Research could lead to improvements in gene therapy and antiviral resistance medications while also possibly leading to a new class of clinical laboratory tests
Scientists at the University of Maryland, Baltimore County (UMBC) have discovered what may be the scariest virus of all—the Vampire Virus. It’s a term that may inspire “Walking Dead” level horror in the wake of the COVID-19 pandemic, and though virologists and microbiologists might be tempted to dismiss them as imaginary, they are all too real. Even more apropos to the Dracula saga, the UM scientists found them in a soil sample. Yikes!
Happily, this ghoulish discovery could have positive implications for gene editing, gene therapy, and the development of new antiviral medications, according to The Conversation. In turn, these positive implications may eventually trigger the need to create new diagnostic tests that clinical laboratories can offer to physicians.
The image above, taken from a University of Maryland news release, shows the satellite virus “latched onto its helper virus.” Discovery of vampire-like viruses that attach at the “neck” of other viruses may lead to important discoveries in the development of gene editing and antiviral therapies. Might clinical laboratories one day collect samples for pharmaceutical developers engaged in combating antiviral drug resistance? (Photo copyright: University of Maryland.)
Spotting a Vampire Virus
According to IFLScience, these tiny vampire viruses were first discovered by undergraduates who believed they were looking at sample contamination when analyzing sequences of bacteriophages from environmental soil samples. But upon repeating the experiment they realized it was no mistake.
In the UMBC news release, bioinformatician Ivan Erill, PhD, Professor of Biological Sciences at the University of Maryland, noted that “some viruses, called satellites, depend not only on their host organism to complete their life cycle, but also on another virus, known as a helper.
“The satellite virus needs the helper either to build its capsid, a protective shell that encloses the virus’ genetic material, or to help it replicate its DNA,” he added. “These viral relationships require the satellite and the helper to be in proximity to each other at least temporarily, but there were no known cases of a satellite actually attaching itself to a helper—until now.”
Although scientists have witnessed viruses working together before, this is the first known instance of a virus directly latching onto another virus’ capsid—rather like a vampire going for the neck.
“When I saw it, I was like, I can’t believe this,” said Tagide deCarvalho, PhD, Assistant Director of Natural and Mathematical Sciences at the University of Maryland and first author of the study, in a UM news release, “No one has ever seen a bacteriophage—or any other virus—attach to another virus.”
“Not everyone has a TEM at their disposal. [With the TEM] I’m able to follow up on some of these observations and validate them with imaging. There’s elements of discovery we can only make using the TEM,” said deCarvalho in the UMBC news release.
Using Vampire Viruses to Develop Better Gene Therapies
Spookily, the comparisons to Dracula and his parasitic brethren do not stop with their freeloading tendencies. The researchers found that some viruses without a satellite attached still showed signs of having been leeched onto before. Those viruses had the equivalent of “bite marks” showing evidence of encountering vampiric viruses in the past.
“It’s possible that a lot of the bacteriophages that people thought were contaminated were actually these satellite-helper systems,” said deCarvalho in the ISME paper.
But what does UMBC’s breakthrough mean for the greater scientific and medical community? Do we need to arm host viruses with silver crosses and necklaces of garlic? Jokes aside, this discovery could lead to further development in research of how to genetically alter viruses and deliver therapeutic elements into cells.
According to Healthline, some gene therapy or “gene editing” already involves the use of viruses. Scientists switch out the programming on a virus and trick it into healing, instead of harming the cells it infiltrates. Therefore, UMBC’s discovery could lead to new breakthroughs battling deadly viruses by using their own parasitic tricks to infiltrate other viruses.
Although groundbreaking and extremely interesting, the research is still in early stages. Any developments from this discovery aren’t likely to impact clinical laboratories any time soon. But after the past few years of battling the COVID-19 variants, this exciting discovery could help find new ways to prevent the next pandemic.
Through their proposal, the AMA has developed principles and recommendations surrounding the benefits and potentially harmful consequences of relying on AI-generated medical advice and content to advance diagnoses.
