Antibiotic Resistance has slowly risen to become one of the biggest health crises of our time, leading to hundreds of thousands of deaths annually [1]. However, this issue extends beyond medical consequences, affecting global development, and undermining the Sustainable Development Goals (SDGs) for greater health, economic growth, and inequality; it also further exacerbates gender disparity and poverty. Antibiotic resistance is not merely a clinical challenge but also reflects systemic failure in healthcare, socioeconomic inequality, and inadequate government [1].

Addressing this multifaceted issue requires a solution that goes beyond conventional methods. Plasmid AI represents the use of innovative technology to address antibiotic resistance. While we have developed functional plasmids, our current focus is on laying the groundwork for future applications.

Central to our approach is the understanding that scientific innovation thrives with a people-centered approach. Our Human Practices journey emphasizes that scientific innovation cannot thrive without the voices of the communities we aim to serve. We have engaged with and incorporated feedback from diverse stakeholders to ensure that our project has a holistic perspective and our solution resonates with real-life experiences and needs. This year, our community engagement project AImersion has allowed us to understand the cultural contexts that surround community perspectives.

Building on the efforts of iGEM Toronto’s 2023 Human Practices team, our team has taken a more streamlined approach to inclusivity. We conducted an impact assessment to better understand how our project may affect Indigenous communities.

We acknowledge the rapid evolution of AI and its integral role in our project. To responsibly harness this new technology, we have developed a tailored framework, incorporating elements from three existing models. Our approach emphasizes ethical AI usage, transparency, community engagement, and a comprehensive risk assessment to ensure safety.

In light of the current ecological crisis, we have conducted a thorough impact analysis, tracking our carbon footprint under the guidance of an Associate Engineer from the Ministry of Environment. Based on their advice, we have developed a plan to reduce our carbon footprint as the project scales up. This strategy ensures that our efforts to combat antibiotic resistance remain sustainable

We understand the importance of inspiring individuals through synthetic biology. This year, we hosted a seminar for incoming freshmen to share our project and showcase the exciting possibilities that synthetic biology offers. We made sure to be transparent about our methods, giving attendees a clear understanding of our research. To spark meaningful discussions, we held a debate exploring the ethical side of what we do. Right now, we are teaming up with the Science Communication Club at our University to write an article on antibiotic resistance. These efforts show our commitment to engaging various communities and raising awareness about the real-world impact of synthetic biology on health and society.

Our stakeholder framework was designed to ensure that there is a safe space for expressing assorted opinions. We recognize that though certain modifications might be challenging to implement, our goal is to find a middle ground that balances stakeholder satisfaction with feasibility.

To achieve this, we designed a five-part approach:

1. Identification of Key Stakeholders

Our first step in our holistic approach was to map stakeholders into specific sectors. This process helped ensure that we consider diverse viewpoints essential for the success of our project. We identified the following sectors:

• Healthcare: Engaging with healthcare professionals to understand the implications of antibiotic resistance in a medical field, we also aimed to explore existing treatment methods as well as medical benchmarks for new solutions.
• History: Reaching out to historians to gather insights on how past medical advances have influenced public perceptions of healthcare inventions. Our aim was to understand how previous innovations, along with colonial legacies and historical inequalities, shape contemporary attitudes towards medical inventions today.
• Bioethics: Consulting with bioethics experts to address safety and regulatory considerations of our project, such as the potential for dual use.
• Community: Connecting with community members to gather diverse perspectives on AI integration into synthetic biology. We aim to promote inclusivity by understanding different viewpoints and have encouraged individuals to also share their stories that led to this viewpoint to explore the cultural contexts that shape these perspectives. This approach helps us create a more holistic understanding of community values and concerns related to our project.
• Indigenous Communities: While we intended to engage with Indigenous communities to understand their perspectives on AI in healthcare and antibiotic resistance, we faced challenges in connecting with relevant organizations. Despite these hurdles, we acknowledge the importance of engaging with vulnerable communities. We have done a preliminary impact assessment to identify the connection between our project and the health concerns of Indigenous populations. Moving forward, we remain committed to continuing our efforts to contact and collaborate with Indigenous communities.
• Environmental Sector: Reaching out to environmental experts to assess the ecological impacts of our project and how we can reduce our carbon footprint if our project were to scale up, to ensure that our project is sustainable and eco-friendly.
• Academia: Contacting academic researchers specializing in antibiotic resistance and related fields to enhance our scientific approach. This helped ground our project in rigorous research.

2. Impact Assessment

Our next step was conducting an impact assessment focused on Indigenous communities. In Canada, the past continues to shape the present in profound ways. Historical trauma— including mistreatment, forced family separation, land dispossession, and cultural suppression—has had lasting effects on the physical and mental health of Indigenous populations [2]. These intergenerational harms have been widely documented and emphasize the need for approaches that are grounded in respect and understanding.

With this in mind, we conducted an impact assessment focused on ensuring our project impacts Indigenous communities before initiating outreach. Our assessment involved reviewing recent scholarly articles to identify how antibiotic resistance impacts Indigenous populations. Our goal was to foster respectful, informed partnerships without perpetuating harm or exploitation.

3. Stakeholder Engagement

Two-way communication with diverse stakeholders has been central to our project. Initially, we reached out to professionals through perceptual questions to understand their perspectives. We followed up with questions to gather insights from their respective fields as well. This helped us identify critical areas that needed attention in our project.

For community engagement, we launched the AImerion project, which allowed community members to express their perspectives through art and storytelling. This created an interactive platform where communities can express their perspectives in a safe space. We also reached out to the community through public forums to understand vaccine hesitance. Additionally, we explored public perspectives on the commercialization of our project, to shape the long-term goals of Plasmid AI. These initiatives have helped us ensure that our stakeholders have been co-creators in shaping our solution.

4. Feedback Integration

We designed our feedback integration process to ensure that stakeholder input directly informs and shapes our project. By collecting feedback through various channels such as art, interviews, and feedback forms, we aimed to capture diverse perspectives and concerns. Analyzing this feedback has helped us to identify key themes and insights that we have incorporated into different aspects of Plasmid AI.

5. Continuous Monitoring

Continuous Monitoring is essential to our approach, allowing us to assess the impact of our initiative and allowing open lines of communication with stakeholders. We have scheduled follow-up meetings in October to share how we have integrated the feedback we received. During these meetings, we will ask our stakeholders if they are satisfied with the integration and discuss any additional concerns or suggestions they may have. This ongoing dialogue ensures that our project remains responsive to community needs and fosters a collaborative environment.

References

ReAct. (2021, June 3). Antibiotic resistance – far more than a medical problem. ReAct Group. https://www.reactgroup.org/news-and-views/news-and-opinions/year-2021/antibiotic-resistance-far-more-than-a-medical-problem/

The Lancet. (2021). The past is not the past for Canada's Indigenous peoples. The Lancet, 397(10293), 2439. https://doi.org/10.1016/S0140-6736(21)01685-8

The rapid advancement of synthetic biology and artificial intelligence (AI) compels us to establish a robust regulatory framework to assess the associated risks of these converging technologies. Recognizing the complexity of this task, we initially conducted extensive research on various regulatory approaches. However, after a thoughtful consultation with Bioethics Professor Dr. Jonathan Herrington, we found our focus solidified and were encouraged to adopt the comprehensive framework outlined in the paper "Risk Assessments for Converging Technologies" [1]. This framework deftly synthesizes elements from the AAAS-FBI-UNICRI, NASEM, and Tucker frameworks, providing us with a versatile tool for evaluating the risks that arise from technological convergence [1]. Dr. Herrington’s insights were invaluable, reinforcing our belief in the importance of a holistic assessment in this rapidly evolving landscape.

Assessing risks associated with converging technologies is inherently challenging due to the intricate interplay between various domains. Traditional risk assessment frameworks often focus narrowly on specific technologies or scenarios, which can limit their applicability to emerging risks. In contrast, a more generalized framework allows for comparative analysis across different technologies, aiding decision-makers in prioritizing risks effectively [1].

These complexities are compounded when we consider the uncertainties of technological convergence. Each component—be it AI or biotechnology—presents distinct yet interconnected risks. For instance, concentrating solely on the biological aspects of cloud laboratories may obscure vulnerabilities inherent in the underlying AI infrastructure. Moreover, the scarcity of data and the challenges of quantifying emerging risks further complicate our assessments [1].

The framework we have adopted integrates the strengths of three previously established approaches, offering a comprehensive lens through which we can evaluate risks associated with Plasmid AI:

This framework emphasizes the inclusion of multidisciplinary experts, which facilitates a nuanced evaluation of risks associated with big data in the life sciences. However, its specific focus on big data may limit its applicability to other converging technologies [1].

This framework recognizes the synergy between technologies and assesses the level of concern regarding the capabilities of synthetic biology and its potential applications for harm. Despite its strengths, its specificity to synthetic biology may pose challenges when considering the broader implications of AI and biotechnology convergence [1].

This framework incorporates a governability assessment, highlighting the relationship between risk and governance. It emphasizes that technologies with lower governability are more susceptible to misuse. However, its reliance on a limited ordinal scale may result in a loss of granularity in risk categorization [1].

By combining these frameworks, our adapted approach facilitates a more comprehensive understanding of the risks associated with Plasmid AI.

