Transcriptome analysis reveals key genes and pathways associated with piglet fetal mummification
Bibliographic record
Abstract
China has the largest pork consumption worldwide. However, the high incidence of piglet fetal mummification (3%–5%) is an important factor that causes the slow improvement of pig reproductive capacity, and the genetic mechanism is still unclear. This study aimed to identify candidate genes associated with piglet fetal mummification. RNA-seq technology was used to compare transcriptome profiling of blood from healthy and mummified piglets at different stages of pregnancy (35, 56, 77, and 98 days). A total of 137–420 differentially expressed genes (DEGs) were detected at each stage. Seven DEGs were significantly differentially expressed at various stages. IL-9R, TLR8, ABLIM3, FSH-α, ASCC1, PRKCZ, and GCK may play important roles in the course of piglet fetal mummification. The differential genes we identified between the groups were mainly enriched in immune and inflammation regulation, while others were mainly enriched in reproduction. Considering the function of candidate genes, IL-9R and TLR8 were suggested as the most promising candidate genes involved in mummified piglet traits. We speculate that during pregnancy, it may be the combined effects of the above-mentioned inflammation, immune response, and reproduction-related signaling pathways that affect the occurrence of mummified piglets and further affect pig reproduction.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".