MétaCan
Menu
← Back to cohort
Record W4214854153 · doi:10.1101/2022.02.28.482233

Early Tactile Stimulation Influences the Development of Alzheimer Disease in Gestationally Stressed APP <sup>NL-G-F</sup> Adult Offspring <sup> <i>NL-G-F/NL-G-F</i> </sup> mice

2022· preprint· en· W4214854153 on OpenAlexafffund
Shakhawat R. Hossain, Hadil Karem, Zahra Jafari, Bryan Kolb, Majid H. Mohajerani

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Lethbridge
FundersRIKENAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaAlzheimer Society
KeywordsStimulationOffspringSensory stimulation therapyPsychologyAnxietyDementiaNeuroscienceCognitionDevelopmental psychologyDiseaseAlzheimer's diseaseInternal medicineEndocrinologyMedicineBiologyPsychiatryPregnancy

Abstract

fetched live from OpenAlex

Abstract Alzheimer Disease (AD) is associated with cerebral plaques and tangles, reduced synapse number, and shrinkage in several brain areas and these morphological effects are associated with the onset of compromised cognitive, motor, and anxiety-like behaviours. The focus of this study was to examine the effect of neonatal tactile stimulation on AD-like behavioural and neurological symptoms on APP NL-G-F/NL-G-F mice, a mouse model of AD. Our findings indicate that neonatal tactile stimulation improves cognition, motor skills, and anxiety-like symptoms in both gestationally stressed and non-stressed adult APP mice and that these alterations are associated with reduced Aβ plaque formation. Thus, tactile stimulation appears to be a promising non-invasive preventative strategy for slowing the onset of dementia in aging animals.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.270
Teacher spread0.249 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicAlzheimer's disease research and treatments→French-language works237,207→