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Record W4388510461 · doi:10.1002/adhm.202302899

Silk Fibroin Encapsulated Icariin Nanoparticles Mitigate Bisphenol A‐Induced Spermatogenesis Dysfunction

2023· article· en· W4388510461 on OpenAlexaff
Chunyan Fang, Fan Luo, Zheng Li, Yifeng Shen, Fangyue Wang, Jingyi Zhang, Ren‐You Gan, Yulong Ye

Bibliographic record

VenueAdvanced Healthcare Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsMinistry of Agriculture
FundersNatural Science Foundation of Sichuan Province
KeywordsIcariinFibroinBisphenol ASpermatogenesisMaterials scienceNanoparticleNanotechnologyMedicineInternal medicineComposite materialSILKEpoxy

Abstract

fetched live from OpenAlex

Bisphenol A (BPA) is a prevalent endocrine disruptor found in natural environments. Exposure to BPA has been associated with male infertility. The natural phytochemical icariin (ICA) has demonstrated significant promise for the treatment of male infertility. However, its effectiveness is limited due to its low bioavailability, poor water solubility, and insufficient targeting abilities. Herein, novel nanoparticles are generated from the natural silk fibroin, which are used to load ICA. The efficient drug delivery system (ICA-SNPs) result in significantly focused drug distribution to spermatogonium, enhancing the anti-infertility properties of ICA, and can effectively mitigate spermatogenesis dysfunction induced by BPA, control serum sex hormone levels, and enhance testicular ultrastructure. Additionally, the ICA-SNPs restore spermatogenesis dysfunction primarily via the hormone biosynthesis, spermatogonium meiosis process, and glycerophospholipid metabolism.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.299
Teacher spread0.267 · 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 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

Citations18
Published2023
Admission routes1
Has abstractyes

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