Understanding the Complete Blood Count

Not everyone likes getting their blood drawn at the doctor’s. There’s a needle in the arm, it’s pumped into vials, and then sent off to a mysterious lab. What really happens there, and what do doctors look at to examine your blood and review your health? One of the most common blood tests done that will help answer this question is the Complete Blood Count.

The Complete Blood Count, or CBC test, that test that all the doctor’s on TV yell for. This test evaluates the proportions and patterns of different parts of your blood. CBCs are often ordered as a part of a routine check-up, because they provide a useful indicator of overall health. CBCs are also important because they detect abnormalities in blood composition, help to diagnose disease, and monitor progress of treatments or medications.

components of blood graphic

Analyzing a blood sample to obtain CBC results only takes a few hours. When blood is drawn from the patient, it is collected in a test tube that contains an anticoagulant, which prevents blood clots from forming in the sample. At the lab, dyes are added to identify different parts of the blood when the sample is put under a microscope. The counting and analysis of the blood is done automatically by a machine called a hematology analyzer.

A standard CBC includes a red blood cell count, which simply counts the number of these cells present in the given blood sample. CBCs evaluate red blood cells carefully because abnormalities in red blood cells play a large role in diagnosing diseases such as anemia and leukemia, which is cancer of the blood. CBC tests measure the physical attributes of red blood cells and analyze the amount of hemoglobin (a protein that carries oxygen) within the cells. An important part of the CBC is evaluating hematocrit, which measures the proportion of red blood cells relative to the volume of blood in the body. Low or high levels of hematocrit can signal dehydration, anemia, or problems with bone marrow, where red blood cells are created.

CBCs perform white blood cell counts and differentials, which tracks the proportions of different types of white blood cells. The white blood cell count of CBCs are used to detect infections, tissue damage, and autoimmune problems. CBC tests also count the number of platelets in the sample, which can be useful in predicting the risk of dangerous blood clots.

The Complete Blood Count test is a simple yet effective way for doctors to evaluate health and it can lead to more rapid and accurate diagnoses. Understanding how the CBC is done and how doctors use the results can make appointments and blood tests seem like a lot less of a mystery!

Mayo Clinic: https://www.mayoclinic.org/tests-procedures/complete-blood-count/about/pac-20384919
Scripps: https://www.scripps.org/news_items/6595-what-do-common-blood-tests-check-for
Leukemia and Lymphoma Society: https://www.lls.org/managing-your-cancer/lab-and-imaging-tests/blood-tests

Four Women Who Made Major Contributions to Genetics and Medicine (Whose names you might not know)

Nettie Stevens: Discoverer of Sex Chromosomes

Women like Nettie Stevens, who were born in the early 1860s, didn’t have a plethora of career options to choose from. They could either be secretaries, or they could be teachers. Stevens went down the teaching route. What she really wanted to do, however, was continue her education. Eventually, at the age of 36, she saved up enough money from her teaching jobs, moved from Vermont to California, and enrolled in Stanford University, and later in Bryn Mawr college for her PhD.

Stevens entered the field of genetics at a time when the field was rapidly expanding. Mendel’s seminal work on the principles of inheritance had been rediscovered in 1900, and in a few short years, Thomas Morgan – who taught Stevens at Bryn Mawr – would go on to show that genes are carried on chromosomes. While this might seem unremarkable now, Morgan’s research provided physical evidence for the heredity described by Mendel. However, despite the increasing evidence that physical traits are determined by genes, scientists still believed that either the mother’s environment or the chemical balance of the cytoplasm of eggs determined sex.

Nettie Stevens’ research put the issue to rest. Stevens studied mealworms – insects that resemble garden grubs – and after spending endless hours peering through microscopes, found that during spermatogenesis, the 20 chromosomes of the mealworm form “9 symmetrical pairs and 1 unsymmetrical [pair] composed of [a] small chromosome and a much larger mate.” This asymmetrical pair, she observed, was replaced by a tenth symmetrical pair during the formation of egg cells. She also found that somatic (non-reproductive) cells of mealworms followed a similar pattern: 10 symmetrical pairs of chromosomes in females, and 9 symmetrical pairs and 1 asymmetrical pair in males. This discovery was conclusive proof that chromosomes – in the form of the X and Y chromosomes in most animals – were what led to sex determination, and not maternal characteristics.

Nettie Stevens unfortunately died of breast cancer at the age of 50, a mere four years after she discovered sex chromosomes. Her reputation – both then and now – does not match the significance of her research. Morgan, her mentor and professor, is considered the most influential figure in modern genetics and often gets credited for all chromosome-related discoveries. Morgan’s name appears frequently in relation to his research on chromosomes, but Nettie Stevens’ doesn’t.

Alice Ball: The chemist who developed a cure for leprosy

Alice Ball grew up around chemicals. Her grandfather, James Presley Ball, was a famous African American photographer. Chemicals used in developing photographic prints, such as silver, iodine, chlorine, and bromine were likely part of her life years before she entered a chemistry lab.

Ball was born in Seattle on July 24, 1892. Her family moved to Hawaii in 1903 hoping that the salubrious weather would alleviate her grandfather’s arthritis. Her family moved back to Seattle in 1905, following her grandfather’s death. She earned two bachelor’s degrees in Pharmaceutical Chemistry and the Science of Pharmacy in 1912 and 1914, respectively. She then decided to pursue a master’s degree at the College of Hawaii, now called the University of Hawaii, and eventually became the first female and first African-American chemistry professor at the College.

