Parts of a Human Cell

The cell is the main building unit for all living organisms, from bacteria to humans. All complex organs and systems are made up from billions of individual cells working together, but also individually. In the human body, cells come in different shapes depending on the tissue they belong to. However, all cells have four parts in common: the plasma membrane, cytoplasm, ribosomes, and DNA.

The plasma membrane (also called the cell membrane) is a thin coat of lipids that surrounds the cell. It forms the physical boundary between the cell and its environment and therefore is very important for all interactions between the cell and the outside. Many proteins assist with these interactions by docking on the plasma membrane and binding to outside molecules. Furthermore, cells can send signaling molecules through their plasma membrane to communicate messages to other parts of the tissue or body.

Next, the cytoplasm refers to all of the cellular material inside the plasma membrane, other than the nucleus. Cytoplasm is gel-like in its texture and contains mostly water, salt and other molecules, as well as all organelles that assist with cellular function, such as ribosomes.

Ribosomes are structures in the cytoplasm where proteins are made. They consist of RNA molecules and proteins, and their main role is to translate genetic sequences to proteins. Each cell may have a large number of ribosomes which either exist as free particles in the cytoplasm, or are organized in larger structures.

Finally, the DNA is a nucleic acid that contains all the necessary genetic information for the cell to function. The DNA is usually tightly packed in the nucleus of the cell, where it is protected from the outside environment. If we were to measure the length of a DNA molecule, each cell alone contains about 6 feet of DNA. The human DNA contains about 25,000 gene sequences, as well as many sequences that do not encode proteins and are either evolutionary remnants or regulatory components.

What is RNA and What are its Types?

RNA is the second most well-known ribonucleic acid after DNA. When it comes to structure, it is highly similar to DNA, but has some important differences. For example, RNA has one distinct nucleotide called uracil (or U) instead of thymin (or T). Furthermore, RNA commonly exists in single-strand format, while DNA is almost always double-stranded.

Human DNA GraphicAs we discussed in the previous post, all human cells contain genetic information for protein expression in the form of DNA. Our DNA is tightly packed in the cell nucleus to ensure protection from outside factors. However, almost all processes that are required for protein generation are taking place outside the nucleus. For this reason, there are mechanisms in place for creating copies of genetic information that can traverse from the nucleus to other locations. This is where RNA comes into play, since it can be synthesized in a complementary manner to DNA and contain the same type of genetic information. Furthermore, RNA can interact with proteins and create necessary complexes for various cell processes.

The main types of RNA are the following:

  • mRNA: Also known as messenger RNA, mRNA is a single-stranded RNA molecule that contains information for generating a protein sequence. It is created in the nucleus through the process of transcription, during which the two DNA strands are temporarily separated to copy the information of one gene onto the new mRNA molecule. Then, mRNA can exit the nucleus and go to the ribosomes, where proteins are generated using its information.

  • tRNA: tRNA, or transfer RNA, is a small RNA molecule that is essential for protein synthesis. Proteins consist of amino acids, and each amino acid has a 3-letter code (or codon) in the DNA or mRNA that corresponds to it. This way a DNA or RNA sequence can be translated to a protein sequence. Codons are examined in series, and tRNA is responsible for bringing the correct amino acid depending on each codon. This is done by utilizing the complementary structure of one tRNA part to the codon triplet.

  • rRNA: Ribosome RNA (or rRNA) is an RNA molecule that is a fundamental component of ribosomes. As mentioned before, ribosomes are the machinery for protein synthesis in the cell. There are two rRNA subunits in each ribosome, a large one and a small one. Along with proteins and enzymes, they facilitate the process of translating an mRNA sequence into protein.

Honorable Mentions

There are some other RNA types in human cells that are not commonly known but are often used in molecular biology research. For example, micro RNA or miRNA is a small single-strand RNA molecule that can silence other types of RNA (such as mRNA) by binding to them using the rules of complementarity. Small interfering RNA or siRNA is a double-stranded RNA molecule that has a similar goal, but acts by triggering an mRNA degradation response called RNA interference. Overall, these molecules can affect protein expression levels by effectively silencing certain genes without affecting the DNA itself.

More sources on RNA:

— Vicky Kanta

How is DNA Organized in the Human Cell?

