Biomedical subjects

Ling Guan

Publications and source records attributed to Ling Guan.

5 recordsLinked to original sources

PubMed2026

Integrated single-cell transcriptomics, Mendelian randomization, and machine learning identify CEBPZ as an immune-related biomarker in oral lichen planus.

BACKGROUND: Oral lichen planus (OLP) is a chronic, immune-mediated oral mucosal disease with complex pathophysiology and potential for malignant transformation. Understanding its molecular basis is critical for the development of precise diagnostic and therapeutic strategies. OBJECTIVES: We aimed to identify key immune-related biomarkers and characterize cellular dynamics in OLP, with a particular focus on the role of CEBPZ in disease pathogenesis. MATERIAL AND METHODS: We analyzed single-cell RNA sequencing (scRNA-seq) data from OLP lamina propria samples (GSE211630) to identify disease-specific T-cell subpopulations using high-dimensional weighted gene co-expression network analysis (hdWGCNA) for oxidative stress-related gene modules.-data-based Mendelian randomization (SMR) integrated FinnGen genome-wide association study (GWAS; 342,499 Europeans) data with Genotype-Tissue Expression (GTEx) expression quantitative trait loci (eQTL) data to identify causal genes. Machine learning (ML) models (least absolute shrinkage and selection operator (LASSO) and convolutional neural network (CNN)) were developed using bulk RNA-seq datasets (GSE52130 and GSE38616) for diagnostic purposes. RESULTS: We identified OLP-specific T-cell populations (clusters 0, 3, 5, 7, 13, and 15) with enhanced migration inhibition factor (MIF) pathway signaling toward B cells and monocytes. Two oxidative stress-associated modules contained hub genes, including CEBPZ. Summary-data-based Mendelian randomization analysis identified 231 OLP-associated genes, with CEBPZ uniquely intersecting LASSO-selected markers (odds ratio (OR) = 1.057, 95% confidence interval (95% CI) = 1.013-1.102, p = 0.010). Machine learning models achieved area under the curve (AUC) values ranging from 0.653 to 0.745, with the CNN model reaching a validation accuracy of 0.735. CEBPZ showed elevated expression in OLP T cells and correlated with enhanced MIF-(CD74+CXCR4) signaling. CONCLUSIONS: This integrative approach identifies CEBPZ as a pivotal biomarker linking genetic susceptibility, oxidative stress, and immune dysregulation in OLP. Our diagnostic models offer promising tools for OLP management.

CEBPZ↗

PubMed2006

[Essences of Mr. Xie Xi-liang's acupuncture experience].

Mr. Xie Xi-liang studied acupuncture from Mr. Cheng Dan-an in his early years. He has been engaged in clinical and teaching work of acupuncture and moxibustion for near 60 years. He attaches importance to basic training of acupuncture and moxibustion, advocates carefully choice of acupoints clinically, lays stress on using acupuncture and moxibustion to treat diseases, especially using direct moxibustion. He is good at deeply needing Fengfu (GV 16) to treat mental diseases. He has very rich knowledge and experience, and he deserves to be studied for younger generation.

Acupuncture Points↗

PubMed2004

Retrieval for color artistry concepts.

This paper presents a work on the retrieval of artworks for color artistry concepts. First we affirm the view that the Query-by-Example paradigm fundamental to the current content-based retrieval systems is able to extend only limited usefulness. We then propose a concept-based retrieval engine based on the generative grammar of elemental concepts methodology. In the latter, the language by which color artistry concepts are communicated in artworks is used to operate semantic searches. The color artistry language is explicated into elemental concepts and the associated generative grammar. The elemental concepts are used to index the artworks, while the generative grammar is used to facilitate post-coordinate expression of color artistry concept queries by using the elemental concepts.

Abstracting and Indexing↗

PubMed1998

A combined evolution method for associative memory networks.

In the study of associative memory networks, the updating rule remains unchanged during neuron evolution. When evolution stops at an undesired minimum, simulated annealing is used to resolve the problem. In this paper, a combined neuron evolution method based on a multipath network architecture is presented. It is shown that, by controlling the evolution path, the probability that evolution terminates in undesired, minima is significantly reduced. Once evolution is trapped in an undesired minimum with respect to one path, the method seeks an alternative path to carry on the evolution. The process continues until a desired minimum is reached. Visual examples are used to demonstrate the performance of the method.

Journal Article↗

PubMedDate not supplied

Refining competition in the self-organising tree map for unsupervised biofilm image segmentation.

The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that the efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents.

Algorithms↗