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Zheng Guo

Publications and source records attributed to Zheng Guo.

At least 19 recordsLinked to original sources
PubMed2026

Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.

Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.

Humans↗
PubMed2006

Characterization of the chemical composition of lotus plumule oil.

Characterizations of lotus plumule and plumule oil, focusing on approximate composition analysis of lotus plumule powder and fatty acid composition, lipid classes, triglyceride (TG) profiles, and sterol analysis of the plumule oil, were conducted in this work. The results revealed that the lotus plumule constitutes 7.8% moisture, 4.2% ash, and 12.5% crude oil and 26.3% protein on the dry base. Lotus plumule oil is rich in linoleic acid (50.4%) and oleic acid (13.5%), and the dominating saturated fatty acids are palmitic acid (18.0%) and behenic acid (6.8%). The principal components of TG in lotus plumule oil are LLL (12.80%), beta-PLL (11.27%), beta-POL (8.28%), beta-PLO (8.58%), and beta-BeLL (8.32%). Lipid class assay of the crude oil gave the saponification value of 153.4 KOH mg/g and tocopherol content 390 mg/100 g. A distinct characteristic of lotus plumule oil is that its unsaponifiable matter is incredibly high, up to 14-19%, which consists mainly of beta-sitosterol (32%), Delta(5)-avenasterol (20%), and campesterol (6.3%). The major occurring form of sterols was found to be steryl ester. This work might be useful to develop innovative applications of lotus plumule oil.

Chemical Phenomena↗
PubMed2006

Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules.

MOTIVATION: Microarrays datasets frequently contain a large number of missing values (MVs), which need to be estimated and replaced for subsequent data mining. The focus of the paper is to study the effects of different MV treatments for cDNA microarray data on disease classification analysis. RESULTS: By analyzing five datasets, we demonstrate that among three kinds of classifiers evaluated in this study, support vector machine (SVM) classifiers are robust to varied MV imputation methods [e.g. replacing MVs by zero, K nearest-neighbor (KNN) imputation algorithm, local least square imputation and Bayesian principal component analysis], while the classification and regression tree classifiers are sensitive in terms of classification accuracy. The KNNclassifiers built on differentially expressed genes (DEGs) are robust to the varied MV treatments, but the performances of the KNN classifiers based on all measured genes can be significantly deteriorated when imputing MVs for genes with larger missing rate (MR) (e.g. MR > 5%). Generally, while replacing MVs by zero performs relatively poor, the other imputation algorithms have little difference in affecting classification performances of the SVM or KNN classifiers. We further demonstrate the power and feasibility of our recently proposed functional expression profile (FEP) approach as means to handle microarray data with MVs. The FEPs, which are derived from the functional modules that are enriched with sets of DEGs and thus can be consistently identified under varied MV treatments, achieve precise disease classification with better biological interpretation. We conclude that the choice of MV treatments should be determined in context of the later approaches used for disease classification. The suggested exclusion criterion of ignoring the genes with larger MR (e.g. >5%), while justifiable for some classifiers such as KNN classifiers, might not be considered as a general rule for all classifiers.

Algorithms↗
PubMed2006

Functional dependency of structures of ionic liquids: do substituents govern the selectivity of enzymatic glycerolysis?

The concept of regulating the preference of a reversible multi-step reaction by adjusting the substituents of ionic liquids (ILs) has been successfully exemplified with a group of tetraammonium-based ionic liquids as medium for the enzymatic glycerolysis. Simultaneous existence of long chain hydrophobic substituents and hydrophilic ethoxyl or hydroxyl moieties is found, respectively, to be essential for triglycerides (TG) dissolving and equilibrium shifting. The reactions in the ILs with cations consisting of long chain and free hydroxyl groups gave markedly higher conversion of TG and better preference to monoglyceride formation. Interestingly the predicted results from COSMO-RS (a quantum chemical model programme) achieved a good agreement with the experimental data, mapping out the specific solvation from the ILs as well as demonstrating the interaction between ILs, substrates and products being the intrinsic causes that govern reaction evolution and direct equilibrium shifting.

Glycerol↗
PubMed2006

Mechanism and regioselectivity for the reactions of propylene oxide with X(100)-2x1 surfaces (X = C, Si, Ge): a density functional cluster model investigation.

