Mesenchymal stem cell application in pulmonary disease treatment with emphasis on their interaction with lung-resident immune cells.

Due to the vital importance of the lungs, lung-related diseases and their control are very important. Severe inflammatory responses mediated by immune cells were among the leading causes of lung tissue pathology and damage during the COVID-19 pandemic. In addition, uncontrolled immune cell responses can lead to lung tissue damage in other infectious and non-infectious diseases.

DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.

Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information.

Dual blockage of both PD-L1 and CD47 enhances the therapeutic effect of oxaliplatin and FOLFOX in CT-26 mice tumor model.

Colorectal cancer is a poorly immunogenic. Such property can be reverted by using ICD. However, ICD inducers can also induce the expression of inhibitory checkpoint receptors CD47 and PD-L1 on tumor cells, making CRC tumors resistant to mainly CD8 T cell killing and macrophage-mediated phagocytosis.

BACTERIOPHAGE M13 MODULATES THE SEPSIS-RELATED INFLAMMATORY RESPONSES AND ORGAN DAMAGE IN A CLP MODEL.

Background: Sepsis is a life-threatening disorder that leads to the induction of inflammatory responses and organ failure. Phage therapy is a new approach to controlling infections resistant to common treatments, including sepsis. Several studies have shown the effect of lytic bacteriophages on infection control by reducing the bacterial load.

Immune cells-derived exosomes function as a double-edged sword: role in disease progression and their therapeutic applications.

Exosomes, ranging in size from 30 to 150 nm as identified initially via electron microscopy in 1946, are one of the extracellular vesicles (EVs) produced by many cells and have been the subject of many studies; initially, they were considered as cell wastes with the belief that cells produced exosomes to maintain homeostasis. Nowadays, it has been found that EVs secreted by different cells play a vital role in cellular communication and are usually secreted in both physiological and pathological conditions. Due to the presence of different markers and ligands on the surface of exosomes, they have paracrine, endocrine and autocrine effects in some cases.

Association of the genetic polymorphisms in inhibiting and activating molecules of immune system with rheumatoid arthritis: A systematic review and meta-analysis.

Several studies have demonstrated that the genetic polymorphisms in the genes encoding immune regulatory molecules, namely cytotoxic T-lymphocyte-associated protein 4 (CTLA4) and CD28, play a fundamental role in susceptibility to rheumatoid arthritis (RA). Several disperse population studies have resulted in conflicting outcomes regarding the genetic polymorphisms in these genes and RA risk. This systematic review and meta-analysis study was performed to reach a conclusive understanding of the role of single-nucleotide polymorphisms (SNPs) of CTLA4-rs231775, CTLA4-rs5742909, and CD28-rs1980422 in susceptibility to RA.

Efficient Multitask Multiple Kernel Learning With Application to Cancer Research.

Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications.

A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.

Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest.

Discriminating early- and late-stage cancers using multiple kernel learning on gene sets.

Motivation: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data.