Ending the HIV Epidemic: Equitable Access, Everyone’s Voice

A guest post by Amanda J. Wilson, Chief, Office of Engagement and Training; Leigh Samsel, NLM Planning and Evaluation Officer, Office of Strategic Initiatives; and Elizabeth A. Mullen, Manager of Web Development and Social Media, History of Medicine Division, National Library of Medicine at the National Institutes of Health.

This year’s theme for World AIDS Day is Ending the HIV Epidemic: Equitable Access, Everyone’s Voice. As the world’s largest biomedical library, NLM has a long history of supporting NIH’s efforts to end the HIV epidemic by providing equitable access to trusted biomedical information, supporting biomedical research, and highlighting the historical, social, and cultural context of this research. Expedient, reliable, free public access to NLM’s trusted biomedical information resources and literature collections advances the knowledge and treatment of HIV/AIDS worldwide, helps progress research to end the HIV epidemic and improves the health of people living with HIV.

Improving HIV/AIDS Health Information Access

NLM integrates multiple types of HIV literature, sequence, testing, and clinical trials data into a portfolio of resources that enables researchers to easily find related information and robustly supports global research to end the HIV epidemic. Free public access to citations to the scholarly literature is provided through NLM’s PubMed and Bookshelf, while free access to full-text is provided through PubMed Central and NLM Digital CollectionsGenBank and the Sequence Read Archive allow researchers to deposit and access publicly available DNA sequences, including HIV experimental and clinical genome sequences. NCBI Virus makes HIV and other virus sequences from RefSeq, GenBank, and other NLM repositories available. The HIV-1 Human Interaction Database includes information gleaned from literature about the interaction between human and HIV-1 genes and proteins.

NLM also provides a rich resource of easy-to-understand online health and wellness information available in English and Spanish via MedlinePlus. Among its wealth of content, MedlinePlus contains several HIV/AIDS topical pages. NLM supports an equitable distribution of information by providing resources and support to strengthen skills and literacies needed to access and use biomedical information for those affected by HIV/AIDS. The Network of the National Library of Medicine provides free health information training, from webinars to instructor-led classes to on-demand tutorials. Additional trainings on a variety of topics are added weekly.

A Long History of Support

NLM’s engagement and collections activities strive to capture the many voices in and around the HIV epidemic. A key historical partnership for engagement with the NIH Office of AIDS Research was the HIV/AIDS Community Information Outreach Program; beginning in 1994 as a resource for community-based organizations to improve the knowledge, skills, and technical means to access and provide the latest authoritative prevention, treatment, and research information electronically. The current iteration of the program concludes this month with 200 plus organizations conducting more than 350  projects in 38 states providing training, communications campaigns, and enhanced products and apps to raise awareness of HIV/AIDS information and discoveries over the 40-year history.

NLM has been collecting publications and archival materials related to HIV/AIDS since the first Morbidity and Mortality Weekly Report on the topic was issued in June 1981. The NLM HIV/AIDS Web Archive offers more than 150 websites documenting the biomedical, clinical, cultural, and social aspects of HIV/AIDS in the early 21st century. This month you can look for new additions and resources.

World AIDS Day 2021

Screen shot of NLM's History of Medicine Division's exhibit entitled "AIDS, Posters and Stories of Public Health"

This month, to help mark World AIDS Day, NLM will launch a new online exhibition—A People’s History of Pandemic: AIDS, Posters, and Stories of Public Health. The NLM exhibition will cover the archive of public health posters about AIDS rooted in the cultural output of artists, activists, and community workers. Their work, specifically the use of personal narrative as a visual-art strategy, along with language and the collective process of creating AIDS posters, continues to broadcast the message that, 40 years after the crisis began, attention to AIDS has not diminished. In mid-December, NLM’s Profiles in Science, will release a curated collection of digitized primary source materials related to the United States National Commission on AIDS, dating from the mid-1980s through the early 1990s. Learn more about the diversity of NLM’s historical collections related to HIV/AIDS on Circulating Now.

As we mark this year’s World AIDS Day, NLM is proud to continue its efforts to provide global access to trusted resources; share the voices of those affected by HIV; and provide the foundation for researchers, clinicians, patients, and families to engage and answer the call to end the HIV epidemic.

Photo of Amanda Wilson

Amanda J. Wilson is Chief of the NLM Office of Engagement and Training (OET), bringing together general engagement, training, and outreach staff from across NLM to focus on NLM’s presence across the U.S. and internationally. OET is also home to the Environmental Health Information Partnership for NLM and coordinates the Network of the National Library of Medicine.

Photo of Leigh Samsel

Leigh Samsel, MS, is responsible for formal reporting of NLM activities and for providing staff leadership to strategic planning activities. Leigh is currently serving as NLM’s AIDS Coordinator to the NIH Office of AIDS Research.