In response to valuable insights from our stakeholders, we are committed to ensuring that all our decision-making processes remain transparent. Stakeholders expressed a strong desire for clarity in how decisions are made regarding the development and deployment of Plasmid AI technologies. By integrating their feedback, we will implement the following measures:

1. Documentation of Decisions: All significant decisions will be documented and made available upon request. Interested parties can reach out to our team to review the rationale behind our actions.
2. Stakeholder Feedback Mechanisms: Our Continuous Monitoring section within our stakeholder framework will be made mandatory if a stakeholder expresses any form of concern regarding our project. By doing so, we can assess whether their concerns have been adequately addressed, and, if necessary, explore ways to find a middle ground. This approach not only fosters accountability but also encourages collaboration in addressing any evolving concerns.
3. Incorporation of Diverse Perspectives: We recognize the importance of incorporating diverse perspectives in our decision-making. We will actively seek input from various stakeholder groups to ensure that our approaches are well-rounded and considerate of different viewpoints.

By embedding transparency into our regulatory framework, we aim to build trust and foster collaboration among all stakeholders involved in the Plasmid AI project.

To illustrate the application of the adapted framework, we present several convergent risk scenarios related to Plasmid AI:

We have also delved into the synergy of other technologies with Plasmid AI, which you can find here

• 1. O’Brien, J. T., & Nelson, C. (2020). Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology. Health Security, 18(3), 228-235. https://doi.org/10.1089/hs.2019.0122

This year our Integrated Human Practices efforts were driven by meaningful engagements with a diverse array of stakeholders, including academic experts, healthcare workers, patients, environmental specialists, pharmacists, and members of the community. Their feedback has helped refine and shape Plasmid AI into a project that is technologically safe, scientifically robust, ethically grounded, and sustainability-focused. This year, we have also focused on including the historical and cultural perspectives of community members in our feedback cycles. We are grateful for all the insights shared throughout this journey.

On this page, we will detail the diverse methods employed to engage our stakeholders, emphasizing the breadth of perspectives considered throughout the process. We will outline the background research that has shaped our project, providing context for our design and decision-making. Additionally, we will reflect on the key lessons learned during our journey, highlighting how these insights have informed our approach and how we have integrated feedback received into our project. This iterative process has ensured that Plasmid AI not only meets technical challenges but also serves the broader community's needs, positioning it as a meaningful advance in the field.

Our journey in Integrated Human Practices began with a critical examination of the pressing issues our technology could address, marking a step in the development of Plasmid AI as a foundational advance in synthetic biology. We explored significant environmental challenges such as bioremediation and wastewater management, as well as opportunities to improve gut microbiome therapy, biofuel generation, conservation efforts, agricultural practices, and antibiotic resistance.

Through extensive background research and engaging conversations with stakeholders, we identified antibiotic resistance as an urgent global health threat that our technology could lay the groundwork for to tackle in the future. The following section dives into the background research and conversations we had with stakeholders, explaining why we chose to focus on antibiotic resistance (ABR) for Plasmid AI.

One of the applications our research focused on, was bioremediation and wastewater management, critical areas that blend environmental sustainability with synthetic biology. We explored using microbes to degrade harmful pollutants in wastewater, emphasizing the importance of Horizontal Gene Transfer, which enables bacteria to acquire genes for enhanced degradation. However, wastewater treatment plants can also facilitate the spread of antibiotic resistance genes, creating both opportunities and challenges [1][2].

Although genetically engineered microbes show promise, gaps in models predicting gene transfer permissivity exist, highlighting an area our project could address. The economic implications are significant, with implementation costs ranging from $200,000 to over $4 million [3][4]. Despite their potential benefits, bioremediation methods require optimization for scalability [5][6]. To deepen our insights, we consulted with an Associate Engineer from the Ministry of Environment.

The emerald ash borer (EAB), an invasive beetle from Asia, poses a significant threat to North American ash trees, leading to rapid tree death and an estimated loss of 2 billion trees in Canada over the next two decades [8]. Current management primarily involves the costly application of insecticides, which can be toxic to non-target organisms[9].

Our project considered leveraging RNA interference (RNAi) as an innovative pest management strategy. RNAi targets specific genes in EAB to induce mortality, with the potential to use machine learning for optimizing the design of double-stranded RNA (dsRNA) plasmids. Application methods such as foliar sprays and trunk injections were explored to deliver the dsRNA effectively [10][11].

Ultimately, we decided not to pursue this approach due to the complexities of dsRNA production, potential environmental impacts, and the need for extensive optimization before practical application could be realized. Our focus shifted toward other solutions that better align with our project goals and capabilities.

Major depressive disorder (MDD) represents a significant global mental health challenge, with growing evidence linking gut microbiota dysbiosis to its pathogenesis [12]. Alterations in microbial diversity and specific bacterial taxa, particularly increases in pro-inflammatory bacteria and decreases in anti-inflammatory ones, have been observed in MDD patients. Key affected phyla include Firmicutes, Actinobacteria, and Bacteroidetes, suggesting that dysbiosis may exacerbate MDD symptoms [13]. Various microbiota-targeted therapeutics—such as dietary interventions, fecal microbiota transplantation (FMT), probiotics, prebiotics, synbiotics, and postbiotics—show potential in alleviating depressive symptoms, though further research is needed to validate their clinical application [14].

Despite the promising therapeutic avenues, we chose not to pursue gut microbiome therapy for MDD due to the complexities of gut-brain interactions and the need for extensive research to establish causality between microbial composition and MDD. Additionally, the variations in individual responses to microbial-targeted therapies and concerns regarding the safety of live probiotics in vulnerable populations led us to focus on other, more straightforward therapeutic strategies that align better with our project objectives [15].

Agriculture has seen transformative advancements through artificial intelligence (AI) and synthetic biology. AI applications encompass crop management, water management, soil management, and pest control, utilizing techniques like machine learning and robotics to optimize efficiency [16]. However, the implementation challenges, particularly the high costs and the need for digital literacy among small and medium producers, hinder accessibility and scalability. Similarly, synthetic biology presents innovative strategies, such as improving nitrogen fixation and enhancing food nutrition [17], yet these require substantial research, regulatory approvals, and careful consideration of ecological impacts.

Despite the potential benefits of these technologies, we chose not to pursue them for our project. The complexity and costs associated with AI technologies present significant barriers to adoption, particularly for smaller agricultural units.

Artificial bacteria are engineered to convert biomass into biofuels, such as ethanol or butanol, representing a compelling application of synthetic biology [19]. Biomass, which encompasses organic materials derived from plants and agricultural residues, serves as a sustainable feedstock for biofuel production due to its abundance and renewability [20]. However, direct conversion of biomass into biofuels poses significant challenges due to its complex structure, particularly the lignocellulosic components that are difficult to break down [21]. While recent advancements in metabolic engineering and synthetic biology show promise for enhancing biomass degradation through artificial microbial consortia, we ultimately chose not to pursue this application due to uncertainties regarding the integration of artificial intelligence (AI) into this process and how it would contribute to overcoming the existing challenges in biofuel production.

While the potential to create artificial bacteria for biofuel production is intriguing, we felt that the current state of research did not provide a clear pathway for effective implementation.

Research has indicated that artificial intelligence (AI) can play a transformative role in the discovery of new antibiotics, particularly against critical pathogens like Acinetobacter baumannii [16]. This finding sparked our interest to further explore this direction, especially given the pressing need to combat antibiotic resistance, which poses significant public health risks. With agriculture accounting for approximately 82% of antibiotic use in Canada, understanding its implications is vital [22]. While we acknowledged the challenges posed by biases in AI models and the potential for unreliable outputs [17], we felt that the urgent need for solutions to antibiotic resistance [ABR] outweighed these concerns. This awareness motivated us to explore a plasmid-based AI approach, which we believe can effectively contribute to addressing the critical issue of antibiotic resistance.

Current strategies to combat antibiotic resistance (ABR) include the discovery of novel antibiotics through modifications of existing compounds and exploration of unconventional sources [23]. Enhancing the efficacy of current antibiotics involves metabolic stimulation and novel delivery systems [24]. Alternative treatments, such as bacteriophages, anti-biofilm drugs, probiotics, nanomaterials, vaccines, and antibody therapies, are also under investigation [25]. However, antibiotic resistance primarily arises from plasmid-encoded genes that confer resistance to multiple antibiotic classes [26]. Plasmids can be transferred between bacterial cells, allowing the rapid spread of resistance traits [27]. This mechanism of resistance transfer has prompted our focus on the possibility of using our technology to combat this issue in the future.

After our background research, we decided to engage with individuals from the Ministry of Environment and the healthcare sector to help us choose between bioremediation, wastewater management, and tackling antibiotic resistance. This outreach was essential for understanding the practical challenges and needs associated with each area. By speaking with stakeholders, we aimed to gather insights on current practices and the efficacy of existing solutions. Ultimately, this engagement helped clarify our project focus on antibiotic resistance, as it presented the most pressing public health risks and aligned well with our goal of leveraging plasmid-based AI to combat superbugs.

Who is this stakeholder?

Bikramjit is an Associate Engineer at the Ministry of Environment in Hamilton, whom we contacted to gain insights into the potential application of our project for bioremediation of wastewater management.

What did they say?

Bikramjit offered valuable insights into the bioremediation and wastewater management landscape in Canada, highlighting current solutions and their challenges. He explained that efforts focus on reducing pollutants and managing waste using methods like constructed wetlands and activated sludge systems, with advanced technologies such as membrane bioreactors and biological nutrient removal systems proving effective. Techniques like phytoremediation and bioaugmentation also enhance pollutant breakdown.