Ball became an expert in extracting active ingredients from plants, and caught the attention of Harry T. Hollmann, medical director of the Kahili Leprosy Hospital. He had been trying to treat leprosy patients but hadn’t been making much progress. In a pre-antibiotic world, there was no clear cure for leprosy, although a potential candidate had been known for years. Chaulmoogra had been used to alleviate skin diseases, including leprosy, in India and China for centuries. Eventually, in the 19th Century, Western doctors started experimenting with Chaulmoogra oil to see if it could be used to treat leprosy. But success had been limited. Ingestion had proven to be ineffective and injecting the oil had proven disastrous – the viscous oil clumped under the skin to form blisters, due to which the patient’s skin looked as though it “had been replaced by bubble wrap.” What doctors needed was a form of Chaulmoogra oil that could be absorbed by the body.

Enter Alice Ball, the 23-year-old chemist whose master’s thesis was on the extraction of the active ingredient from a root called the Ava root. In less than a year, Ball devised a way to create a water-soluble injectable form of Chaulmoogra oil.

Ball died shortly thereafter, on December 31, 1916, at the age of 24. It is unclear why she died, although it is possible that she could have gotten chlorine poisoning while teaching in the lab.

Ball did not live to see 84 patients in the Hospital get cured because of the extraction method she had developed. She was also not given due credit for her discovery, as Arthur Dean, president of the College of Hawaii, published Ball’s extraction technique as his own. In was only in 1922 that she got credit for her work, when Hollmann, the surgeon who had initially encouraged her to develop the drug, wrote about the extraction process and called it “The Ball Method.” The injectable form of Chaulmoogra oil became the principal method of treating leprosy until the 1940s. In 2000, then Hawaii Lieutenant Governor Mazie Horono declared February 29 Alice Ball Day.

Barbara McClintock: Discoverer of Transposons

From the time Barbara McClintock was a young girl, it was clear that she was not going to grow up to become a conventional woman. She preferred sports over dolls, and her mother even made her bloomers so that she would be able to play all the sports she wanted “unhindered by dresses.” As her desire to pursue higher education grew, however, her mother’s support of her idiosyncrasies became less enthusiastic. Worried that an academic daughter would be unmarriageable, she was reluctant to allow McClintock to go to college. Her father interfered, however, and McClintock went off to Cornell to pursue a degree in Agriculture.

By the time she graduated, McClintock became an expert at preparing cells for the microscope. She began studying maize and became so familiar with maize chromosomes that she noticed that certain sections of the chromosome broke off and reattached to different chromosomes and that this corresponded with changes in the coloration of the maize. McClintock called these regions controlling elements (they are now called transposons). This discovery greatly expanded what scientists believed that genes could do. Previously genes were thought to be stationary – like, as the popular analogy goes, beads on a string. McClintock developed a strong reputation in the scientific community and was elected president of the Genetics Society of America in 1945, becoming the first woman to serve in the position.

However, what McClintock really wanted to study was how genetic expression was regulated. It was a question that had plagued scientists for decades: how could neurons and skin cells can look so different despite having the same genetic code? McClintock hypothesized that if a transposon landed near a gene, it would turn off its expression, and turn it on when it left. She presented this theory at a prominent symposium in 1951, but her theory – lacking data to back it up – baffled scientists. McClintock withdrew from the scientific limelight after the symposium and didn’t publish her research after 1953. In 1983, Evelyn Keller published a popular biography of McClintock that brought McClintock back into the public consciousness. McClintock was awarded the Nobel Prize in Physiology or Medicine the same year – the first and only woman to receive an unshared Nobel Prize in the category. However, despite the honor, she never succeeded in proving the regulatory functions of transposons, and indeed, subsequent research showed that it is proteins such as transcription factors, promotors, enhancers, and repressors that control gene expression.

Tu Youyou: A cure for malaria

Tu Youyou was born in 1930 to a family that greatly valued education. At university, she trained under a phytochemist who taught her how to extract active ingredients from plants using appropriate solvents. After graduating, Youyou was recruited to the Institute of Materia Medica, Academy of Traditional Chinese Medicine. Her interest in traditional medicine had deep roots. Growing up, she had seen folk recipes being used to treat a variety of diseases and had seen that some of them were quite effective. The Institute of Chinese Materia Medica provided a unique environment for the combination of Traditional Chinese Medicine and Western medicine. It was an institution where historians, who poured over ancient recipes, and chemists and medical doctors, who had modern tools at their disposal, worked side by side.

It was under these conditions that in 1967, Youyou was tasked with developing a drug to treat chloroquine-resistant malaria. Many Chinese and American soldiers were dying due to malaria in Vietnam – and both the United States and China launched campaigns to develop a treatment, and Youyou was recruited to the Chinese campaign.

Youyou’s team collected over 2000 recipes based on over 600 herbs. One of the most promising candidates was Qinghao, the Chinese name for six herbs falling under the genus Artemisia. Handbooks detailing traditional recipes were helpful in refining their techniques of extraction. One recipe, for example, made Youyou’s team attempt a cold extraction instead of performing extractions at boiling temperatures, leading to better results. Youyou extracted the active ingredient from Artemisia annua and it proved to be effective against rodent malarias. In the absence of robust protocols on how to conduction clinical trials in China in the 1960s and 1970s Youyou and her team volunteered inject themselves to ensure that the active ingredient wasn’t toxic. The team then used the drug to treat 21 malaria patients and saw that their fever disappeared. Their drug was 100 percent effective.

