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Record W3127228941 · doi:10.3233/hab-200438

Infertility cell therapy and epigenetic insights

2021· review· en· W3127228941 on OpenAlexaff
Nahal Eshghifar, Behnam Kamali Dehghan, Atieh Abedin‐Do, Saeideh Zamani Koukhaloo, Mohsen Habibi, Farkhondeh Pouresmaeili

Bibliographic record

VenueHuman Antibodies · 2021
Typereview
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEpigeneticsInfertilityBiologyMale infertilityAssisted reproductive technologyStem cellEtiologyBioinformaticsDiseaseEmbryonic stem cellIdentification (biology)GeneticsComputational biologyMedicinePregnancyInternal medicineGene

Abstract

fetched live from OpenAlex

Recent advances in assisted reproductive technology (ART) have allowed couples with severe infertility to conceive, but the methods are not effective for all cases. Stem cells as undifferentiated cells which are found in different stages of embryonic, fetal and adult life are known to be capable of forming different cell types, tissues, and organs. Due to their unlimited resources and the incredible power of differentiation are considered as potential new therapeutic biological tools for treatment of infertility. For reproductive medicine, stem cells are stimulated in vitro to develop various specialized functional cells including male and female gametes. The epigenetic patterns can be modified in the genome under certain drugs exposure or lifestyle alterations. Therefore, epigenetics-related disorders may be treated if the nature of the modifications is completely admissible. It is proved that our understanding of epigenetic processes and its association with infertility would help us not only to understand the etiological factors but also to treat some type of male infertilities. Exploration of both genetic and epigenetic variations in the disease development could help in the identification of the interaction patterns between these two phenomena and possible improvement of therapeutic methods.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.179
GPT teacher head0.414
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2021
Admission routes1
Has abstractyes

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