Biomedical subjects

Zhenqiu Liu

Publications and source records attributed to Zhenqiu Liu.

10 recordsLinked to original sources

PubMed2026

Large-Scale Plasma Proteomics Reveals Preclinical Biomarkers of Incident Severe Liver Disease.

The absence of robust biomarkers for early detection of severe liver disease (SLD) highlights the critical need for high-throughput proteomics-driven discovery. In this prospective cohort study, we aimed to identify plasma protein signatures associated with incident SLD and assess their clinical utility. Using the large-scale Olink Explore 1536 platform, we quantified 1461 plasma proteins in 46951 participants from the UK Biobank community-based cohort without baseline liver disease. Over a median follow-up of 14.1 years, we identified 490 proteins significantly associated with incident SLD risk. Growth differentiation factor 15 (GDF15) emerged as the strongest predictor, achieving a C-index of 0.80 and outperforming conventional clinical indices (LiverRisk score: 0.75; FIB-4: 0.68; APRI: 0.68). Temporal trajectories revealed that GDF15 levels began increasing up to 10 years before diagnosis, with progressive elevation as the diagnosis timepoint approached. Mendelian randomization analysis supported genetic associations linking higher protein levels of GDF15, FABP1, SPON2, CHI3L1, and PIGR with SLD risk. Our large-scale proteome-wide study not only reveals significant proteomic changes preceding SLD diagnosis but also establishes GDF15 as both a promising preclinical biomarker, opening new avenues for early intervention in at-risk individuals.

Humans↗

PubMed2017

Genome-wide analysis suggests a differential microRNA signature associated with normal and diabetic human corneal limbus.

Small non-coding RNAs, in particular microRNAs (miRNAs), regulate fine-tuning of gene expression and can impact a wide range of biological processes. However, their roles in normal and diseased limbal epithelial stem cells (LESC) remain unknown. Using deep sequencing analysis, we investigated miRNA expression profiles in central and limbal regions of normal and diabetic human corneas. We identified differentially expressed miRNAs in limbus vs. central cornea in normal and diabetic (DM) corneas including both type 1 (T1DM/IDDM) and type 2 (T2DM/NIDDM) diabetes. Some miRNAs such as miR-10b that was upregulated in limbus vs. central cornea and in diabetic vs. normal limbus also showed significant increase in T1DM vs. T2DM limbus. Overexpression of miR-10b increased Ki-67 staining in human organ-cultured corneas and proliferation rate in cultured corneal epithelial cells. MiR-10b transfected human organ-cultured corneas showed downregulation of PAX6 and DKK1 and upregulation of keratin 17 protein expression levels. In summary, we report for the first time differential miRNA signatures of T1DM and T2DM corneal limbus harboring LESC and show that miR-10b could be involved in the LESC maintenance and/or their early differentiation. Furthermore, miR-10b upregulation may be an important mechanism of corneal diabetic alterations especially in the T1DM patients.

Adult↗

PubMed2006

A method for automatic identification of reliable heart rates calculated from ECG and PPG waveforms.

OBJECTIVE: The development and application of data-driven decision-support systems for medical triage, diagnostics, and prognostics pose special requirements on physiologic data. In particular, that data are reliable in order to produce meaningful results. The authors describe a method that automatically estimates the reliability of reference heart rates (HRr) derived from electrocardiogram (ECG) waveforms and photoplethysmogram (PPG) waveforms recorded by vital-signs monitors. The reliability is quantitatively expressed through a quality index (QI) for each HRr. DESIGN: The proposed method estimates the reliability of heart rates from vital-signs monitors by (1) assessing the quality of the ECG and PPG waveforms, (2) separately computing heart rates from these waveforms, and (3) concisely combining this information into a QI that considers the physical redundancy of the signal sources and independence of heart rate calculations. The assessment of the waveforms is performed by a Support Vector Machine classifier and the independent computation of heart rate from the waveforms is performed by an adaptive peak identification technique, termed ADAPIT, which is designed to filter out motion-induced noise. RESULTS: The authors evaluated the method against 158 randomly selected data samples of trauma patients collected during helicopter transport, each sample consisting of 7-second ECG and PPG waveform segments and their associated HRr. They compared the results of the algorithm against manual analysis performed by human experts and found that in 92% of the cases, the algorithm either matches or is more conservative than the human's QI qualification. In the remaining 8% of the cases, the algorithm infers a less conservative QI, though in most cases this was because of algorithm/human disagreement over ambiguous waveform quality. If these ambiguous waveforms were relabeled, the misclassification rate would drop from 8% to 3%. CONCLUSION: This method provides a robust approach for automatically assessing the reliability of large quantities of heart rate data and the waveforms from which they are derived.

Algorithms↗

PubMed2006

A novel multiple FISH array for the detection of genetic aberrations in cancer.