“We’re trying to look around the corner for our patients to understand the promise and limitations of AI,” said Alexander Ding, MD (above), AMA Trustee and Associate Vice President for Physician Strategy and Medical Affairs at Humana, in a press release. “There is a lot of uncertainty about the direction and regulatory framework for this use of AI that has found its way into the day-to-day practice of medicine.” Clinical laboratory professionals following advances in AI may want to remain informed on the use of generative AI solutions in healthcare. (Photo copyright: American Medical Association.)
In addition to creating human-like text and content, GPTs have the ability to answer questions in a conversational manner. They can analyze language queries and then predict high-quality responses based on their understanding of the language. GPTs can perform this task after being trained with billions of parameters on massive language datasets and then generate long responses, not just the next word in a sequence.
“AI holds the promise of transforming medicine,” said diagnostic and interventional radiologist Alexander Ding, MD, AMA Trustee and Associate Vice President for Physician Strategy and Medical Affairs at Humana, in an AMA press release.
“We don’t want to be chasing technology. Rather, as scientists, we want to use our expertise to structure guidelines, and guardrails to prevent unintended consequences, such as baking in bias and widening disparities, dissemination of incorrect medical advice, or spread of misinformation or disinformation,” he added.
The AMA plans to work with the federal government and other appropriate organizations to advise policymakers on the optimal ways to use AI in healthcare to protect patients from misleading AI-generated data that may or may not be validated, accurate, or relevant.
Advantages and Risks of AI in Medicine
The AMA’s proposal was prompted by AMA-affiliated organizations that stressed concerns about the lack of regulatory oversight for GPTs. They are encouraging healthcare professionals to educate patients about the advantages and risks of AI in medicine.
“AI took a huge leap with large language model tool and generative models, so all of the work that has been done up to this point in terms of regulatory and governance frameworks will have to be treated or at least reviewed with this new lens,” Sha Edathumparampil, Corporate Vice President, Digital and Data, Baptist Health South Florida, told Healthcare Brew.
According to the AMA press release, “the current limitations create potential risks for physicians and patients and should be used with appropriate caution at this time. AI-generated fabrications, errors, or inaccuracies can harm patients, and physicians need to be acutely aware of these risks and added liability before they rely on unregulated machine-learning algorithms and tools.”
According to the AMA press release, the organization will propose state and federal regulations for AI tools at next year’s annual meeting in Chicago.
In a July AMA podcast, AMA’s President, Jesse Ehrenfeld, MD, stressed that more must be done through regulation and development to bolster trust in these new technologies.
“There’s a lot of discomfort around the use of these tools among Americans with the idea of AI being used in their own healthcare,” Ehrenfeld said. “There was a 2023 Pew Research Center poll [that said] 60% of Americans would feel uncomfortable if their own healthcare provider relied on AI to do things like diagnose disease or recommend a treatment.”
WHO Issues Cautions about Use of AI in Healthcare
In May, the World Health Organization (WHO) issued a statement advocating for caution when implementing AI-generated large language GPT models into healthcare.
A current example of such a GPT is ChatGPT, a large language-based model (LLM) that enables users to refine and lead conversations towards a desired length, format, style, level of detail and language. Organizations across industries are now utilizing GPT models for Question and Answer bots for customers, text summarization, and content generation and search features.
“Precipitous adoption of untested systems could lead to errors by healthcare workers, cause harm to patients, erode trust in AI, and thereby undermine (or delay) the potential long-term benefits and uses of such technologies around the world,” commented WHO in the statement.
WHO’s concerns regarding the need for prudence and oversight in the use of AI technologies include:
Data used to train AI may be biased, which could pose risks to health, equity, and inclusiveness.
LLMs generate responses that can appear authoritative and plausible, but which may be completely incorrect or contain serious errors.
LLMs may be trained on data for which consent may not have been given.
LLMs may not be able to protect sensitive data that is provided to an application to generate a response.
