According to the Centers for Disease Control and Prevention (CDC), the 1918 influenza (aka, the Spanish Flu) pandemic took place worldwide between 1918 and 1919. It was caused by the H1N1 virus (A/H1N1), a subtype of the Influenza A virus, and infected approximately 500 million people worldwide (a third of the human population at the time). Fifty million people died. Many were children or otherwise healthy individuals, but people from all age groups perished.
The CDC calls the Spanish Flu the “deadliest pandemic of the 20th century.” Past pandemics have generally concluded after 2.5 to 3.5 years. That’s how long it takes for new viruses to mutate and become endemic diseases, Healthline reported.
The COVID-19 pandemic has been around for about that long. It stands to reason the natural end of the COVID-19 pandemic may be just around the corner. But is it? And is the Omicron variant an indicator that the COVID-19 pandemic is winding down?
“Our analysis suggests that in the US, this combination of characteristics would lead to Omicron replacing Delta as the dominant variant in the next few months and to a higher peak burden of disease than the country saw in the second half of 2021 (but likely below the peak reached in the winter of 2020-21),” the report states.
McKinsey analysts also acknowledged the possible impact of new therapeutics, COVID-19 vaccine booster doses, and public health measures on Omicron spread. “In the short term, an accelerated rollout of booster doses of COVID-19 vaccines is likely to be one of the best protections against an Omicron-fueled wave of the disease,” the analysts wrote.
Does How the Spanish Flu Came to an End Mirror the COVID-19 Pandemic?
Virologists and infectious disease experts explained that the Spanish Flu virus did what viruses still do: mutate and become less dangerous. Herd immunity also helped end the 1918 pandemic.
“The 1918 influenza virus eventually mutated to the point of not having a high number of deaths—eventually over three years or so. We may very well be witnessing this process with ongoing variants of SARS-CoV-2,” virologist Rodney Rohde, PhD, Director of the Clinical Laboratory Science Program at Texas State University, told Healthline.
Today’s flu strains have “ancestral links” to the 1918 flu, and thus, the SARS-CoV-2 coronavirus will most likely also leave its mark, The Boston Herald reported. “The coronavirus will evolve and hopefully morph into a seasonal illness to which we pay little mind, but it’s still too early to tell,” Todd Ellerin, MD (above,) Director of Infectious Diseases, South Shore Health, South Weymouth, Mass., told The Boston Herald. (Photo copyright: Greg Derr/The Patriot Ledger.)
“If you think about the way viruses behave, biologically, their reason for living is to replicate and spread, and there’s really no advantage for the virus to kill the host,” infectious disease specialist Keith Armitage, MD, Professor of Medicine, Division of Infectious Diseases at Case Western Reserve University, told Healthline. “The hope is, that if the pandemic doesn’t go away, we will get new variants that are highly contagious but don’t produce much of a clinical illness,” he added.
In “2021’s Top 10 Lab Stories Confirm Important Trends,” Dark Daily’s sister publication, The Dark Report (TDR), posed a similar question in its number one story of 2021: “COVID-19: Will it Become Endemic and a Respiratory Virus that Shows Up Every Year like Influenza?”
“The question of whether SARS-CoV-2 is a pandemic that fades, as did SARS in 2003, or becomes endemic and a respiratory virus that shows up every season like influenza and the common cold, is of major concern to clinical lab administrators. That’s because clinical labs and pathology groups must continue to serve physicians and patients with the usual menu of routine, reference, and esoteric testing,” TDR noted.
Clinical Laboratories to Continue COVID Testing
It would be most helpful for medical laboratories and pathology groups to have some idea of when the pandemic will end. Unfortunately, such predictions would not be very useful.
“Since COVID-19 infections have a high number of asymptomatic transmitters, we may not fully understand how societal and environmental pressures—masks, distancing, remote working, etc.—on the virus will allow it to evolve,” Rohde told Healthline.
For now, clinical laboratories will need to continue to remain prepared as COVID-19 cases rise and people seek SARS-COV-2 tests, vaccinations, and treatments. COVID-19 testing is likely to be in demand throughout the coming year. The current surge in demand for COVID-19 tests is putting additional stress on the supply chain.
“We know pandemics end; it’s just a matter of time,” Sara Paton, PhD, Associate Professor of Epidemiology, Wright State University, told the Journal-News. “It could be in 2022, maybe later in the year, but I can’t say for sure. It could be 2023.”
