Identification of microRNAs as potential markers of ovarian toxicity
Bibliographic record
Abstract
Exposure to environmental toxicants has been associated with ovarian dysfunction yet sensitive biomarkers of adverse effect are lacking. We previously demonstrated that cigarette smoke exposure induced decreased relative ovarian weight, increased follicle loss and granulosa cell autophagy in mice. We postulate that cigarette smoke exposure will induce changes in the epigenome that can be used to reveal potential sensitive biomarkers of ovarian toxicity. Therefore, we evaluated differences in expression of 940 microRNAs (miRNAs), environmentally responsive small non-coding genes that regulate expression of genes at the post-transcriptional level, in ovarian tissue from 8-week-old female C57BL/6 mice exposed to room air or cigarette smoke 5 days per week for 8 weeks. A total of 152 miRNAs were dysregulated in expression, 17 of which were examined with quantitative polymerase chain reaction analysis. Using an online miRNA database tool, complete lists of predicted miRNA gene targets were generated, 12 of which were measured for their expression levels with quantitative polymerase chain reaction. An online bioinformatics resource database, DAVID generated functional classification lists of the target genes and their associated biological pathways. Results of the present pilot study suggest that miR-379, miR-15b, miR-691, miR-872 and miR-1897-5p are potentially useful markers of ovarian toxicity and dysfunction. Examination of the expression pattern of the target mRNA for these miRNA species demonstrated that cigarette smoke exposure induced significant changes that affect mitogen-activated protein kinase signaling pathways. We therefore suggest that miRNAs could serve as sensitive markers of ovarian toxicity and elucidate affected pathways.
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 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 | 0.000 |
| 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".