Dosimetrist Workload Management Framework for Radiation Therapy Pre-treatment Operations

Dosimetrists are responsible for treatment plan formulation in radiation therapy. Management of dosimetrist workload should balance utilization and encourage fulfillment of plan deadlines. In this work we develop a methodology to manage dosimetrist workload using a mixed-integer linear program which considers partial future patient demand, dosimetrist skillsets, and capacity constraints. Dosimetrists’ workload and schedule are represented by state variables which incorporate plan’s planning time, deadline, and dosimetrists’ autonomy. We show a greater utilization balance between dosimetrists and decreased overtime compared to historical policies using a simulation designed with data from Odette Cancer Center in Toronto, Ontario. Finally, we introduce and evaluate methods to increase assignment flexibility through selective dosimetrist training and relaxation of dosimetrist assignment constraints.M.A.S

Power-to-Gas Implementation for a Polygeneration System in Southwestern Ontario

Canada has stockpiles of waste petroleum coke, a high carbon waste product leftover from oil production with little positive market value. A polygeneration process is proposed which implements “power-to-gas” technology, through the use of electrolysis and surplus grid electricity, to use waste petroleum coke and biomass to create a carbon monoxide-rich stream after gasification, which is then converted into a portfolio of value-added products with the addition of hydrogen. A model implementing mixed-integer linear programming integrates power-to-gas technology and AspenPlus simulates the polygeneration process. The downstream production rates are selected using particle swarm optimization. When comparing 100% electrolysis vs. 100% steam reforming as a source of hydrogen production, electrolysis provides a larger net present value due to the carbon pricing introduced in Canada and the cost reduction from removal of the air separation unit by using the oxygen from the electrolysers. The optimal percent of hydrogen produced from electrolysis is about 82% with a hydrogen input of 7600 kg/h. The maximum net present value is $332 M when over 75$203 M when the dimethyl ether is capped at 50% production. The polygeneration plant is an example of green technology used to environmentally process Canada’s petroleum coke

Optimization of biofuel production from corn stover under supply uncertainty in Ontario

In this paper, a biofuel production supply chain optimization framework is developed that can supply the fuel demand for 10% of Ontario. Different biomass conversion technologies are considered, such as pyrolysis and gasification and subsequent hydro processing and the Fischer-Tropsch process. A supply chain network approach is used for the modeling, which enables the optimization of both the biorefinery locations and the associated transportation networks. Gasification of corn stover is examined to convert waste biomass into valuable fuel. Biomass-derived fuel has several advantages over traditional fuels including substantial greenhouse gas reduction, generating higher quality synthetic fuels, providing a use for biomass waste, and potential for use without much change to existing infrastructure. The objective of this work is to show the feasibility of the use of corn stover as a biomass feedstock to a hydrocarbon biofuel supply chain in Ontario using a mixed-integer linear programming model while accounting for the uncertainty in the availability of corn stover. In the case study, the exact number of biorefineries is left as a policy decision and the optimization is carried out over a range of the possible numbers of facilities. The results obtained from the case study suggests implementing gasification technology followed by Fischer-Tropsch at two different sites in Ontario. The optimal solution satisfied 10% of the yearly fuel demand of Ontario with two production plants (14.8 billion L of fuel) and requires an investment of $42.9 billion, with a payback period of about 3 years

Machine Learning Approaches for the Image-Based Identification of Surgical Wound Infections: Scoping Review