He pointed out critical issues in wastewater management, starting with aging infrastructure that increases contamination risks. Industrial wastewater from mining, oil, and gas presents another challenge; while vital to Canada's economy, these sectors often lack effective treatment protocols, reflecting the need to balance environmental protection with job preservation. Stormwater management is also a significant concern, as many treatment plants weren't designed for mixed water streams. Historically, stormwater was discharged untreated, but regulations now require it to go through facilities that often can't handle the increased volume and pollutants like heavy metals and oils, leading to untreated discharges during heavy rains.

Bikramjit emphasized broader challenges, including high costs, excessive energy consumption, incomplete treatment processes, and bacterial resistance, all of which hinder the adoption of newer technologies.

When discussing artificial plasmids for bioremediation and wastewater management, he acknowledged their potential but raised concerns about effectiveness, environmental impact, regulatory approval, and monitoring. The use of Genetically Modified Organisms [GMOs] requires rigorous safety trials, and cost remains a significant barrier, as municipalities may hesitate to adopt these solutions due to high expenses. His insights highlight the need for a careful balance between innovation and practical implementation.

Reflections or Next Steps

Following our meeting with Bikramjit, we recognized that for our plasmid-based solutions to be effective, they must clearly outperform existing methods. This challenge is heightened by ongoing issues with current solutions, including high costs, excessive energy consumption, and incomplete treatment processes.

We also discussed the reluctance of municipalities to adopt new technologies without compelling, cost-effective evidence of their benefits. This perspective led us to reflect on the practicality of implementing our technology in this sector. Moving forward, we decided to explore alternative future applications for our technology, taking these concerns into account.

Who is this stakeholder?

To ensure the success of our project, it was important to gather insights from healthcare workers. To start, we interviewed a nurse practitioner from the Toronto Western Hospital, who remains anonymous to protect their privacy. We wanted to explore the perspective of healthcare workers on using our technology to treat antibiotic resistance in comparison to traditional methods of treatment. Additionally, we sought to understand potential concerns and methods to ease them.

What did they say?

The nurse practitioner offered critical insights into the high costs and lengthy timelines associated with traditional methods of tackling antibiotic resistance, particularly in the development of new antibiotics. They noted that artificial intelligence could potentially streamline this lengthy process. They emphasized that complex challenges like antibiotic resistance require ongoing research and should complement established methods. While AI can swiftly analyze large datasets and identify patterns, the nurse pointed out the importance of maintaining data quality.

They stressed that integrating our solution into existing healthcare practices must be equitable to ensure effectiveness. Collaboration among AI experts, microbiologists, policymakers, and healthcare professionals is essential for developing practical solutions that maintain a balanced approach. Additionally, they highlighted the need for transparency from the project's outset, particularly regarding decision-making processes.

Reflections or Next Steps

Our discussion with the nurse practitioner provided us with valuable insights into the complexities of addressing antibiotic resistance. We were surprised by the high costs and lengthy timelines associated with traditional antibiotic development pipelines. This reinforced the urgency of our project, highlighting the need for solutions that quicken the process. Their emphasis on using artificial intelligence as a tool to enhance the approach was enlightening. It made us realize the potential impact our project could have in combating antibiotic resistance in the future.

Their concerns regarding data quality gave us insights on the importance of using reliable data in our research. We recognized that the success of our project depends on the integrity and size of the data we use. Fortunately, our drylab team had already planned to expand our datasets, allowing us to incorporate a wide range of high-quality data. The nurse’s emphasis on transparency motivated us to include a section in our regulatory framework that documented our vital decision-making processes in our project. Overall, speaking with the nurse practitioner gave us more confidence to pursue antibiotic resistance as an application and prompted us to consider the importance of our datasets as well as strengthening our regulatory frameworks.

Who is this stakeholder?

Dr. Bath is a family practitioner in Ontario. We wanted to reach out to a family doctor, to gain insights into the rationale behind antibiotic prescriptions, and how antibiotic resistance manifests in patients. We also wanted to learn about the best ways to approach antibiotic resistance stewardship. Understanding these factors will help us refine our approach and make informed decisions regarding the application of our project.

What did they say?

Dr. Bath noted that antibiotic resistance is a growing crisis highlighting its urgent nature. She emphasized that the first line of treatment for antibiotic-related issues is patient education. This she believes is the most crucial step to manage antibiotic use effectively. Dr. Bath’s suggestion to effectively educate the public was to leverage social media in order to reach a broader audience; she said shifting modern communication strategies would be most effective.

Furthermore, she noted the importance of considering all side effects a solution might have, expressing that every side effect is equally important. Dr. Bath also shared her observations regarding patient preferences in knowing the origins and technology behind their medications; she expressed that some patients like to understand this information, while others prefer not to go into detail.

Reflections or Next Steps

Dr. Bath’s insights on antibiotic resistance being a growing crisis helped us prioritize this issue as a critical focus area for our project. Her emphasis on patient education as the first line of treatment reaffirms our commitment to developing educational resources that effectively educate the public on antibiotic resistance. Discussing follow-up antibiotics helped us understand how complex antibiotic resistance is and reminded us to ensure that our solution is thoroughly researched and backed by evidence to support its efficacy. The variability in patient interest regarding the origin and technology behind medicine gave us insights on how we must tailor educational strategies regarding our methods to interact with individuals with varying levels of interest. Moving forward, we plan to conduct interviews to gain a deeper understanding of the public's perspectives on antibiotic resistance, ensuring our approach resonates with their preferences and concerns.

As part of our efforts to understand how the general public perceives antibiotic resistance and its broader implications, we reached out to three individuals who agreed to share their perspectives. Their insights offer a glimpse into the challenges of public awareness and engagement around this issue. To maintain privacy, their identities remain anonymous.

One participant defined antibiotic resistance as the evolution of bacteria that become resistant to antibiotics once effective at treating infections. They pointed out that, while some people who follow health news are aware of the issue, many still lack a deep understanding of its potential severity. They emphasized the need for more accessible information to raise awareness, suggesting the use of public health campaigns, such as posters in healthcare facilities, and interactive social media content.

Another interviewee shared a personal experience where they had been prescribed antibiotics for a severe throat infection, but the treatment was initially ineffective due to bacterial resistance. It wasn’t until they switched to a stronger medication that the infection was resolved. The interviewee remarked that people often do not realize the risks of overusing antibiotics and tend to see them as a "quick fix." To improve awareness, they suggested using personal stories and case studies to demonstrate the real-life consequences of antibiotic resistance.

A third participant discussed the issue from a more systemic perspective, noting that antibiotic resistance extends beyond healthcare into areas like agriculture, where antibiotics are overused. They voiced concerns that public education on the topic is insufficient and often overshadowed by more immediate health concerns. To address this, they advocated for more comprehensive approaches, such as integrating antibiotic resistance into school curricula and launching social media campaigns to engage broader audiences.

Reflecting on the feedback we've gathered from various stakeholders and public insights, it's clear that while there is awareness of antibiotic resistance, there remains a significant gap in deeper understanding. The personal experiences and broader concerns shared offer valuable perspectives that reinforce the importance of our project. These insights not only highlight the urgency of the issue but also point us toward new ways of engaging the public and shaping our communication strategies.

The insights we gathered from healthcare professionals like the nurse practitioner and Dr. Bath, along with public feedback, highlighted the urgency of addressing antibiotic resistance. Healthcare workers revealed the limitations of traditional antibiotic development and the potential of AI to accelerate solutions. Meanwhile, public feedback stressed the lack of awareness around resistance and the real-world dangers of antibiotic misuse, emphasizing the need for education. These insights solidified our decision to focus our technology on this issue, recognizing the powerful opportunity AI and synthetic biology present to develop innovative, impactful solutions.

As we started reaching out to more stakeholders, we reaffirm our commitment to ensuring meaningful engagement with Indigenous communities. To this end, we have conducted a thorough impact assessment to evaluate the direct effects of antibiotic resistance in these populations. For detailed findings and analysis, please visit our dedicated impact assessment page.

Who is this stakeholder?

Professor Albert Burghuise is a professor in the Biochemistry Department at McGill University, whose research is focused on the development of antimicrobials, antibiotic resistance, and crystallography. We reached out to Dr. Burghuise as we wanted his insights on our methods as well as to learn more about antibiotic resistance from his perspective.

What did they say?

Professor Burghuise helped us understand the urgent nature of antibiotic resistance, describing it second only to climate change. He also helped us understand that perspectives on antibiotic resistance can be subjective and may be underestimated due to the presence of alternative antibiotics. We learned how many existing antibiotics are derived from natural compounds that have co-evolved with bacteria for a long time, and bacteria have evolved resistance mechanisms against them over that time. We were surprised when he shared with us how scientists have observed resistance in places that are untouched by humans. He talked about a study in an isolated cave in Mexico that had never been exposed to antibiotics; however, scientists found bacteria that were already resistant to many of our modern drugs. He highlighted that we are not discovering new forms of resistance; rather, we are selecting for bacteria that already possess these defenses, and that is why resistance seems to appear rapidly after new antibiotics are introduced.

Professor. Burghuise also advised that our dataset’s quality and relevance are crucial aspects of our project and eminent for its success. He also shared some insights to keep in mind for our hardware sample management system and colony detection. He pointed out that the integration of Internet of Things (IoT) devices could lead to a large amount of data and that this is an existing problem in hospitals as well. He explained how, despite there being a large interest in incorporating IoT, existing systems are not functioning as effectively as desired. We also learned that there is a concern regarding the information in these systems not being effectively utilized; this issue stems from data overload, making it challenging for researchers to extract meaningful insights. A suggestion from the professor to address data overload was to develop AI algorithms that can sift through vast amounts of data and identify relevant patterns or insights.

