Radically different
Nature Medicine, Published online: 15 June 2026; doi:10.1038/s41591-026-04432-4
Neural decoding algorithms that leverage physiological principles of locomotor encoding support activity-dependent deep brain stimulation therapies that improve locomotor deficits in people with Parkinson’s disease.
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Good morning. At a Cambridge bar on Saturday, I watched straight-seeming couples congregate by a television showing basketball, while a more queer-coded crowd lingered at another showing soccer. I don’t think that’s anything, really, but it was fun.
A child born with congenital syphilis could suffer dire consequences: bone deformities, brain damage, blindness, deafness, and more. But that should be a ‘never event’ as public health officials say: A pregnant person can receive an injectable form of penicillin to prevent the infection. Somehow, rates keep going up anyway. Between 2012 and 2024, the U.S. saw an 800% increase in babies born with the disease. And since last year, there’s been a shortage of the drug.
The request was an emergency. In late March, a woman in Gila County, Arizona, was diagnosed with syphilis, and she was pregnant. She needed an injection of penicillin — if possible, 30 days before delivery — but the bacteria corkscrewing through her body increased her risk of delivering early. Without timely treatment, her pregnancy could end in miscarriage, stillbirth, or infant death, and if the infant survived, the child might live with bone deformities, brain damage, blindness, and deafness, among other complications.
All of that could be prevented with one of the oldest antibiotics in the book — specifically, an injectable form of penicillin sold under the brand name Bicillin L-A.
It was the only treatment approved in the United States for syphilis during pregnancy, and there’d been a national shortage since July 2025. But Pfizer, the only company that sells it, had a protocol in place for exactly this scenario: an emergency request system “to be used for confirmed congenital and risk of congenital syphilis patients only.”
That was what was unfolding in Gila County — and on Friday, March 27, public health officials submitted a request. Pfizer confirmed receipt the next Monday, according to emails obtained by STAT. But over a week after that, on April 7, the medication still hadn’t arrived.
“What can be done to get bicillin for this patient ASAP? These are the kind of delays that make the emergency request line not a viable option for public health response,” a leader at the National Coalition of STD Directors wrote to a Pfizer representative, stepping in to help secure the drug.
“Did they submit a medical request form first? I checked with Customer Service and they can’t locate. If we can get this medical request we will get it processed,” the Pfizer representative wrote back that same day.
But by the time this exchange was taking place, it was already too late. “Mom has delivered and we have missed our opportunity to prevent congenital syphilis,” wrote an official at the Arizona Department of Health Services, adding, “Yes, the county completed the medical request form.”
It was the outcome everyone was trying to avert. In February, the National Coalition of STD Directors had asked Pfizer to donate a fraction of its Bicillin L-A reserves to state health departments, so they’d have it on hand as a backstop in case this kind of emergency arose. But Pfizer hadn’t — and in early June, nearly four months after the coalition’s proposal, the company said it was still evaluating the idea of proactively sending out doses. This case in Arizona was a realization of the fears that had spurred the suggestion in the first place.
Researchers in the Netherlands have developed a DNA test for rare diseases that can provide much more comprehensive results than standard diagnostics in a shorter amount of time. A study published today in the New England Journal of Medicine reports that this new approach could replace 15 other genetic tests with a single analysis while increasing the number of patients who successfully receive a diagnosis.
Taken together, all rare diseases affect approximately 400 million people worldwide. Of the more than 7,000 rare diseases that have been identified so far, about 80% are caused by genetic mutations. Obtaining a diagnosis can be critical for people suspected of having a rare disease, offering them perspective for the future, better guidance and treatment, and less uncertainty. However, these patients often have to undergo multiple rounds of testing and wait years before receiving a definitive answer.
The new test is based on long-read genome sequencing, a technology that can read significantly longer stretches of DNA before assembling them into a complete genome. While conventional genomic tests typically read fragments around 300 nucleotides long, long-read sequencing can analyze stretches of up to 20,000 nucleotides at a time. The longer reads make it easier to accurately assemble the full genome, providing a more complete picture of the patient’s genetic makeup.