We have performed density functional cluster model calculations to explore the mechanism and regioselectivity for the reactions of propylene oxide with X(100)-2x1 surfaces (X = C, Si, and Ge). The computations reveal the following: (i) the reactions on Si(100) and Ge(100) are barrierless and highly exothermic; (ii) the reactions on X(100) (X = Si and Ge) are initiated by the formation of a dative-bonded precursor state followed by regioselective cleavage of the C2-O bond (C2 directly connected to the methyl-substituent) in propylene oxide, giving rise to a five-membered ring surface species; and (iii) the reaction on C(100), although highly exothermic, requires a large activation energy and would be kinetically forbidden at room temperature.

Epoxy Compounds↗
PubMed2006

Decision forest analysis of large-scale sib-pair identical-by-decent profiles for locating the underlying disease genes for alcoholism in human.

OBJECTIVE: To extract the relevant SNPs for alcoholism using sib-pair IBD profiles of pedigrees. METHODS: We used the ensemble decision approach, a supervised learning approach based on decision forests, to locate alcoholism relevant SNPs using genome-wide SNP data. RESULTS: Application to a publicly available large dataset of 100 simulated replicates for three American populations (http://www.gaworkshop.org/) demonstrates that the proposed approach has successfully located all of the simulated true loci. CONCLUSION: The numerical results establish the proposed decision forest analysis to be a powerful and practical alternative for large-scale family-based association study.

Alcoholism↗
PubMed2006

Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.

BACKGROUND: It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. RESULTS: In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. CONCLUSION: We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.

Algorithms↗
PubMed2006

A novel model-free approach for reconstruction of time-delayed gene regulatory networks.

Reconstruction of genetic networks is one of the key scientific challenges in functional genomics. This paper describes a novel approach for addressing the regulatory dependencies between genes whose activities can be delayed by multiple units of time. The aim of the proposed approach termed TdGRN (time-delayed gene regulatory networking) is to reversely engineer the dynamic mechanisms of gene regulations, which is realized by identifying the time-delayed gene regulations through supervised decision-tree analysis of the newly designed time-delayed gene expression matrix, derived from the original time-series microarray data. A permutation technique is used to determine the statistical classification threshold of a tree, from which a gene regulatory rule(s) is extracted. The proposed TdGRN is a model-free approach that attempts to learn the underlying regulatory rules without relying on any model assumptions. Compared with model-based approaches, it has several significant advantages: it requires neither any arbitrary threshold for discretization of gene transcriptional values nor the definition of the number of regulators (k). We have applied this novel method to the publicly available data for budding yeast cell cycling. The numerical results demonstrate that most of the identified time-delayed gene regulations have current biological knowledge supports.

Algorithms↗
PubMed2006

[Analysis of diffuse large B-cell lymphoma heterogeneity based on coupled two-way clustering].

Microarray technology has proposed a powerful tool in dealing with the heterogeneity of disease. Currently, many methods in the field are based on traditional hierarchical clustering to discover subtypes of disease using a large number of genes on microarray.However, they did not considered that large unrelated noise (genes)may mask significant partitions and correlations of disease samples. To avoid the shortcoming, this paper presented a heterogeneous analysis based on coupled two-way clustering (HCTWC) to search interesting gene signature and find the natural partitions of disease samples. The method was applied to diffuse large B-cell lymphoma (DLBCL) microarray dataset. By identifying significant gene signature, we were able to discover the two new subtypes of DLBCL with survival rate 55% and 25% respectively. The results showed that HCTWC had the potential to be a powerful tool for solving the heterogeneity of disease on gene expression profile.

Cluster Analysis↗
PubMed2006

[Changes in substance P in myocardium and dorsal root ganglion following coronary artery occlusion in rats].