LLMs can be misused to generate and disseminate highly convincing disinformation in the form of text, audio, or video that may be difficult for people to differentiate from reliable health content.
Tech Experts Recommended Caution
Generative AI will continue to evolve. Therefore, clinical laboratory professionals may want to keep a keen eye on advances in AI technology and GPTs in healthcare diagnosis.
“While generative AI holds tremendous potential to transform various industries, it also presents significant challenges and risks that should not be ignored,” wrote Edathumparampil in an article he penned for CXOTECH Magazine. “With the right strategy and approach, generative AI can be a powerful tool for innovation and differentiation, helping businesses to stay ahead of the competition and better serve their customers.”
GPT’s may eventually be a boon to healthcare providers, including clinical laboratories, and pathology groups. But for the moment, caution is recommended.
Clinical laboratories and pathologists should expect to receive increase referrals from oncologists with younger patients
More people are getting serious cases of cancer at younger and younger ages. So much so that some anatomic pathologists and epidemiologists are using the term “Turbo Cancers” to describe “the recent emergence of aggressive cancers that grow very quickly,” Vigilant News reported.
Cancer continues to be the second leading cause of death in the United States and current trends of the disease appearing in younger populations are causing alarm among medical professionals and scientists.
It’s anatomic pathologists who receive the biopsies and analyze them to diagnose the cancer. Thus, they are on the front lines of seeing an increased number of biopsies for younger patients showing up with the types of cancers that normally take many years to grow large enough to be discovered by imaging and lumps leading to biopsy and diagnosis. It’s a medical mystery that may have long term effects on younger populations.
“What clinicians have been seeing is very strange things,” said Harvey Risch, MD, PhD (above), Professor Emeritus of Epidemiology at the Yale School of Public Health and Yale School of Medicine, in an Epoch TV interview. “For example, 25-year-olds with colon cancer, who don’t have family histories of the disease—that’s basically impossible along the known paradigm for how colon cancer works—and other long-latency cancers that they’re seeing in very young people.” Epidemiologists and anatomic pathologists are describing these conditions as “turbo cancers.” (Photo copyright: Yale University.)
Early-Onset Cancer Rates Jump Sharply
According to the federal Centers for Disease Control and Prevention (CDC), about 3.3 million Americans died in 2022, and 607,800 of those deaths were attributed to cancer. This statistic translates to approximately 18.4% of US deaths being due to cancer last year.
The largest increase in cancer diagnoses occurred in people in the 30 to 39-year-old age group. This number represents a jump of almost 20% for the years analyzed for individuals in that demographic. The researchers also found that cancer rates decreased in individuals over the age of 50.
Breast cancer, which increased by about 8% in younger people, accounted for the most diagnoses in this age group. However, the biggest increase was 15% for gastrointestinal cancers, including colon, appendix, bile duct, and pancreatic cancer.
Because cancer can recur or progress, researchers have concerns about what happens to young cancer patients as they grow older and what effect cancer may have on their lives.
“They are at a transitional stage in life,” Chun Chao, PhD, Research Scientist, Division of Epidemiologic Research at Kaiser Permanente, told The Hill. “If you think about it, this is the age when people are trying to establish their independence. Some people are finishing up their education. People are trying to get their first job, just start to establish their career. And people are starting new families and starting to have kids. So, at this particular age, having a cancer diagnosis can be a huge disruption to these goals.”
“The increase in early-onset cancers is likely associated with the increasing incidence of obesity as well as changes in environmental exposures, such as smoke and gasoline, sleep patterns, physical activity, microbiota, and transient exposure to carcinogenic compounds,” according to the JAMA study.
“Suspected risk factors may involve increasing obesity among children and young adults; also the drastic change in our diet, like increasing consumption of sugar, sweetened beverages, and high fat,” Hyuna Sung, PhD, Cancer Surveillance Researcher at the American Cancer Society, told US News and World Report. “The increase in cancers among young adults has significant implications. It is something we need to consider as a bellwether for future trends.”