Our DNA contains the genetic code for creating every single protein in our bodies. All cells contain an almost identical copy of our DNA in their nuclei. However, since DNA molecules can be over 6 feet long if stretched out, they are packed and organized in a very specific manner in order to fit in the very small space of the nucleus. Here are the different levels of DNA organization:

  • Human DNA GraphicDouble helix DNA: This is the unpacked form of our DNA, in which two complementary strands are chemically linked together and are spiraling around counterclockwise, forming a “ladder-like” double-stranded molecule. Each “step” of the ladder consists of a pair of nucleotides, which are the basis of DNA sequences and are commonly known by their initials (A and T, G and C). In every cell, genetic information exists in two copies, with one coming from each of the two parents.

  • Nucleosome: This is the first step of “packaging” inside the nucleus. In a nucleosome, a segment of double helix DNA is wrapped around a set of proteins called histones. Usually, the length of DNA in each nucleosome is fixed to about 150 nucleotide pairs. When many nucleosomes are seen together in series, they have the appearance of “beads on a string”, which is a common term used for this level of organization.

  • Chromatin: In this next level, nucleosomes are tightly grouped together forming a fiber that is about 30 nm in diameter. There are two types of chromatin, namely euchromatin and heterochromatin, that differ in how compact they are and whether they allow unpacking for gene expression. These two types can be located on a microscope in different parts of the nucleus.

  • Chromosome: This is the final form of DNA organization inside the nucleus. In the chromosome form, chromatin is even more tightly packed. In the end, the entirety of the double helix DNA is packaged in 23 pairs of chromosomes, that fit in the 6μm wide cell nucleus.

It is worth mentioning here that DNA does not only exist in the cell nucleus. There is some amount of DNA in other organelles inside our cells called the mitochondria. Mitochondrial DNA is also organized in small chromosomes and contains genetic information for mitochondrial function. Unlike nuclear DNA, which is inherited from both parents, mitochondrial DNA is only inherited from the mother via the egg.

More DNA organization sources:

— Vicky Kanta

The Differences between SBIR and STTR Cheat Sheet

The Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs both provide Small Business Administration (SBA) grants to small businesses for research. The two programs share the same three phases, with separate federal contracts awarded for Phases I and II. Phase I is innovation and research, where the scientific merit and commercial viability of an innovation is studied.  Phase II focuses on the innovation’s development, demonstration and delivery. Phase III involves preparation for commercial rollout and is funded by outside sources rather than the SBA.

While the two programs share many similarities, they have some important differences as well. While SBIR is geared at small businesses conducting independent research, STTR grants are only available to small businesses teamed up with a nonprofit research institution such as universities or Federal Laboratories. Below, we compare some other ways in how the two programs differ.

SBIR vs. STTR

  SBIR STTR
Who applies? For-profit U.S. small business For profit U.S. small business (partnered with nonprofit research institution)
Phases Phase I: 6 months
Phase II: 24 months
Phase III: Variable (Not SBIR funded)
Phase I: 13 months
Phase II: 24 months
Phase III: Variable (Not STTR funded)
Principal Investigator Employed at least 51% by the small business with at least 10% effort May be employed by either the small business or non-profit with at least 10% effort
Intellectual Property (IP) Requires the small business and non-profit to have an agreement concerning IP and further research/development
Amounts and timelines Phase I: $150,000 for 6 months
Phase II: $1M for 2 years
Phase I: $150,000 for 1 year
Phase II: $1,000,000 for 2 years
Is collaboration required? Allowed Required
Work distribution (R&D) Small business: minimum 66% Phase I, 50% Phase II
Non-profit: maximum 33% Phase I, 50% Phase II
Small business: minimum 40% both phases
Non-profit minimum 30% both phases
Number of participating agencies 12 5

Outside the Office: Volunteering with Professional Organizations

Several employees at the Office of Technology Transfer extend their expertise in the technology industry to outside professional organizations.

Among these employees are Laura Fritts, Director of Patent and License Strategy and Chief Intellectual Property Officer; Kimberly Dunn, Compliance Associate; Linda Kesselring, Operations Director; Kevin Lei, Director of Faculty and Start-Up Services; Patrick Reynolds, Assistant Director of Faculty and Start-up Services; Quentin Thomas, Marketing Manager; and Sarah Wilkening, Licensing Associate.

