Unraveling the mystery of genetics and male infertility
Bibliographic record
Abstract
Unraveling the mystery of genetics and male infertilityTaking 13 years to complete, the Human Genome Project sequenced the entire euchromatic human genome.The accomplishment was hoped to offer solutions to many of man's most befuddling medical conditions, male infertility a prime example.What was discovered, however, was the tip of the iceberg.The sequence of base pairs only begins to shed light on the complex interplay between pre-translational, translational, and related factors that determine fertility potential.This focused series of Translational Andrology and Urology explores our current understanding of the genetic causes and management of male infertility.Beginning with reviews of the basic genetic etiologies and relevant tests, this series then delves into well-known causes of male infertility including Y chromosome copy number variations, Y chromosome microdeletions, cystic fibrosis transmembrane conductance regulator (CFTR) gene mutations, and hypogonadotropic hypogonadism.A novel review collates the available literature on nutrigenomics, detailing the interplay between diet, genetic makeup, and fecundity.The latter half of this series focuses on management options for the above-mentioned and other genetic etiologies of male infertility.Two articles on Y chromosome microdeletions detail sperm retrieval techniques and assisted reproductive outcomes.Two additional reviews address sperm retrieval techniques and success rates for Klinefelter's syndrome and CFTR mutations.Equally as important, a special review discusses genetic counseling recommendations for men with known and unknown causes of infertility.The last article looks to the future, exploring research on the verge of bettering our understanding of genetic causes of male infertility.On behalf of myself and my co-editor, Dr. Keith A. Jarvi, we would like to thank the editors and copyediting staff at Translational Andrology and Urology for their tireless efforts guiding this special edition from concept to final form.And we would be remiss without offering our sincerest thanks to our review article authors for their efforts in summarizing the available literature on these important topics, even during these trying times.We all are living in a constant state of flux related to the ongoing COVID pandemic-from social isolation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".