“Thanks to long reads, we obtain an even more complete view of DNA and can detect complex and hard-to-find abnormalities. We then link these to specific conditions,” says Alexander Hoischen, PhD, professor of genomic technologies at Radboud University Medical Center. “In this way, our knowledge grows and we can make more diagnoses.”
In addition, the test can detect epigenetic modifications in the genome that affect gene function without altering the underlying DNA sequence. Although these modifications can be responsible for some rare disorders, conventional testing methods are currently unable to detect them.
“With current diagnostics, this requires additional specialized tests, but with long reads we capture these modifications as a bonus—two in one,” explains Christian Gilissen, PhD, professor of genome bioinformatics at Radboud University Medical Center.
Earlier this year, the technology was used as part of the National Undiagnosed Hackathon, where over 140 experts across the Netherlands came together to search for a diagnosis for 33 families. Long-read sequencing was used to map their DNA in detail, leading to five new confirmed diagnoses within two days as well as strong suspicion of a diagnosis for another eight families.
As the number of rare disease diagnoses continues to rise, this new test could make the diagnostic process much faster and more efficient. Long-read sequencing could also help researchers identify and investigate the genetic drivers of rare conditions, many of which remain largely understudied.
Based on these findings, Lisenka Vissers, PhD, professor of translational genomics at Radboud University Medical Center, calls for the technology to be adopted worldwide as the first choice diagnostic approach when testing patients suspected to have a rare genetic disorder.
The post Long-Read DNA Test Could Replace 15 Existing Tests for Rare Diseases appeared first on Inside Precision Medicine.
A technique called non-invasive fetal sequencing (NIFS), developed by scientists at Harvard University, allows accurate and comprehensive genetic screening of a fetus using only a blood test from the expectant mother.
At the European Society of Human Genetics conference in Gothenburg this week, the researchers report that the technique allowed them to identify 97% of all the genetic variants normally identified using more invasive testing during pregnancy such as amniocentesis or chorionic villus sampling.
Non-invasive prenatal testing (NIPT) that focuses on cell free DNA in maternal blood has been offered for some time but has only historically tested for a limited set of mutations, mostly conditions with chromosome number abnormalities such as Down syndrome, as these were easiest to detect accurately in the small amount of fetal DNA found in maternal blood.
However, a combination of improvements in sequencing accuracy and coverage and other advances such as developments in machine learning can now help interpret such tests and it is now possible to detect a wide range of genetic variants in the fetus using only maternal blood.
In this study, the team applied NIFS to 565 pregnancies at a median gestational age of 17.5 weeks. Blood samples were sequenced to a high level of coverage (an average of 860x), and ensemble machine learning was used to call heterozygous, hemizygous, and homozygous sequence variants, and copy number variants across the full exome. The NIFS results were validated against matched genome sequencing from invasive procedures in 388 samples.
The researchers looked at nearly 7.9 million fetal variants, and the test had a median sensitivity of 94.0% and precision of 94.9% for detecting these mutations. The test identified 97.2% of the reportable fetal variants that could be found by invasive testing.
Notably, the NIFS test was more sensitive at detecting de novo and paternal variants at around 97% or higher and less sensitive at detecting inherited maternal variants at around 95%.
“The test performed really well in capturing all of the clinically relevant variants found by invasive genome sequencing that would have been missed by all current non-invasive tests,” said presenting scientist Christopher Whelan, PhD, a senior computational scientist working at the Broad Institute of Massachusetts Institute of Technology and Harvard, in a press statement.
“There were also some unexpected discoveries, such as twin pregnancies with abnormal tissue, and evidence that some mothers had received a bone marrow transplant from a male donor that confounded NIPT results. This provided further evidence of the strength of the technique.”
Whelan works in the lab of Michael Talkowski, PhD, the director of the Center for Genomic Medicine at Massachusetts General Hospital, who was also involved in the research. Talkowski is co-founder of the diagnostics company First Genomic Insights, which is developing this test for commercialization. If successful it will likely be the first U.S. company to bring an exome scale non-invasive prenatal test like this to the market.
The post Maternal Blood Test Allows Comprehensive Genetic Fetal Screening appeared first on Inside Precision Medicine.