Teams from multiple Swedish organizations are investigating the relationship of protein-coding genes to antibodies
Scientists in Sweden are discovering new ways to map the expression of genes in cells, tissues, and organs within the human body thanks to advances in molecular profiling. Their study has successfully combined the analysis of single-cell transcriptomics with spatial antibody-based protein profiling to produce a high-resolution, single-cell mapping of human tissues.
The data links protein-coding genes to antibodies, which could help researchers develop clinical laboratory tests that use specific antibodies to identify and target infectious disease. Might this also lead to a new menu of serology tests that could be used by medical laboratories?
This research is another example of how various databases of genetic and proteomic information—different “omics”—are being combined to produce new understanding of human biology and physiology.
In a Human Protein Atlas (HPA) project press release, Director of the HPA consortium and Professor of Microbiology at Royal Institute of Technology in Stockholm, Mathias Uhlén, PhD, said, “The [Science Advances] paper describes an important addition to the Human Protein Atlas (HPA) which has become one of the world’s most visited biological databases, harboring millions of web pages with information about all the human protein coding genes.”
“We are excited that the new open-access Single Cell Type section constitutes a unique resource for studying the cell type specificity and exact spatial localization of all our proteins”, said Cecilia Lindskog, PhD (above), Head of the HPA Tissue Atlas and Associate Professor, Experimental Pathology, Uppsala University, in the Protein Atlas press release. Medical laboratories may soon have new serology tests to perform that were developed based on HPA data. (Photo copyright: Human Uterus Cell Atlas.)
Distinct Expression Clusters Consistent to Similar Cell Types
To perform their research, the scientists mapped the gene expression profile of all protein-coding genes across different cell types. Their analysis showed that there are distinct expression clusters which are consistent to cell types sharing similar functions within the same organs and between organs of the human body.
The scientists examined data from non-diseased human tissues and organs using three main criteria:
Publicly available raw data from human tissues containing good technical quality with at least 4,000 cells analyzed and at least 20 million read counts by the sequencing for each tissue.
High correlation between pseudo-bulk transcriptomics profile from the scRNA-Seq data and bulk RNA-Seq generated as part of the Human Protein Atlas (HPA).
High correlation between the cluster-specific expression and the expected expression pattern of an extensive selection of marker genes representing well-known tissue- and cell type-specific markers, including both markers from the original publications and additional markers used in pathology diagnostics.
According to the HPA press release, “across all analyzed cell types, almost 14,000 genes showed an elevated expression in particular cell types, out of which approximately 2,000 genes were found to be specific for only one of the cell types.”
The press release also states, “cell types in testis showed the highest numbers of cell type elevated genes, followed by ciliated cells. Interestingly, only 11% of the genes were detected in all analyzed cell types suggesting that the number of essential genes (‘house-keeping’) are surprisingly few.”
Omics-based Biomarkers for Accurate Diagnosis of Disease
The Human Protein Atlas is the largest and most comprehensive database for spatial distribution of proteins in human tissues and cells. It provides a valuable tool for researchers who study and analyze protein localization and expression in human tissues and cells.
Ongoing improvements in gene sequencing technologies are making research of genes more accurate, faster, and more economical. Advances in gene sequencing also could help medical professionals discover more personalized care for patients leading to improved outcomes. A key goal of precision medicine.
One of the conclusions to be drawn from this work is that clinical laboratories and anatomic pathology groups will need to be able to handle immense amounts of data, while at the same time having the capabilities to analyze that data and identify useful patterns that can help diagnose patients earlier and more accurately.
DeepMind hopes its unrivaled collection of data, enabled by artificial intelligence, may advance development of precision medicines, new medical laboratory tests, and therapeutic treatments
‘Tis the season for giving, and one United Kingdom-based artificial intelligence (AI) research laboratory is making a sizeable gift. After using AI and machine learning to create “the most comprehensive map of human proteins,” in existence, DeepMind, a subsidiary of Alphabet Inc. (NASDAQ:GOOGL), parent company of Google, plans to give away for free its database of millions of protein structure predictions to the global scientific community and to all of humanity, The Verge reported.
Pathologists and clinical laboratory scientists developing proteomic assays understand the significance of this gesture. They know how difficult and expensive it is to determine protein structures using sequencing of amino acids. That’s because the various types of amino acids in use cause the [DNA] string to “fold.” Thus, the availability of this data may accelerate the development of more diagnostic tests based on proteomics.
