Biology subjects

Carr, P.

Publications and source records attributed to Carr, P..

3 recordsLinked to original sources

bioRxiv2022

Design, development, and preliminary assessment of a novel peripheral intravenous catheter aimed at reducing early failure rates

BackgroundPeripheral intravenous catheters (PIVCs) are the most commonly used invasive medical device, yet despite best efforts by end-users, PIVCs experience unacceptably high early failure rates. We aimed to design a new PIVC that reduces the early failure rate of in-dwelling PIVCs and we conducted preliminary tests to assess its efficacy and safety in a large animal model of intravenous access. MethodsWe used computer-aided design and simulation to create a PIVC with a ramped tip geometry, which directs the infused fluid away from the vein wall; we called the design the FloRamp. We created FloRamp prototypes (test device) and tested them against a market-leading device (BD Insyte; control device) in a highly-controlled setting with five insertion sites per device in four pigs. We measured resistance to infusion and visual infusion phlebitis (VIP) every six hours and terminated the experiment at 48 hours. Veins were harvested for histology and seven pathological markers were assessed. ResultsComputer simulations showed that the optimum FloRamp tip reduced maximum endothelial shear stress by 60%, from 12.7Pa to 5.1Pa, compared to a typical PIVC tip, and improved the infusion dynamics of saline in the blood stream. In the animal study, we found that 2/5 of the control devices were occluded after 24 hours, whereas all test devices remained patent and functional. The FloRamp created less resistance to infusion (0.73{+/-}0.81 vs 0.47{+/-}0.50, p=0.06) and lower VIP scores (0.60{+/-}0.93 vs 0.31{+/-}0.70, p=0.09) that the control device, although neither findings were significantly different. Histopathology revealed that 5/7 of the assessed markers were lower in veins with the FloRamp. ConclusionsAs PIVCs are used in almost every hospitalized patient, there is an urgent need to reduce failure rates. Herein we report preliminary assessment of a novel PIVC design, which could be advantageous in clinical settings through decreased device occlusion.

bioengineering↗

bioRxiv2020

An atlas of Arabidopsis protein S-Acylation reveals its widespread role in plant cell organisation of and function.

S-acylation is the addition of a fatty acid to a cysteine residue of a protein. While this modification may profoundly alter protein behaviour, its effects on the function of plant proteins remains poorly characterised, largely as a result to the lack of basic information regarding which proteins are S-acylated and where in the proteins the modification occurs. In order to address this gap in our knowledge, we have performed a comprehensive analysis of plant protein S-acylation from 6 separate tissues. In our highest confidence group, we identified 5185 cysteines modified by S-acylation, which were located in 4891 unique peptides from 2643 different proteins. This represents around 9% of the entire Arabidopsis proteome and suggests an important role for S-acylation in many essential cellular functions including trafficking, signalling and metabolism. To illustrate the potential of this dataset, we focus on cellulose synthesis and confirm for the first time the S-acylation of all proteins known to be involved in cellulose synthesis and trafficking of the cellulose synthase complex. In the secondary cell walls, cellulose synthesis requires three different catalytic subunits (CESA4, CESA7 and CESA8) that all exhibit striking sequence similarity. While all three proteins have been widely predicted to possess a RING-type zinc finger at their N-terminus, for CESA4 and CESA8, we find evidence for S-acylation of cysteines in this region that is incompatible with any role in coordinating metal ions. We show that while CESA7 may possess a RING type domain, the same region of CESA4 and CESA8 appear to have evolved a very different structure. Together, the data suggests this study represents an atlas of S-acylation in Arabidopsis that will facilitate the broader study of this elusive post-translational modification in plants as well as demonstrates the importance of undertaking further work in this area.

plant biology↗

bioRxiv2018

A Framework for Predicting Design Failures in Engineered Genetic Codes

Extreme engineering of an organisms genetic code could impart true genetic incompatibility, even blocking effects of horizontal gene transfer and viral infection. Recent experiments exploring this possibility demonstrate that such radical genome engineering achievements are plausible. However, it is unclear when the modifications will compromise the fitness of an organism. Efforts to reformat an entire genome are difficult and expensive; computational methods predicting fruitful experimental trajectories could play a pivotal role in advancing such efforts. We present a framework for building in silico models to assist genome-scale engineering. Genetic code engineering requires choosing from many possible codon-usage schemes, to find a design that is viable and effective. We use machine learning to identify which alternative codon-usage schemes are likely to result in no observed viable cells. Our data-driven approach employs observations of how modifying codon usage in individual genes impacted observed viability in E. coli, revealing salient features for early identification of problematic genetic code designs. We achieved an average area under the receiver operating characteristic of 0.72 on out-ofsample data.\n\nAuthor SummaryAs machine learning and artificial intelligence play an increasingly central role in science and engineering, it will be important to establish standardized techniques that facilitate the dialogue between experimentation and modeling. Biological experimental techniques are concurrently evolving at a rapid pace, providing unique opportunities to collect high-quality, novel information that was previously unobtainable. This work navigates the landscape of this vast, new territory, identifies interesting landmarks for exploration and posits new approaches towards advancing our research efforts in these areas. In this work, we show that, using a small dataset of 47 observations and rigorous nested cross validation techniques, we can build a model that makes better-than-random predictions of how codon usage changes in essential genes influence viability in E. coli. These predictions can be used to inform experimental trajectories in both genetic code and codon optimization experiments. We discuss ways to improve this model, iteratively, by performing high value experiments that decrease uncertainty in predictions and extrapolation error. Finally, we present novel visualization methods to aid in developing intuitions for how re-coding impacts groups of genes. These methods are also useful tools in building important insights into how well machine learning algorithms can generalize to new data.

bioengineering↗