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Record W2791436546 · doi:10.1038/s41598-018-22211-1

Echinacoside Increases Sperm Quantity in Rats by Targeting the Hypothalamic Androgen Receptor

2018· article· en· W2791436546 on OpenAlexaff
Zhihui Jiang, Bo Zhou, Xinping Li, Gordon M. Kirby, Xiaoying Zhang

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicHerbal Medicine Research Studies
Canadian institutionsUniversity of Guelph
FundersScience and Technology Innovation Talents in Universities of Henan ProvinceAnyang Institute of Technology
KeywordsAndrogen receptorSpermAndrogenReceptorEndocrinologyTestosterone (patch)AndrologyBiologyInternal medicineMedicineBioinformaticsHormoneProstate cancer

Abstract

fetched live from OpenAlex

Male infertility is a major health issue with an estimated prevalence of 4.2% of male infertility worldwide. Our early work demonstrated that Cistanche extracts protect against sperm damage in mice and that echinacoside (ECH) is one of the major active components. Here we report an essential role for ECH, a natural product that reverses or protects against oligoasthenospermia in rats. ECH was assayed by HPLC, the quantity and quality of sperm was evaluated and hormone levels were determined by radioimmunosorbent assay. ECH reduced levels of androgen receptor (AR) and key steroidogenic-related genes as determined by Western blot and qPCR analysis. The interaction between ECH and AR were evaluated by indirect ELISA and molecular docking. The results show that ECH combined with hypothalamic AR in the pocket of Met-894 and Val-713 to inhibit transfer of AR from the cytoplasm to nuclei in the hypothalamus. While negative feedback of sex hormone regulation was inhibited, positive feedback was stimulated to increase the secretion of luteinizing hormone and testosterone subsequently enhancing the quantity of sperm. Taken together, these data demonstrate that ECH blocks AR activity in the hypothalamus to increase the quantity of sperm and protect against oligoasthenospermia in rats.

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.024
GPT teacher head0.318
Teacher spread0.294 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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