“For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. Attraction and repulsion between the 20 different types of amino acids cause the string to fold in a feat of ‘spontaneous origami,’ forming the intricate curls, loops, and pleats of a protein’s 3D structure. This grand scientific challenge is known as the protein-folding problem,” a DeepMind statement noted.
Enter DeepMind’s AlphaFold AI platform to help iron things out. “Experimental techniques for determining structures are painstakingly laborious and time consuming (sometimes taking years and millions of dollars). Our latest version [of AlphaFold] can now predict the shape of a protein, at scale and in minutes, down to atomic accuracy. This is a significant breakthrough and highlights the impact AI can have on science,” DeepMind stated.
Release of Data Will Be ‘Transformative’
In July, DeepMind announced it would begin releasing data from its AlphaFold Protein Structure Database which contains “predictions for the structure of some 350,000 proteins across 20 different organisms,” The Verge reported, adding, “Most significantly, the release includes predictions for 98% of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures.”
According to Edith Heard, PhD, Director General of the European Molecular Biology Laboratory (EMBL), the open release of such a dataset will be “transformative for our understanding of how life works,” The Verge reported.
“I see this as the culmination of the entire 10-year-plus lifetime of DeepMind,” company CEO and co-founder Demis Hassabis (above), told The Verge. “From the beginning, this is what we set out to do: to make breakthroughs in AI, test that on games like Go and Atari, [and] apply that to real-world problems, to see if we can accelerate scientific breakthroughs and use those to benefit humanity.” The release of DeepMind’s entire protein prediction database will certainly do that. Clinical laboratory scientists worldwide will have free access to use it in developing new precision medicine treatments based on proteomics. (Photo copyright: BBC.)
Free Data about Proteins Will Accelerate Research on Diseases, Treatments
Research into how protein folds and, thereby, functions could have implications to fighting diseases and developing new medicines, according to DeepMind.
“This will be one of the most important datasets since the mapping of the human genome,” said Ewan Birney, PhD, Deputy Director General of the EMBL, in the DeepMind statement. EMBL worked with DeepMind on the dataset.
DeepMind protein prediction data are already being used by scientists in medical research. “Anyone can use it for anything. They just need to credit the people involved in the citation,” said Demis Hassabis, DeepMind CEO and Co-founder, in The Verge.
In a blog article, Hassabis listed several projects and organizations already using AlphaFold. They include:
“As researchers seek cures for diseases and pursue solutions to other big problems facing humankind—including antibiotic resistance, microplastic pollution, and climate change—they will benefit from fresh insights in the structure of proteins,” Hassabis wrote.
Because of the deep financial backing that Alphabet/Google can offer, it is reasonable to predict that DeepMind will make progress with its AI technology that regularly adds capabilities and accuracy, allowing AlphaFold to be effective for many uses.
This will be particularly true for the development of new diagnostic assays that will give clinical laboratories better tools for diagnosing disease earlier and more accurately.
Jurors are expected to hear closing arguments beginning on December 16 and then will decide Holmes’ fate in criminal fraud trial
It was seven days of testimony from former Theranos CEO Elizabeth Holmes, reported in detail by most major news outlets. The jury in her criminal fraud trial heard the once-high-flying Silicon Valley executive attempt to explain away charges of deception. She acknowledged that she made mistakes while leading the clinical laboratory blood-testing company but claimed that others were ultimately responsible for the company’s failures.
Rosendorff left Theranos in November 2014. He was followed by three more Theranos laboratory directors, all of whom have testified in the fraud case against Holmes.
Presumably, in her testimony, Holmes was laying the blame for key failures in the accuracy of the lab tests performed for patients, along with major deficiencies in how her medical lab company complied with CLIA regulations, on these former Theranos laboratory directors (as the clinical laboratory company’s CLIA lab directors of record during those years).
“Whether you have an intent to defraud is really a state of mind,” she said.
The illustration above depicts ex-Theranos CEO and founder Elizabeth Holmes concluding seven days of testimony in her criminal fraud trial in San Jose, California. Closing arguments are scheduled to begin December 16. Clinical laboratory directors and pathologists following the fraud trail may soon learn whether the four clinical laboratory directors who worked for Theranos may in some way be held accountable for some of the company’s activities. (Graphic copyright: Vicki Behringer/The Wall Street Journal.)