Youyou was awarded the Lasker DeBakey Clinical Medical Research Award in 2011 and the Nobel Prize in Physiology or Medicine in 2014 for her work, which, the presenter of the Lasker award described as “arguably the most important pharmaceutical intervention in the last half-century.”

Check out our blog honoring five of Emory’s female inventors and their work here.


Nettie Stevens

Alice Ball

Barbara McClintock

  • “Barbara McClintock and the discovery of jumping gene” by Sandeep Ravindran: https://www.pnas.org/content/109/50/20198

  • “’The Real Point is Control’: The Reception of Barbara McClintock’s Controlling Elements” by Nathaniel Comfort: https://www.jstor.org/stable/4331511?seq=1

  • The Tangled Field by Nathaniel Comfort

  • The Violinist’s Thumb: And Other Lost Tales of Love, War, and Genus, as Written by Our Genetic Code by Sam Kean1

Tu Youyou

Understanding AI Lingo in Healthcare

With the ever-growing incorporation of technology into medicine over the past decade, healthcare industries have advanced to integrate novel technology innovations. Such innovations include artificial intelligence (AI), virtual reality (VR), 3D-printing, robotics, and so on. One of these innovations, artificial intelligence (AI), holds promise in improving patient care while reducing costs. This technology has been applied in areas such as patient diagnosis and monitoring, treatment protocol development, radiology, and drug development. While some of this might seem like science-fiction, it’s being incorporated every day in the healthcare field. To help introduce you to this new world below, we’ve compiled a list of some of the most common terms in this field.

Basics of AI and machine learning

  • AI: “The study and design of intelligent agents.” In healthcare these agents gain information, process it, and provide specific output. Often healthcare AI programs in healthcare use pattern recognition and data analysis to evaluate the intersection of prevention or treatment and patient outcomes.

  • Algorithm: Instructions or rules for a computer to execute that solve problems or perform calculations. AI is dependent on algorithms to make calculations, process data, and automate reasoning.

  • Machine learning: Is a type of algorithm that “self teaches” or improves through experience. It uses pattern recognition, rule-based logic, and reinforcement techniques that help algorithms give preference to “good” outcomes. Quite often in healthcare, this is done through training data, in other words medical records. Machine learning can be supervised, unsupervised, semi-supervised, reinforced, self-learning and a number of other learning approaches.

  • Supervised vs. unsupervised learning: Refers to whether programs are given both inputs and outputs, this can also be described as labeled and unlabeled data. Supervised learning means that “training data” that identifies both the input (labeled) and output (answer key) so the algorithm can be “trained” to distinguish between good and bad results. Unsupervised learning, on the other hand, occurs when the algorithm is not provided with outputs (answer key) and must identify patterns, features, clusters and so forth to provide output (solutions).

  • Artificial Neural Networks: Algorithms that imitate human brains with artificially-constructed neurons and synapses. They are typically constructed in layers that each perform different functions that are then used to simulate how a brain actually works. These algorithms can obtain and process information from large quantities of data.

  • Decision Trees: A tool that maps out information according to possibilities that come from making a decision. With each decision made, there are a multitude of consequences, and a decision tree maps out the possible outcomes from making different choices in a tree-like model. AI inputs data from decision trees and determines which options will yield the best, least-costly outcomes by considering all possible options.

Applications of AI and Machine Learning in Healthcare

  • Radiology: AI assists in radiology primarily through speeding up patient diagnoses and treatment recommendations. It also can produce more accurate quantitative imaging and identify unknown characteristics that individuals with particular diseases have.

  • Imaging:  When programmed correctly, AI can identify signs of particular diseases in patient images acquired through CT scans, MRIs, and x-rays through finding abnormalities. Examples of typically identified injuries include cardiovascular abnormalities, musculoskeletal injuries like fractures, neurological diseases, thoracic complications like pneumonia, and various cancers.
  • Diagnosis: AI software has recently been able to diagnose patients more accurately than physical healthcare professionals using imaging. In the past, AI was mainly used when identifying cancers based on pictures of skin lesions. The number of AI-identifiable diseases has since expanded with technological advancements. Based on the disease diagnosed, AI tools can recommend treatment options and help develop drug treatments if they don’t already exist

  • Telehealth/Telemedicine: Telehealth/telemedicine enable healthcare to be delivered over long distances using technologies involving telecommunication and information dispersed electronically. AI uses predictive analytics to better serve rural or elderly populations from afar through diagnosing patients faster, functioning as robots to physically assist people, remotely checking in with patients to monitor progress, and reducing visits to specialty healthcare professionals.

  • Electronic Health Records: AI holds potential to simplify complicated Electronic Health Records (EHR) through making networks more flexible and intelligent by using key terms to obtain data, using predictive algorithms in EHR to warn professionals of potential diseases in patients, and simplifying data collection and entry.

  • Drug Development: Because AI can identify abnormalities in patients and what disease these physical abnormalities are linked to, it can use this information to develop drug treatments. Drug development is often slowed by human error found in the need to test several variations until it is approved by the FDA. AI speeds this process up and makes it cheaper by using pattern analysis and decision-making processes to analyze biomedical information more accurately, eliminate drug options that are likely to fail, and recruit the best patients for trials.

