Teat number plays an important role in the reproductive performance of sows and the growth of piglets. However, the quantitative trait loci (QTLs) and candidate genes for the teat number-related traits in Qingping pigs remain unknown. In this study, we performed GWAS based on whole-genome single-nucleotide polymorphisms (SNPs) and insertions/deletions (Indels) for the total number of teats and five other related traits in 100 Qingping pigs. SNPs and Indels of all 100 pigs were genotyped using 10× whole genome resequencing. GWAS using General Linear Models (GLM) detected a total of 28 SNPs and 45 Indels as peak markers for these six traits. We also performed GWAS for the absolute difference between left and right teat number (ADIFF) using Fixed and random model Circulating Probability Unification (FarmCPU). The most strongly associated SNP and Indel with a distance of 562,788 bp were significantly associated with ADIFF in both GLM and FarmCPU models. In the 1-Mb regions of the most strongly associated SNP and Indel, there were five annotated genes, including TRIML1, TRIML2, ZFP42, FAT1 and MTNR1A. We also highlighted TBX3 as an interesting candidate gene for SSC14. Enrichment analysis of candidate genes suggested the Wnt signaling pathway may contribute to teat number-related traits. This study expanded significant marker-trait associations for teat number and provided useful molecular markers and candidate genes for teat number improvement in the breeding of sows.

Labor is a process of inflammation and hormonal changes involving both fetal and maternal compartments. MicroRNA-132-3p (miR-132-3p) has been reported to be involved in the development of inflammation-related diseases. However, little is known about its potential role in labor onset. This study aimed to explore the mechanism of miR-132-3p in amnion for labor initiation. In the mouse amnion membranes, the expression of miR-132-3p was found to increase gradually during late gestation. In human amniotic epithelial cell line (WISH), upregulation of miR-132-3p was found to increase proinflammatory cytokines and cyclooxygenase 2 (COX2) as well as prostaglandin E2 (PGE2), which was suppressed by miR-132-3p inhibitor. Dual-specificity phosphatase 9 (DUSP9) was identified as a novel target gene of miR-132-3p, which could be negatively regulated by miR-132-3p. DUSP9 was present in the mouse amnion epithelial cells, with a decrease in its abundance at 18.5 days post coitum (dpc) relative to 15.5 dpc. Silencing DUSP9 was found to facilitate the expression of proinflammatory cytokines and COX2 as well as PGE2 secretion in WISH cells, which could be attenuated by p38 inhibitor SB203580 or JNK inhibitor SP600125. Additionally, intraperitoneal injection of pregnant mice with miR-132-3p agomir not only caused preterm birth, but also promoted the abundance of COX2 as well as phosphorylated JNK and p38 levels, and decreased DUSP9 level in mouse amnion membranes. Collectively, miR-132-3p might participate in inflammation and PGE2 release via targeting DUSP9-dependent p38 and JNK signaling pathways to cause preterm birth.

AI-enabled smart pig farming has evolved from isolated sensing applications to integrated systems for health monitoring, precision feeding, welfare assessment, and environmental control. However, evidence remains fragmented across computer vision, sensor engineering, nutritional management, and thermal monitoring, obscuring mature and commercially viable technological pathways. This review evaluates deployment-relevant AI technologies for commercial pig production through structured evidence mapping and critical thematic synthesis. We analyzed 707 publications from the Web of Science Core Collection (WoSCC; 1991–2025) and evaluated candidate themes based on publication activity, citation patterns, temporal persistence, and thematic convergence. Three technical streams were examined in depth: vision-based pig detection, precision feeding, and AI-assisted infrared body-temperature monitoring. Across these areas, research has progressed from proof-of-concept algorithms to integrated sensing-to-decision systems. Vision-based detection is advancing toward robust, lightweight models; precision feeding toward individualized closed-loop control; and thermal monitoring toward automated region-of-interest (ROI) localization and AI-assisted temperature interpretation. Major gaps remain in dataset representativeness, cross-farm generalizability, methodological consistency, field-scale validation, system reliability, economic feasibility, thermal calibration, surface-to-core temperature inference, ROI localization, and false-alarm control. This review is limited by its reliance on a single bibliographic database, predefined search terms, and potential publication and citation biases. Future progress requires cross-site validation, multimodal sensing, interpretable decision models, cost-effective deployment, adaptive thermal calibration, and reliable alert strategies. Overall, AI-enabled smart pig farming is not only an algorithmic challenge but also a systems-integration task that must translate sensing and prediction into actionable farm management.