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Record W1990766662 · doi:10.1016/s2005-2901(11)60006-x

Promotion of Blood Fluidity by Inhibition of Platelet Adhesion Using Electroacupuncture Stimulation

2011· article· en· W1990766662 on OpenAlexaff
Shintaro Ishikawa, Makoto Murai, Takao Sato, Masataka Sunagawa, Erika Tokita, Steven Aung, Kazuhito Asano, Tadashi Hisamitsu

Bibliographic record

VenueJournal of Acupuncture and Meridian Studies · 2011
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsZusanliElectroacupuncturePlateletStimulationMedicineInternal medicineMembrane fluidityEndocrinologyPlatelet activationPlatelet adhesivenessAcupunctureChemistryPlatelet aggregationPathologyBiochemistry

Abstract

fetched live from OpenAlex

Stress applied to rats is known to result in a quick decrease in blood fluidity. Although electrical acupuncture stimulation (ACU) attenuates stress responses, the influence of ACU on blood fluidity has not been well examined. In the present study, the effect of ACU on blood fluidity and platelet adhesion was examined using a Micro Channel Array Flow Analyzer and a laser scattering platelet aggregometer (PA-20), respectively. Male Wistar rats (7-8 weeks old) were used. ACU (1 Hz, 3-5 V), which causes slight muscle twitching, was applied to acupoints for 60 minutes/day once or on 2 consecutive days. Stimulated acupoints were as follows: ZuSanli (ST-36), Sanyinjiao (SP-6), Hegu (L-I4), Neiguan (P-6), and Shenshu (BL-23). ACU applied to ST-36, SP-6, and L-14 revealed significant increases in blood fluidity while platelet adhesion activity decreased. No significant changes were observed when ACU was applied to P-6 and BL-23. Results indicate that ACU affects blood fluidity depending on the acupoints. Blood fluidity changed with ACU within 1 day. In other words, the effect of acupuncture has an immediate effect. In addition, platelet aggregation decreased with ACU, suggesting that an increase in blood fluidity is associated with platelet aggregation ability.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.084
GPT teacher head0.337
Teacher spread0.253 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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