Reflections or Next Steps

Our conversation with Professor Burghuis significantly influenced how we engage stakeholders and approach discussions on antibiotic resistance. We came to understand that the perceived threat of antibiotic resistance is often subjective and can be downplayed due to the availability of alternative antibiotics. However, Professor Burghuis emphasized that antibiotic resistance has deep historical roots—it’s not just a modern issue. This realization is key to how we educate the public, many of whom view antibiotic resistance as a recent development. By highlighting its long-standing presence, we can more effectively convey the urgency of addressing this growing problem and emphasize its pervasive, ongoing impact.

We also took his concerns about data overload seriously, and subsequently had a discussion with our hardware team about the issue. They explained the challenges they face, particularly with bandwidth limitations and database size. One critical point was that our communication servers have limited bandwidth, and storing large amounts of data isn’t feasible. This led us to rethink our database structures, ensuring they are as compact as possible, storing only essential data such as timestamps to track sample age, without unnecessary information. We also learned that the larger the database, the more expensive and difficult it becomes to maintain, particularly with the microcontrollers we’re using, which have very limited memory capacity.

Regarding the development of AI algorithms, our team admitted that we don’t currently have the capacity to create sophisticated data-sifting tools. There is also a disconnect between industry practitioners and AI experts, as the manufacturing environment is slow to adopt these technologies. Implementing machine learning or AI into our system would require a significant commitment and shift in our current methods, which may not be feasible in the short term.

In conclusion, while we continue to navigate the complexities of the data overload challenge, this conversation has significantly clarified our project's direction regarding feedback integration and data management. As we move forward, we acknowledge that enhancing our data storage processes is a longer-term objective.

Who is this stakeholder?

Dr. Karine Auclair is a professor at McGill University and a Canada Research Chair Antimicrobials. Her research focuses on antibiotic resistance, and she is currently developing a novel treatment for intracellular infections that is free of antibiotics, enhancing the immune system's ability to combat infections. We reached out to her to gain insights into our project, particularly concerning current trends, ethical considerations, and practical challenges in the field of antibiotic resistance.

What did they say?

Dr. Auclair highlighted several key areas for us to consider. In terms of current trends, she pointed out that antibiotic resistance continues to evolve rapidly, particularly as researchers turn to artificial intelligence (AI) for designing inhibitors targeting resistant bacterial strains. She emphasized the importance of keeping an eye on medical chemistry developments and their potential applications in our plasmid designs.

Regarding strategies for plasmid development, Dr. Auclair noted the complexity of plasmid mutation and resistance mechanisms, advising us that the main challenge is the diversity of resistance pathways. This might require the development of multiple systems to address resistance across different bacterial species.

On the topic of ethical challenges, Dr. Auclair observed that research funding often follows economic interests, with more resources directed toward diseases affecting wealthier nations, such as cancer. This makes securing funding for conditions like tuberculosis, which primarily affects less affluent countries, more difficult.

She also pointed out several practical challenges, such as the stringent regulations involved in working with pathogenic bacteria, which require certification for handling level 2 bacteria. Additionally, when designing plasmids, she advised us to be mindful of the different parts involved, such as multiple cloning sites.

Lastly, she mentioned the influence of new technologies, including natural product biosynthesis and advances in AI and RNA sequencing, which have revolutionized how researchers approach antibiotic resistance today.

Reflections or Next Steps

Dr. Auclair highlighted the complexity of resistance mechanisms and the diversity of resistance pathways among different bacterial species. This insight reinforces our approach to designing plasmids that offer a stronger selective advantage than existing resistance-carrying plasmids. By focusing on creating plasmids that are less metabolically burdensome, easier to replicate, and fundamentally incompatible with target plasmids, we aim to address the multifaceted nature of antibiotic resistance. Our strategy directly responds to the challenge of diversity in resistance mechanisms by ensuring that our plasmids can effectively compete across various bacterial species.

Dr. Auclair’s insights on funding disparities in antibiotic research emphasizes the need for diverse funding sources. While our project does not focus on diseases like tuberculosis, understanding these challenges is important. Our finance team has effectively secured the necessary resources, allowing us to advance our technology by laying the groundwork to combat antibiotic resistance.

Additionally, her insights into regulatory challenges underscore the importance of compliance. Our wet lab has conducted thorough safety assessments to ensure we meet all necessary certifications and adhere to safety standards in our practices. Overall, our engagement with Dr. Auclair has refined our strategies for addressing antibiotic resistance by highlighting key considerations in plasmid design, funding, and regulatory compliance. We will use these insights to improve our project and ensure it aligns with the ethical and practical aspects of the field. This approach will help us navigate the complexities of antibiotic resistance more effectively.

Who is this stakeholder?

Dr. Garton is an Assistant Professor at the Institute of Biomedical Engineering at the University of Toronto. He is the Primary Investigator on the iGEM Toronto project and he has provided crucial guidance throughout the program. He has a strong background in high-throughput computational protein design and is a Canada Research Chair for Synthetic Biology.

What did they say?

Dr. Garton shared the eventual goal of making a fully functional genome. He additionally helped contextualize the role of antibiotic resistance in this project. Through discussion about how to address this pressing issue, iGEM and Dr. Garton decided to generate novel plasmids to increase biodiversity in an effort to combat novel diseases.

Reflections or Next Steps

Discussion with Dr. Garton was a springboard for the remainder of the project and from his insights we were able to proceed with the direction of our project. We evaluated the feasibility of generating plasmids using AI and began to synthesize plasmids as well as test them in vitro.

Who is this stakeholder?

Dr. Lan is an Assistant Professor with the Institute of Biomedical Engineering at the University of Toronto. He specialises in utilising high-throughput experimental methods to study complex disease, which aligns closely with our project.

What did they say?

Dr. Lan provided essential counsel on which specific sequences would be most beneficial to generate and test in vitro. Originally, we were operating with the belief that the model would generate whole plasmids, however, it was quickly realised that this may not be the best approach as this would not help us isolate our observations to any one region of the plasmid. Our initial plan was then to evaluate the role of the origin of replication (oriV) and Toxin-Antitoxin (TA) systems. We discovered, with the help of Dr. Lan, that the oriV would be the most vital sequence to assess.

Reflections or Next Steps

From our conversations with Dr. Lan, we were able to glean that the oriV is the sequence of the plasmid where we should be focusing our efforts. As such, for the remainder of the project, the oriV was our primary target and we deprioritised evaluation of TA systems.

Who is this stakeholder?

Endang (Susie) Susilawati is the Lab Manager of BioZone at the University of Toronto, where iGEM was able to execute the wet lab portion of the project. She is generally responsible for the safety and maintenance of lab equipment.

What did they say?

Through the conduction of lab tours and equipment demonstrations, Susie provided our team with invaluable safety training. She was also our point of contact when there were any issues with the lab equipment.

Reflections or Next Steps

The lessons imparted to our team ensured that our team would be kept safe throughout the project. Our actions after safety training largely reflected the workflows and best practices indicated to us by Susie.

Who is this stakeholder?

Dr. Nasr is a Postdoctoral Fellow with BioZone at the University of Toronto.

What did they say?

Dr. Nasr provided some additional training on lab equipment as well as providing some of it directly, which was greatly beneficial to our wet lab endeavors.

Reflections or Next Steps

We made use of the know-how granted by Dr. Nasr to ensure that our project moved at a consistent and safe pace.

Who is this stakeholder?

Dr. Abo-Hashesh is an Assistant Professor at Port Said University and a Research Fellow/Associate with BioZone at the University of Toronto.

What did they say?

Dr. Abo-Hashesh provided relevant feedback and experimental analysis when our PCRs did not work as expected. Specifically, our positive control was not working and she provided counsel that allowed us to troubleshoot and get back on track. We later determined that the issue was we had received the incorrect plasmids and had to reassess our strategy and pivot to alternative plasmids.

Reflections or Next Steps

Moving forward from speaking with Dr. Abo-Hashesh, we were able to more clearly ascertain any errors in future PCR and improve the efficiency of our workflow.

Who is this stakeholder?

Victoria Ivey is a PhD student studying Chemical and Biomolecular Engineering at the University of Toronto. Her history is primarily based in biochemistry and molecular biology with her PhD focusing on the characterisation of microorganisms, which overlaps with our project.

What did they say?

Victoria aided in ensuring that our positive control worked consistently.

Reflections or Next Steps

As a result of Victoria’s support, we were able to develop a benchmark by which to compare future experiments and improve our understanding of our experimental results.

Who is this stakeholder?

Mark Spahl is a PhD student in Metabolic Engineering at the University of Toronto.

What did they say?

Mark helped us fix our positive control, situate ourselves in the lab, and understand some equipment.

Reflections or Next Steps

Mark’s advice allowed us to quickly and smoothly lift our wet lab work off the ground and understand how to operate moving forward.

Who is this stakeholder?

Matthew is a research assistant.

What did they say?

Similarly to Mark and Victoria, Matthew helped us with our positive control. He additionally helped with the operation of some equipment.

Reflections or Next Steps

Due to Matthew’s help with our project, we were able to operate machinery and proceed with our experiment in a more efficient manner.

What did they say?

Prof. Garton was also instrumental in developing the foundations and approach of the project and determining key milestones. He directed us to existing research in the field and helped highlight gaps. He also connected us with graduate students who could give feedback.

Reflections or Next Steps

Prof. Garton’s mentorship was crucial in determining the essential milestones for the project. We used his advice to design the validation pipeline and set the milestone of testing oris.

What did they say?