Photo of Elizabeth Mullen

Elizabeth A. Mullen, MS, is Manager of Web Development and Social Media for the History of Medicine Division (HMD) and Managing Editor of Circulating Now, a blog featuring new research, curatorial insights, and news about NLM’s historical collections.

Artificial Intelligence, Imaging, and the Promising Future of Image-Based Medicine

In mid-October I gave the NLM Research in Trustable, Transparent AI for Decision Support keynote speech to the 50th Institute of Electrical and Electronics Engineers (IEEE) Applied Imagery Pattern Recognition conference in Washington, D.C. (virtually, for me). The IEEE continues to advance new topics in applied image and visual understanding, and the focus this year was to explore artificial intelligence (AI) in medicine, health care, and neuroscience.

To prepare for my talk, I reviewed our extramural research portfolio so I could highlight current research on these topics. NLM’s brilliant investigators are using a range of machine learning and AI strategies to analyze diverse image types. Some of the work fosters biomedical discovery; other work is focused on creating novel decision support or quality improvement strategies for clinical care. As I did with the audience at IEEE, I’d like to introduce you to a few of these investigators and their projects.

Hagit Shatkay and her colleagues from the University of Delaware direct a project titled Incorporating Image-based Features into Biomedical Document Classification. This research aims to support and accelerate the search for biomedical literature by leveraging images within articles that are rich and essential indicators for relevance-based searches. This project will build robust tools for harvesting images from PDF articles and segment compound figures into individual image panels, identify and investigate features for biomedical image-representation and categorization of biomedical images, and create an effective representation of documents using text and images grounded in the integration of text-based and image-based classifiers.

Hailing from the University of Michigan, Jenna Wiens leads a project called Leveraging Clinical Time Series to Learn Optimal Treatment of Acute Dyspnea. Managing patients with acute dyspnea is a challenge, sometimes requiring minute-to-minute changes in care approaches. This team will develop a novel clinician-in-the-loop reinforcement learning (RL) framework that analyzes electronic health record (EHR) clinical time-series data to support physician decision-making. RL differs from the more traditional classification-based supervised learning approach to prediction; RL “learns” from evaluating multiple pathways to many different solution states. Wiens’ team will create a shareable, de-identified EHR time-series dataset of 35,000 patients with acute dyspnea and develop techniques for exploiting invariances (different approaches to the same outcome) in tasks involving clinical time-series data. Finally, the team will develop and evaluate an RL-based framework for learning optimal treatment policies and validating the learned treatment model prospectively.

Quynh Nguyen from the University of Maryland leads a project called Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects Research. Using geographic information systems and images to assemble a national collection of all road intersections and street segments in the United States, this team is developing informatics algorithms to capture neighborhood characteristics to assess the potential impact on health.

Corey Lester from the University of Michigan leads a multidisciplinary team using machine intelligence in a project titled Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence. Machine intelligence is a branch of AI distinguished by its reliance on deductive logic, and the ability to make continuous modifications based in part on its ability to detect patterns and trends in data. The team is designing interpretable machine intelligence to double-check dispensed medication images in real-time, evaluate changes in pharmacy staff trust, and determine the effect of interpretable machine intelligence on long-term pharmacy staff performance. More than 50,000 images are captured and put through an automated check process to predict the shape, color, and National Drug Code of the medication product. This use of interpretable machine intelligence methods in the context of medication dispensing is designed to provide pharmacists with confirmatory information about prescription accuracy in a way that reduces cognitive demand while promoting patient safety.

Alan McMillan from the University of Wisconsin-Madison and his team are examining how image interpretation can improve noisy data in a project called Can Machines be Trusted? Robustification of Deep Learning for Medical Imaging. Noisy data is information that cannot be understood and interpreted correctly by machines (such as unstructured text). While deep learning approaches (methods that automatically extract high-level features from input data to discern relationships) to image interpretation is gaining acceptance, these algorithms can fail when the images themselves include small errors arising from problems with the image capture or slight movements (e.g., chest excursion in the breathing of the patient). The project team will probe the limits of deep learning when presented with noisy data with the ultimate goal of making the deep learning algorithms more robust for clinical use.

In the work of Joshua Campbell’s team at Boston University, the images emerge at the end of the process to allow for visualization of large-scale datasets of single-cell data. The project, titled Integrative Clustering of Cells and Samples Using Multi-Modal Single-Cell Datauses a Bayesian hierarchical model developed by the team to perform bi-clustering of genes into modules and cells into subpopulations. The team is developing innovative models that cluster cells into subpopulations using multiple data types and cluster patients into subgroups using both single-cell data and patient-level characteristics. This approach offers improvements over discrete Bayesian hierarchical models for classification in that it will support multi-modal and multilevel clustering of data.