‘We Wanted to Help People’
Holmes’ testimony may have both helped and hurt her case. According to The Wall Street Journal (WSJ), Holmes “hasn’t flinched during questioning by her lawyer or the government.
“The persona of the confident yet traumatized chief executive could create reasonable doubt in the minds of jurors, legal observers following the trial say, and muddy the evidence prosecutors put forward over 11 weeks to prove she intended to defraud investors and patients about the reach of Theranos’ technology,” the WSJ wrote.
During testimony, Holmes maintained that her goal in founding Theranos was to increase access to healthcare. “We wanted to help people who were scared of needles,” she told jurors, the WSJ reported.
In building its case, prosecutors presented witness testimony and other evidence strongly suggesting Holmes lied to investors about Theranos’ laboratory testing capabilities and deployment, concealed its use of commercial blood testing machines, and hid ongoing issues with its Edison device.
One of the most damaging moments of Holmes’ own testimony may have been when she admitted to affixing the logos of pharmaceutical giants Pfizer and Schering-Plough to reports sent to Walgreens and potential investors.
Holmes told jurors that her intent was to give credit to others, not to deceive and her defense attorneys attempted to show that many of Holmes’ more questionable decisions were aimed at protecting Theranos trade secrets.
“We had a huge amount of invention that was happening in our laboratories,” Holmes testified, according to CNN’s trial coverage. “We had teams of scientists and engineers that were working really hard on coming up with new ideas for patents and trade secrets, and we needed to figure out how to protect them.”
Holmes Claims No Responsibility for Theranos’ Lab Operations and Product Development
On the witness stand, Holmes acknowledged she was the final decisionmaker at Theranos. However, she worked to distance herself from the company’s medical laboratory troubles. She pointed out that others within the company had control over laboratory operations and scientific decision-making.
The WSJ reported that defense lawyer Kevin Downey asked Holmes, “Who was responsible for operational management of the lab?”
Holmes replied, “Sunny Balwani.” She explained that her former No. 2 executive oversaw all the “business parts” of the lab. Meanwhile, the clinical/scientific decision-making, Holmes stated, was the job of the laboratory director and laboratory leadership.
When given the opportunity to cross-examine Holmes, prosecutors focused on Holmes’ response to the 2015 WSJ investigation into Theranos and her retaliatory actions against whistleblower Erika Cheung, a former lab employee who became a source for the WSJ’s expose and a prosecution witness.
According to WSJ live coverage, Holmes testified that Theranos hired a law firm and threatened Cheung with litigation after she left the company, but only did so to protect Theranos’ trade secrets. Holmes acknowledged that Cheung’s concerns about Theranos’ blood-testing technology ultimately were proven correct.
“I think I mishandled the entire process of the Wall Street Journal reporting,” Holmes said.
Closing Arguments
In her closing day of testimony, Holmes was asked if she ever intentionally misrepresented Theranos’ technology to patients and investors, the WSJ reported.
“Never,” Holmes responded.
Asked if investors lost money because of her attempting to mislead them, she answered, “Of course not.”
Clinical laboratory directors and pathologists who have taken a keen interest in the Holmes fraud trial will soon learn if the jury buys her arguments. Closing arguments are set for December 16, after which the jury must decide whether Holmes intended to defraud patients and investors or is guilty only of falling short in her goal of revolutionizing clinical laboratory medicine.
WASE-COVID Study also found that use of artificial intelligence technology minimized variability among echocardiogram scan results
Many physicians—including anatomic pathologists—are watching the development of artificial intelligence (AI)-powered diagnostic tools that are intended to analyze images and analyze the data with accuracy comparable to trained doctors. Now comes news of a recent study that demonstrated the ability of an AI tool to analyze echocardiograph images and deliver analyses equal to or better than trained physicians.
Conducted by researchers from the World Alliance Societies of Echocardiography and presented at the latest annual sessions of the American College of Cardiology (ACC), the WASE-COVID Study involved assessing the ability of the AI platform to analyze digital echocardiograph images with the goal of predicting mortality in patients with severe cases of COVID-19.
To complete their research, the WASE-COVID Study scientists examined 870 patients with acute COVID-19 infection from 13 medical centers in nine countries throughout Asia, Europe, United States, and Latin America.