  • Drug Interactions: Since combining drugs is a common but potentially dangerous treatment practice, AI can warn providers about possible side effects from interactions between drugs. Penn State researchers created an algorithm using artificial neural networks that screens drug contents and look for combinations that could potentially cause harm to a patient when put into the human body. This application of AI may have large ramifications for healthcare because many patients use multiple drugs when dealing with more severe health issues and need to know that what they’re consuming won’t cause more harm.

  • Treatment Planning: Implementing AI into treatment planning has been especially heavily studied in radiotherapy. Because new technology has resulted in more options becoming available, treatment planning is more complex and labor intensive than before. AI can automate planning processes through using algorithms to identify benefits and drawbacks of treatments at much faster rates and note effects from combinations of treatments.

15 Good Minutes: Ichiro Matsumura

For Emory Professor of Biochemistry Ichiro Matsumura, PhD, inspiration to pursue a career in research came from an unlikely source: a concussion. When Matsumura was in college at MIT, he got into a bike accident that left him hospitalized for several months. After being released from the hospital, Matsumura was prepared to retake all his courses from that semester over the summer. However, one of Matsumura’s professors, Harry Lodish, gave him the option to write a report from a list of topics instead of retaking the course, given that he had done well on the class’s first midterm. The topic Matsumura chose was evolution, which he would later dedicate his career to studying.

“That [summer] was what got me excited about evolution,” Matsumura said. “Eventually when I went to grad school a couple years later, I already knew I already who the leaders of the field were, and so I just applied to those specific departments.”

Matsumura credits that summer project with helping him identify key research questions. Given the field of molecular evolution was young at the time, Matsumura was able to read every issue of Molecular Biology and Evolution and learn the names of all the contributors in the field. Later as a grad student, Matsumura learned how to formulate hypotheses and design informative experiments. He would use these skills to apply for a competitive NSF postdoc fellowship, and to develop an independent research program within the lab of his advisor, Andy Ellington.

Today, Matsumura leads a lab at Emory that studies evolution on a molecular level. His work has yielded discoveries of proteins with pharmaceutical and industrial uses, as well as illuminated the evolutionary process within cells and microorganisms. Recently, Matsumura has explored what factors account for variation in how bacteria grows. When examining bacterial cultures with the same initial genotype, Matsumura found that the cultures develop variations, even if grown in similar environments. Eventually, he began to realize that these variations could not simply be accounted for by different copy numbers or multicopy plasmids. Instead, he was witnessing evolution taking place on a molecular level.

“If you think about how much a bacterium can replicate itself over say 30 Generations, it’s a lot of opportunity for mutation,” Matsumura said. And so especially with multicopy plasmids, you have so many copies per cell, so many generations, and so many cells per milliliter, it just sort of becomes inevitable that some of them start getting mutated.”

Matsumura’s work has implications for a wide range of topics, including novel gene therapy technologies. Gene therapy relies on the interstation of a “stressor DNA” into a cell as the impetus for genetic change that improves the health of the cell. Based on Matsumura’s findings regarding molecular evolution however, such changes on a genetic level can lead to unintended mutations. Matsumura is working on techniques that could prevent damaging consequences as a result of this process, by forcing the cell to express specific proteins. While he is currently exploring the technique using bacteria, it could potentially be used on human cells as well.

Balancing the need to protect intellectual property while publishing work has sometimes proved challenging for Matsumura, as he believes it can be for many scientists. While publishing work in a timely manner is essential for obtaining research grants, doing so can be considered a “public disclosure,” starting the clock on a limited amount of time to obtain a patent. To help augment his knowledge of the patent process, Matsumura took a class on intellectual property at Emory Law School, offered as part of a program where Emory faculty can take courses for free. There he worked with his professor to discuss which projects he was currently working on could be suitable for patenting.

“It, to some extent, falls upon the shoulders of us investigators to make a case and to prove that [an innovation] could of be value and therefore worth patenting,” Matsumura said. “And that’s not always an easy case to make.”

Given his long and successful career, Matsumura has two key pieces of advice for those seeking to follow his path. The first essential piece is worrying more about establishing strong working relationships than raw talent. Matsumura believes that he overestimated the role of measures such as test scores in predicting future success.

“You need a certain threshold of talent to get into grad school and to get that first job, but once you’re along a certain way, it really ends up becoming more a matter of personality that determines who succeeds and who doesn’t,” Matsumura said. “I did spend a fair amount of time when I was younger, thinking about what I’m good at and how good I am at those things, and I think I may have spent a little bit too much time thinking about that.”

The second key piece of advice that Matsumura would give is not being afraid of failure and learning from mistakes. He emphasizes willingness to learn the “right lessons,” as opposed to just the easy lessons from mistakes, as an important part of this process. Ultimately, learning from mistakes has been defining for Matsumura’s career path, even as he recognizes that he was privileged to receive the benefit of the doubt and the ability to learn from these mistakes.

“It’s really hard I think to go through life and to get everything right the first time, and so for me learning how to solve problems and make good decisions all requires doing things wrong, figuring out that I did them wrong, and trying to do better the next time,” Matsumura said. “Since I had to figure it out learning the hard way, at the very least, I think that I taught my younger self that that’s okay.”

