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Record W2388267448

Impact of Aqueous Extract of Red Beet Hemorrhagic Anemia in Mice

2015· article· en· W2388267448 on OpenAlexaff
Wang Chun-l

Bibliographic record

VenueShipin yanjiu yu kaifa · 2015
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsScience North
Fundersnot available
KeywordsHemoglobinRed blood cellAnemiaDistilled waterChemistryMedicineAqueous extractAnimal sciencePharmacologyInternal medicineTraditional medicineBiologyChromatography
DOInot available

Abstract

fetched live from OpenAlex

To investigate the effects of aqueous extract of the red beet on loss anemia mice, the 50 mice were randomly divided into normal control group, model group and red beet water extract high, medium and low dose groups, each group contained 10 mice. The blood loss anemia mice model was constructed by the method of angular vein plexus. Gastric perfusion was started after 24 hours, whereas the mice in the control group were given the distilled water in the gastric. The number of red blood cells in mice and hemoglobin content were determined before the loss of blood, blood loss after 24 hours, after dosing 5 days and 10 days, respectively.Our model reduced the number of red blood cells in mice, and decreased the content of hemoglobin. After treated by the aqueous extract of red beet orally, the red blood cell count and hemoglobin content of mice increased variously. After gavaging 5 days with red beet aqueous extract, there was significant difference between the hemoglobin content, red blood cell count of the high dose group and the control group. The former group was significantly higher than those in control group. Red beet aqueous extract of high, middle dose group mice returned to normal after 10 days of gavaging. Aqueous extract of red beet hemorrhagic anemia could promote recovery.

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 score0.270
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.031
GPT teacher head0.316
Teacher spread0.285 · 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
Published2015
Admission routes1
Has abstractyes

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