“Increased efforts are required to combat the risk factors for early-onset cancer, such as obesity, heavy alcohol consumption, and smoking,” said Daniel Huang, MD, Assistant Professor of Medicine at the National University of Singapore, one of the authors of the study, in the US News and World Report interview.
Other studies also have shown a rise in so-called turbo cancers.
“Cancer as a disease takes a long time to manifest itself from when it starts. From the first cells that go haywire until they grow to be large enough to be diagnosed, or to be symptomatic, can take anywhere from two or three years for the blood cancers—like leukemias and lymphomas—to five years for lung cancer, to 20 years for bladder cancer, or 30 to 35 years for colon cancer, and so on,” Risch told the Epoch Times.
Not the Occurrence Oncologists Expect
“Some of these cancers are so aggressive that between the time that they’re first seen and when they come back for treatment after a few weeks, they’ve grown dramatically compared to what oncologists would have expected,” Risch continued. “This is just not the normal occurrence of how cancer works.”
Risch believes that damage to the immune system is the most likely cause of the rise in turbo cancers. He said the immune system usually recognizes, manages, and disables cancer cells so they cannot progress. However, when the immune system is impaired, cancer cells can multiply to the point where the immune system cannot cope with the number of bad cells.
It is a statistical fact that more people are being diagnosed with serious cases of cancer at younger and younger ages. If this trend continues, clinical laboratories and pathologists can expect to see more oncology case referrals and perform more cancer diagnostic tests for younger patients.
Initial analyses also shows AI screening lowers associated radiologist image reading workload by half
Both radiologists and pathologists analyze images to make cancer diagnoses, although one works with radiological images and the other works with tissue biopsies as the source of information. Now, advances in artificial intelligence (AI) for cancer screenings means both radiologists and pathologists may soon be able to detect cancer more accurately and in significantly less time.
Pathologists may find it instructive to learn more about how use of this technology shortened the time for the radiologist to sign out the case without compromising accuracy and quality.
Even better, AI screenings reduced doctors’ workload in interpreting mammography images by nearly 50%, the news release states. Such an improvement would also be a boon to busy pathology practices were this technology to become available for tissue biopsy screenings as well.
“The greatest potential of AI right now is that it could allow radiologists to be less burdened by the excessive amount of reading,” said breast radiologist Kristina Lång, MD, PhD, Associate Professor in Diagnostic Radiology at Lund University. Pathologists working with clinical laboratories in cancer diagnosis could benefit from similar AI advancements. (Photo copyright: Lund University.)
Can AI Save Time and Improve Diagnoses?
One motivation for conducting this study is that Sweden, like other nations, has a shortage of radiologists. Given ongoing advances in machine learning and AI, researchers launched the study to assess the accuracy of AI in diagnosing images, as well as its ability to make radiologists more productive.
The MASAI trial was the first to demonstrate the effectiveness of AI-supported screening, the Lund news release noted.
“We found that using AI results in the detection of 20% (41) more cancers compared with standard screening, without affecting false positives. A false positive in screening occurs when a woman is recalled but cleared of suspicion of cancer after workup,” said breast radiologist Kristina Lång, MD, PhD, clinical researcher and associate professor in diagnostic radiology at Lund University, and consultant at Skåne University Hospital, in the news release.
Not only did the researchers explore the accuracy of AI-supported mammography compared with radiologists’ standard screen reading, they also looked into AI’s effect on radiologists’ screen-reading workload, the Lancet paper states.
Impetus for the research was the shortage of radiologists in Sweden and other countries. A Lancet news release noted that “there is a shortage of breast radiologists in many countries, including a shortfall of around 41 (8%) in the UK in 2020 and about 50 in Sweden, and it takes over a decade to train a radiologist capable of interpreting mammograms.”
More Breast Cancer Identified with Lower Radiologist Workload When Using AI Screening
Here are study findings, according to the Lancet paper:
AI-supported screening resulted in 244 cancers of 861 women recalled.