Ichiro Matsumura: https://med.emory.edu/departments/biochemistry/research-labs/mastumura/index.html

Algorithms and Healthcare: The Future is Coming

Computers are everywhere it seems, even in our healthcare. While they aren’t quite at the level of (find some movie reference with something futuristic) they are making significant contributions. One of those contributions is algorithms which are contributing in areas from imaging, diagnosing, and predicting.

To help solve dilemmas such as this, healthcare professionals are increasingly turning to algorithms, which use machine learning techniques that enable computers to learn information without human input. Algorithms create a formulaic process for healthcare professionals to evaluate patient symptomology and decide on the best course of treatment. While they cannot replace human decision-making or medical expertise, algorithms can help guide doctors and nurses through a logical thought process. An ideal algorithm is structured to help prevent flawed decisions that can harm patients.

Taking available input concerning a patient’s age, weight, risk factors, and symptoms, algorithms can offer the probability of whether a patient has a given condition. A basic example of this can be seen with online services such as WebMD that allow people to enter symptoms and quickly obtain a preliminary diagnosis. At a much higher level, algorithms can monitor hospital patients and predict when a patient is at high risk of deteriorating or going into cardiac arrest. Such algorithms are oftentimes complex versions of “decision trees,” where the algorithm’s judgment is based on answers to many yes/no questions. This method minimizes the potential for bias by using a formulaic and straightforward process to diagnose medical conditions.

Algorithms can be used for much higher-level diagnostics as well. Algorithms excel at detecting patterns and can analyze large amounts of information quickly, making them perfect for predicting diagnosing many types of conditions. For example, researchers at the U.C. San Francisco created an algorithm that scans echocardiogram images, looking for heart issues. The algorithm achieved an accuracy rate over 10% higher in detecting heart issues from these images than human doctors. Algorithms can even predict human behavior in some cases, with a Vanderbilt University Medical Center algorithm being able to predict with 84% accuracy whether a patient admitted for self-harm or suicide attempts would try again within two weeks. Algorithms such as these can be helpful predictive tools, allowing doctors to tailor their treatment to best serve patients.

Several important algorithms have been developed at Emory, including one deployed during the 2009 H1N1 pandemic that prevented unnecessary patient visits. Patients experiencing flu symptoms were asked to input their age and answer a few questions about their symptoms. The algorithm could then determine their risk of developing complications and recommend whether they seek medical attention or stay home. Emory Healthcare has deployed a similar tool during the COVID-19 pandemic. Those exhibiting COVID-symptoms are asked to visit C19check.com, which assesses their risk of serious illness based on several questions. The website has helped Emory manage emergency room capacity by allowing doctors to recommend recovery from home for low-risk patients.

Algorithms developed at Emory have had substantial impacts on other areas of healthcare as well. One algorithm allows for more precise measurements during eye scans, which can greatly improve the accuracy of such scans in picking up signs of Alzheimer’s and dementia. Another  created a uniform protocol system for blood transfusions, allowing doctors to track and monitor adverse reactions. Finally, a third improves machine learning itself, creating stronger neural networks that give algorithms greater accuracy and learning ability.

With algorithms having such a substantial impact on healthcare, a question that inevitably arises is how liability is determined. If a healthcare provider misdiagnoses or mistreats a patient, it is easy to establish the party at fault. If a machine does the same, the issue becomes much more complex. While there are no laws specifically pertaining to medical algorithms, manufacturers can be held liable under general product liability law. The Consumer Protection Act of 1987 allows plaintiffs injured by defective products to sue. Under this act, patients can recover damages if they prove that an algorithm is defective in some way. However, significant legal grey areas still exist. Creators of medical algorithms may be shielded from liability their algorithms go through an FDA approval process. Under the concept of preemption, the Supreme Court has ruled that in some, but not all cases, manufacturers of medical products cannot be held liable state courts if their product received FDA approval.

More specific regulations by the Food and Drug Administration (FDA) covering algorithms may be forthcoming. Currently, most medical devices require premarket approval by the FDA. Since these regulations were implemented before the development of machine learning techniques, algorithms fall into a grey area and do not generally require such approval. In September 2019, the FDA published a draft guidance regulatory framework for algorithms, which would create an approval process for the software. Since machine-learning algorithms constantly change and adapt, FDA approval would not be needed for every modification. Instead, the manufacturer would have to provide a “predetermined change control plan” to the FDA, containing the algorithm’s underlying methodology and anticipated changes. Only software for high-risk medical conditions would be covered by these regulations, and algorithms for self-diagnosis of low-risk medical conditions would remain unregulated.

From helping prevent suicide, to diagnosing heart conditions, to calibrating treatments for patients in ICU units, algorithms are now a critical component of our healthcare system. Just like human judgment, algorithms are not infallible. Used correctly however, they can make our healthcare system safer and more efficient. Look for algorithms to take on new roles and an even more active role in patient care in the future as artificial intelligence continues to advance.


