Publications by authors named "Y Liu"

148,201 Publications

Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.

Brain 2022 Aug 12. Epub 2022 Aug 12.

IRCCS Istituto Giannina Gaslini, Genova 16147, Italy.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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http://dx.doi.org/10.1093/brain/awac224DOI Listing
August 2022

Single-cell RNA-sequencing analysis and characterisation of testicular cells in giant panda (Ailuropoda melanoleuca).

Reprod Fertil Dev 2022 Aug 12. Epub 2022 Aug 12.

Context: The giant panda (Ailuropoda melanoleuca) is a rare and endangered species to be preserved in China. The giant panda has a low reproductive capacity, and due to the scarcity of samples, studies on testes from giant panda are very limited, with little knowledge about the process of spermatogenesis in this species.

Aims: To establish the gene expression profiles in cells from the testis of a giant panda.

Methods: The 10×Genomics single-cell RNA-sequencing platform was applied to cells from the testis of an adult giant panda.

Key Results: We identified eight testicular cell types including six somatic and two germ cell types from our single-cell RNA-sequencing datasets. We also identified the differentially expressed genes (DEGs) in each cell type, and performed functional enrichment analysis for the identified testicular cell types. Furthermore, by immunohistochemistry we explored the protein localisation patterns of several marker genes in testes from giant panda.

Conclusions: Our study has for the first time established the gene expression profiles in cells from the testis of a giant panda.

Implications: Our data provide a reference catalogue for spermatogenesis and testicular cells in the giant panda, laying the foundation for future breeding and preservation of this endangered species.
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http://dx.doi.org/10.1071/RD22039DOI Listing
August 2022

Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information.

J Agric Food Chem 2022 Aug 11. Epub 2022 Aug 11.

National Engineering Laboratory for Animal Breeding, Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.

Fat deposition in pigs is not only closely related to pig production efficiency and pork quality but also an ideal model for human obesity. Transcriptome sequencing is widely used to study fat deposition. However, due to small sample sizes, high false positive rates, and poor consistency of results from different studies, new strategies are urgently needed. Machine learning, a new analysis method, can effectively fit complex data and accurately identify samples and genes. In this study, 36 samples of adipose tissue, muscle tissue, and liver tissue were collected from Songliao black pigs and Landrace pigs, and the mRNA of all the samples was sequenced. In addition, we collected transcriptome data for 64 samples in the GEO database from four different sources. After standardization and imputation of missing values in the data set comprising 100 samples, traditional differential expression analysis was carried out, and different numbers of expressed genes were selected as features for the training model of eight machine learning methods. In the 1000 replications of fourfold cross validation with 100 samples, AdaBoost performed best, with an average prediction accuracy greater than 93% and the highest mean area under the curve in predicting the high- and low-fat content groups among the eight ML methods. According to their performance-based ranks inferred by AdaBoost, 12 genes related to fat deposition were identified; among them, and were specifically expressed in adipose tissue, and was specifically expressed in the liver, which could be important candidate biomarkers affecting fat deposition.
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http://dx.doi.org/10.1021/acs.jafc.2c03339DOI Listing
August 2022

HAO1 negatively regulates liver macrophage activation via the NF-κB pathway in alcohol-associated liver disease.

Cell Signal 2022 Aug 8:110436. Epub 2022 Aug 8.

Department of Geriatrics, The First Affiliated Hospital, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China. Electronic address:

Inflammation is a key factor contributing to the progression of alcohol-associated liver disease (ALD). Accumulating data have shown that ethyl alcohol (EtOH) induced liver macrophages activation along with an inflammatory response that contributes to the development of ALD. The liver-specific peroxisomal enzyme hydroxyacid oxidase 1 (HAO1) has been found to be associated with chronic liver disease. But the role of HAO1 remains unknown in ALD. In our study, HAO1 was found to be decreased in ALD patients and EtOH-fed mice. Interestingly, HAO1 expression was reduced in primary hepatocytes, whereas HAO1 was elevated in peripheral blood monocytes from ALD patients and EtOH-fed mice liver macrophages as well as LPS-treated RAW264.7 cells. Moreover, HAO1 knockdown exacerbated the inflammatory response, while HAO1 overexpression inhibited inflammation in LPS-stimulated RAW264.7 cells. Additionally, overexpression or silencing of HAO1 in vitro significantly affected NF-κB signaling pathway. Collectively, the results revealed a key role of HAO1-mediated macrophage activation and may provide a potential target for treating ALD.
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http://dx.doi.org/10.1016/j.cellsig.2022.110436DOI Listing
August 2022

Glia maturation factor-β supports liver regeneration by remodeling actin network to enhance STAT3 proliferative signals.

Cell Mol Gastroenterol Hepatol 2022 Aug 8. Epub 2022 Aug 8.

Biomedical Research Center, Southern Medical University, Guangzhou, China; Department of Hepatology, Southern Medical University Affiliated Shenzhen Hospital, Shenzhen, China. Electronic address:

Background & Aims: Glia maturation factor-β (GMFB) is a bona fide member of actin depolymerizing factor homology family. Recently, emerging evidence suggests its implication in liver diseases. But data on its role in liver remain limited.

Methods: Assessment of GMFB in liver histology, impact on liver regeneration and hepatocytes proliferation, and the underlying molecular pathways were conducted using mice models with acute liver injury.

Results: GMFB is widely distributed in normal liver. Its expression increases within 24h following partial hepatectomy (PHx). Adult Gmfb knockout (GKO) mice and wild-type littermates are similar in gross appearance, body weight, liver function and histology. However, compared with wild-type control, GKO mice post-PHx develop more serious liver damage and steatosis and have the delayed liver regeneration; the dominant change in liver transcriptome at 24h post-PHx is the significantly suppressed acute inflammation pathways; the top downregulated gene-sets relate to IL6/JAK/STAT3 signaling. Another mice model intoxicated with carbon tetrachloride replicates the findings. Furthermore, GKO and wild-type groups have the similar numbers of Kupffer cells, but GKO Kupffer cells once stimulated produce less IL6, TNF, and IL1β. In hepatocytes treated with IL6, GMFB positively associates with cell proliferation and STAT3/CyclinD1 activation, but without any direct interaction with STAT3. In GKO hepatocytes, cytoskeleton-related genes expression is significantly changed, appearing abnormal morphology of actin-networks. In hepatocytes modelling actin-filaments turnover, STAT3 activation and metabolite excretion show strong reliance on the status of actin-filaments organization.

Conclusions: GMFB plays a significant role in liver regeneration by promoting acute inflammatory response in Kupffer cells and by intracellularly coordinating the responsive hepatocytes proliferation.
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http://dx.doi.org/10.1016/j.jcmgh.2022.07.016DOI Listing
August 2022
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