A growing measles outbreak in South Carolina, combined with rising cases nationwide, is putting renewed pressure on clinical and public health laboratories as the US risks losing its measles elimination status.
A rapidly expanding measles outbreak in South Carolina is reinforcing the critical role laboratories play in outbreak detection and response. State health officials report 188 measles cases statewide, with 185 linked to a concentrated outbreak in the Upstate region.
The outbreak is centered around elementary schools with low vaccination rates, a setting that often drives sudden spikes in testing demand. For laboratory leaders, these environments can translate into urgent diagnostic needs and close coordination with public health officials.
Health authorities say most new cases are tied to known exposures. Four recent infections were linked to household transmission, and one resulted from a previously reported school exposure.
However, signs of broader spread are emerging. The source of three new cases remains unknown, and another is still under investigation, suggesting transmission may be extending beyond clearly identified clusters.
As of the week of Dec. 29, 2025, 223 individuals in South Carolina were under quarantine following measles exposure. Each quarantine case increases reliance on timely laboratory confirmation to support isolation, contact tracing, and clearance decisions.
Photo credit: CDC
Vaccination status among patients is low and a likely contributor. Of the 185 cases in the Upstate cluster, 172 individuals were unvaccinated. Four had unknown vaccination status, and only one patient was fully vaccinated.
Children make up the vast majority of infections. Forty patients are under age five, while 123 are between ages five and 17, reinforcing the role of school-based transmission and pediatric testing demand.
Regional Spread Raises Stakes
South Carolina’s surge mirrors similar outbreaks in the western United States. Arizona has now reported 205 measles cases, while neighboring Utah is tracking 156.
Many of those infections are linked to a multistate outbreak centered in Mohave County, Arizona, and Southwest Utah. The pattern highlights how quickly localized outbreaks can expand across jurisdictions.
Nationally, the situation is becoming more serious. By the end of December, the United States surpassed 2,000 measles cases.
At that level, the country risks losing its measles elimination status, first achieved in 2000. Losing the designation would signal the return of sustained endemic transmission and likely intensify surveillance and reporting requirements.
For laboratory leaders, these developments may bring increased volumes of measles PCR and serology testing, particularly in pediatric and outpatient settings. Rapid turnaround times will remain essential for guiding quarantine and infection control decisions.
Public health laboratories may also face expanded workloads related to confirmatory testing and molecular tracking of transmission chains. As measles resurges, laboratories once again serve as a frontline defense—where preparedness, capacity planning, and coordination can directly shape outbreak control.
Researchers have identified thousands of protein-free, circular RNA molecules living inside human-associated bacteria, raising new questions for microbiology labs about how life is classified.
Laboratory leaders accustomed to classifying organisms as bacteria, viruses, or parasites may soon need to account for something entirely different. Researchers have identified a previously unknown class of RNA molecules living inside bacteria associated with the human body—entities that replicate but do not fit into any existing biological category.
The structures, called “obelisks,” are circular RNA molecules found primarily in bacteria from the human mouth and gut. They are neither living cells nor conventional viruses. Instead, they exist as short loops of RNA that replicate within microbial hosts through mechanisms that scientists do not yet understand.
Protein-Free RNA Replicators Emerge from Large-Scale Metagenomic Analysis
What makes obelisks particularly unusual is what they lack. According to the researchers, the RNA loops are “protein-free, RNA-only replicators.” They do not encode proteins, nor do they form protective capsids or membranes. This places them outside established definitions of viruses, plasmids, or other mobile genetic elements.
The discovery emerged from a large-scale analysis of publicly available metagenomic data drawn from human-associated microbial communities. Using computational tools designed to identify circular RNA structures, the research team screened vast genomic libraries from oral and intestinal microbiomes.
That effort revealed more than 3,000 distinct obelisk sequences, many of which appeared repeatedly across samples from different individuals and geographic regions. The work was led by Nobel laureate Andrew Fire of Stanford University and published as a preprint on bioRxiv.
To ensure the findings were not artefacts of sequencing or data processing, the team applied stringent filtering criteria. After removing false positives, they identified conserved genetic motifs shared among multiple obelisks. Many of the RNA loops were found embedded within bacterial genomes, suggesting they replicate inside microbial cells and may have adapted to specific bacterial hosts over time.
Unknown Function, Broad Implications for Microbiology and Evolution
Although obelisks resemble plant viroids, non-coding, circular RNAs that infect plants, the researchers note a key difference: obelisks have so far been identified only in bacteria associated with humans. Their biological role remains unknown.