Prof. Lan supported the dry lab team by brainstorming the validation pipeline and finding useful datasets and tools. He helped shape our initial thinking around metrics needed to validate plasmids as well as useful components to test for, such as oris and toxin/anti-toxin pairs.

Reflections or Next Steps

Based on Prof. Lan’s advice, the dry lab team explored existing research on ori identification more thoroughly and established specific metrics for testing those components.

Who is this stakeholder?

Professor Jonathan Herrington is an Assistant Professor at the University of Rochester, specializing in the philosophy of science, health, and technology. We reached out to him to explore the bioethical dimensions of our project, particularly its dual-use nature, equity concerns, model bias, and to gather insights on structuring a regulatory framework that aligns with ethical considerations.

What did they say?

Additionally, Dr. Herrington highlighted the importance of aligning our technology with global principles of justice and equity. He cautioned that our AI-generated plasmids could exacerbate existing global health disparities, particularly in countries like India.

To address the dual-use nature of biotechnology, he advocated for the establishment of ethical safeguards to prevent the misuse of our plasmids. He recommended reinforcing our current efforts by establishing a hybrid regulatory framework that combines three existing models: the NASM framework, the FBI-UNICRI framework, and the Tucker framework. This approach will ensure comprehensive oversight and enhance our commitment to responsible biotechnology practices.

Reflections or Next Steps

In response to Dr. Herrington's insights, we recognized the importance of identifying and mitigating biases in the design of our plasmids. Acknowledging our limitations as a student team, particularly regarding funding for comprehensive bias mitigation, we have prioritized transparency about our dataset's limitations. This commitment has helped us navigate the ethical implications of deploying our technology across diverse populations.

To further this goal, we proactively reached out to various healthcare professionals in India to gain a deeper understanding of antibiotic resistance and its implications in various contexts. This engagement has informed our approach and helped ensure that our technology aligns with global principles of justice and equity.

In addressing the dual-use nature of biotechnology, Dr. Herrington emphasized the need for ethical safeguards to prevent the misuse of our plasmids. We have already integrated his recommendations into our regulatory framework by building upon established models such as the NASM framework, the TUCKER framework, and the FBI-UNICRI framework. Moving forward, we will continue to prioritize transparency and ethical considerations in our project, ensuring that our efforts align with best practices in biotechnology governance.

Who is this stakeholder?

Dr. Jeeva Venkatesan is a practicing dentist based in India, specializing in oral health and the role of antibiotics in dental practices. Our engagement with Dr. Venkatesan followed discussions with Professor Herrington, as we sought to understand antibiotic use and resistance, particularly in the dental field. Her insights are crucial for informing our project on antibiotic resistance, especially regarding the impact of socio-economic factors on treatment adherence.

What did they say?

In our discussions, Dr. Venkatesan highlighted that antibiotic resistance in dentistry often develops from both overuse and underuse of medications like amoxicillin. She explained that antibiotics are typically prescribed to manage oral infections and prevent systemic complications. However, economic challenges significantly influence patient behavior, leading many to take antibiotics for only a short duration or to stop treatment once their pain subsides. This incomplete adherence contributes to the development of resistance.

Dr. Venkatesan emphasized the need for improved access to affordable antibiotics, suggesting that government intervention is necessary to reduce medication prices and ensure equitable access for all patients. While most healthcare professionals are aware of these issues, she stressed the importance of prioritizing these concerns at the policy level to foster responsible prescribing practices.

Reflections or Next Steps

Dr. Venkatesan’s insights have deepened our understanding of the socio-economic factors influencing antibiotic use and resistance. We now recognize the importance of community engagement in our project, and we will actively seek to learn more from the communities affected by antibiotic resistance.

• Community Interaction: We will engage with local communities to better understand their experiences and challenges regarding antibiotic use in dental care. This will involve listening sessions and focus groups to gather feedback that can inform our approach.
• Integration of Insights: By incorporating Dr. Venkatesan's feedback, we will ensure that our project not only develops innovative solutions to combat antibiotic resistance but also addresses the socio-economic barriers faced by patients. This holistic approach will enhance the relevance and impact of our work, aiming to create a more sustainable healthcare model.

We have also learned the importance of policy advocacy and will keep this in mind as a long-term goal for our project. This awareness will guide our future efforts to ensure that our initiatives align with broader healthcare policies that promote equitable access to antibiotics and responsible prescribing practices.

Through these actions, we aspire to foster a more equitable approach to tackling antibiotic resistance.

Who is this stakeholder?

Dr. Barathi Dhasan is a veterinarian based in India with extensive knowledge of animal health and antibiotic use. Our discussions with Dr. Dhasan focused on antibiotic resistance in livestock and poultry, providing valuable insights into the implications of antibiotic use in veterinary medicine.

What did they say?

In our conversations, Dr. Dhasan explained that high levels of antibiotics are commonly used in the treatment of infections in cows, particularly for conditions like mastitis caused by coliform bacteria. He noted that the increasing prevalence of antibiotic resistance is a significant concern, particularly due to the indiscriminate use of these drugs.

He highlighted that livestock farmers often administer antibiotics to prevent secondary bacterial infections that arise following viral infections in poultry. This practice is driven by economic necessity, as poultry is typically kept for short durations to maximize yield, leading to extensive antibiotic use to ensure they reach market weight efficiently.

Dr. Dhasan pointed out that the overuse of certain classes of antibiotics, such as cephalosporins and aminoglycosides, contributes to resistance in both large animals and poultry. While penicillin may yield good results, drugs like sulfonamides are becoming less effective. He emphasized the need for better education among veterinarians regarding responsible antibiotic use, given that some practitioners may lack adequate training.

Economic barriers play a crucial role in antibiotic use, as many pet owners and farmers cannot afford diagnostic testing. This often leads to a reliance on empirical treatments without proper culture and sensitivity testing. Dr. Dhasan mentioned that injectable antibiotics are often preferred for their effectiveness, though they come with higher rejection rates, complicating treatment further.

Reflections or Next Steps

From our discussions with Dr. Dhasan, we learned about the critical issues surrounding antibiotic resistance in veterinary medicine and the potential parallels with human health. We now recognize that our project could potentially extend its applications to veterinary practices, particularly in addressing antibiotic resistance.

• Potential Applications: We will explore how the principles of our project, especially the development of resistance-nullifying plasmids, could be adapted for veterinary applications, particularly in the treatment of mastitis and secondary infections in poultry.
• Interdisciplinary Insights: The insights gained from Dr. Dhasan have highlighted the interconnectedness of antibiotic use across species. Understanding these dynamics will inform our broader strategy in combating antibiotic resistance.
• Long-Term Goals: We will keep in mind the importance of responsible antibiotic stewardship as a long-term goal of our project, recognizing that solutions in human medicine can also have relevance in veterinary contexts.

Overall, we gained valuable insights into antibiotic resistance in veterinary medicine, which have highlighted the potential for our project to address similar challenges across both human and animal health sectors.

Who is this stakeholder?

Professor Elizabeth Koester is a former lawyer currently serving as a professor at the University of Toronto in the Institute for the History & Philosophy of Science & Technology. She is the author of In the Public Good: Eugenics and Law in Ontario, a work that examines the intersection of law, history, and societal values. Our discussions with Dr. Koester focused on how historical perceptions might influence current views of our project. We also explored the relationship between traditional medical practices and the adoption of modern treatments like antibiotics in various communities. Additionally, we learned how historical factors such as colonialism and healthcare disparities have shaped contemporary attitudes toward antibiotic use in marginalized populations.

What did they say?

In our discussions, we sought to address the potential for inequality in implementing our project. Dr. Koester highlighted the significant disparities in access to medical services and information, which could be exacerbated by advancements in medicine. She cautioned against focusing solely on the solutions offered by AI without acknowledging the accompanying medical inequalities.

Regarding public reception, she noted that some communities remain suspicious of medical involvement, stemming from a historical context where traditional practices were often disregarded in favor of Western medicine. Despite this, many traditional remedies have a scientific basis; for instance, aspirin was derived from tree bark used in traditional practices. Dr. Koester observed that while some individuals express concern about vaccines, acceptance can vary depending on the type of medication.

To foster positive public engagement, she stressed the importance of respecting community opinions, providing education on AI and antibiotic resistance, and maintaining transparency. She drew parallels to vaccine skepticism, emphasizing that cautious reactions could similarly manifest towards our project.

Dr. Koester also discussed how historical factors, such as colonialism, have contributed to stereotypes and unequal opportunities in healthcare. She cited instances of unethical medical practices, such as drug testing in India and the Tuskegee syphilis study, which have left lasting distrust in certain communities. Successful integration of traditional and modern medical practices requires a respectful collaboration with these communities.

Regarding ethical considerations, Dr. Koester expressed confidence in the stakeholder consultation process, noting that ample thought has gone into preventing ethical issues. However, she reiterated the potential for inequality, emphasizing that disparities in access to medical services persist, even in urban areas like Toronto. Understanding the historical context of medicine is crucial for informing our ethical approach, particularly in communities that have experienced healthcare disparities.

Reflections or Next Steps

Given the existing inequalities in healthcare, we acknowledge that our project could inadvertently exacerbate these issues if not made accessible to all. We are committed to addressing this challenge by fostering education and respecting diverse opinions. Following Professor Koester's guidance, we have embraced full transparency in our project, recognizing that public knowledge is vital for building trust.