BackgroundSurgical site infections (SSIs) occur frequently and impact patients and health care systems. Remote surveillance of surgical wounds is currently limited by the need for manual assessment by clinicians. Machine learning (ML)–based methods have recently been used to address various aspects of the postoperative wound healing process and may be used to improve the scalability and cost-effectiveness of remote surgical wound assessment. ObjectiveThe objective of this review was to provide an overview of the ML methods that have been used to identify surgical wound infections from images. MethodsWe conducted a scoping review of ML approaches for visual detection of SSIs following the JBI (Joanna Briggs Institute) methodology. Reports of participants in any postoperative context focusing on identification of surgical wound infections were included. Studies that did not address SSI identification, surgical wounds, or did not use image or video data were excluded. We searched MEDLINE, Embase, CINAHL, CENTRAL, Web of Science Core Collection, IEEE Xplore, Compendex, and arXiv for relevant studies in November 2022. The records retrieved were double screened for eligibility. A data extraction tool was used to chart the relevant data, which was described narratively and presented using tables. Employment of TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) guidelines was evaluated and PROBAST (Prediction Model Risk of Bias Assessment Tool) was used to assess risk of bias (RoB). ResultsIn total, 10 of the 715 unique records screened met the eligibility criteria. In these studies, the clinical contexts and surgical procedures were diverse. All papers developed diagnostic models, though none performed external validation. Both traditional ML and deep learning methods were used to identify SSIs from mostly color images, and the volume of images used ranged from under 50 to thousands. Further, 10 TRIPOD items were reported in at least 4 studies, though 15 items were reported in fewer than 4 studies. PROBAST assessment led to 9 studies being identified as having an overall high RoB, with 1 study having overall unclear RoB. ConclusionsResearch on the image-based identification of surgical wound infections using ML remains novel, and there is a need for standardized reporting. Limitations related to variability in image capture, model building, and data sources should be addressed in the future

Liquid-Infused Silicone As a Biofouling-Free Medical Material

There is a dire need for infection prevention strategies that do not require the use of antibiotics, which exacer- bate the rise of multi- and pan-drug resistant infectious organisms. An important target in this area is the bacterial attach- ment and subsequent biofilm formation on medical devices (e.g., catheters). Here we describe non-fouling, lubricant-infused slippery polymers as proof-of-concept medical materials that are based on oil–infused polydimethylsiloxane (iPDMS). Pla- nar and tubular geometry silicone substrates can be infused with non-toxic silicone oil to create a stable, extremely slippery interface that exhibits exceptionally low bacterial adhesion and prevents biofilm formation. Analysis of a flow culture of Pseudomonas aeruginosa through untreated PDMS and iPDMS tubing shows at least an order of magnitude reduction of bio- film formation on iPDMS, and almost complete absence of biofilm on iPDMS after a gentle water rinse. The iPDMS materials can be applied as a coating on other polymers or prepared as simply as taking a silicone tubing and immersing it in a sili- cone oil, and are compatible with traditional sterilization methods. As a demonstration, we show the preparation of sili- cone-coated polyurethane catheters and significant reduction of Escherichia coli and Staphylococcus epidermidis biofilm formation on the catheter surface. This work represents an important first step towards a simple and effective means of preventing bacterial adhesion on a wide range of materials used for medical devices.Chemistry and Chemical Biolog

Liquid-Infused Silicone As a Biofouling-Free Medical Material

There is a dire need for infection prevention strategies that do not require the use of antibiotics, which exacerbate the rise of multi- and pan-drug resistant infectious organisms. An important target in this area is the bacterial attachment and subsequent biofilm formation on medical devices (e.g., catheters). Here we describe nonfouling, lubricant-infused slippery polymers as proof-of-concept medical materials that are based on oil-infused polydimethylsiloxane (iPDMS). Planar and tubular geometry silicone substrates can be infused with nontoxic silicone oil to create a stable, extremely slippery interface that exhibits exceptionally low bacterial adhesion and prevents biofilm formation. Analysis of a flow culture of <i>Pseudomonas aeruginosa</i> through untreated PDMS and iPDMS tubing shows at least an order of magnitude reduction of biofilm formation on iPDMS, and almost complete absence of biofilm on iPDMS after a gentle water rinse. The iPDMS materials can be applied as a coating on other polymers or prepared by simply immersing silicone tubing in silicone oil, and are compatible with traditional sterilization methods. As a demonstration, we show the preparation of silicone-coated polyurethane catheters and significant reduction of <i>Escherichia coli</i> and <i>Staphylococcus epidermidis</i> biofilm formation on the catheter surface. This work represents an important first step toward a simple and effective means of preventing bacterial adhesion on a wide range of materials used for medical devices