1877-08-09

The Old Commonwealth was a weekly newspaper published in Harrisonburg, Va., between 1865 and 1884

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers

MicroRNA as Therapeutic Targets for Chronic Wound Healing

Wound healing is a highly complex biological process composed of three overlapping phases: inflammation, proliferation, and remodeling. Impairments at any one or more of these stages can lead to compromised healing. MicroRNAs (miRs) are non-coding RNAs that act as post-transcriptional regulators of multiple proteins and associated pathways. Thus, identification of the appropriate miR involved in the different phases of wound healing could reveal an effective third-generation genetic therapy in chronic wound care. Several miRs have been shown to be upregulated or downregulated during the wound healing process. This article examines the biological processes involved in wound healing, the miR involved at each stage, and how expression levels are modulated in the chronic wound environment. Key miRs are highlighted as possible therapeutic targets, either through underexpression or overexpression, and the healing benefits are interrogated. These are prime miR candidates that could be considered as a gene therapy option for patients suffering from chronic wounds. The success of miR as a gene therapy, however, is reliant on the development of an appropriate delivery system that must be designed to overcome both extracellular and intracellular barriers. Keywords: microRNA, wound healing, gene therapy, chronic wound

Linear regression for estimating bladder volume with voltage signals

Urinary incontinence is a common condition that can severely impact the lives of those who have it. Bladder volume monitoring solutions that exploit the electrical differences of different tissues in the pelvis have the potential to help medical personnel in the decision-making process with urinary incontinence. In this work, we investigate linear regression as a means of assigning bladder volume to the measured voltage values. We found that linear regression outperforms the previously studied machine learning regression algorithms by nearly a factor of 4. This linear regression approach is also more effectively able to handle volumes outside the training boundaries in comparison to previous work in the field. More work is needed to further improve the estimate of bladder volume based on the voltage signals, especially at high noise levels.This research was supported by the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n. 637780 and the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728. This publication is based upon work from COST Action EMF-MED, supported by COST (European Cooperation in Science and Technology).peer-reviewe

Linear regression for estimating bladder volume with voltage signals

Urinary incontinence is a common condition that can severely impact the lives of those who have it. Bladder volume monitoring solutions that exploit the electrical differences of different tissues in the pelvis have the potential to help medical personnel in the decision-making process with urinary incontinence. In this work, we investigate linear regression as a means of assigning bladder volume to the measured voltage values. We found that linear regression outperforms the previously studied machine learning regression algorithms by nearly a factor of 4. This linear regression approach is also more effectively able to handle volumes outside the training boundaries in comparison to previous work in the field. More work is needed to further improve the estimate of bladder volume based on the voltage signals, especially at high noise levels.This research was supported by the European Research Council under the European Union’s Horizon 2020 Programme/ERC Grant Agreement BioElecPro n. 637780 and the charity RESPECT and the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA Grant Agreement no. PCOFUND-GA-2013-608728. This publication is based upon work from COST Action EMF-MED, supported by COST (European Cooperation in Science and Technology).peer-reviewe