• Community Engagement: To enhance public understanding of AI in healthcare, we have organized engaging workshops, such as "Paint and Sip," where participants expressed their perspectives on AI in synthetic biology through art. These workshops not only encourage critical thinking but also provide an avenue for the public to engage with our approach to antibiotic resistance via engineered plasmids, fostering informed perspectives.
• Broader Engagement: Additionally, we designed AImersion, a community engagement project, inviting the general public to participate in the same artistic exploration, thereby gathering diverse viewpoints for a more holistic approach.
• Understanding Traditional Medicine: In our efforts to understand broader perspectives, we reached out to individuals who practice traditional medicine. This engagement allowed us to explore how these practices align or differ from modern treatments, offering valuable insights that could shape our project’s accessibility and acceptance in various communities.
• Vaccine Skepticism: We also took steps to engage in conversations with individuals regarding vaccine skepticism. Our aim was to understand their concerns without judgment, and we found this to be a crucial exercise in drawing parallels with how some may view AI-driven medical interventions. This approach has deepened our understanding of public attitudes and helped us refine our strategy.

By focusing on education, transparency, and respect, we aim to contribute positively to the ongoing discourse surrounding healthcare and antibiotic resistance.

Who is this stakeholder?

Professor Luis Campos serves as an Associate Professor and Co-Director at Rice University, focusing on the History of Science, Technology, and Innovation. He has a particular interest in the history of synthetic biology, among other topics.

What did they say?

During our consultation with Professor Campos regarding our approach to human practices, we learned that some opinions towards modern science, often expressed as “conspiracy theories” or “antiscience,” could actually stem from a place of caution. We learned that these perspectives reflect valid concerns and emphasized the importance of approaching them with understanding and empathy.

Reflections or Next Steps

Inspired by our discussions with Professor Koester and Professor Campos, we took the initiative to engage with individuals who are vaccine hesitant. We posted on a Reddit forum dedicated to vaccine-related discussions, where we explained our project and asked them to share their insights on vaccine hesitancy. Our aim was to understand the diverse perspectives surrounding vaccine hesitancy, to ensure our project is inclusive of all forms of caution.

Our engagement does not fall under the definition of "research" as outlined in the TCPS2. Research is characterized by "an undertaking intended to extend knowledge through a disciplined inquiry or systematic investigation." [28] In this instance, our activities involved soliciting insights rather than conducting a structured research study [28]. We do not collect or retain any identifiable data, as the discussions are anonymous and no personal information is linked to the insights shared [28]. Furthermore, there was no systematic investigation or analysis of the insights gathered, and we are not disseminating findings in the conventional sense, but rather publishing the perspectives as they were shared. Importantly, we obtained explicit consent from all participants to share their views, reinforcing the ethical consideration in our approach [28]. Given these points, our activities align with the TCPS2 definition that does not necessitate research ethics review. We are committed to transparency and ethical engagement in all our interactions.

These are the unique perspectives that we received:

• 1. Concerns about Vaccines: One individual expressed profound fear stemming from a personal experience at age 17, where they experienced severe pain shortly after vaccination, leading them to associate vaccines with potential death or injury. They emphasized that their trust in healthcare professionals is crucial, as without it, they feel unwilling to accept vaccinations.
• 2. Trust and Healthcare: Another perspective highlighted a deep-seated mistrust in healthcare institutions. They noted a history of pharmaceutical companies misrepresenting their products, leading to distrust in healthcare recommendations. This skepticism is compounded by personal experiences with adverse reactions in their family following vaccinations, which has ultimately led them to question the integrity of the medical establishment.
• 3. Community Narratives: A participant mentioned the significance of personal experiences in shaping their views on vaccines. They observed that while community narratives can influence perceptions, the trustworthiness of the individuals sharing those narratives plays a critical role. They also remarked that communities tend to become polarized, making it difficult to change entrenched beliefs on either side of the vaccine debate.
• 4. Alternatives to Vaccination: One perspective suggested allowing the body’s natural defenses to handle diseases, citing personal health success without further vaccinations as evidence of this approach's safety. They expressed a preference for alternatives that do not involve vaccination, emphasizing their belief in the body's inherent mechanisms.
• 5. Developing Solutions: This individual noted that solutions aligning with their perspective already exist; they seek to be left alone regarding vaccine mandates. They stressed the need for transparency and ethical considerations in developing any health solutions, reflecting their desire for autonomy in health decisions.
• 6. Toxic Ingredients and Safety: Concerns were raised regarding the ingredients in vaccines, particularly aluminum adjuvants, and their potential neurotoxicity. The individual called for more rigorous safety studies conducted by independent parties to ensure that the risks associated with vaccine ingredients are thoroughly evaluated.
• 7. Personal Experiences of Adverse Reactions: This individual recounted their family’s experiences with severe adverse reactions to vaccines, which led them to seek answers that contradicted established medical narratives. They emphasized the importance of sharing experiences to build a clearer understanding of vaccine safety.
• 8. Trust in Healthcare Evolution: Another individual discussed the evolving nature of trust in healthcare professionals. They noted that their upbringing instilled trust in medical institutions, but ongoing negative experiences have significantly eroded this trust. They highlighted the necessity for healthcare to distance itself from pharmaceutical influences to regain public confidence.
• 9. Skepticism Towards Medical Research: A perspective emerged emphasizing the need for a more critical examination of medical treatments beyond vaccines. This individual expressed a desire for alternative medical options to be more vigorously explored, advocating for freedom of choice in health decisions without social stigma or mandates.
• 10. Expertise and Skepticism: This individual emphasized the importance of recognizing cultural biases in public health and the need for alternative perspectives in medical discussions. Their personal health challenges, including long COVID and adverse reactions to medications, further shape their skepticism.

Reflections or Next Steps

Moving forward, our aim was to implement the suggestions provided by the individuals we engaged with, particularly focusing on enhancing educational outreach and building trust within communities. Our recent seminar, which reached over 600 students through social media, exemplifies our commitment to this mission. During the seminar, we raised awareness about antibiotic resistance and provided a transparent overview of the methods used in our project, fostering an open dialogue about our approach and the implications for public health.

We wanted to hold a debate to explore the long-term concerns and opinions surrounding Plasmid AI. Our debate topic was, “Is it ethical to commercialize AI-generated antibiotic resistance solutions without long-term studies?” This discussion aimed to address the varying perspectives on whether immediate commercialization is responsible and ethical, considering the potential benefits alongside the risks associated with a lack of long-term safety data.

Conversely, Dawei Wang supporting the commercialization of a solution without long terms studies. Highlighting the following points.

1. Urgency of the Antibiotic Resistance Crisis: The speaker argued that the escalating threat of antibiotic resistance demands immediate action. Delaying the introduction of potentially life-saving treatments due to the absence of long-term studies could result in unnecessary loss of life as the crisis deepens.
2. Innovative Potential of AI: They highlighted AI's ability to rapidly generate new antibiotic compounds and analyze extensive datasets to stay ahead of evolving pathogens. This innovative capability significantly reduces the time required to develop effective treatments compared to traditional approaches.
3. Role of Commercialization in Progress: The speaker emphasized that commercialization is critical for translating research into practical applications. It drives rapid development, secures funding for ongoing research, and facilitates access to essential innovations in healthcare settings. Regulatory bodies could impose safety measures and conduct post-market surveillance to mitigate safety concerns without hindering access to necessary treatments.
4. Balancing Safety and Urgency: Finally, the argument acknowledged common safety concerns while asserting that AI could help mitigate risks through predictive modeling during development. Thus, the ethical commercialization of these solutions does not involve bypassing safety protocols but rather finding a balance that enables timely action in the face of a public health emergency.

Another argument supporting the commercialization of AI-generated antibiotic resistance solutions emphasized the distinction between efficacy and commercialization. This anonymous speaker noted that the challenge of demonstrating the effectiveness of these solutions is fundamentally a scientific issue, separate from the process of bringing them to market.

1. Scientific Challenge vs. Market Process: They highlighted that individuals possess the autonomy to choose whether or not to utilize these solutions, even if they are released without extensive long-term studies. The speaker argued that this autonomy is crucial, as it allows people to weigh the risks and benefits based on the information available to them at the time.
2. Autonomy and Choice: Additionally, they pointed out that long-term studies cannot guarantee safety or efficacy for every individual. Instead, real-world usage can provide invaluable data that contributes to the optimization of these solutions. By allowing the public to access these treatments, we foster an environment where feedback can drive continuous improvement, ultimately offering better options for those in need.
3. Real-World Data and Continuous Improvement: In reflecting on this perspective, we recognize that our educational initiatives, such as seminars, have been instrumental in fostering transparency about the technology we are developing. Engaging in discussions about historical injustices with experts has also enriched our understanding of public concerns, highlighting the importance of responsible innovation and informed consent in our approach to combating antibiotic resistance.

Reflecting on these diverse perspectives has helped us better understand the ethical implications of our project. Our debate highlighted the importance of transparency regarding the technology we are developing. By discussing the potential benefits and risks of AI-generated solutions, we aim to address public concerns and build trust. Collaborating with experts like Dr. Koester and Dr. Herington has deepened our understanding of historical injustices and ethical considerations in antibiotic resistance. These discussions emphasize our commitment to responsible innovation and informed consent.

Our wiki has been fully transparent about our work, and we remain committed to keeping our stakeholders informed. We've also developed educational materials, such as our article with the Science Communication Club [SCC], to further engage the public. Insights from the debate will guide our long-term goals, ensuring that we prioritize public safety and autonomy as we work on effective solutions for antibiotic resistance.

We asked people from all walks of life to draw what AI integration in synthetic biology looks like to them. Each art piece is unique to every individual's opinion and perception of the impact AI would have on the science community. To encourage participation, we distributed instructions and invited participants to spread the word through social media, creating a broader conversation around the impact of AI in the scientific community.