WASHINGTON — The Trump administration on Friday proposed to change a policy that is designed to prevent drugmakers from avoiding Medicare price negotiation by adding active ingredients to drugs.
The policy is part of an annual proposed rule that establishes the process that the Centers for Medicare and Medicaid Services uses to choose the next 20 drugs and biologics for price negotiation. Those drugs will be announced by Feb. 1, 2027, and their negotiated prices will take effect in 2029. The administration also considered a similar policy last year but put off a decision to study it further.
Medicare must wait seven to 11 years after a product is approved by the Food and Drug Administration before it can negotiate its price, depending on the type of medicine. Biologics that are typically administered in doctor offices get more time than drugs taken orally.
First coined over a decade ago in the aerospace industry to describe a digital replica of a physical object, the concept of a “digital twin” has since found its way into medicine, where it refers to the simulation of a patient’s unique biology. Drawing on multiple layers of patient health data, these computer models promise to predict how a person’s health will evolve over time and how they will respond to any given intervention.
Digital twins represent a transformative shift in medicine, moving from reactive health interventions toward preventive strategies. While this technology is still in early stages, it is already being used to guide personalized cancer treatment, simulate the outcomes of cardiology interventions, and manage complex metabolic diseases like diabetes. However, most applications today are closer to small-scale digital models of a specific tissue or condition rather than a complete digital twin that dynamically adapts to real-world data from each simulated patient.
A convergence of rapid technological advances across multi-omics and artificial intelligence (AI) is priming the development of powerful computational models that can capture intricate biological processes beyond the capabilities of any of their predecessors. As large-scale multi-omics datasets are increasingly combined with clinical and real-time physiological data, digital twins are laying the foundation for a more precise and individualized understanding of human health.
Digital twins could have a particularly meaningful impact in areas of medicine where knowledge is limited and currently available technologies have fallen short. One such area is rare diseases. Although rare diseases collectively affect more than 300 million people worldwide, each of the over 7,000 conditions covered under this definition only affects a small number of patients—sometimes even just a single person. This scarcity makes it difficult to study the underlying biology and hinders the development of much-needed treatments and diagnostics.
“We can use digital twins to address the fact that, with a rare disease, you might only have a handful of patients with that diagnosis,” said Ellen M. McDonagh, PhD, group team lead at the European Bioinformatics Institute (EMBL-EBI) in the U.K. and translational informatics director at Open Targets.
Through a project funded by the Chan Zuckerberg Initiative, McDonagh’s team is developing digital twins of human tissues that combine multi-omics data with a patient’s clinical history and additional phenotype data. Their approach begins by modeling biological processes in healthy tissue, and then bringing in data from common diseases affecting the same tissue to train AI models to predict patterns of dysfunction. This would allow researchers to feed the algorithm data from patients with rare diseases to better understand the underlying biological mechanisms driving each condition.
Integrating diverse layers of multi-omics data will be critical to achieving a more comprehensive understanding of the molecular basis of these rare conditions. In some countries, including the U.K., patients with rare diseases routinely undergo whole-genome or whole-exome sequencing as part of diagnostic testing. However, many of the identified genetic variants remain difficult to interpret with limited current knowledge. By combining genomics with other modalities such as transcriptomics, proteomics, and metabolomics, researchers can develop a more complete picture of the underlying molecular interactions and better determine the relevance of these previously uncharacterized variants.
On this front, a major challenge lies in collecting and integrating data across a wide range of modalities, cohorts, and institutions. To address this, McDonagh’s team is actively developing workflows to standardize data collected from the scientific literature, public datasets, and research environments, enabling more reliable comparisons across datasets and facilitating their integration into digital twin models.
This work also involves efforts to fill gaps in the data, as not all data modalities will be available for every patient. For instance, a computer model could predict what the transcriptomic profile will look like based on genomics data, and vice versa.
“We are benchmarking different methods that can help with predicting missing data, but also evaluating how confident we are in those predictions,” said McDonagh. Knowing which biological processes can be predicted with high confidence, and which cannot, can help researchers draw more robust conclusions and guide future data collection efforts.