The majority of the above OTT employees volunteer with the Association of University Technology Managers (AUTM). AUTM is a nonprofit organization that supports individuals involved in technology transfer through education, professional development, and advocacy, among other activities. Through these methods, the organization provides knowledge of technology transfer beyond Emory and forges connections between members, which is why many employees opted to join.

Each involved OTT member contributes to AUTM in a different capacity, ranging from assisting with marketing to hosting instructional webinars. For example, while Fritts serves on the AUTM Public Policy Legal team, Dunn previously served on the Distance Learning Committee organizing speakers for AUTM-hosted webinars and was Chair of the Intellectual Property Portfolio Management Committee. As chair, Dunn helped develop and manage an annual course for three years before recently joining AUTM’s TOOLs Committee.

Having been an AUTM member for over 20 years, Kesselring currently serves as chair of the website committee. As chair, she works with AUTM staff focused on marketing and communications and organizes work, activities, and conference calls to “meet objectives for the AUTM website.” This is done through tasks like supporting the website’s redesign and providing regular statistics.

Reynolds chairs AUTM’s Better World Project committee, a group that selects stories submitted by technology transfer offices about how their technologies “make the world a better place.”

“It’s easy to get in the cycle of only knowing what is happening in your own office,” he said. “The Better World Project allows me to see the great work that institutions and [technology transfer offices] around the world are doing.”

Thomas, another AUTM volunteer, occasionally serves as a presenter on Annual Meeting webinar sessions and recently left the Marketing Course Committee after working with this group for three years. He noted that his work with AUTM increases exposure of Emory’s OTT office and fuels additional opportunities, while also allowing him to learn new skills and gain knowledge from colleagues at other institutions.

In addition to those who volunteer with AUTM, several OTT employees cite benefits from volunteering with outside organizations. Lei volunteers with Certified Licensing Professional (CLP), Inc. as a member of The CLP Exam Development and Maintenance Committee. The CLP program certifies professionals that demonstrate experience and proficiency in the licensing and commercialization of intellectual property after they take and pass a 150 multiple choice question exam. When the organization recruited volunteers to revise exam questions in 2019, Lei took this opportunity.

“I thought it would be important to be involved and help maintain the high standard of the CLP program, because it takes dedicated volunteers to develop a quality certification program,” he said.

Wilkening primarily volunteers with the Patent Agents of Georgia, an organization she co-founded under the Georgia Intellectual Property Alliance. This group aims to foster community among Georgia’s past, present, and future patent agents and coordinates networking events for “science-lovers that want to stay at the frontlines of research without having to do the research.”

“I wish I would have known about this career path earlier in my life … Being a patent agent can be a rewarding career opportunity,” she said. “This organization has brought together technology transfer and patent professionals from all over Georgia, and I have had the honor to help branch those networking opportunities.”

Fritts has volunteered as a lawyer with the Executive Committee for Atlanta’s IP Inn of Court and the Advisory Board for the United States Patent and Trademark Office (USPTO) Georgia PATENTS. As a volunteer, she has helped inventors file patent applications, saying, “The benefits to the organizations, my office, and me personally far outweigh the burden of the work.”

Dunn also volunteered with the Georgia Association of Paralegals’s Atlanta Legal Aid Society. With this organization, she assisted domestic violence victims in filing appropriate documents involving scenarios like temporary restraining orders and coordinating temporary housing. She listed obtaining a “diverse network of knowledge and experience” among several benefits she received from volunteering.

“Volunteering is like continuing education: we constantly learn something new,” she explained. “The most important benefit is the feeling of being a part of something bigger than yourself.”

What is Telemedicine?

Telemedicine is the use of available telecommunications technologies to diagnose and provide care to patients from a remote location. Although frequently mentioned in the news lately due to COVID-19, telemedicine is a field that has been around for more than 50 years.

The need to treat patients without seeing them in person arose mostly because of people in rural areas, who were unable to travel long distances to see a doctor or go to a hospital. To solve this issue, doctors would talk to patients on the phone and even use the telegraph to send and receive medical information. Nowadays, modern video-conferencing is widely used for telemedicine, providing an easy and convenient way to share information with providers. Since file sharing is easier than ever, doctors can electronically send their patients their lab results, X-Rays, and other types of diagnostic information. Furthermore, doctors can share patient records with each other, enabling them to provide better collaborative care to their patients.