Human versus Artificial Intelligence Analysis
Echocardiograms were analyzed with automated, machine learning-derived algorithms to calculate various data points and identify echocardiographic parameters that would be prognostic of clinical outcomes in hospitalized patients. The results were then compared to human analysis.
All patients in the study had previously tested positive for COVID-19 infection using a polymerase chain reaction (PCR) or rapid antigen test (RAT) and received a clinically-indicated echocardiogram upon admission. For those patients ultimately discharged from the hospital, a follow-up echocardiogram was performed after three months.
“What we learned was that the manual tracings were not able to predict mortality,” Federico Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab at MedStar Health Research Institute in Washington, DC, told US Cardiology Review in a video interview describing the WASE-COVID Study findings.
Asch is also Associate Professor of Medicine (Cardiology) at Georgetown University. He added, “But on the same echoes, if the analysis was done by machine—Ultromics EchoGo Core, a software that is commercially available—when we used the measurements obtained through this platform, we were able to predict in-hospital and out-of-hospital mortality both with ejection fraction and left ventricular longitudinal strain.”
“When compared to the manual reads, the AI algorithms had a much higher predictive value for mortality,” Federico Asch, MD (above), told US Cardiology Review. “Indeed, they were predictive where the manual ones were not.” These findings may have implications in the development and adoption of artificial intelligence driven clinical laboratory diagnostics and for predicting risk of COVID-19 deaths in hospitalized heart patients. Click here to review the entire video interview. (Photo copyright: US Cardiology Review.)
Nearly half of the 870 hospitalized patients were admitted to intensive care units, 27% were placed on ventilators, 188 patients died in the hospital, and 50 additional patients died within three to six months after being released from the hospital.
10 of 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice and three out of the 13 centers performed comprehensive exams.
In-hospital mortality rates ranged from 11% in Asia, 19% in Europe, 26% in the US, to 27% in Latin America.
Left ventricular longitudinal strain (LVLS), right ventricle free wall strain (RVFWS), as well as a patient’s age, lactic dehydrogenase levels and history of lung disease, were independently associated with mortality. Left ventricle ejection fraction (LVEF) was not.
Fully automated quantification of LVEF and LVLS using AI minimized variability.
AI-based left ventricular analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.
The WASE-COVID Study also revealed the varying international use of cardiac ultrasound (echocardiography) on COVID-19 patients.
“By using machines, we reduce variability. By reducing variability, we have a better capacity to compare our results with other outcomes, whether that outcome in this case is mortality or it could be changes over time,” Asch stated in the US Cardiology Review video. “What this really means is that we may be able to show associations and comparisons by using AI that we cannot do with manual [readings] because manual has more variation and is less reliable.”
He said the next steps will be to see if the findings hold true when AI is used in other populations of cardiac patients.
COVID-19 Pandemic Increased Need for Swift Analyses
An earlier WASE Study in 2016 set out to answer whether normal left ventricular heart chamber quantifications vary across countries, geographical regions, and cultures. However, the data produced by that study took years to review. Asch said the COVID-19 pandemic created a need for such analysis to be done more quickly.
“When the pandemic began, we knew that the clinical urgency to learn as much as possible about the cardiovascular connection to COVID-19 was incredibly high, and that we had to find a better way of securely and consistently reviewing all of this information in a timely manner,” he said in the Ultromics new release.
Coronary artery disease (CAD) is the most common form of heart disease and affects more than 16.5 million people over the age of 20. By 2035, the economic burden of CAD will reach an estimated $749 billion in the US alone, according to the Ultromics website.
“COVID-19 has placed an even greater pressure on cardiac care and looks likely to have lasting implications in terms of its impact on the heart,” said Ross Upton, PhD, Founder and CEO of Oxford, UK-based Ultromics, in a news release announcing the US Food and Drug Administration’s 510(k) clearance for the EchoGo Pro, which supports clinicians’ diagnosing of CAD. “The healthcare industry needs to quickly pivot towards AI-powered automation to reduce the time to diagnosis and improve patient care.”
Use of AI to analyze digital pathology images is expected to be a fast-growing element in the anatomic pathology profession, particularly in the diagnosis of cancer. As Dark Daily outlined in this free white Paper, “Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now,” anatomic pathology laboratories can expect adoption of AI and digital technology to gain in popularity among pathologists in coming years.