Standard screening found 203 screen-detected cancers among 817 women who were recalled.
The false positive rate of 1.5% was the same in both groups.
41 (20%) more cancers were detected in the AI-enabled screening group.
Screen readings by radiologists in the AI-supported group totaled 46,345, as compared to 83,231 in the standard screening group.
Workload dropped by 44% for physicians using screen-reading with AI.
“We need to see whether these promising results hold up under other conditions—with other radiologists or other algorithms,” Lang said in the Lund news release.
“The results from our first analysis show that AI-supported screening is safe since the cancer detection rate did not decline despite a reduction in the screen-reading workload,” she added.
Is AI a Threat to Radiologists?
The use of AI in the Swedish study is an early indication that the technology is advancing in ways that may contribute to increased diagnostic accuracy for radiologists. But could AI replace human radiologists’ readings. Not anytime soon.
“These promising interim safety results should be used to inform new trials and program-based evaluations to address the pronounced radiologist shortage in many countries. But they are not enough on their own to confirm that AI is ready to be implemented in mammography screening,” Lång cautioned. “We still need to understand the implications on patients’ outcomes, especially whether combining radiologists’ expertise with AI can help detect interval cancers that are often missed by traditional screening, as well as the cost-effectiveness of the technology.”
“These tools work best when paired with highly trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist,” she added.
Whether a simple tool or an industry-changing breakthrough, pathology groups and clinical laboratories that work with oncologists can safely assume that AI advances will lead to more cancer research and diagnostic tools that enable earlier and more accurate diagnoses from tissue biopsies and better guidance on therapies for patients.
Technology could enable patients to monitor their own oxygen levels and transmit that data to healthcare providers, including clinical laboratories
Clinical laboratories may soon have a new data point to add to their laboratory information system (LIS) for doctors to review. Researchers have determined that smartphones can read blood-oxygen levels as accurately as purpose-built pulse oximeters.
This could mean that patients at risk of hypoxemia, or who are suffering a respiratory illness such as COVID-19, could eventually add accurate blood-oxygen saturation (SpO2) readings to their lab test results at any time and from any location.
“In an ideal world, this information could be seamlessly transmitted to a doctor’s office. This would be really beneficial for telemedicine appointments or for triage nurses to be able to quickly determine whether patients need to go to the emergency department or if they can continue to rest at home and make an appointment with their primary care provider later,” Matthew Thompson, DPhil, Professor of Global Health and Family Medicine at University of Washington, told Digital Health News. Clinical laboratories may soon have a new data point for their laboratory information systems. (Photo copyright. University of Washington.)
UW/UC San Diego Study Details
The researchers studied three men and three women, ages 20-34. All were Caucasian except for one African American, Digital Health News reported. To conduct the study, a standard pulse oximeter was placed on a finger and, on the same hand, another of the participant’s fingers was placed over a smartphone camera.
“We performed the first clinical development validation on a smartphone camera-based SpO2 sensing system using a varied fraction of inspired oxygen (FiO2) protocol, creating a clinically relevant validation dataset for solely smartphone-based contact PPG [photoplethysmography] methods on a wider range of SpO2 values (70–100%) than prior studies (85–100%). We built a deep learning model using this data to demonstrate an overall MAE [Mean Absolute Error] = 5.00% SpO2 while identifying positive cases of low SpO2 < 90% with 81% sensitivity and 79% specificity,” the researchers wrote in NPJ Digital Medicine.
When the smartphone camera’s flash passes light through the finger, “a deep-learning algorithm deciphers the blood oxygen levels.” Participants were also breathing in “a controlled mixture of oxygen and nitrogen to slowly reduce oxygen levels,” Digital Health News reported.
“The camera is recording a video: Every time your heart beats, fresh blood flows through the part illuminated by the flash,” Edward Wang, PhD, Assistant Professor of Electrical and Computer Engineering at UC San Diego and senior author of the project, told Digital Health News. Wang started this project as a UW doctoral student studying electrical and computer engineering and now directs the UC San Diego DigiHealth Lab.