Since the use of smart devices has increased in the past few years, many doctors now have remote access to important health information, such as their heart rate, activity levels and even their blood pressure. That way patients can receive medical advice from the comfort of their home, without unnecessary travel and long wait times at doctor’s offices. Telemedicine helps doctors with office-related costs while also saving them precious time and allowing for more appointments. As evidenced during this pandemic, an additional benefit of telemedicine is the reduced risk of exposure to contagious diseases that are frequently found in hospitals and doctor’s offices, therefore protecting both patients and medical personnel.

The telemedicine industry is rapidly growing, with more and more tech companies creating HIPAA-compliant video platforms and e-health environments. With the inevitable future developments in the medical device sector, it is very likely that telemedicine is here to stay as a regular part of medical care.

More resources on telemedicine:

What is Herd Immunity?

Infectious diseases caused by microorganisms such as bacteria and viruses can spread widely within a community. In particular, densely populated areas like big cities allow rapid disease transmission, because people are frequently in close proximity. When a certain proportion of the community acquires immunity for a disease, then further spread is limited, because immune individuals usually cannot get re-infected or infect others. This is when we reach herd immunity for this particular disease.

There are two main ways to achieve herd immunity. The first way is for the pathogen to infect a large number of people, who then recover and become immune. The second way is through vaccination, which provides future immunity against the disease by stimulating the natural pathways that lead to it. Nowadays, many infectious diseases are either eliminated or controlled through vaccination, such as measles, chickenpox, and the seasonal flu. In fact, a vaccine is the safest way towards herd immunity, leading to less sick people and therefore a lower overall number of disease-related deaths and complications.

Depending on the size of the community, as well as the infectiousness of the disease, the percentage of people that must become immune before herd immunity is achieved can vary. For example, measles requires 93-95% vaccination rates, whereas polio requires around 80-86%. This is why timely vaccination of children at the recommended schedule is really important; skipping or delaying a dose does not only increase chances of disease susceptibility for the child, but also contributes to a decrease in herd immunity and therefore allows previously controlled diseases to reappear like the recent resurgence of the measles.

Herd immunity is highly important for our society, because it is an important tool to curtail or even eliminate infectious diseases. Furthermore, it protects some of the most vulnerable members in our communities from getting sick, such as the elderly, those who can’t have a vaccine for medical reasons, or the immunocompromised, as well as pregnant women and infants. For this reason, there is extensive research and funding dedicated towards the development of safe and effective vaccines.

More resources on herd immunity and vaccination:

Access to Experimental Drugs

Experimental drugs can be a lifeline for those with conditions for which conventional treatments are not working or readily available. At the same time, doctors and patients want to have confidence that they are making safe and informed choices when requesting the use of drugs that have not have fully completed the clinical trial process.  With many doctors turning to experimental treatments for COVID-19 patients, this topic has recently gained renewed attention. Two processes in particular: compassionate use and Right to Try, give patients with serious or terminal conditions the ability to gain access to treatments which have not received FDA approval. These treatments can be potentially lifesaving for those whom conventional treatment is either unavailable or not working.

Compassionate use, formally known as Expanded Access, allows patients access to experimental treatments with approval from their doctor, the FDA, and an Institutional Review Board (IRB). The patient must meet several criteria, including having a serious disease or condition for which no comparable satisfactory treatment is available and being unable to enroll in clinical trials for the drug. If patients decide to pursue compassionate use, they must first speak to their physician, who then files a request to both the FDA and IRB. Eligibility is granted by the FDA if the agency determines that both (1) the benefits of using the experimental treatment outweigh any risks and (2) that its use will not jeopardize ongoing clinical trials. The FDA must respond to such a request within thirty days, and approval has historically been granted in 99% of cases. Secondly, the use must be approved by a registered IRB. Only once both of these steps are complete is the patient allowed access to the drug.

In May 2018 Congress passed the Right to Try Act, establishing a second process for patients to gain access to experimental treatments. Under Right to Try, patients with life-threatening conditions no longer need the approval of the FDA and IRBs to gain access to certain experimental drugs. Those with a terminal illness can submit a request directly to the manufacturer of an experimental treatment with the consent of their doctor. They may then obtain the treatment if the manufacturer approves. Similar to with compassionate use, patients using the Right to Try process must have exhausted all available conventional treatments and be unable to participate in clinical trails involving the experimental drug. The medication must also have completed Phase 1 clinical trials, an additional requirement not present in the compassionate use process. Right to Try has proven controversial in the scientific community since its conception. Proponents claim it cuts red tape patients for terminally ill patients in obtaining treatment, but critics point to the lack of reporting requirements and say that Right to Try removes safeguards in the compassionate use process that ensure patient safety.