We also organized a Paint and Sip night, where university students came together to discuss and share their thoughts on AI in synthetic biology while engaging in artistic expression. This event fostered a creative atmosphere for open dialogue and collaboration, allowing participants to explore their ideas through art.

Ethical Consideration

Our engagement does not fall under the definition of "research" as outlined in the TCPS2 [28]. Research is characterized by "an undertaking intended to extend knowledge through a disciplined inquiry or systematic investigation." In this instance, our activities involved soliciting insights rather than conducting a structured research study. We do not collect or retain any identifiable data, as the discussions are anonymous and no personal information is linked to the insights shared. Furthermore, there was no systematic investigation or analysis of the insights gathered, and we are not disseminating findings in the conventional sense, but rather publishing the perspectives as they were shared. Importantly, we obtained explicit consent from all participants to share their views, reinforcing the ethical consideration in our approach. Given these points, our activities align with the TCPS2 definition that does not necessitate research ethics review. We are committed to transparency and ethical engagement in all our interactions.

To ensure ethical use of the artwork, each artist has signed a consent form, confirming their understanding and agreement to the inclusion of their creations in our project. This commitment reflects our dedication to respecting the rights and contributions of all participants involved [28].

We hosted a paint and sip event where we provided paint, canvases, and brushes. Through this project, we learned of many perspectives.

The Dangers of AI

Some artwork highlights the potential dangers of AI integration, particularly concerns surrounding the possibility of an AI takeover. This topic has sparked widespread debate and raises ethical questions about the use of AI in biology. Recognizing that AI can be an unpredictable tool accompanied by significant risks, we engaged with various experts to explore ways to foster public confidence in our technology. Through these conversations, we learned that transparency is essential in addressing the public's concerns. By prioritizing open communication about our processes and intentions, we aim to build a foundation of trust and understanding as we navigate the complexities of AI integration in health technologies.

The Middle Ground

Some individuals expressed a nuanced view of AI. They acknowledged the potential benefits of AI while also conveying a belief that there is no "silver bullet" solution to its integration in various fields, including health technologies. This perspective highlights the importance of balancing enthusiasm for technological advancement with a realistic understanding of the complexities and limitations inherent in AI. These individuals appreciated the innovative capabilities of AI and recognized its role in advancing scientific research and medical practices. However, they also emphasized the necessity for careful consideration of the ethical implications, risks, and the need for robust oversight. This balanced viewpoint reinforces our commitment to fostering informed discussions around AI, ensuring that all voices are heard as we navigate the challenges and opportunities that come with integrating AI into society.

The Positive Potential of AI

Many individuals express optimism about the potential of AI to benefit humanity and advance various fields of science, particularly in medicine. This enthusiasm underscores a widespread recognition of AI as a valuable tool for driving scientific innovation. Through our interactions, we discovered that many are eager to explore the possibilities AI offers and its diverse applications. To foster this interest, we organized events such as the Paint and Sip night, which provided an engaging platform for participants to share their thoughts on AI integration. These enlightening conversations revealed excitement about the future of AI in science and its transformative capabilities, further inspiring our commitment to advancing this critical dialogue.

Reflections or Next Steps

AImersion revealed a spectrum of perspectives on AI in synthetic biology. Many attendees voiced their concerns about the unpredictability of AI and its ethical implications, while others highlighted its potential to drive scientific innovation, especially in medicine. Notably, some participants expressed a balanced view, recognizing AI's benefits while cautioning against the belief in a singular solution to its integration. This diversity of opinions is invaluable as we continue our journey.

As our project moves on we will:

• Enhance Transparency: We will prioritize open communication regarding our processes for integrating AI, ensuring the public feels informed and included in our journey.
• Encourage Continued Engagement: We plan to continue AImersion to further invite community input and facilitate discussions around AI. This will help us remain connected to the varied perspectives of the public.
• Balance Perspectives: By presenting both the advantages and risks of AI, we will help the public gain a well-rounded understanding of its implications and foster informed discussions.
• Collaborate with Experts: Engaging with thought leaders will aid us in addressing ethical considerations, navigating challenges, and refining our approach to AI integration, ensuring we remain accountable to the diverse viewpoints we've gathered.

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As our project has developed, we've encountered some delays in directly engaging with Indigenous communities, which has made it even more crucial for us to ensure that our work is genuinely relevant to those most affected by antibiotic resistance. To this end, we conducted an impact assessment to confirm that our efforts are focused on real needs rather than simply aiming for inclusivity. We remain dedicated to reaching out and building connections with these communities, ensuring that our strategies are tailored to address their unique health challenges and that our interventions are meaningful and equitable.

Antibiotic resistance, particularly in the form of methicillin-resistant Staphylococcus aureus (MRSA), poses a significant health challenge for Indigenous communities in Canada. These populations experience higher infection rates due to a combination of intersecting risk factors, including social determinants of health and limited access to healthcare resources[1]. Both hospital-acquired MRSA (HA-MRSA) and community-acquired MRSA (CA-MRSA) are prevalent in these communities, where overcrowding, inadequate sanitation, and poor housing conditions facilitate the rapid spread of infections [1].

The burden of MRSA among Indigenous populations is substantial, with studies indicating a prevalence rate of 14.78% for CA-MRSA in skin and soft tissue infections (SSTIs) within First Nations communities across five provinces [1]. The prevalence of MRSA is particularly pronounced in northern and remote regions, with rates reaching as high as 168.1 per 10,000 residents in northern Saskatchewan in 2006 and even higher in subsequent years [2].

Several environmental and socioeconomic factors drive the high prevalence of MRSA in Indigenous communities. Overcrowded housing, lack of sanitation, and limited healthcare access create conditions where infections can spread easily [2]. Indigenous populations are also more likely to have comorbidities, such as diabetes and alcoholism, which increase susceptibility to infections. For instance, approximately 21.43% of individuals in affected populations have diabetes, while 18.75% have cardiac diseases, both of which impair the immune response and complicate recovery [1].

Inadequate infection control measures in healthcare settings also contribute to the problem. Delays in diagnosis and treatment arise from a shortage of diagnostic tools and trained healthcare personnel. Tracking MRSA colonization in these areas is challenging due to insufficient surveillance systems, highlighting the need for improved healthcare infrastructure and infection control protocols [2].

Patterns of antibiotic resistance in MRSA strains isolated from Indigenous populations reveal critical insights. While MRSA strains are uniformly resistant to beta-lactam antibiotics, such as penicillin and cefazolin, there is variability in resistance to other classes of antibiotics. In remote First Nations communities, nearly all MRSA isolates have shown susceptibility to clindamycin, with about 60% remaining susceptible to erythromycin [1]. This suggests that although treatment options are available, evolving resistance patterns emphasize the necessity for tailored antibiotic stewardship programs.

Additionally, high susceptibility to trimethoprim-sulfamethoxazole (TMP-SMX), a common treatment for mild to moderate MRSA infections, has been observed [2]. However, resistance to fluoroquinolones and macrolides remains a concern, with over 50% of MRSA strains demonstrating resistance to these antibiotic classes. Continuous monitoring of antibiotic resistance patterns is essential to ensure effective treatment for MRSA infections in Indigenous communities.

The data clearly indicates that Indigenous communities are significantly affected by antibiotic resistance, particularly through high rates of antibiotic prescriptions and infections caused by CA-MRSA. To address these challenges effectively, it is essential to develop targeted strategies that not only focus on the development of new solutions, such as Plasmid AI, but also take into account the unique health profiles and needs of these vulnerable communities. This will ensure that interventions are inclusive, equitable, and responsive to the specific challenges faced by Indigenous populations in Canada.

1. Jeong D, Nguyen HNT, Tyndall M, Schreiber YS. (2020). Antibiotic use among twelve Canadian First Nations communities: a retrospective chart review of skin and soft tissue infections. BMC Infect Dis, 20(118):1-8. doi:10.1186/s12879-020-4842-1.
2. Mitevska E, Wong B, Surewaard BGJ, Jenne CN. (2021). The prevalence, risk, and management of methicillin-resistant Staphylococcus aureus infection in diverse populations across Canada: a systematic review. Pathogens, 10(393):1-22. doi:10.3390/pathogens10030393.

In our mission to raise awareness about antibiotic resistance [ABR], we aimed to highlight the potential of synthetic biology in addressing this pressing issue. Understanding that transparency in our methods is essential, we sought to educate future University of Toronto [UofT] students about our project and the broader implications of ABR. To accomplish this, we reached out to the SMC Mentorship program via email to explore the possibility of collaborating on a joint presentation aimed at incoming students. The primary objective was to introduce these students to the iGEM team and engage them in an informative online seminar focused on the critical issue of antibiotic resistance.

After establishing contact and receiving a positive response, we decided to broaden our outreach by involving two additional student clubs: Step-UOFT and Global Brigades. Both of these clubs are STEM-focused and emphasize the intersection of advocacy and science, making them ideal partners for this initiative.

To ensure smooth coordination, we organized and facilitated several meetings with representatives from iGEM, SMC Mentorship, Global Brigades, and Step-UOFT. During these meetings, we discussed logistics, including the seminar’s structure, content, and promotional strategies. Each team was tasked with developing personalized presentations that highlighted their unique perspectives and expertise on antibiotic resistance. Additionally, we planned comprehensive advertisement campaigns to maximize our reach. These campaigns included emails, posters, and social media posts, which were strategically designed to attract a large audience.