15 Good Minutes: William Wuest

Antibiotics have been one of the most consequential innovations in human history, allowing us to treat a wide variety of bacterial diseases that could otherwise be damaging or fatal. However, bacterial resistant to these antibiotics is on the rise, necessitating a constant drive to discover new antibiotic drugs as older ones are rendered less effective. One of the scientists on this forefront of this push is Emory Associate Professor and Georgia Research Alliance Distinguished Investigator, William Wuest, PhD. Wuest runs a lab that is focused on finding novel antibiotics to fight bacterial infections. Recently he and his team have made several notable discoveries, including drugs that can be used against antibiotic-resistant staph (MSRA), as well as bacteria that can cause tooth decay and heart disease.

Wuest originally obtained a degree in chemistry/business from the University of Notre Dame. Between his PhD at the University of Pennsylvania and postdoctoral position at Harvard Medical School, he grew interested in antibacterial development. A major incentive for him to study this subject was the relative lack of interest by pharmaceutical companies in a field that had a growing need.

“The fact that humans have created compounds de novo, that are effective against specific diseases, and have saved countless lives is truly remarkable,” Wuest said. “However, companies’ recent lack of interest in antibiotics has left a convenient void for academics to fill.”

As Wuest’s career advanced, antibiotic-resistant bacteria became a growing problem. Today, these strains of bacteria infect over 2 million people worldwide each year and are responsible for 23,000 annual deaths. A 2014 study by KPMG estimated that 2050, antibiotic-resistant bacteria could cause more deaths than cancer. To combat this problem, Wuest and his team are always looking for new compounds with the potential to become antibiotic drugs. They start by looking at structures in nature that are known to kill bacteria. They then attempt to “strip down” the molecule in the lab to create a simplified form where it can be used in therapies, a process which Wuest says can be challenging.

“Although organic synthesis is a mature field, and we can create virtually any molecule we want, it is still a time consuming and frustrating practice,” Wuest said. “I’m fortunate to lead an incredibly talented group of graduate students, undergraduates, and postdocs at Emory who work very hard day-in and day-out toward these goals.”

Wuest’s work is uncertain by nature, as the outcomes of the trials his lab runs on new drugs are unpredictable. One time, for example, Wuest discovered a compound that appeared to be highly potent at killing Staph bacteria. It was later found, with further testing however, that the compound also damaged human cells, making it impossible to use as a therapy. To Wuest, however, such experiences are just part of his job and make it even more rewarding when he does find a successful antibiotic.

“To me, the most exciting part of every project is to see if our hypotheses are accurate,” Wuest said. “I am the type of person who always loves to be right, but in this field that outcome is typically rare.”

For those seeking a career in his field, Wuest emphasizes intellectual curiosity, particularly through reading scientific literature, as an essential quality to have. He also advises students and young scientists to network, saying such connections have broadened the scope of his own research.

“Our research has been expanding in ways I never would have thought possible through one-off meetings during seminar visits or a dinner after conferences,” Wuest said. “These collaborations have expanded our potential, leveraged our resources, and enabled my students to have broad training experiences.”

William Wuest: http://biomed.emory.edu/academics/faculty-detail.html?action=getFacultyDetail&gdbbsId=07FD72BF-FE9C-4F05-AC97-AB470D7DF98F

The Differences Between Small Molecule Drugs and Biological Drugs?

What are small molecule drugs?
Small molecule drugs, as their name suggests, are chemical compounds that have low molecular weight – a single molecule of a small molecular drug typically contains only 20 to 100 atoms. They can enter cells easily where they interact with molecules within the cell.

What are some examples of small molecule drugs?
Despite the development of more targeted drugs, small molecule drugs are immensely popular, and account for 90% of drugs in the market. Examples of common small molecule drugs include aspirin, penicillin, paracetamol, and esomeprazole (sold under the brand name Nexium and helps reducing stomach acid).

What are biological drugs?
Biological drugs are drugs that are manufactured or extracted from living organisms. These drugs can consist of genetic material or proteins such as hormones or antibodies. These are typically larger in size than small molecule drugs, with a single molecule consisting of anywhere between 200 to 50,000 atoms.

Unlike small molecule drugs that are characterized by their specific chemical composition, it can be difficult to determine the exact chemical composition of biological drugs because they are often large, complex molecules. Instead, these are characterized by the process by which they are obtained.

What are some examples of biological drugs?
Some examples of biological drugs include:

  • Insulin
  • Vaccines
  • Trastuzumab, a drug used to treat breast cancer. Trastuzumab is an antibody that binds to a receptor involved in the development of breast cancer and prevents it from firing cellular signals.
  • Adalimumab, also an antibody, that is used to treat rheumatoid arthritis.

How does drug delivery differ between the two types of drugs?
Small molecule drugs are typically administered orally. Biological drugs, on the other hand, are not as stable as small molecule drugs. If they are consumed orally, they degrade in the gastrointestinal tract. As a result, biological drugs are typically administered by injection or infusion.  

What are some of the pros and cons of the two types of drugs?

  • Small molecule drugs are a lot easier to administer than biological drugs.
  • Biological drugs are highly targeted drugs. They don’t bind to non-target molecules, and as a result, lead to fewer side effects.
  • Biological drugs are much more expensive to develop and hence are much more expensive for patients.
  • Innovations are assisting the development of both small molecule and biological drugs. Sophisticated gene editing tools such as CRISPR/Cas9 have transformed medical research, and have pushed the boundaries of the kinds of targeted therapies and drugs that can be discovered. At the same time, small molecule drugs are likely going to remain important, and their discovery is projected to be aided by the use of technologies like artificial intelligence.