At present, there is no evidence linking obelisks to disease. However, their presence in bacteria that support digestion, immune function, and other critical processes raises questions about whether they may have indirect effects on human health. Researchers also observed that different obelisk variants appear in specific body sites, hinting at localized adaptation within the microbiome.
Beyond immediate clinical relevance, the discovery has broader implications for microbial classification and evolutionary biology. Obelisks do not conform to known categories, challenging long-standing assumptions about what constitutes a replicating biological entity.
Some scientists suggest these RNA structures could inform theories about early life on Earth, when self-replicating RNA may have existed before cells and proteins. As one review in Royal Society Open Science notes, such entities sit at the edge of life as currently defined.
For laboratory leaders, the finding highlights the expanding reach of metagenomic sequencing and bioinformatics. As clinical and research labs generate and analyze ever-larger datasets, they are increasingly likely to encounter biological signals that defy traditional taxonomy.
Whether obelisks prove to be ancient evolutionary relics or modern molecular passengers, their discovery is a reminder that the microbiome—and the lab tools used to study it—still holds fundamental surprises.
Accuracy gaps in pathology AI affecting nearly 30% of diagnostic tasks highlight risks for clinical decision-making and patient outcomes, according to new research.
A new study is raising important questions for pathologists as artificial intelligence (AI) becomes more embedded in diagnostic workflows. Researchers report that AI systems used to interpret pathology slides for cancer diagnosis do not perform equally across all patient populations, with accuracy varying by race, gender, and age. The findings highlight why pathologists, who rely on objective tissue evaluation to guide treatment decisions, need to understand how bias can enter AI tools designed to support their work.
The study, published in Cell Reports Medicine, shows that pathology AI models can extract demographic information directly from tissue images, even though such details are invisible to human experts. That capability can influence diagnostic performance and potentially reinforce disparities in cancer care if left unaddressed.
Testing Pathology AI Reveals Widespread Diagnostic Gaps
To assess the scope of the problem, Yu and his colleagues evaluated four commonly used deep-learning models under development for cancer diagnosis. These systems are trained on large collections of labeled pathology slides, learning visual patterns associated with disease that can then be applied to new samples. The team tested the models using a large, multi-institutional dataset spanning 20 cancer types.
Across all four models, the researchers found consistent performance gaps linked to patient demographics. Diagnostic accuracy was lower for certain groups defined by race, gender, and age. For example, the models struggled to distinguish lung cancer subtypes in African American patients and in male patients. They also showed reduced accuracy when classifying breast cancer subtypes in younger patients, and lower detection performance for breast, renal, thyroid, and stomach cancers in specific demographic groups. Overall, these disparities appeared in roughly 29% of the diagnostic tasks analyzed.
The findings were unexpected, Yu said, because pathology has long been considered one of the most objective areas of medicine. “Because we would expect pathology evaluation to be objective,” he said, “when evaluating images, we don’t necessarily need to know a patient’s demographics to make a diagnosis.” The results raised a fundamental question for the research team: why were AI systems failing to meet the same standard of objectivity expected of human pathologists?
Further analysis revealed three main contributors to bias in pathology AI. One factor is uneven training data. Pathology samples are often easier to obtain from some populations than others, resulting in imbalanced datasets that make accurate diagnosis more difficult for underrepresented groups. But Yu noted that data imbalance alone did not fully explain the observed disparities. “The problem turned out to be much deeper than that,” he said.
From Demographic Shortcuts to Fairer Diagnosis
Differences in disease incidence also play a role. Some cancers occur more frequently in certain populations, allowing AI models to become highly accurate for those groups while struggling in populations where those diseases are less common. In addition, the models appear capable of detecting subtle molecular and biological differences linked to demographics, such as mutations in cancer driver genes.
Kun-Hsing Yu (Photo credit: Harvard Medical School)
Kun-Hsing Yu, associate professor of biomedical informatics at Harvard Medical School and assistant professor of pathology at Brigham and Women’s Hospital noted, “We found that because AI is so powerful, it can differentiate many obscure biological signals that cannot be detected by standard human evaluation.”
When models rely on these demographic-linked signals as shortcuts, accuracy can suffer across diverse patient groups.
To address these issues, the researchers developed a new framework called FAIR-Path. Built on a machine-learning approach known as contrastive learning, FAIR-Path trains models to focus on clinically meaningful differences—such as distinctions between cancer types—while minimizing attention to less relevant features, including demographic characteristics.
