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Record W4293920664 · doi:10.54097/hset.v11i.1382

Potential Health Benefit of Houttuynia Cordata on Inflammation, Cancer and Diabetes

2022· article· en· W4293920664 on OpenAlexaff
Sijia Chen, Qifei Huang

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldMedicine
TopicNephrotoxicity and Medicinal Plants
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHouttuynia cordataInflammationDiabetes mellitusMedicineCancerTraditional medicineCancer preventionDiseasePharmacologyOncologyInternal medicineEndocrinologyChemistry

Abstract

fetched live from OpenAlex

In Asian areas, Houttuynia cordata thunb (HCT) has traditionally been utilized as medication. Recent research reveals the bioactive ingredients in HCT and tests its efficacy to counter several diseases using the HCT extract. In this article, we focus on HCT’s potency in treating inflammation, cancer, and diabetes. Collectively, the data suggests HCT can effectively reduce inflammation, promote apoptosis in cancer cells, smoothen symptoms of diabetes and increase glucose tolerance. These findings are essential for the future development of drugs targets at those disease. More research is needed to further unveil the mechanisms behind HCT pharmaceutical efficacy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0040.000

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 head0.007
GPT teacher head0.242
Teacher spread0.235 · 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 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
Published2022
Admission routes1
Has abstractyes

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