What are generic drugs and biosimilars and how are they regulated?
A generic drug is a drug that has the same active ingredients as the drug that was originally patented. Generic drugs have the same dosage, intended use, and method of administration as the brand-name drug, although the manufacturing process might differ.
Biosimilars are drugs that are “highly similar” to biological drugs but are not necessarily identical to them. Because of the large size and complexity of biological drugs, biosimilars do not have to be exact copies of the original biological drug in order to have the same therapeutic function.

The regulations governing generic drugs are fairly straight forward: the Hatch-Waxman Act of 1984 provides drug innovators with 5 years of market exclusivity. After this period expires, generic drugs can enter the market, as long as clinical trials are conducted, and the FDA had ensured that manufacture of drugs is consistent. These regulations have been vital in lowering prescription drug costs. Today, nearly 90% of prescription drugs are generic drugs which can cost up to 80% lesser than brand-name drugs.

Regulations governing biosimilars are more complicated. These regulations were signed into law in 2010 under the Biologics Price Competition and Innovation Act (BPCIA) give 12 years of market exclusivity to the drug innovator. Additionally, for biosimilars to be approved, manufacturers have to conduct more rigorous clinical trials compared to those required for generic drugs. These stricter regulations have led to an increase in biological drug costs – Medicare and Medicaid spending biological drugs has ballooned from $5.3 billion in 2012 to $10.3 billion in 2016.


What’s the Difference Between Apoptosis, Necroptosis, and Pyroptosis?

The word “death” often brings up negative feelings and is associated with harm. Within the human body, however, cell death happens every second and the processes that regulate cell death are often beneficial and necessary to preventing infections, cancer, and other abnormalities. Apoptosis, necroptosis, and pyroptosis are all methods of programmed cell death, regulated by genes and signal molecules within the cell. These forms of cell death have distinct attributes that can help or hurt the body.

Apoptosis was the first type of programmed cell death to be discovered, and it is often referred to as “cell suicide”. Apoptosis occurs due to an activation of instructions within cell DNA. Apoptosis is commonly activated through an intrinsic pathway, which starts when signal molecules within the cell detect abnormal cell growth or damage in cellular DNA. The signal molecules activate genes within the cell that cause the cell to commit apoptosis. An important example of the intrinsic pathway is within cell division. The p53 protein is an example of a tumor suppressor, which causes cells to commit apoptosis when it detects that cells are dividing too quickly. This prevents tumors and abnormal cell growth. When signal molecules activate genes within the cell, the cell releases proteases, which are enzymes that break bonds between proteins. These proteases cause the cell membrane to disintegrate and causes DNA to condense and break up into fragments. The material inside the cell is released in small membrane-bound capsules called apoptotic bodies. Then cells called phagocytes engulf and dispose of these apoptotic bodies, acting as the cleanup crew for the dead cell. Although apoptosis is necessary for preventing cancer and regulating cell growth, too much apoptosis can lead to serious diseases such as Parkinson’s disease and Alzheimer’s disease.

Necroptosis is a type of regulated cell death triggered by outside trauma or deprivation, compared to apoptosis which can start from signals within the cell. Necroptosis is a regulated form of necrosis, which is uncontrolled cell death due to factors outside the cell. The most common way that necroptosis takes place is through the activation of the RIPK3 gene in human cells. When a signal from outside the cell binds to a receptor on the cell membrane, the RIPK3 gene is activated and causes a chain reaction inside the cell. This chain reaction leads to lysis of the cell, which is when the cell membrane bursts and the contents of the cell spill out. Unlike apoptosis, after the cell bursts, phagocytes do not engulf the dead cell material and it is not removed from cell circulation. Therefore, the remnants of the dead cell often trigger reactions with nearby cells and activate the immune system. The bursting of cells that happens during necroptosis is used to fight infection and combat viruses through the release of substances called DAMPs, which stands for Damage-Associated Molecular Patterns. DAMPs alert surrounding cells of danger and promote inflammation, which is how the body fights injuries and infections. When the signals triggering necroptosis malfunction and necroptosis happens too often, it can contribute to inflammatory diseases such as psoriasis, ulcerative colitis, and Crohn’s disease.

Pyroptosis is the primary response of the cell to infectious organisms and is triggered by the immune system. The main difference between pyroptosis and necroptosis is how it is activated: while the RIPK3 gene commonly activates necroptosis, pyroptosis is activated by the enzyme caspase-1. For this reason, pyroptosis is also called caspase-1 dependent cell death. Caspase-1 activates proteins that prompt an immune response from the cell. This response causes the same lysis seen in necroptosis where the cell membrane bursts and the contents of the cell spill out. The release of DAMPs from the ruptured cell triggers inflammation and a larger immune response from surrounding cells and organs. Pyroptosis causes more inflammation than necroptosis and is often dangerous to the body. Pyroptosis triggered by pathogens often contributes to symptoms of infectious disease because of the release of DAMPs and inflammatory molecules. Because of the strength of pyroptosis, this form of programmed cell death is used with apoptosis to kill cancerous cells. However, because pyroptosis causes inflammation, it can also make the environments around cells more suitable for tumors to grow.

Apoptosis, necroptosis, and pyroptosis are all forms of programmed cell death that activate genes and molecules inside the cell. These different types of cell death promote inflammation, respond to pathogens, and suppress cancer. Programmed cell death is an important field of study because if scientists can find a way to control cell death, they can trigger responses to tumors, injuries, and disease. Cell death is a necessary part of human life, and these three forms are constantly being studied to understand how they operate and how scientists can harness them to create better treatments for the future.


