnxietyMedicineBiologyPsychiatryBioinformaticsHippocampal formation
DOInot available

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a devastating disease usually characterized by its physical symptoms caused by central nervous system inflammation and demyelination. Behavioral and cognitive co-morbid symptoms frequently afflict those living with MS and precede the onset of physical symptoms. This suggests these co-morbidities may be caused by a mechanism independent of demyelination. Structural changes in small projections extending off of dendrites (dendritic spines), the post-synaptic portion of a synapse, may underlie the functional deficits in behaviour and cognition seen in MS. Spines will be imaged from the mouse basolateral amygdala due to its regulation of anxiety, emotionality and fear memory, which coincide with the observed co-morbid symptomology. Dendritic spines will be measured using ImarisTM software to separate spines into three groups: thin, mushroom or stubby, based on the ratio of spine diameter to spine length. It is hypothesized that at day 7 post induction, to correlate with the onset of co-morbid symptoms, there will be a pronounced shift in spine morphology from more infantile (thin) classifications to more mature (mushroom) structures. This research aims to examine dendritic spine morphological changes as a potential mechanism for the onset of co-morbid symptoms in individuals afflicted with MS. * Indicates faculty mentor.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.086
GPT teacher head0.357
Teacher spread0.271 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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