Several things struck me as I reviewed these research projects. The first was a sense of excitement over the engagement of so many smart young people at the intersection of analytics, biomedicine, and technology. The second was the variety of types of images considered within each project. While one study explores radiological images, another study examines how image data types vary from figures in journal papers to pictures of the built environment and images of workflows in a pharmacy. Two of these studies use AI techniques to analyze the impact of the physical environment to better understand its influence on patient health and safety, and one study uses images as a visualization tool to better support inference of large-scale biomedical research projects. Images appear at all points of the research process, and their effective use heralds an era of image-based medicine. Let’s see what lies ahead!

40 Years of Progress: It’s Time to End the HIV Epidemic

Guest post by Maureen M. Goodenow, PhD, Associate Director for AIDS Research and Director, Office of AIDS Research, National Institutes of Health

On June 5th, the National Institutes of Health (NIH) Office of AIDS Research (OAR) joined colleagues worldwide to commemorate the 40th anniversary of the landmark 1981 Centers for Disease Control and Prevention (CDC) Morbidity and Mortality Weekly Report (MMWR) that first recognized the syndrome of diseases later named AIDS. June 5th also marks HIV Long-Term Survivors Awareness Day. 

Forty years ago, the CDC’s MMWR described five people who were diagnosed with Pneumocystis carinii pneumonia—catalyzing a global effort that led to the identification of AIDS, and later, the virus that causes AIDS.

Over the years, much of the progress to guide the response to HIV has emerged from research funded by the NIH, and helped turn a once fatal disease into a now manageable chronic illness. This progress is attributable in large part to the nation’s longstanding HIV leadership and contributions at home and abroad.

NIH is taking action to recognize the milestones achieved through science, pay tribute to more than 32 million people who have died from AIDS-related illness globally (including 700,000 Americans), and support the goal of Ending the HIV Epidemic in the U.S. (EHE) and worldwide. OAR is coordinating with NIH Institutes, Centers, and Offices (ICOs) to share messaging that will continue through NIH’s World AIDS Day commemoration on December 1, 2021.

The NIH remains committed to supporting basic, clinical, and translational research to develop cutting-edge solutions for the ongoing challenges of the HIV epidemic. The scientific community has achieved groundbreaking advances in the understanding of basic virology, human immunology, and HIV pathogenesis and has led the development of safe, effective antiretroviral medications and effective interventions to prevent HIV acquisition and transmission.

Nevertheless, HIV remains a serious public health issue.

NIH established the OAR in 1988 to ensure that NIH HIV/AIDS research funding is directed at the highest priority research areas, and to facilitate maximum return on the investment. OAR’s mission is accomplished in partnership within the NIH through the ICs that plan and implement specific HIV programs or projects, coordinated by the NIH HIV/AIDS Executive Committee. As I reflect on our progress against HIV/AIDS, I would like to note the collaboration, cooperation, innovation, and other activities across the NIH ICOs in accelerating HIV/AIDS research.

Key scientific advances using novel methods and technologies have emerged in the priority areas of the NIH HIV research portfolio. Many of these advances stem from NIH-funded efforts, and all point to important directions for the NIH HIV research agenda in the coming years, particularly in the areas of new formulations of current drugs, new delivery systems, dual use of drugs for treatment and prevention, and new classes of drugs with novel strategies to treat viruses with resistance to current drug regimens.

Further development of long-lasting HIV prevention measures and treatments remains at the forefront of the NIH research portfolio on HIV/AIDS research.

NIH-funded investigators continue to uncover new details about the virus life cycle, which is crucial for the development of next generation HIV treatment approaches. Additionally, the NIH is focused on developing novel diagnostics to detect the virus as early as possible after infection.

Results in the next two years from ongoing NIH-supported HIV clinical trials will have vital implications for HIV prevention, treatment, and cure strategies going forward. For example, two NIH-funded clinical trials for HIV vaccines, Imbokodo and Mosaico, are evaluating an experimental HIV vaccine regimen designed to protect against a wide variety of global HIV strains. These studies comprise a crucial component of the NIH’s efforts to end the HIV/AIDS epidemic.

As we close on four decades of research, I look forward to the new advances aimed at prevention and treatment in the years to come.

You can play a role in efforts to help raise awareness and get involved with efforts to end the HIV epidemic. Visit OAR’s 40 Years of Progress: It’s Time to End the HIV Epidemic webpage, and use the toolkit of ready-to-go resources.

Dr. Goodenow leads the OAR in coordinating the NIH HIV/AIDS research agenda to end the HIV pandemic and improve the health of people with HIV. In addition, she is Chief of the Molecular HIV Host Interactions Laboratory at the NIH.