Interphase multicolor fluorescence in situ hybridization (IM-FISH) has great promise for improving cancer diagnosis because it can directly visualize multiple changes in chromosomes and gene copy number on a cell-to-cell basis. However, no more than four targets can be detected simultaneously by current commercially available IM-FISH protocols, and the DNA probes used are too large to detect the single-gene aberrations that characterize tumorigenesis. As a result, multiple FISH has a low sensitivity in detecting cancer cells. To overcome such limitations, we first developed specific genomic probes for the genes relevant to primary lung cancer. We next designed a multiple FISH array by arranging four different compositions of cocktails of four probes for each gene on a coverslip, which allowed four four-color FISH experiments to be performed in parallel on a single slide. We then tested the multiple FISH array on bronchial brushing samples from lung cancer patients to determine its ability to detect genetic abnormalities. A comparison of the data with the results of cytology and commercial four-color FISH suggested that the multiple FISH array had the highest sensitivity for cancer detection. The technique may thus be a powerful laboratory strategy for cancer prevention and early detection and for improved patient management.

Bronchoalveolar Lavage Fluid↗

PubMed2006

Genome-wide tagging SNPs with entropy-based Monte Carlo method.

The number of common single nucleotide polymorphisms (SNPs) in the human genome is estimated to be around 3-6 million. It is highly anticipated that the study of SNPs will help provide a means for elucidating the genetic component of complex diseases and variable drug responses. High-throughput technologies such as oligonucleotide arrays have produced enormous amount of SNP data, which creates great challenges in genome-wide disease linkage and association studies. In this paper, we present an adaptation of the cross entropy (CE) method and propose an iterative CE Monte Carlo (CEMC) algorithm for tagging SNP selection. This differs from most of SNP selection algorithms in the literature in that our method is independent of the notion of haplotype block. Thus, the method is applicable to whole genome SNP selection without prior knowledge of block boundaries. We applied this block-free algorithm to three large datasets (two simulated and one real) that are in the order of thousands of SNPs. The successful applications to these large scale datasets demonstrate that CEMC is computationally feasible for whole genome SNP selection. Furthermore, the results show that CEMC is significantly better than random selection, and it also outperformed another block-free selection algorithm for the dataset considered.

Algorithms↗

PubMed2005

Comparisons of methods for linkage analysis and haplotype reconstruction using extended pedigree data.

We compare and contrast the performance of SIMPLE, a Monte Carlo based software, with that of several other methods for linkage and haplotype analyses, focusing on the simulated data from the New York City population. First, a whole-genome scan study based on the microsatellite markers was performed using GENEHUNTER. Because GENEHUNTER had to drop individuals for many of the pedigrees, we performed a follow-up study focusing on several regions of interest using SIMPLE, which can handle all pedigrees in their entirety. Second, 3 haplotyping programs, including that in SIMPLE, were used to reconstruct haplotypic configurations in pedigrees. SIMPLE emerges clearly as a preferred tool, as it can handle large pedigrees and produces haplotypic configurations without double recombinant haplotypes. For this study, we had knowledge of the simulating models at the time we performed the analysis.

Chromosome Mapping↗

PubMed2005

Selecting genes by test statistics.

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.

Journal Article↗

PubMed2005

Gene expression data classification with Kernel principal component analysis.

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Journal Article↗

PubMed2005

Multilocus LD measure and tagging SNP selection with generalized mutual information.

Linkage disequilibrium (LD) plays a central role in fine mapping of disease genes and, more recently, in characterizing haplotype blocks. Classical LD measures, such as D' and r(2), are frequently used to quantify relationship between two loci. A pairwise "distance" matrix among a set of loci can be constructed using such a measure, and based upon which a number of haplotype block detection and tagging single nucleotide polymorphism (SNP) selection algorithms have been devised. Although successful in many applications, the pairwise nature of these measures does not provide a direct characterization of joint linkage disequilibrium among multiple loci. Consequently, applications based on them may lead to loss of important information. In this report, we propose a multilocus LD measure based on generalized mutual information, which is also known as relative entropy or Kullback-Leibler distance. In essence, this measure seeks to quantify the distance between the observed haplotype distribution and the expected distribution assuming linkage equilibrium. We can show that this measure is approximately equal to r(2) in the special case with two loci. Based on this multilocus LD measure and an entropy measure that characterizes haplotype diversity, we propose a class of stepwise tagging SNP selection algorithms. This represents a unified approach for SNP selection in that it takes into account both the haplotype diversity and linkage disequilibrium objectives. Applications to both simulated and real data demonstrate the utility of the proposed methods for handling a large number of SNPs. The results indicate that multilocus LD patterns can be captured well, and informative and nonredundant SNPs can be selected effectively from a large set of loci.

Algorithms↗

PubMed2005

Clustering gene expression data with kernel principal components.

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

Algorithms↗