“The camera records how much that blood absorbs the light from the flash in each of the three color channels it measures: red, green, and blue. Then we can feed those intensity measurements into our deep-learning model,” he added.
The deep learning algorithm “pulled out the blood oxygen levels. The remainder of the data was used to validate the method and then test it to see how well it performed on new subjects,” Digital Health News reported.
“Smartphone light can get scattered by all these other components in your finger, which means there’s a lot of noise in the data that we’re looking at,” Varun Viswanath, co-lead author in the study, told Digital Health News. Viswanath is a UW alumnus who is now a doctoral student being advised by Wang at UC San Diego.
“Deep learning is a really helpful technique here because it can see these really complex and nuanced features and helps you find patterns that you wouldn’t otherwise be able to see,” he added.
Each round of testing took approximately 15 minutes. In total the researchers gathered more than 10,000 blood oxygen readings. Levels ranged from 61% to 100%.
“The smartphone correctly predicted whether the subject had low blood oxygen levels 80% of the time,” Digital Health News reported.
Smartphones Accurately Collecting Data
The UW/UC San Diego study is the first to show such precise results using a smartphone.
“Other smartphone apps that do this were developed by asking people to hold their breath. But people get very uncomfortable and have to breathe after a minute or so, and that’s before their blood-oxygen levels have gone down far enough to represent the full range of clinically relevant data,” said Jason Hoffman, a PhD student researcher at UW’s UbiComp Lab and co-lead author of the study.
The ability to track a full 15 minutes of data is a prime example of improvement. “Our data shows that smartphones could work well right in the critical threshold range,” Hoffman added.
“Smartphone-based SpO2 monitors, especially those that rely only on built-in hardware with no modifications, present an opportunity to detect and monitor respiratory conditions in contexts where pulse oximeters are less available,” the researchers wrote.
“This way you could have multiple measurements with your own device at either no cost or low cost,” Matthew Thompson, DPhil, Professor of Global Health and Family Medicine at University of Washington, told Digital Health News. Thompson is a professor of both family medicine and global health and an adjunct professor of pediatrics at the UW School of Medicine.
What Comes Next
The UW/UC San Diego research team plans to continue its research and gather more diversity among subjects.
“It’s so important to do a study like this,” Wang said. “Traditional medical devices go through rigorous testing. But computer science research is still just starting to dig its teeth into using machine learning for biomedical device development and we’re all still learning. By forcing ourselves to be rigorous, we’re forcing ourselves to learn how to do things right.”
Though no current clinical laboratory application is pending, smartphone use to capture biometrics for testing is increasing. Soon, labs may need a way to input all that data into their laboratory information systems. It’s something to consider.
Study may also result in new clinical laboratory tools for determining antimicrobial resistance and efficacy of existing antibiotics
Researchers find it increasingly difficult to develop antibiotics that are effective against strains of bacteria that display antibiotic resistance—a subset of antimicrobial resistance (AMR). However, a new study provides a glimmer of hope and may spur clinical laboratories to look at this research in novel ways.
Conducted at the University of California Santa Barbara, the study looked at more than 500 antibiotic-bacteria combinations. The researchers discovered that several widely used, FDA-approved, antibiotics may be more useful than previously thought against a large range of bacterial infections, said infectious disease specialist Judy Stone, MD, in an article she penned for Forbes titled, “Why Antibiotics Fail—and How We Can Do Better.”
The researchers also discovered a common culture medium that enables a better assessment of the properties of various strains of bacteria to resist different antibiotics.
Clinical laboratories and microbiologists are tasked with plating and growing bugs to identify a specific bug, what strain of bug, and whether that strain has resistance to specific antibiotics. Thus, this research touches on what they do daily. It is something that may provide microbiologists with new approaches to detect AMR more accurately.