Be Ware of Preprints: Protect Your Intellectual Property First

Who owns the rights to a new innovation described in a research paper? If a patent is in place, the answer is simple: the owner. Generally, the lengthy, and confidential, peer-review process means that authors of unpublished work have ample time to submit an invention disclosure and to have their technology transfer office review and if necessary, file a patent, ensuring that any new invention is protected prior to any public disclosure. However, the rise of “preprint” services, which allow authors to publish preliminary findings ahead of peer-review, has complicated this process. Preprints can severely hamper the ability of authors and their universities to patent new inventions described in their work if appropriate steps are not taken prior to public disclosure. Whenever possible, university researchers should consult with their technology transfer office prior to submission of manuscripts to a preprint server to ensure that any patentable inventions are adequately protected.

Scientific manuscripts accepted for publication as preprints have become increasingly common in the past decade, as searchable databases such as bioRvix and MedRxiv allow authors to publish their work online with relative ease. Many authors choose to publish preprints because their findings are negative or contradictory and have a low chance of being published in a journal. Others may publish preprints to obtain quick and wide feedback for work that is concurrently receiving peer-review. Preprints often gain substantial exposure, especially for the many studies concerning research related to COVID-19. In fact, many scientific studies cited in the media are preprints awaiting peer-review.

Preprints certainly have their place and help to enable rapid distribution of scientific results and can help give an article beneficial attention, but authors should be aware that they also complicate the process for protecting new, and potentially valuable innovations that may be described in a paper. While traditional peer-review can often take months to complete, the preprint services often compress this process into days or weeks, meaning articles may be published before authors or inventors and their respective universities have had a chance to evaluate the invention and to file a patent application. Under most countries’ patent laws, any invention that is disclosed publicly before a patent application is filed may be considered “prior art,” and therefore may be ineligible for patent protection. It is therefore critical that authors and innovators work with their university to file the appropriate patent applications for their work before submitting manuscripts to preprint services to protect any potentially valuable intellectual property.

Researchers seeking to commercially market new innovations should be aware of the consequences of foregoing intellectual property rights by publishing early. Without adequate protection, it is unlikely that a commercial partner will be able to further develop and eventually distribute products based on the innovation. Patents provide an incentive to commercial partners to invest in new inventions, and without some guarantee of exclusivity, industry partners are likely unable to move promising technology to market. The university technology transfer office is accustomed to working with researchers (even on tight timelines) to ensure that new innovation can be successfully protected and commercialized while balancing the need for rapid publication.

University researchers should contact the tech transfer office early in the study, especially if the resulting data look promising. “We’re always happy to work with our researchers on filing a patent before a public disclosure, and the earlier we can be involved, the better protection we can secure for our intellectual property,” notes licensing associate John Nicosia. Preprint manuscripts often generate positive attention for the research within the scientific community, the greater public, and those business development professionals looking for the next big discovery. However, researchers seeking to patent new innovations arising from their work must be aware of potential pitfalls before submitting a manuscript for preprint publication.

15 Good Minutes: Cassandra Quave

When most people think about medicine, plants are not what immediately jumps to mind. However, for Emory Assistant Professor Cassandra Quave, PhD, the relationship between plants and medicine is career-defining. Quave is an ethnobotanist, meaning she studies human interaction with plants and their potential medical properties. Her work has led to important discoveries including treatments for eczema and skin infections. Quave describes her research as investigating compounds on a fundamental level, derived from their source in plants. She and her lab then determine whether the compound has properties that would allow it to be used in medicine.

“In a single plant species, you have hundreds or thousands of unique molecules, and so there’s a lot of chemical diversity found in nature to still explore,” Quave said. “There are over 28,000 species of plants used by humans on earth for medicinal purposes, and we’ve barely scratched the surface in exploring their medical potential.”

Quave decided to pursue her career studying plants based on her interests in microbiology and nature. Today, in addition to her role as an Emory Professor, she also serves as a curator of the Emory Herbarium and as CEO of the start-up company PhytoTEK LLC. In her role at PhyoTek, which she co-founded, Quave helped the company discover innovative plant-based medications for fighting antibiotic-resistant drugs. Currently, PhytoTEK is working on the technology for a new line of medicated bandages.

Protecting the intellectual property of her innovations can be more complicated for Quave than it is for many other researchers. This is because plants themselves can only be patented in a narrow set of circumstances, while the medical use of compounds isolated or formulated from the plant can be patented more broadly. Quave’s company PhyoTEK holds one patent, and Quave has worked with Emory’s Office of Technology Transfer (OTT) to secure additional patents for innovations discovered through her academic research. Quave says working with OTT has generally been a smooth and quick process.

“I’ve been really impressed by Emory’s OTT because they’re pretty fast in getting provisional patents filed and then converting them when the year is up,” Quave said.

For those also hoping to pursue a career in ethnobotany or biology in general, Quave recommends that they polish their writing skills. She spends much of the time writing, from grant proposals to academic papers and a science memoir.

“Start writing earlier and practice writing,” Quave said. “Write a lot more grants because grants are what make the research possible. So just building skills in the field of scientific writing and communicating science from an early stage is really important.”