Interleukin-34 and immune checkpoint inhibitors: Unified weapons against cancer.

Interleukin-34 (IL-34) is a cytokine that is involved in the regulation of immune cells, including macrophages, in the tumor microenvironment (TME). Macrophages are a type of immune cell that can be found in large numbers within the TME and have been shown to have a role in the suppression of immune responses in cancer. This mmune suppression can contribute to cancer development and tumors' ability to evade the immune system. Immune checkpoint inhibitors (ICIs) are a type of cancer treatment that target proteins on immune cells that act as "checkpoints," regulating the activity of the immune system. Examples of these proteins include programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). ICIs work by blocking the activity of these proteins, allowing the immune system to mount a stronger response against cancer cells. The combination of IL-34 inhibition with ICIs has been proposed as a potential treatment option for cancer due to the role of IL-34 in the TME and its potential involvement in resistance to ICIs. Inhibiting the activity of IL-34 or targeting its signaling pathways may help to overcome resistance to ICIs and improve the effectiveness of these therapies. This review summarizes the current state of knowledge concerning the involvement of IL-34-mediated regulation of TME and the promotion of ICI resistance. Besides, this work may shed light on whether targeting IL-34 might be exploited as a potential treatment option for cancer patients in the future. However, further research is needed to fully understand the mechanisms underlying the role of IL-34 in TME and to determine the safety and efficacy of this approach in cancer patients.

scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene-gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.

Correction to: Computational study on hemodynamic changes in patient-specific proximal neck angulation of abdominal aortic aneurysm with time-varying velocity.

The name of the third author was incorrect in the initial online publication. The original article has been corrected.

Computational study on hemodynamic changes in patient-specific proximal neck angulation of abdominal aortic aneurysm with time-varying velocity.

Aneurysms are considered as a critical cardiovascular disease worldwide when they rupture. The clinical understanding of geometrical impact on the flow behaviour and biomechanics of abdominal aortic aneurysm (AAA) is progressively developing. Proximal neck angulations of AAAs are believed to influence the hemodynamic changes and wall shear stress (WSS) within AAAs. Our aim was to perform pulsatile simulations using computational fluid dynamics (CFD) for patient-specific geometry to investigate the influence of severe angular (≥ 60°) neck on AAA's hemodynamic and wall shear stress. The patient's geometrical characteristics were obtained from a computed tomography images database of AAA patients. The AAA geometry was reconstructed using Mimics software. In computational method, blood was assumed Newtonian fluid and an inlet varying velocity waveform in a cardiac cycle was assigned. The CFD study was performed with ANSYS software. The results of flow behaviours indicated that the blood flow through severe bending of angular neck leads to high turbulence and asymmetry of flows within the aneurysm sac resulting in blood recirculation. The high wall shear stress (WSS) occurred near the AAA neck and on surface of aneurysm sac. This study explained and showed flow behaviours and WSS progression within high angular neck AAA and risk prediction of abdominal aorta rupture. We expect that the visualization of blood flow and hemodynamic changes resulted from CFD simulation could be as an extra tool to assist clinicians during a decision making when estimation the risks of interventional procedures.