The Significance of Network Biology

Guest post by Teresa Przytycka, PhD, Senior Investigator, Computational and Systems Biology section of the Computational Biology Branch at the National Library of Medicine’s National Center for Biotechnology Information, National Institutes of Health.

The functioning of any complex system involves interactions between elements of that system. This is true at the cellular level, the macro level, and every point in between.

For example, within a cell, diverse molecules coordinate their activities and work together to carry out specific cellular functions. Cells then interact with each other to shape an organism’s development, tissue-level organization, and immune response. In turn, the organisms themselves interact to form various types of connections. In the case of people, those connections form the backbone of social systems.

These many interactions, from the microscopic to the macroscopic, can be described as networks, which comprise nodes connected via links that designate the relationships between nodes.

But how can we discover which nodes are connected? And how can we learn about the nature of those connections?

My research group and I try to answer these questions using network analysis, that is, working at the network level to uncover insights about the underlying system.

We can trace the beginnings of network analysis (also known as graph theory in mathematical circles) to Gottfried Leibniz’s geometria situs (“geometry of position”), a mathematical discipline focused on the relationship between positions and objects. The first recorded application of this new way of thinking was the famous solution to the problem known as “The Bridges of Königsberg,” published by Leonhard Euler in 1736.

Königsberg (now Kaliningrad) straddles the Pregel River. In Euler’s time, seven bridges connected the various parts of the city, including two islands in the middle of the river. The question asked was whether one could chart a walk through the city that required crossing each bridge only once and return to the start.

Euler tackled this question using what we today call network theory. If the regions of Königsberg are nodes and the bridges are links, Euler showed that, for such a walk to be possible, each node must have an even number of links. Why an even number? Because if we cannot cross the same bridge twice, then for every way into each region of the city there must be a new way out. Because that property did not hold for Königsberg, Euler concluded that no such walk could be devised.

As Euler’s argument shows, representing a complex system as a network of nodes and links can help uncover properties of the system that might have otherwise been obscured.

In biology and medicine, such network-centric approaches coincided with the emergence of high-throughput experimental techniques and advanced methods for collecting and storing diverse biomedical data. The protein interaction network for yeast became one of the first large biological networks obtained from high-throughput experiments. Analyzing that network revealed that a small fraction of proteins interacted with a disproportionately large number of other proteins. Additional research showed that these “hub” proteins are essential to the cell’s survival.

This intriguing relationship between a network property and a biological property begged for an explanation.

Our 2008 paper demonstrated that the majority of protein hubs are essential because of their involvement in complex, densely connected modules that carry out functions essential for cell survival. These results illustrate that, in addition to reporting binary relationships between individual nodes, interaction networks encode hidden higher-level organization.

In many networks, including biological and social ones, groups of nodes that interact with each other more tightly than with the rest of the network can be identified. We call these groups “modules.” In the context of biological networks, modules are often associated with groups of genes that work together to perform a specific biological function. At the same time, we’re beginning to see that complex diseases, such as cancer, are more likely caused by the dysregulation of a specific functional module than a dysfunction of an individual gene. That’s why, in recent years, cancer research has turned its attention to identifying dysregulated modules.

This effort includes several methods developed by our research group—Module Cover, Mutual Exclusivity Module Cover, and BeWith. These methods combine information from the human-interaction network with disease-specific information, such as abnormally expressed or mutated genes, to identify disease-associated modules. These modules can shed light on the mechanism of the disease, suggesting areas for further study and possible means of intervention.

We also use networks to discover how information flows between individual nodes. For example, by applying the principles of current flow within an electric circuit, we have been able to identify the causal (altered) disease genes and the pathways they dysregulate, providing another way to discover groups of genes involved in a disease.

Unfortunately, most currently available interaction networks are static depictions of a dynamically changing system. They typically do not account for tissue types, developmental stages, disease status, and other factors. As a result, these networks cannot tell us the full story.

To consider these and other factors, we need a context-specific network. But to build one, we must use context-specific data. How can we do that, given the myriad conditions we must consider? Our new method, NetREX, moves us in that direction.

Network biology has facilitated progress in many areas of biomedical science. This simple, yet powerful, concept allows us to abstract the essence of relations between genes and proteins, predict interactions between drugs, study disease comorbidity, and discover important associations. Of course, discovering an association is just the first step in uncovering a mechanism, but it is often a crucial step.

headshot of Dr. Teresa Przytycka

Teresa M. Przytycka, PhD, leads the Algorithmic Methods in Computational and Systems Biology section at the National Center for Biotechnology Information. Dr. Przytycka is particularly interested in the dynamical properties of biological systems, including spatial, temporal and contextual variations, and exploring how such variations impact gene expression, the functioning of biological pathways, and the phenotype of the organism.

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