While compassionate use and Right to Try have different processes, the programs share several key limitations. Importantly, manufacturers have no obligation to provide experimental drugs under either process, and the FDA has no power to compel companies to do so. Many manufacturers are reluctant to provide patients access to experimental drugs due to concerns that adverse reactions could lead to negative publicity or interfere with the FDA approval process.  Additionally, insurance companies are under no obligation to cover experimental treatments. The fact that many patients must pay out-of-pocket for experimental treatments has led to a stratified system where low-income patients are unable to obtain experimental drugs.

Despite these constraints, both compassionate use and Right to Try continued to be utilized by doctors and patients across the country. Many individuals with severe or terminal illness can’t afford to wait the seven to twelve years it takes for drugs to be approved by the FDA. Through compassionate use and Right to Try, these patients with few options left get a renewed chance.

Clinical Trials 101

Any time a new drug appears on the market, the final product is a result of years of research and testing. The time from conception to FDA approval for medications takes 12 years on average. An important and time-consuming aspect of this process are clinical trials. By testing a drug on gradually increasing numbers of patients and volunteers, researchers are able verify that it is both safe and effective in treating the disease or condition. Only when the first three steps of this process are complete can the drug be approved and commercially distributed.

Phase 1 is the shortest of the three phases and involves the smallest sample size. This phase is the first time a new drug is tested on humans and establishes the product’s safety. During Phase 1, the drug is given to 20 to 50 volunteers. Generally, this process takes several months. By examining subjects over this period, researchers are able to determine the appropriate dosage, observe side effects, and glean limited information about the drug’s effectiveness. These data are taken into consideration as researchers plan for Phase 2, which approximately 70% of drugs move onto.

Phase 2 gives researchers a clearer picture of a drug’s efficacy and safety. With preliminary safety data available, testing can be expanded to groups of up to several hundred patients. While these samples typically aren’t large enough to fully test a drug’s effectiveness, the results provide further insight into whether it is safe. Phase 2 trials last several months to two years. They are critical in winnowing out unsafe or unsuccessful drugs, with only approximately 33% of drugs proceeding to Phase 3.

The longest and most complex step of clinical trials is Phase 3, which involves 30 to 300 patients and takes 1 to 4 years. Phase 3 answers the most critical question for any drug: whether it provides the beneficial treatment it was designed to. Researchers test this by randomizing the study’s participants. Half of patients receive the experimental drug, while the other half are given a placebo. Studies are usually double-blind, which means neither the participant nor the researchers know who is in which group. Results from the two groups are then compared to determine the effectiveness of the drug. Phase 3 also provides additional safety data by sometimes revealing side effects that went undetected in smaller sample groups. Approximately 25 to 30% of drugs complete this phase and are ready for FDA approval.

Once drugs are FDA approved and commercially available, further testing continues in Phase 4 clinical trials. Although the drug has already been approved, Phase 4 allows for analysis of any long-term effects. Additionally, Phase 4 allows for more organic testing among groups that may not have been studied due to the controlled nature of previous phases, such as those simultaneously taking other drugs. Sometimes, drugs are banned from use by the FDA after harmful side effects emerge during Phase 4.

Clinical trials are a time-consuming, expensive, and selective process. Drugs that make it through and are FDA approved have an average cost of $41,117 per patient just for clinical trials. The federal government has created a database that is home to information about both privately and publicly funded clinical studies around the world. However, clinical trials remain the most comprehensive method available to ensure that every drug marketed in the U.S. is both safe and effective. Doctors and patients can have confidence knowing the drugs they prescribe, and use, have been put to this test and passed.

drug discovery timeline graphic

Introduction to Artificial Intelligence and Healthcare

The healthcare industry generates a lot of data. X-rays, pathology slides, patient vitals, clinical trial information; we have mountains of information accessible at the touch of a button. But it’s costly and time-inefficient for humans to manually pour over it. So what do we do with all of this data?