When applied to the tested models, FAIR-Path reduced diagnostic disparities by about 88%. “We show that by making this small adjustment, the models can learn robust features that make them more generalizable and fairer across different populations,” Yu said. Importantly, the improvement did not require perfectly balanced training datasets.
For pathologists, the findings underscore why careful evaluation of AI tools is essential as these technologies move closer to routine clinical use. The authors are now working with institutions worldwide to study pathology AI bias in different regions and clinical settings, and to adapt FAIR-Path for use in data-limited environments.
Finally, Yu said, the goal is not to replace human expertise, but to support it. “I think there’s hope that if we are more aware of and careful about how we design AI systems, we can build models that perform well in every population,” he said. For pathologists, the study reinforces the importance of remaining actively involved in how AI is developed, validated, and deployed, so that these tools enhance diagnostic confidence and equity, rather than introducing new sources of error into cancer care.
Ontario medical labs face critical staffing shortages, delaying test results. MLPAO urges $6M funding for student training to expand workforce capacity.
Medical laboratories across Ontario are facing persistent staffing shortages that are delaying test results and straining a workforce critical to clinical care and public health surveillance. In response, the Medical Laboratory Professionals Association of Ontario (MLPAO) is calling on the province to fund lab-based educator positions to expand student clinical placements and ease workforce pressures.
A recent MLPAO survey found that 68% of Ontario medical laboratories report shortages of medical laboratory technologists (MLTs). These professionals perform the diagnostic testing that supports physician decision-making, disease surveillance, and outbreak investigations across the healthcare system.
Staff Shortages Create Training Bottlenecks, Extend Test Turnaround Times
Staffing gaps are increasingly affecting laboratory operations. The association’s 2025 report indicates that shortages are contributing to longer turnaround times for diagnostic testing, including cancer diagnostics and sexually transmitted infection panels, delaying results for patients and clinicians.
Although laboratories remain understaffed, the MLPAO says the issue is not a lack of student interest. MLT students are required to complete clinical placements as part of their training, but many laboratories are unable to accept trainees because they lack sufficient staff to supervise and educate them.
Michelle Hoad, CEO of the MLPAO, told CBC Radio-Canada, “When a lab is short-staffed, they’re not able to take a student from that program.” She described the situation as a training bottleneck, noting that many laboratories maintain waitlists for clinical placements. (Photo credit: MLPAO)
The MLPAO is requesting $6 million over three years to fund educator positions within medical laboratories. The association argues that targeted funding would expand placement capacity, accelerate workforce entry for new graduates, and reduce reliance on overtime among existing staff.
Hoad said Ontario’s staffing challenges have been building for more than a decade and were intensified by the COVID-19 pandemic. “A lot of people were understaffed, overworked,” she said. “A lot of people that were eligible to take retirement, took early retirement.”
Lab Staffing Pressures Persist Across North America
Similar workforce pressures are being reported in the United States. The American Society for Clinical Pathology’s (ASCP) 2024 Vacancy Survey, reported on recently by Dark Daily, found that while vacancy rates in US medical laboratories have declined from pandemic-era highs, they remain well above pre-pandemic levels as retirements accelerate faster than the pipeline of newly trained professionals. Survey authors warned that workforce recovery has been “uneven and incomplete,” with sustained recruitment and retention challenges continuing to affect laboratory operations nationwide.
Ontario has made some investments aimed at strengthening the MLT pipeline. In 2024, the province added 700 new seats to MLT education programs, according to Ema Popovic, spokesperson for Health Minister Sylvia Jones. The government also expanded the Ontario Learn and Stay Grant to include MLT students, covering tuition and book costs in exchange for post-graduation service commitments in underserved areas.
The MLPAO welcomed those measures but said they have not resolved immediate operational challenges inside laboratories. Hoad noted that many technologists continue to work double shifts or delay vacation time to maintain testing volumes.
“It’s a very giving group,” Hoad said. “But that being said, we need to make sure that we don’t take advantage of them and we make sure that they’re properly staffed.”
Popovic did not respond to questions from CBC Radio-Canada about whether the Ministry of Health has received or is considering the MLPAO’s funding request for lab-based educators.
According to the MLPAO study, targeted funding could quickly expand training capacity. Among laboratories that currently do not accept students for clinical placements, 37% said they would be able to do so if a government-funded trainer were available.
