Accurate staging of liver fibrosis (LF) is essential for clinical management in chronic liver disease. While non-contrast MRI (NC-MRI) yields valuable information for liver assessment, its effectiveness in predicting LF remains underexplored. This study aimed to develop and validate artificial intelligence (AI)-powered models utilizing NC-MRI for staging LF. A total of 1726 patients from Shengjing Hospital of China Medical University, registered between October 2003 and October 2022, were retrospectively collected, and divided into development (n = 1208) and internal test (n = 518) cohorts. An external test cohort consisting of 337 individuals from six centers, registered between June 2015 and November 2022, were also included. All participants underwent NC-MRI (T1-weighted imaging, T1WI; and T2-fat-suppressed imaging, T2FS) and liver biopsies. Two classification models (CMs), named T1 and T2FS, were trained on respective image types using 3D contextual transformer networks and evaluated on both test cohorts. Additionally, three CMs—Clinic, Image, and Fusion—were developed using clinical features, T1 and T2FS scores, and their integration via logistic regression. Classification effectiveness of CMs was assessed using the area under the receiver operating characteristic curve (AUC). A comparison was conducted between the optimal models (OMs) with highest AUC and other methods (transient elastography, five serum biomarkers, and six radiologists). Fusion models (i.e., OM) yielded the highest AUC among the CMs, achieving AUCs of 0.810 for significant fibrosis, 0.881 for advanced fibrosis, and 0.918 for cirrhosis in the internal test cohort, and 0.808, 0.868, and 0.925, respectively, in the external test cohort. The OMs demonstrated superior performance in AUC, significantly surpassing transient elastography (only for staging ≥ F2 and ≥ F3 grades), serum biomarkers, and three junior radiologists for staging LF. Radiologists, with the aid of the OMs, can achieve a higher AUC in LF assessment. AI-powered models utilizing NC-MRI, including T1WI and T2FS, accurately stage LF. 10.13039/501100001809 National Natural Science Foundation of China (No. 82071885); General Program of the Liaoning Provincial Department of Education (LJKMZ20221160); Liaoning Province Science and Technology Joint Plan (2023JH2/101700127); the Leading Young Talent Program of Xingliao Yingcai in Liaoning Province (XLYC2203037).

Background Human demand for meat and dairy products will increase as a result of economic development and population growth, and the farming of ruminants, such as cattle and sheep, will also increase. Methane (CH₄) emission from the enteric fermentation of ruminant livestock is a major source of greenhouse gas emissions and a significant contributor to global warming. Meanwhile, growth performance is often limited and animals are more vulnerable to diseases in high-density, intensive farming, greatly reducing livestock productivity, so developing ways to reduce CH₄ emissions and improve ruminant productivity has become a research hotspot. Studies have reported that fenugreek (Trigonella foenum-graecum L.) as feed additives have the potential to reduce ruminant methane and improve the productivity. However, systematic reviews of such studies are lacking. Methodology In this review, databases of Google Scholar, Web of Science, PubMed, Scopus and Science Direct were used for the literature search. The initial keywords search was fenugreek or Trigonella foenum-graecum L. For more focused search, we added terms such as methane, rumen fermentation, growth, milk production and antioxidants. All were done for ruminants. The literature that conforms to the theme of this article is selected, summarized, and finally completed this article. Results By regulating the rumen microbiome (suppressing protozoans, methanogenic bacteria, and fungi), fenugreek can lower CH₄ emissions according to many in vitro anaerobic fermentation experiments. Fenugreek secondary metabolites (saponins and tannins) are responsible for this impact, but it is still unclear exactly how they work. Therefore, more long-term in vivo experiments are needed to verify its efficacy. Fenugreek is also rich in alkaloids, amino acids, flavonoids, saponins and phenolic acids. These compounds have been shown to have beneficial effects on ruminant growth, lactation, and total antioxidant capacity. Therefore, fenugreek has a great opportunity to develop into a new green feed additive. Conclusions This review provides a summary of the effect of fenugreek and its bioactive compounds on rumen fermentation, CH₄ emissions and production performance by ruminants. In addition, based on the available data, the possible biochemical pathway of fenugreek to reduce CH₄ emissions in ruminants was described. Overall, the livestock feed industry has the opportunity to develop natural, environmentally-friendly feed additives based on fenugreek.

In the present study, we investigated the function of thyroid hormones (TH) in visual remodeling during Japanese flounder (Paralichthys olivaceus) metamorphosis through cellular molecular biology experiments. Our results showed that the expression of the five opsin genes of the flounder were highest in eye tissue and varied with the metamorphosis process. The expression of rh1, sws2aβ and lws was positively regulated by exogenous TH, but inhibited by thiourea (TU) compared to the control group. In addition, there was a significant increase in sws2aβ and lws in the rescue experiments performed with TU-treated larvae (p < 0.05). Meanwhile, T3 levels in flounder larvae were increased by TH and decreased by TU. Based on the differences in the expression of the three isoforms of the thyroid hormone receptor (TR) (Trαa, Trαb and Trβ), we further hypothesized that T3 may directly or indirectly regulate the expression of sws2aβ through Trαa. This study demonstrates the regulatory role of TH in opsins during flounder metamorphosis and provides a basis for further investigation on the molecular mechanisms underlying the development of the retinal photoreceptor system in flounders.

Grass Carp Reovirus (GCRV) and Aeromonas hydrophila (Ah) are the causative agents of haemorrhagic disease in grass carp. This study aimed to investigate the molecular mechanisms and immune responses at the miRNA, mRNA, and protein levels in grass carp kidney cells (CIK) infected by Grass Carp Reovirus (GCRV, NV) and Aeromonas hydrophilus (Bacteria, NB) to gain insight into their pathogenesis. Within 48 h of infection with Grass Carp Reovirus (GCRV), 99 differentially expressed microRNA (DEMs), 2132 differentially expressed genes (DEGs), and 627 differentially expressed proteins (DEPs) were identified by sequencing; a total of 92 DEMs, 3162 DEGs, and 712 DEPs were identified within 48 h of infection with Aeromonas hydrophila. It is worth noting that most of the DEGs in the NV group were primarily involved in cellular processes, while most of the DEGs in the NB group were associated with metabolic pathways based on KEGG enrichment analysis. This study revealed that the mechanism of a grass carp haemorrhage caused by GCRV infection differs from that caused by the Aeromonas hydrophila infection. An important miRNA–mRNA–protein regulatory network was established based on comprehensive transcriptome and proteome analysis. Furthermore, 14 DEGs and 6 DEMs were randomly selected for the verification of RNA/small RNA-seq data by RT-qPCR. Our study not only contributes to the understanding of the pathogenesis of grass carp CIK cells infected with GCRV and Aeromonas hydrophila, but also serves as a significant reference value for other aquatic animal haemorrhagic diseases.