Determining how dogs do this may lead to biomarkers for new clinical laboratory diagnostics tests
Development of new diagnostic olfactory tools for prostate and other cancers is expected to result from research now being conducted by a consortium of researchers at different universities and institutes. To identify new biomarkers, these scientists are studying how dogs can detect the presence of prostate cancer by sniffing urine specimens.
Funded by a grant from the Prostate Cancer Foundation, the pilot study demonstrated that dogs could identify prostate samples containing cancer and discern between cancer positive and cancer negative samples.
Canine Olfactory Combined with Artificial Intelligence Analysis Approach
The part of a canine brain that controls smell is 40 million times greater than that of humans. Some dog breeds have 300 to 350 million sensory receptors, compared to about five million in humans. With their keen sense of smell, dogs are proving to be vital resources in the detection of some diseases.
The pilot study examined how dogs could be trained to detect prostate cancer in human urine samples.
Claire Guest, CEO and Chief Scientific Officer of UK-based Medical Detection Dogs and one of the study authors, is shown above with one of her cancer detecting dogs. In a Prostate Cancer Foundation article, she said, “Prostate cancer is not going to turn out to be a single note. What dogs are really good at discovering is a tune. Think of Beethoven’s Fifth Symphony, those first few notes. We suspect the cancer signature is something like that. It’s a pattern; the dogs are really good at recognizing the pattern. Machines that recognize the notes but can’t read the pattern are not reliable biomarkers,” she noted. The researchers believe the best solution for developing a clinical laboratory diagnostic that detects prostate cancer may be a combined approach using canine olfaction and AI neural networks. (Photo copyright: Janine Warwick/NPR.)
To perform the study, the researchers trained two dogs to sniff urine samples from men with high-grade prostate cancer and from men without the cancer. The two dogs used in the study were a four-year-old female Labrador Retriever named Florin, and a seven-year-old female wirehaired Hungarian Vizsla named Midas. The dogs were trained to respond to cancer-related chemicals, known as volatile organic compounds, or VOCs, the researchers added to the urine samples, and to not respond to the samples without the VOCs.
Both dogs performed well in their cancer detection roles, and both successfully identified five of seven urine samples from men with prostate cancer, correlating to a 71.4% accuracy rate. In addition, Florin correctly identified 16 of 21 non-aggressive or no cancer samples for an accuracy rate of 76.2% and Midas did the same with a 66.7% accuracy rate.
“We wondered if having the dogs detect the chemicals, combined with analysis by GC-MS, bacterial profiling, and an artificial intelligence (AI) neural network trained to emulate the canine cancer detection ability, could significantly improve the diagnosis of high-grade prostate cancer,” said Alan Partin, MD, PhD, Professor of Urology, Pathology and Oncology, Johns Hopkins University School of Medicine and one of the authors of the study, told Futurity.
The researchers determined that canine olfaction was able to distinguish between positive and negative prostate cancer in the samples, and the VOC and microbiota profiling analyses showed a qualitative difference between the two groups. The multisystem approach demonstrated a more sensitive and specific way of detecting the presence of prostate cancer than any of the methods used by themselves.
In their paper, the researchers concluded that “this study demonstrated feasibility and identified the challenges of a multiparametric approach as a first step towards creating a more effective, non-invasive early urine diagnostic method for the highly aggressive histology of prostate cancer.”
Can Man’s Best Friend be Trained to Detect Cancer and Save Lives?
Prostate cancer is the second leading cause of cancer deaths among men in the developed world. And, according to data from the National Cancer Institute, standard clinical laboratory blood tests, such as the prostate-specific antigen (PSA) test for early detection, sometimes miss the presence of cancer.
Establishing an accurate, non-invasive method of sensing the disease could help detect the disease sooner when it is more treatable and save lives.
The American Cancer Society estimates that there will be about 248,530 new cases of prostate cancer diagnosed in 2021 and that there will be approximately 34,130 deaths resulting from the disease during the same year.
Of course, more testing will be needed before Man’s best friend can be put to work detecting cancer in medical environments. But if canines can be trained to detect the disease early, and in a non-invasive way, more timely diagnosis and treatment could result in higher survival rates.
Meanwhile, as researchers identify the elements dogs use to detect cancer and other diseases, this knowledge can result in the creation of new biomarkers than can be used in clinical laboratory tests.
