Biomedical subjects

Rui Mao

Publications and source records attributed to Rui Mao.

4 recordsLinked to original sources

PubMed2024

Construction of a molecular diagnostic system for neurogenic rosacea by combining transcriptome sequencing and machine learning.

Patients with neurogenic rosacea (NR) frequently demonstrate pronounced neurological manifestations, often unresponsive to conventional therapeutic approaches. A molecular-level understanding and diagnosis of this patient cohort could significantly guide clinical interventions. In this study, we amalgamated our sequencing data (n = 46) with a publicly accessible database (n = 38) to perform an unsupervised cluster analysis of the integrated dataset. The eighty-four rosacea patients were partitioned into two distinct clusters. Neurovascular biomarkers were found to be elevated in cluster 1 compared to cluster 2. Pathways in cluster 1 were predominantly involved in neurotransmitter synthesis, transmission, and functionality, whereas cluster 2 pathways were centered on inflammation-related processes. Differential gene expression analysis and WGCNA were employed to delineate the characteristic gene sets of the two clusters. Subsequently, a diagnostic model was constructed from the identified gene sets using linear regression methodologies. The model's C index, comprising genes PNPLA3, CUX2, PLIN2, and HMGCR, achieved a remarkable value of 0.9683, with an area under the curve (AUC) for the training cohort's nomogram of 0.9376. Clinical characteristics from our dataset (n = 46) were assessed by three seasoned dermatologists, forming the NR validation cohort (NR, n = 18; non-neurogenic rosacea, n = 28). Upon application of our model to NR diagnosis, the model's AUC value reached 0.9023. Finally, potential therapeutic candidates for both patient groups were predicted via the Connectivity Map. In summation, this study unveiled two clusters with unique molecular phenotypes within rosacea, leading to the development of a precise diagnostic model instrumental in NR diagnosis.

Humans↗

PubMed2006

Glucagon-like peptide-1 stimulates GABA formation by pancreatic beta-cells at the level of glutamate decarboxylase.

Pancreatic beta-cells are the major extraneural site of glutamate decarboxylase expression (GAD). During culture of isolated beta-cells, the GAD product gamma-aminobutyrate (GABA) is rapidly released in the medium, independently of insulin. It is considered as a possible mediator of beta-cell influences on alpha-cells, acinar cells, and/or infiltrating lymphocytes. In this perspective, we investigated the regulation of GABA release by rat beta-cells during a 24-h culture period. Glucose was previously reported to inhibit GABA release by diverting cellular GABA to mitochondrial breakdown through activation of GABA transferase (GABA-T). In the present study, glucagon-like peptide-1 (GLP-1) was shown to stimulate GABA formation at the level of GAD, its effect being suppressed by the GAD inhibitor allylglycine and remaining unaltered by the GABA-T inhibitor gamma-vinyl-GABA. The stimulatory action of GLP-1 is cAMP dependent, being reproduced by the adenylate cyclase activator forskolin and the cAMP analog N(6)-benzoyladenosine-3',5'-cAMP and inhibited by a PKA inhibitor. It is dependent on protein synthesis and associated with an increased expression of GAD67 but not GAD65. The GLP-1-induced stimulation of GAD activity in beta-cells can elevate medium GABA levels in conditions of glucose-driven intracellular GABA breakdown and thus maintain GABA-mediated beta-cell influences on neighboring cells.

Adenylyl Cyclases↗

PubMed2006

A fast coarse filtering method for peptide identification by mass spectrometry.

MOTIVATION: We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme. RESULTS: We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.

Algorithms↗

PubMed2005

On optimizing distance-based similarity search for biological databases.

Similarity search leveraging distance-based index structures is increasingly being used for both multimedia and biological database applications. We consider distance-based indexing for three important biological data types, protein k-mers with the metric PAM model, DNA k-mers with Hamming distance and peptide fragmentation spectra with a pseudo-metric derived from cosine distance. To date, the primary driver of this research has been multimedia applications, where similarity functions are often Euclidean norms on high dimensional feature vectors. We develop results showing that the character of these biological workloads is different from multimedia workloads. In particular, they are not intrinsically very high dimensional, and deserving different optimization heuristics. Based on MVP-trees, we develop a pivot selection heuristic seeking centers and show it outperforms the most widely used corner seeking heuristic. Similarly, we develop a data partitioning approach sensitive to the actual data distribution in lieu of median splits.

Algorithms↗