Traditional Chinese medicine-based drug delivery systems for anti-tumor therapies.

The treatment of tumors continues to be significantly challenging. The presence of multiple modalities, including surgery, radiation, chemotherapy and immunotherapy, the therapeutic outcomes remain limited and are often associated with adverse effects and inconsistent efficacy across cancer types. Recent studies have highlighted the potential of active components from traditional Chinese medicine (TCM) for their anti-cancer properties, which are attributable to multi-targeted mechanisms and broad pharmacological actions.

Automatically Detecting Anchor Cells and Clustering for scRNA-Seq Data Using scTSNN.

Advancing in single-cell RNA sequencing techniques enhances the resolution of cell heterogeneity study. Density-based unsupervised clustering has the potential to detect the representative anchor points and the number of clusters automatically. Meanwhile, discovering the true cell type of scRNA-seq data in the unsupervised scenario is still challenging.

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications.

Motivation: The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming.

MTGDC: A Multi-Scale Tensor Graph Diffusion Clustering for Single-Cell RNA Sequencing Data.

Single-cell RNA sequencing (scRNA-seq) is a new technology that focuses on the expression levels for each cell to study cell heterogeneity. Thus, new computational methods matching scRNA-seq are designed to detect cell types among various cell groups. Herein, we propose a Multi-scale Tensor Graph Diffusion Clustering (MTGDC) for single-cell RNA sequencing data.

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes.

The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk RNA sequencing data to identify cell types from single-cell transcriptomes. An anchor graph is constructed to depict the relationship between reference samples and cells.

scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome.

Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier.

scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells.

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics.

A Clustering Ensemble Method for Cell Type Detection by Multiobjective Particle Optimization.

Single-cell RNA sequencing (scRNA-seq) is a new technology different from previous sequencing methods that measure the average expression level for each gene across a large population of cells. Thus, new computational methods are required to reveal cell types among cell populations. We present a clustering ensemble algorithm using optimized multiobjective particle (CEMP).

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2.

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published.

Nanocrystal-Loaded Micelles for the Enhanced In Vivo Circulation of Docetaxel.

Prolonging in vivo circulation has proved to be an efficient route for enhancing the therapeutic effect of rapidly metabolized drugs. In this study, we aimed to construct a nanocrystal-loaded micelles delivery system to enhance the blood circulation of docetaxel (DOC). We employed high-pressure homogenization to prepare docetaxel nanocrystals (DOC(Nc)), and then produced docetaxel nanocrystal-loaded micelles (DOC(Nc)@mPEG-PLA) by a thin-film hydration method.

Improving Oral Bioavailability of Luteolin Nanocrystals by Surface Modification of Sodium Dodecyl Sulfate.

Luteolin suffers from drawbacks like low solubility and bioavailability, thus hindering its application in the clinic. In this study, we employed sodium dodecyl sulfate (SDS), an efficient tight junction opening agent, to modify the surface of luteolin nanocrystals, aiming to enhance the bioavailability of luteolin (LUT) and luteolin nanocrystals (LNC). The particle sizes of SDS-modified luteolin nanocrystals (SLNC) were slightly larger than that of LNC, and the zeta potential of LNC and SLNC was -25.

Enhancing Oral Bioavailability by Paclitaxel Polymeric Micelles: Role of Transmembrane Pathways in the Oral Absorption.

The oral administration of paclitaxel suffering obstacles like poorly solubility and low permeability, causing extremely low oral bioavailability. In this study, we aim to study the role of transmembrane pathway on the absorption mechanisms of PTX-Micelles. In addition, we will further study the permeability of PTX-Micelles by employing perfusion model.

A 13-Immune Gene Set Signature for Prediction of Colon Cancer Prognosis.

Background: Colon cancer is one of the most common cancers worldwide and has a poor prognosis. Through the analysis of transcriptome and clinical data of colon cancer, an immune gene-set signature was identified by single sample enrichment analysis (ssGSEA) scoring to predict patient survival and discover new therapeutic targets.

Objective: To study the role of immune gene-set signature in colon cancer.

Huanglian Jiedu Decoction ameliorates DSS-induced colitis in mice via the JAK2/STAT3 signalling pathway.

Background: Ulcerative colitis (UC) is an intestinal disease which was characterized by intestinal inflammation, mucosal injury and fibrosis. In this paper, the effect of Huanglian Jiedu Decoction (HJD), a well-known traditional Chinese medicine with significant anti-inflammatory effect, on dextran sulphate sodium (DSS)-induced UC in mice and inhibition of JAK2/STAT3 pathway were investigated.

Methods: BALB/c mice were randomly divided into 6 groups: HJD group (high, medium and low dose), USAN group, UC group, and control group.