OBJECTIVE: To observe the changes in substance P in myocardium and dorsal root ganglion (DRG) following ligation of the left anterior descending branch of coronary artery. The substance P is commonly considered released mainly from afferent nerve fibers under noxious stimulation and could induce neurogenic inflammation in peripheral tissue. METHODS: Twenty-four adult male Sprague-Dawley rats, weighing 270-300 g, were randomly divided into four groups: sham-operated group, coronary artery occlusion (CAO) 1 hour group, CAO 3 hours group and CAO 6 hours group, and 6 animals were in each group. The left anterior descending branch of coronary artery was occluded to establish the acute myocardial ischemia model except in the sham-operated group. The myocardium in ischemic and non-ischemic (the contralateral side of ischemic myocardium) region and DRG were harvested as scheduled and processed for immunohistochemistry and for substance P mRNA semi-quantitative examination using reverse transcription-polymerase chain reaction (RT-PCR) technique. RESULTS: (1) substance P and substance P mRNA levels in myocardium of ischemic areas and myocardium of non-ischemic areas were significantly elevated after CAO compared with sham-operated group (all P<0.05), and they were highest in rats of CAO 3 hours group (all P<0.05). In the myocardium of non-ischemic areas, substance P and substance P mRNA levels were lower than those in myocardium of ischemic areas in CAO groups (all P<0.05). (2) substance P mRNA levels in DRG were significantly elevated after CAO compared with sham-operated group (all P<0.05), and were highest in rats of CAO 3 hours group (both P<0.05). CONCLUSION: CAO could evoke an increase in substance P in entire myocardium and substance P mRNA expression in DRG in rats, and the results might imply that neurogenic mechanism participates in the pathophysiological changes following myocardial ischemia.

Animals↗
PubMed2005

New opportunity for enzymatic modification of fats and oils with industrial potentials.

Novozym 435 (Candida antarctica lipase)-catalyzed glycerolysis of commercial oils and fats to produce monoglycerides (MGs) was investigated using a tetraammonium-based ionic liquid (IL) as a reaction medium. A 90% yield of MGs and nearly 100% conversion of triglycerides in this ionic liquid were achieved, markedly higher than in normal solvents. The amphiphilic structure of cocosalkyl pentaethoxi methyl ammonium methosulfate (CPMA.MS) was suggested to be capable of creating a compatible system for glycerol, oils and fats, as well as inducing the shift of reaction equilibrium to the formation of MG. Interestingly, over a wide range of solvent dosage, the higher yields of MG were observed, indicating the good bulky substrate-tolerating capacity of the IL. The universal validity of the protocol was verified by being successfully applied to different commercial oils and fats. Excellent operational stability of the lipase and the reusability of IL were also observed in consecutive batchwise reactions. The results indicate that the protocol developed in this work provides a new environmentally benign "solution" to the enzymatic modification of fats and oils with industrial potentials.

Enzymes, Immobilized↗
PubMed2005

Broadly predicting specific gene functions with expression similarity and taxonomy similarity.

Previous studies on computational gene functional prediction have not fully exploited the taxonomy structure of Gene Ontology (GO). They just select a few classes from GO into a set, and conduct classwise learning of these classes. The pre-selection of learning classes, often done according to the annotation sizes, limits the prediction breadth and depth. This way of pre-selecting learning classes ignores the taxonomy relations among classes, and so wastes the valuable functional knowledge encoded in the DAG structure of GO. This paper proposes GESTS, a novel gene functional prediction approach based on both gene expression similarity and GO taxonomy similarity, which circumvents the problem of arbitrary learning class pre-selection. GESTS is a semi-supervised approach that reasonably and efficiently incorporates the ontology-formed gene functional knowledge into automated functional analyses of local gene clustering. By integrating both expression similarity and taxonomy similarity into the learning process, GESTS achieves better prediction breadth, depth, and precision than previous studies on the fibroblast serum response dataset and the yeast expression dataset.

Computational Biology↗
PubMed2005

Towards precise classification of cancers based on robust gene functional expression profiles.

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Algorithms↗
PubMed2005

A reduction of endogenous asymmetric dimethylarginine contributes to the effect of captopril on endothelial dysfunction induced by homocysteine in rats.