For laboratory leaders, the association warns that failure to address training capacity risks prolonging workforce shortages, increasing burnout, and extending turnaround times—structural challenges mirrored across North America and with direct implications for diagnostic quality, patient care, and system resilience.
Investigators identified more than 100 proteins linked to inherited cancer risk and dozens of existing drugs that could be repurposed for cancer prevention.
Researchers at Vanderbilt University Medical Center (VUMC) and the University of Calgary have developed a new analytical framework that integrates genomic, proteomic, and electronic health record (EHR) data to uncover proteins linked to cancer risk and to identify existing drugs that may be repurposed for cancer prevention. The approach, described in a study published Dec. 2 in the American Journal of Human Genetics, represents a step toward translating large-scale genetic discoveries into actionable prevention strategies across multiple cancer types.
For clinical laboratory directors, the new framework offers a glimpse of how combined genomic, proteomic, and EHR datasets could soon reshape biomarker discovery and test development.
Genome-wide association studies (GWAS) have already identified hundreds of genetic variants associated with increased cancer risk, particularly for breast, colorectal, and prostate cancers, as well as dozens of variants linked to lung, pancreatic, and ovarian cancers.
However, most of these studies have focused on genetic variation and gene expression rather than the downstream proteins that ultimately drive biological function and are more directly targetable by drugs.
Xingyi Guo, PhD, associate professor of medicine in the Division of Epidemiology at VUMC and a co–senior author of the study said, “Previous research, including our work, has identified hundreds of putative cancer susceptibility genes that could be regulated by these risk variants; however, most dysregulated gene expression has not been thoroughly investigated at the protein level.” (Photo credit: VUMC)
Integrating GWAS and Proteomics to Identify Druggable Cancer Risk Proteins
To bridge that gap, the investigators combined large GWAS datasets for six major cancers—breast, colorectal, lung, ovarian, pancreatic, and prostate—with population-scale proteomics data drawn from more than 75,000 participants. The data came from multiple large cohorts, including the Atherosclerosis Risk in Communities (ARIC) study, deCODE genetics, and the UK Biobank Pharma Proteomics Project. The goal was to identify proteins whose circulating levels are associated with inherited cancer risk.
“To deepen the understanding of causal mechanisms and enhance drug discovery efforts, it is imperative to explore data from transcriptomic to proteomic studies,” said Zhijun Yin, PhD, MS, associate professor of biomedical informatics at VUMC and co–senior author, along with Quan Long, PhD, associate professor of biochemistry and molecular biology at the University of Calgary.
Using this integrated approach, the research team identified 365 proteins associated with cancer risk across the six cancer types studied. Through additional analyses to prioritize the most robust findings, they narrowed this list to 101 risk proteins. Notably, 74 of these proteins had not been previously reported as being linked to cancer susceptibility, highlighting the potential of proteomics to reveal novel biology that may be missed by gene-level analyses alone.
The researchers then evaluated whether these risk proteins could be therapeutically targeted. By systematically annotating the proteins using multiple pharmaceutical and drug-development databases, they assessed whether any were already the targets of approved drugs or agents in clinical testing. This step was designed to identify opportunities for drug repurposing—using existing medications for new preventive indications.
“Traditional drug discovery faces challenges of escalating costs, lengthy timelines, and high failure rates. Drug repurposing is a promising strategy to identify new applications for existing drugs with well-documented characteristics,” Guo said.
Among the 101 prioritized proteins, the investigators identified 36 that were considered druggable and potentially targetable by 404 drugs that are already approved or undergoing clinical trials. Of these, 19 proteins were targeted by drugs currently approved or in development for cancer treatment, suggesting a possible extension of oncology therapeutics into the prevention setting.
EHR-Based Analyses Suggest Reduced Cancer Risk with Certain Approved Drugs
To explore real-world relevance, the team leveraged more than 3.5 million de-identified EHRs from VUMC. Using this data, they conducted simulated clinical trials to examine associations between drug exposure and cancer risk. Several approved medications showed signals consistent with reduced cancer risk. One example highlighted in the study was acetazolamide, a diuretic, which was associated with a reduced risk of colorectal cancer in the EHR-based analyses.
“Our findings offer additional insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention,” Yin said. “It is essential to evaluate the effects of the reported candidate drugs through both in vitro and in vivo assays in future research.”
EHRs are rich in diagnostic data, so there is a clear connection between the researchers’ drug discovery efforts and the information that clinical laboratory test results can provide.