The field of intelligence (AI) allows not only to let us analyze all our data, but to find subtle and complex patterns in them. Machine learning algorithms are particularly responsible for these advancements. Engineers have developed software that’s able to look at a dataset, find relationships between a bunch of variables, and then make mathematical models that we can use to predict behaviors in the future or analyze other sources of data: images, patient records, etc.

What does that mean in the real world? Computer scientists can make systems that can analyze an image and detect a disease better than humans can. They can make programs that are able to predict the notoriously unpredictable process of how proteins fold, or even make drugs which are currently in human trials. Let’s learn a little more about how machine learning works, and see some of the current applications.

How machine learning systems work

Machine learning systems are complex algorithms. Algorithms are methods of “treating” data: a computer receives an input, some sort of data. The computer recognizes that data and sorts it. For example, let’s say I have a program that can tell me if the color of an animal I’m thinking about makes sense. if I type in the phrase “blue horse,” there might be something in the code that recognizes the word “blue” and the word “horse.” The code then might have some information about the colors a horse can be, and would have an instruction to compare “blue” with “horse.” If “blue” isn’t in the “horse” database, then the computer would have an instruction to tell me “No, horses can’t be blue.” To make it simple, algorithms are sets of instructions that do something with information we give it.

AI Flow Chart

A simple algorithm for calculating interest. Courtesy of Edraw.

They get way more complex than that example. Specific algorithms can take ridiculous amounts of data and look for statistical relationships between them. Machine learning systems are able to make new models by using what are called learning algorithms. There are a bunch of different types, but the most conceptually simple ones involve supervised learning. These learning algorithms are exposed to a “training set” of data with a bunch of examples with known “right answers” — for example, a bunch of CT scan images from patients whom we know have cancer or not. The algorithm “doesn’t know” which patients have the disease. It keeps changing the instructions inside of its algorithm to try to — if the computer sees a white spot in a patient’s liver, it says yes, but this turned out to be wrong, so the next time the computer doesn’t care about that spot when saying yes or no.

Do this a bunch of times, and eventually the algorithm learns what data points lead to the correct output: the machine learns. Over time, these algorithms get very accurate, and we can use them for specific applications.

Radiology and imaging

Image analysis is a very natural application of this sort of software. The cancer example above isn’t science fiction: Google’s already developed a system that’s able to detect breast cancer. A very recent study published information about a deep learning model that’s able to diagnose forms of common hip arthritis at an accuracy rivaling radiologists’ analysis.

But there are concerns about the current state of radiology AI and bringing these technologies to the clinic. Analyzing images is a very complex process, critics argue, and we don’t always know how these machine learning systems actually work. Although applications are rapidly developing, it’s clear that radiologists are still very necessary.

Breast Cancer Image

A picture of an image where an AI system found cancer in a human breast. Courtesy of Northwestern University via The New York Times.

 

Drug discovery

UK-based startup Exscientia became the first company to develop a small-molecule drug entirely designed by artificial intelligence. Small-molecule drugs are tiny chemicals that generally “stick” to proteins of interest. It is very hard to know what sort of molecules will stick and treat a disease and which ones won’t. The scientists at Exscientia use a learning algorithm that takes data from human-run tests to optimize the structure of the chemical they’re making, reducing the amount of time it takes to get a working drug. The compound, titled DSP-181 and targeted toward treating obsessive-compulsive disorder, is set to enter clinical trials in Japan soon.

Point-of-care solutions

AI doesn’t just help us make drugs; it can help us deliver them, too. Electronic health record systems are growing more and more. Nurses say that bureaucratic work cuts their time spent with patients by about 25 percent. Doctors report that less than half of their patients are what they’d describe as “highly engaged” in their course of care. Engineers are already developing systems that can not only manage data, but use electronic information to make predictions about how patients might comply with certain treatments.

From point-of-care solutions and radiology to drug discovery and beyond, it’s clear that artificial intelligence will play an increasingly large role in analyzing healthcare data in the future in ways both known and unknown to us now. New applications of machine learning systems are rapidly developing as engineers aim to create new machine learning algorithms that can analyze and find complex data. Used in tandem with the expertise of doctors, radiologists, and other healthcare professionals, these algorithm-based technologies are already modernizing our healthcare system and have the potential to improve public health on an even larger scale in the future.