MétaCan
Menu
Back to cohort
Record W4251226148 · doi:10.14789/jmj.61.622

Abstracts for 2014th Annual Poster Presentation (Research Institute for Diseases of Old Age)

2015· article· en· W4251226148 on OpenAlexaff
Kenji Yoshimi, Shiori Kumada, Masato Inoue, F Sasaki, Nanase Takahashi, Toshiaki Okuno, M. Ohba, Shintaro Makino, Satoru Takeda, Takehiko Yokomizo, Kaori Hatakeyama, Ichiro Fukunaga, Katsuhisa Ikeda, Kazusaku Kamiya

Bibliographic record

VenueJuntendo Medical Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicGDF15 and Related Biomarkers
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsPresentation (obstetrics)MedicineGerontologyLibrary scienceComputer scienceSurgery

Abstract

fetched live from OpenAlex

Background: In-vivo voltammetry has successfully been used to detect dopamine release in rodent brains, but its application to monkeys has been limited. We have previously detected dopamine release in the caudate of behaving Japanese monkeys using diamond microelectrodes (Yoshimi 2011); however it is not known whether the release pattern is the same in various areas of the forebrain. Recent studies have suggested variations in the dopaminergic projections to forebrain areas. Methods: In the present study, we attempted simultaneous recording at two locations in the striatum. Carbon-fiber microelectrodes were inserted into the striatum of two Japanese monkeys. Dopamine release was detected by fast-scan cyclic voltammetry (FSCV) on the carbon fibers, which has been widely used in rodents. Results: Dopamine responses to unpredicted food and liquid rewards were detected repeatedly. The response to the liquid reward after conditioned stimuli was enhanced after switching the prediction cue. Conclusion: These results are consistent to the "reward-prediction error"theory of dopamine release. The characteristics were generally similar between the ventral striatum and the putamen. Overall, the technical application of FSCV recording in multiple locations was successful in behaving primates, and further voltammetric recordings in multiple locations will expand our knowledge of dopamine reward responses.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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)0.0000.000

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 head0.085
GPT teacher head0.416
Teacher spread0.331 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJuntendo Medical JournalSame topicGDF15 and Related BiomarkersFrench-language works237,207