Report on Interdisciplinary Research Advancing Sustainable Development Goals in Health and Innovation

Introduction

New research from the University of California, Los Angeles (UCLA) presents a significant advancement in biosensor technology, directly contributing to the achievement of several Sustainable Development Goals (SDGs), most notably SDG 3 (Good Health and Well-being). By developing a novel method to identify synthetic DNA-based receptors (aptamers), researchers are creating tools for the precise molecular monitoring of stress, a key factor in major non-communicable diseases. This report outlines the project’s methodology, outcomes, and its alignment with the global agenda for sustainable development.

Advancing SDG 3: Good Health and Well-being

The core of this research initiative is its potential to revolutionize health monitoring and personalized medicine, directly addressing the targets within SDG 3.

Targeting Non-Communicable Diseases (NCDs)

Chronic stress is a recognized contributor to a range of NCDs, including diabetes, heart disease, cancer, and neurodegenerative disorders. The project addresses SDG Target 3.4, which aims to reduce premature mortality from NCDs through prevention and treatment.

• Problem: Existing stress monitors rely on non-specific indicators like heart rate, failing to capture the underlying molecular activity.
• Solution: The new technology enables the creation of wearable sensors that can monitor specific stress hormones, offering a precise understanding of an individual’s physiological response to stress.
• Impact: This allows for early intervention and personalized management strategies to mitigate the health risks associated with chronic stress, thereby promoting well-being.

Breakthrough in Biomarker Detection

A primary achievement of the research is the successful identification of aptamers capable of distinguishing between the nearly identical stress hormones epinephrine and norepinephrine. This breakthrough is a critical step toward creating effective biosensors.

1. Development of a sensor for cortisol, another key stress mediator.
2. Creation of aptamers that can differentiate and detect critical stress hormones at their naturally low levels.
3. Paving the way for wearable devices that provide a comprehensive biochemical profile of stress.

Beyond stress, the aptamer design process has wide-ranging potential for creating biosensors for other human health biomarkers, including female reproductive hormones, further advancing diagnostics and personalized medicine in line with SDG 3.

Fostering SDG 9 and SDG 17: Innovation and Collaborative Partnerships

The project serves as a model for how interdisciplinary collaboration and strategic partnerships can drive scientific innovation, reflecting the principles of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 17 (Partnerships for the Goals).

An Interdisciplinary Approach to Innovation

The research emerged from a collaboration between mathematician Andrea Bertozzi and chemist Anne Andrews, demonstrating the power of integrating diverse fields to solve complex scientific problems. This approach is fundamental to achieving SDG 9, which calls for enhancing scientific research and upgrading technological capabilities.

• Challenge: The traditional method for identifying suitable aptamers was slow, unreliable, and akin to searching for a “needle in a haystack.”
• Innovation: By applying advanced machine-learning concepts to the chemical challenge, the team developed GMfold, an algorithm capable of predicting the structures of thousands of DNA sequences in seconds.
• Result: This innovation has turned an overwhelming problem into a practical, scalable solution, significantly accelerating biosensor development.

Enhancing Global Partnerships and Access to Technology

The project highlights the importance of multi-stakeholder partnerships, a cornerstone of SDG 17. The research was supported by critical funding from the National Institutes of Health (NIH) and the Simons Foundation, which fostered the interdisciplinary collaboration.

In a significant contribution to SDG Target 17.6 (Enhance cooperation on and access to science, technology and innovation), the research team has made the GMfold algorithm open-source. This action ensures that researchers worldwide can leverage this powerful tool, accelerating progress in medical science and personalized health monitoring across the globe.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article highlights research and development efforts that are directly connected to several Sustainable Development Goals. The primary focus on health innovation, scientific research, and collaboration aligns with the following SDGs:

• SDG 3: Good Health and Well-being: The central theme of the article is the development of a new technology to monitor stress and its impact on human health. The research aims to create tools for better diagnostics and personalized medicine, directly contributing to health and well-being.
• SDG 9: Industry, Innovation, and Infrastructure: The article details a significant scientific and technological innovation—the creation of the GMfold algorithm and its application in developing advanced biosensors. This represents an enhancement of scientific research and technological capabilities.
• SDG 17: Partnerships for the Goals: The success of the project is explicitly attributed to collaboration. This includes interdisciplinary partnership (mathematics and chemistry), institutional partnership (UCLA), and financial partnership with funding bodies (NIH, Simons Foundation). The decision to make the GMfold algorithm open-source further promotes global partnership and knowledge sharing.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s content, several specific targets can be identified under the relevant SDGs:

1. SDG 3: Good Health and Well-being

   • Target 3.4: “By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.” The article directly addresses this target by focusing on stress, which it links to non-communicable diseases like “diabetes, heart disease, cancer, and neurodegenerative disorders.” The development of a sensor to “quantify stress to improve health and wellness” is a tool for prevention and management of these conditions.
   • Target 3.d: “Strengthen the capacity of all countries… for early warning, risk reduction and management of national and global health risks.” The creation of wearable biosensors for stress hormones (epinephrine, norepinephrine) and other biomarkers serves as an early warning and risk management tool for individual health. By making the underlying algorithm (GMfold) open-source, the project strengthens the capacity of researchers “worldwide to leverage the tool for their own studies,” contributing to global health risk management capabilities.

2. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.5: “Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation and substantially increasing the number of research and development workers…” The article is a case study for this target. It describes “groundbreaking basic and translational research” funded by the NIH and the Simons Foundation. The development of the GMfold algorithm and its application in creating aptamer-based sensors is a clear example of enhancing scientific research and encouraging innovation.

3. SDG 17: Partnerships for the Goals

   • Target 17.6: “Enhance… international cooperation on and access to science, technology and innovation and enhance knowledge sharing…” The article emphasizes the “power of collaboration and shared knowledge.” The partnership between mathematician Andrea Bertozzi and chemist Anne Andrews is central to the story. Furthermore, the decision to make “GMfold open-source, allowing researchers worldwide to leverage the tool” is a direct action to enhance access to technology and knowledge sharing.
   • Target 17.16: “Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources…” The project exemplifies a multi-stakeholder partnership involving academia (UCLA), and funding agencies (“the NIH and NSF,” “the Simons Foundation”). This partnership successfully mobilized financial resources and shared knowledge and expertise between mathematics and chemistry to create new technology.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article implies several indicators that can be used to measure progress towards the identified targets:

• Development of new diagnostic and monitoring tools: An indicator for Target 3.4 and 3.d is the successful creation of the technology itself. The article states the breakthrough is “leading to wearable devices that will be used to monitor stress biochemistry” and has potential for “detecting a range of human health biomarkers.” The existence and application of these sensors are a direct measure of progress.
• Creation of new scientific methods and technologies: An indicator for Target 9.5 is the development of the “GMfold, an algorithm capable of predicting the secondary structures of thousands of DNA sequences within seconds.” This innovation is a measurable outcome of the research and development effort.
• Availability of open-source scientific tools: A key indicator for Target 17.6 is the public release of research tools. The article explicitly states, “the team made GMfold open-source, allowing researchers worldwide to leverage the tool for their own studies.” The number of downloads or citations of this tool could serve as a metric.
• Formation of interdisciplinary and multi-stakeholder research collaborations: An indicator for Target 17.16 is the establishment of partnerships. The article highlights the “collaboration between chemistry and mathematics” and the critical support from funding bodies like the “NIH and the Simons Foundation,” which serve as a model for such partnerships.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: chemistry.ucla.edu