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Record W2737813711 · doi:10.1186/s13048-017-0346-z

Enterolactone has stronger effects than enterodiol on ovarian cancer

2017· article· en· W2737813711 on OpenAlexafffund
Huidi Liu, Peter Liu, Siwen Wang, Zheng Zeng, Ting Li, Yongfang Liu, Emilio Mastriani, Qinghai Li, Hongxia Bao, Yujie Zhou, Xiaoyu Wang, Sijing Hu, Shan Gao, Yingying Qi, Zhihang Shen, Hongyue Wang, Miao Yu, Tingting Gao, Randal N. Johnston, Shu‐Lin Liu

Bibliographic record

VenueJournal of Ovarian Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of Calgary
FundersNational Institutes of HealthChina Scholarship CouncilHealth and Family Planning Commission of Heilongjiang ProvinceNational Natural Science Foundation of ChinaHarbin Medical UniversityUniversity of Calgary
KeywordsEnterolactoneMedicineOvarian cancerReproductive medicineGynecologyInternal medicineOncologyObstetricsCancerPregnancyBiologyGeneticsEstrogen

Abstract

fetched live from OpenAlex

BACKGROUND: Ovarian cancer is one of the three leading gynecological malignancies, characterized by insidious growth, highly frequent metastasis, and quick development of drug resistance. As a result, this disease has low 5-year survival rates. Estrogen receptor inhibitors were commonly used for the treatment, but only 7% to 18% of patients respond to anti-estrogen therapies. Therefore, more effective therapies to inhibit estrogen-related tumors are urgently needed. Recently, phytoestrogens, such as lignans with estrogen-like biological activities, have attracted attention for their potential effects in the prevention or treatment of estrogen-related diseases. Enterodiol (END) and enterolactone (ENL) are mammalian lignans, which can reduce the risk of various cancers. However, the effects of END and ENL on ovarian cancer are not adequately documented. METHODS: We used in vitro assays on the ES-2 cell line to evaluate the inhibiting effects of END and ENL on ovarian cancer cell proliferation, invasion and migration ability and in vivo xenograft experiments on nude mice to validate the anticancer effects of END and ENL. RESULTS: The in vitro assays demonstrated that high-dose END and ENL could obviously inhibit ovarian malignant properties, including cancerous proliferation, invasion, and metastasis. Compared to END, ENL behaved in a better time-dose dependent manner on the cancer cells. The in vivo experiments showed that END (1 mg/kg), ENL (1 mg/kg) and ENL (0.1 mg/kg) suppressed tumor markedly, and there were statistically significant differences between the experimental and control groups in tumor weight and volume. Compared to END, which have serious side effects to the animals at high concentration such as 1 mg/kg, ENL had higher anticancer activities and less side effects in the animals than END at the same concentrations, so it would be a better candidate for drug development. CONCLUSION: END and ENL both have potent inhibitory effects on ovarian cancer but ENL possesses a more effective anti-cancer capability and less side effects than END. Findings in this work provide novel insights into ovarian cancer therapeutics with phytoestrogens and encourage their clinical applications.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.146
GPT teacher head0.458
Teacher spread0.312 · 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.

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

Citations45
Published2017
Admission routes2
Has abstractyes

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