Genome and Epigenome Disorders and Male Infertility: Feedback from 15 Years of Clinical and Research Experience
Bibliographic record
Abstract
Infertility is affecting around 20 % of couples in the age of procreation but however in some societies as many as one-third of all couples are unable to conceive. Different factors are contributing to male fertility declining such us endocrine disruptors environmental and professional exposures, oxidative stress and life style changes with risks of de novo epigenetics dysregulation. Since the fantastic development of new technologies of Omes and Omics , the contribution of inherited or de novo genomes and epigenome disorders contributions in male infertility are more elucidated and relevant. More than somatic genome investigation of infertile men (from chromosome to single or multiple gene point mutations) many others techniques become available in molecular andrology laboratory to investigate the genome and epigenome integrity, the maturation and the competency of the spermatozoa and its physiological environment. All those new methods of assessment are demonstrating the role of genetics and epigenetics disorders contribution on reproductive pathology and are helping the professional of assisted reproductive technology to propose different management strategy of male infertility to improve the clinical outcomes and minimize the risk of genetics or metabolic disorders at birth.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".