“We know there are a variety of reasons why antibiotics don’t work as predicted, from wrongly prescribed doses to infrequent administration, but another less noticeable reason is that lab testing can show a bacteria is susceptible to antibiotics when it’s actually not. You know, the whole in vitro (culture plate) versus in vivo (life) balance,” wrote Judy Stone, MD, infectious disease expert, in her Forbes article. Clinical laboratories may soon have a better way of identifying antibiotic resistance in deadly bacteria. (Photo: LinkedIn profile.)
UCSB Antimicrobial Study Details
Antibiotic-resistant infections are responsible for more than 32,000 deaths in the US and 1.27 million globally every year, Forbes reported. A study like this can have a far-reaching impact.
The DMEM predicted antibiotic effectiveness better than Mueller Hinton Broth (MHB), another growth medium from Thermo Fisher Scientific that has been used in clinical laboratories by World Health Organization (WHO) decree since 1968, Forbes reported.
Assays were run against 13 isolates from nine species of bacteria to determine the efficacy of 15 different antibiotics. Using DMEM, the team found different sensitivities in 15% of the bacterial isolates tested in vitro compared to MHB.
In Mahan’s follow-up tests, which looked at mice infected with different bacteria, MHB was accurate in 54% of test predictions while DMEM was accurate 77% of the time. Part of the reason, Mahan believes, is because DMEM is more physiologic and closer in conditions to people (in vivo), Forbes reported.
“People are not Petri plates—that is why antibiotics fail. Testing under conditions that mimic the body improves the accuracy by which lab tests predict drug potency,” said Mahan in a UCSB press release.
“I think it has merit. I think this study has been very well-designed … and showed that this makes clinical sense … If it bears out in humans, it will be clinically very significant,” pulmonologist Ken Yomer Yoneda, MD, Professor Emeritus, Department of Internal Medicine at UC Davis Health, told Forbes.
Though the major limitation of the study is that it was conducted on mice and not humans, Yoneda said it gives an indication of potential success with humans. “If it bears out in humans it will be clinically very significant,” he told Stone for her Forbes article.
Rodney Rohde, PhD, Professor and Chair of Clinical Lab Science Program at Texas State University also shared enthusiasm on the findings. According to Stone, “[Rohde] was ‘intrigued’ by the finding that using a physiologic media predicted ‘a change in susceptibility’ thresholds used to categorize patient isolates as susceptible or resistant.
“He was also ‘excited about the results of increasing diagnostic accuracy’ with especially difficult-to-treat organisms,” she noted.
“Rohde added that the issue of these clinical breakpoints—setting the level at which an organism is defined as ‘sensitive’ or ‘resistant’ to an antibiotic is a hot topic, undergoing considerable discussion in lab circles. Multiple agencies need to reach agreement for the standards that are used globally, both in the US and Europe,” Stone wrote.
Old Drug, New Tricks
According to the UCSB press release, “Physicians are aware of the flaws in the gold-standard test [MHB]. When recommended antibiotics do not work, they must rely on their experience to decide on the appropriate antibiotic(s) for their patients. This study provides a potential solution to address the disparity between antibiotics indicated by standard testing and actual patient outcomes.”
Infectious disease physician Lynn Fitzgibbons, MD, remarked in the UCSB press release, “Re-evaluation of FDA-approved antibiotics may be of far greater benefit than the time and cost of developing new drugs to combat antimicrobial resistance, potentially leading to significant life-savings and cost-savings.”
In her Forbes article, Stone wrote, “Pharmaceutical companies are abandoning the acute infectious disease market and few new antibiotics are in sight. Pharma is profit driven and antibiotics are simply not as lucrative as life-style drugs (like Viagra/Cialis or Rogaine for hair loss) or those for chronic diseases. So, Mahan et al.’s findings are welcome news indeed.”
Once further studies validate the UCSB study findings and allow their use in clinical settings for patient care, clinical laboratories and microbiologists may have new tools for accurately determining a bacterium’s ability to resist existing antibiotics or its susceptibility to antibiotics not currently used to treat certain infections.