In the weeks leading up to the seminar, our combined efforts paid off as we successfully reached out to a substantial audience of both future and current UofT students, totaling approximately 600 participants. On the day of the seminar, each team delivered their presentations, providing valuable insights and raising awareness about antibiotic resistance. The collaborative effort not only educated the attendees but also fostered a sense of community and shared responsibility in addressing this global health challenge.

The University of Toronto Science Communication Club focuses on the public communication of current science, technology, engineering, and mathematics [S.T.E.M.] research to non-experts and the general public. The club strives to make science more accessible by producing and sharing accurate, unbiased, and creative science media. In addition to building bridges between professionals and non-experts, the SCC aims to spark interest in science communication both within and beyond the University of Toronto.

While the article is still in progress as illustrators are working on it, we look forward to sharing the draft below. Through this collaboration, we hope to engage a wider audience and foster a deeper understanding of antibiotic resistance and its implications for public health. Together, we are committed to enhancing the dialogue around this critical issue and empowering individuals with the knowledge they need to take action.

From the Digital Research Alliance of Canada monitoring:

• CPU Usage: 0.84 core years
• GPU Usage: 0.30 GPU years

To estimate the carbon footprint, we can use simplified emission factors for CPUs and GPUs:

• Average Emission Factor for CPUs: 0.5 kg CO₂ per core hour[5].
• Average Emission Factor for GPUs: 2.4 kg CO₂ per GPU hour, reflecting higher power usage[1].

CPU Emissions:

To calculate the CPU emissions, we use the formula:

CPU Emissions = 0.84 core years × (0.5 kg CO₂ per core hour) × (24 hours/day × 365 days/year)

Calculation:

CPU Emissions = 0.84 × 0.5 × (24 × 365) = 3,679.2 kg CO₂ (approximately 3.68 tonnes CO₂)

GPU Emissions:

To calculate the GPU emissions, we use the formula:

GPU Emissions = 0.30 GPU years × (2.4 kg CO₂ per GPU hour) × (24 hours/day × 365 days/year)

Calculation:

GPU Emissions = 0.30 × 2.4 × (24 × 365) = 6,307.2 kg CO₂ (approximately 6.31 tonnes CO₂)

Total Dry Lab Emissions:

Total Dry Lab Emissions = 3.68 tonnes (CPU) + 6.31 tonnes (GPU) = 9.99 tonnes CO₂.

Benchmark: According to a study on data center emissions, the average data center emits approximately 200 kg CO₂ per MWh of electricity consumed[2]. Given the high computational power required for AI projects, the emissions from the Plasmid AI Project are relatively significant but can be mitigated by transitioning to renewable energy sources and optimizing code efficiency.

To calculate the carbon footprint of the wet lab, we focus on significant consumables like ethanol, agar, and antibiotics. For simplification, major contributors like ethanol will be calculated, while minor consumables will be approximated.

• Ethanol: 2 liters, with an emission factor of 14 kg CO₂ per liter[1].
• Additional materials (e.g., LB broth, plasmid kits): estimated 50–100 kg CO₂ from production and transportation[1].

Ethanol Emissions:

To calculate the ethanol emissions, we use the formula:

Ethanol Emissions = 2 L × 14 kg CO₂ per L

Calculation:

Ethanol Emissions = 2 × 14 = 28 kg CO₂

Minor Chemical Contributions:

Approximate additional emissions from other chemicals (e.g., LB broth, plasmid kits) are estimated to be 50–100 kg CO₂ for production and transportation.

Total Wet Lab Emissions ≈ 78 kg CO₂.

Benchmark: The carbon footprint of laboratory chemicals and consumables varies widely. For example, the production of 1 kg of ethanol emits about 14 kg CO₂[3]. The emissions from the wet lab are relatively low compared to industrial chemical production but can be further reduced by refining protocols and choosing green suppliers.

The hardware used, including Arduinos, sensors, and 3D printing filaments, contributes to emissions during manufacturing and assembly.

• Arduino Boards: Approximately 1 kg CO₂ per unit (based on production and transportation data).
• Other Components (e.g., sensors, RFID, filament): A rough estimate of 100 kg CO₂ for all components, which includes various components that each contribute differently to the total emissions.

Total Hardware Emissions ≈ 100 kg CO₂.

Benchmark: The production of electronic components, such as Arduinos and sensors, typically involves significant emissions. For instance, the production of a single smartphone can emit around 70 kg CO₂[4]. The emissions from the hardware used in the Plasmid AI Project are moderate and can be reduced by using eco-friendly components and extending hardware life cycles.

Dry Lab: 9.99 tonnes CO₂

Wet Lab: 78 kg (0.078 tonnes) CO₂

Hardware: 100 kg (0.1 tonnes) CO₂

Total Current Carbon Footprint ≈ 10.17 tonnes CO₂.

While the wet lab emissions were estimated based on key consumables like ethanol and other materials, there are notable limitations in the current analysis:

• Lack of Runtime Data for Equipment: The calculations do not account for the energy consumption and associated emissions of equipment used in the wet lab, such as centrifuges, incubators, and refrigerators.
• Incomplete Assessment of Chemical Contributions: The focus on specific consumables (e.g., ethanol) may overlook other significant chemicals used in experiments that could contribute to the carbon footprint.
• Transport Emissions: The current analysis does not factor in the emissions associated with the transportation of materials to the wet lab.

Similar weaknesses exist in the analysis of hardware emissions:

• Absence of Runtime Data: The carbon footprint calculations for hardware do not consider the runtime or operational hours of the various devices used, such as Arduinos and sensors.
• Manufacturing and Disposal Emissions: The calculations do not include emissions associated with the disposal or recycling of these components at the end of their life cycles.
• Variability in Component Lifespan: Different components may have varying lifespans, which affects their total emissions per unit of functionality.

To reduce emissions in the dry lab, several strategies can be employed:

• Transition to renewable energy sources: Utilizing green-energy-powered data centers could significantly lower the emissions associated with CPU/GPU computing clusters.
• Optimize code: By improving computational efficiency, the project can reduce the number of CPU/GPU hours needed, cutting energy use.
• Switch to energy-efficient hardware: Adopting energy-efficient CPUs and GPUs can lower the power requirements per task, directly reducing carbon emissions.

• Reduce chemical usage: By refining protocols and scaling down experimental volumes, the wet lab can minimize the use of consumables like ethanol.
• Choose green suppliers: Sourcing materials from suppliers that use bio-renewable or low-emission production methods can help reduce the indirect footprint of materials.
• Implement recycling and reuse strategies: Using reusable plastic materials and optimizing plasmid preparation protocols to reduce waste can contribute to lowering overall emissions.

• Use eco-friendly components: Opting for components made from recycled or low-impact materials will reduce the embedded carbon in hardware purchases.
• Extend hardware life cycles: Reusing and refurbishing Arduinos, sensors, and RFID components will reduce the need for frequent replacements and their associated emissions.

• Track emissions more accurately: Implementing a Life Cycle Assessment (LCA) will allow for more precise tracking of emissions across the project’s various phases.
• Purchase carbon offsets: When emissions cannot be reduced further, carbon offsets can be purchased to neutralize the remaining footprint.

In light of the pressing ecological challenges we face in the Anthropocene, understanding and mitigating the carbon footprint of projects like the Plasmid AI initiative is imperative. This assessment revealed a total estimated carbon footprint of approximately 10.17 tonnes CO₂, encompassing emissions from the dry lab, wet lab, and hardware components.

Through careful calculations and contextual comparisons with industry benchmarks, we identified significant emissions contributions from various sources, particularly in the computational demands of the dry lab and the materials utilized in the wet lab. This analysis highlighted areas for improvement and underscored the importance of collecting more comprehensive data, such as equipment runtime and the full range of consumables used in experiments.

To reduce the carbon footprint effectively, we proposed a series of actionable strategies, including transitioning to renewable energy sources, optimizing resource use, and implementing recycling and reuse protocols. By adopting these measures, the Plasmid AI Project can not only minimize its environmental impact but also set a precedent for sustainable practices within the field of synthetic biology.

We would like to extend our gratitude to Bikramjit Singh Toor, an Associate Engineer from the Ministry of Environment, for their valuable expertise in helping us calculate our project's carbon footprint and guiding us in our future sustainability plans. Their support has been key in guiding our commitment to environmental responsibility and sustainability.

1. Amasuomo E, Imtiaz MA. (2016). Carbon footprint reduction strategies for different sectors of the economy. Br J Environ Sci, 4(4):1-10.
2. Ritchie H, Roser M. (2020). CO₂ and greenhouse gas emissions. Our World in Data [Internet]. Available from: https://ourworldindata.org/co2-emissions.
3. Schmidt M, Schwegler R. (2009). Uncertainties in a carbon footprint model for detergents; quantifying correlations and sensitivity. Environ Manage, 43(4):684-691. Available from: https://link.springer.com/article/10.1007/s11367-009-0123-3.
4. Schmidt M, Schwegler R. (2023). Framework for comparison of product carbon footprints of different manufacturing scenarios. Cambridge Core [Internet], 1(1):1-15. Available from: https://www.cambridge.org/core/services/aop-cambridge-core/content/view/17B5B1C3CDE5EA6DD7198A7D8C07D6DF/S2732527X23001943a.pdf/framework-for-comparison-of-product-carbon-footprints-of-different-manufacturing-scenarios.pdf.
5. Scrucca F, Barberio G, Fantin V, Porta PL, Barbanera M. (2021). Carbon footprint case studies: municipal solid waste management, sustainable road transport and carbon sequestration. In: Environmental footprints and eco-design of products and processes. Springer; p. 1-10.