We examined whether captopril exerts beneficial effect on homocysteine-induced endothelial dysfunction in vivo and whether this effect of captopril is associated with a reduction of endogenous inhibitor of nitric oxide synthase (NOS) asymmetric dimethylarginine (ADMA) in rats. Male Sprague-Dawley rats were given intravenous injections of homocysteine (10 mg/kg/day) to induce endothelial dysfunction. Captopril treatment (3 mg/kg/day, i.v.) was taken in some rats after homocysteine administration. Endothelium-dependent relaxation was tested in aortic rings. Serum levels of ADMA, nitrite/nitrate, malondialdehyde (MDA), and creatinine were measured. Furthermore, superoxide dismutase activity in liver and angiotensin converting enzyme activity in serum were also assayed. Administration of homocysteine to rats for 4 weeks significantly impaired endothelium-dependent relaxation compared with control rats. This impairment of endothelium-dependent relaxation was accompanied by elevated serum concentration of ADMA and decreased serum content of nitrite/nitrate. Moreover, serum concentration of MDA was remarkably increased, whereas liver superoxide dismutase activity was decreased in homocysteine-treated group compared with control. Chronic captopril treatment not only improved the impaired endothelium-dependent relaxation, but also prevented the elevation of serum ADMA and MDA levels, as well as reduction of serum nitrite/nitrate contents and liver superoxide dismutase activity. Serum angiotensin converting enzyme activity and creatinine had no significant difference between the three groups. These results suggest that chronic captopril treatment reduces endogenous inhibitor of NOS in rats with homocysteine injection, which may contribute to the beneficial effect of captopril on homocysteine-induced endothelial dysfunction in vivo, and may be secondary to the antioxidative action of captopril.

Acetylcholine↗
PubMed2005

Enzymatic modification of phospholipids for functional applications and human nutrition.

Rapid progress in biochemistry of phospholipids and evolution of modern bioengineering has brought forth a number of novel concepts and technical advancements in the modification of phospholipids for industrial applications and human nutrition. Highlights cover preparation of novel phospholipid analogs based on the latest understanding of pivotal role of phospholipids in manifold biological processes, exploration of remarkable application potentials of phospholipids in meliorating human health, as well as development of new chemical and biotechnological approaches applied to the modification of phospholipids. This work reviews the natural occurrence and structural characteristics of phospholipids, their updated knowledge on manifold biological and nutritional functions, traditional and novel physical and chemical approaches to modify phospholipids as well as their applications to obtain novel phospholipids, and brief introduction of the efforts focusing on de novo syntheses of phospholipids. Special attention is given to the summary of molecular structural characteristics and catalytic properties of multiple phospholipases, which helps to interpret experimental phenomena and to improve reaction design. This will of course provide fundamental bases also for the development of enzymatic technology to produce structured or modified phospholipids.

Food Analysis↗
PubMed2005

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset.

Development of a robust and efficient approach for extracting useful information from microarray data continues to be a significant and challenging task. Microarray data are characterized by a high dimension, high signal-to-noise ratio, and high correlations between genes, but with a relatively small sample size. Current methods for dimensional reduction can further be improved for the scenario of the presence of a single (or a few) high influential gene(s) in which its effect in the feature subset would prohibit inclusion of other important genes. We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine. The major goal of this hybridization was to exploit fully their respective merits (e.g., robustness to the size of solution space and capability of handling a very large dimension of feature genes) for identification of key feature genes (or molecular signatures) for a complex biological phenotype. We have applied the approach to the microarray data of diffuse large B cell lymphoma to demonstrate its behaviors and properties for mining the high-dimension data of genome-wide gene expression profiles. The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between genetic algorithm and K nearest neighbors.

Algorithms↗
PubMed2005

[Research on pattern classification methods using gene expression data].

One of the applications of cDNA microarrays is to recognize the class and subclass of diseases such as cancers on the basis of statistical pattern classification methods using gene expression data. In this paper, we apply 2000 genes expression dataset provided by Affymatrix Company: 40 samples of intestine cancer tissue and 22 samples of normal tissue. We compare the performance of four pattern classification methods based on different feature selection methods. These pattern classification methods include: Fisher linear discriminate, Logit nonlinear discriminate, the least distance and K-nearest neighbor classifier. The results show firstly that four pattern classifiers based on the feature selection methods of t-test and classification tree all have better performance than those based on the stochastic feature selection methods, secondly that K-nearest neighbor classifier has the best performance, thirdly that both the least distance classifier and K-nearest neighbor classifier have better generalization, fourthly that four classifiers are less sensitive to the composition of samples.

Gene Expression↗