As digital twin models keep growing and becoming more refined, they will enable the identification of new therapeutic targets and diagnostic markers, while also forecasting the precise effects an intervention will have on a given person. McDonagh highlights their potential to develop more personalized treatment plans for each patient, adding that, “Monitoring patients over time, one could also predict whether a patient might develop resistance to a given drug and switch them to an alternative treatment.”
Integrating multi-omics data with physiological measurements, obtained from continuous sensors and wearable devices, could help digital twins take a significant step forward in accurately simulating complex and dynamic biological processes. In turn, this could help advance healthcare from a reactive model to a more proactive approach.
“Today, much of medicine begins after a disease has become clinically apparent,” said Tadao Ooka, MD, PhD, associate professor at the University of Yamanashi in Japan. “In contrast, preemptive medicine aims to detect subtle biological changes before symptoms or irreversible damage occur, and to intervene earlier through lifestyle, environmental, pharmacological, or behavioral approaches.”
Achieving such a transformative shift could significantly reduce the burden of chronic diseases such as diabetes, cardiovascular disease, and neurodegenerative disorders. This is becoming an increasingly urgent goal in aging societies, including Japan, where preventing health decline and extending healthy life expectancy are currently major public health priorities.
Ooka’s lab is developing digital twins that integrate patient data from longitudinal multi-omics, wearables, and lifestyle questionnaires. Through Taomics, a company he co-founded, Ooka is also building a platform to collect longitudinal data from patients and healthy individuals. This data is used to create digital twins that can provide users with personalized health recommendations while informing drug discovery and identifying target populations for a more precise approach to clinical development.
“One major objective is to identify biological pathways related to insulin resistance and metabolic dysfunction,” he added. “The goal is not only to predict risk, but also to understand which behaviors or interventions may improve a person’s molecular and metabolic state.”
While multi-omics data can tell researchers what is happening within the body at the molecular level, continuous data obtained from sensors and wearables can provide a deeper insight into what a person is experiencing in daily life, including physical activity, sleep, heart rate, and stress levels.
“The key is to connect these two layers,” said Ooka. “Together, they allow us to move from general advice to personalized, testable, and adaptive recommendations. For example, if a person’s sleep, physical activity, or dietary pattern changes, we can examine how their inflammatory, metabolic, or insulin resistance-related protein signatures change afterward. Conversely, if a molecular pathway appears to be deteriorating, [sensor] data may help identify the behavioral or environmental context behind that change.”
Across all medical specialties, Ooka expects digital twins to make the greatest early impact in diseases where progression is continuous, multifactorial, and strongly influenced by the patient’s lifestyle and environment. These include metabolic diseases, which develop over many years and are shaped by interactions between genetics, environment, and behavioral patterns. Oncology will also be particularly relevant given the complexity of treatment response and resistance processes at the molecular level.
To reach these ambitious goals, however, a number of challenges must be addressed. In addition to ensuring the data used to train digital twin models is robust and reliable, implementation needs to be carefully planned so that digital twins can adapt to and integrate into real-world clinical workflows, reimbursement systems, regulatory frameworks, and ethical governance structures.
“The goal should be to create systems that benefit the broader population,” explained Ooka. “We need to ensure that prediction does not become discrimination, that data is handled securely, and that people receive understandable and actionable recommendations.”
In the future, experts expect to see digital twins that integrate multi-omics data with wearable, imaging, clinical, and environmental data to capture the full complexity of human biology, becoming intelligent decision-support platforms. This progress will be underpinned by continued improvements in multi-omics technology, with the coming decade being primed for advances in longitudinal data collection and spatial multi-omics. Coupled with increasingly lower prices, this technology is expected to become much more accessible to researchers and clinicians alike.
“While omics data were once confined to laboratory analysis, emerging wearable technologies now allow real-time detection of certain metabolites and protein markers,” wrote Kyung-In Jang, PhD, associate professor at the Daegu Gyeongbuk Institute of Science and Technology (DGIST) in South Korea. “These innovations support integrating omics into everyday health monitoring, contributing to the accessibility and responsiveness of precision healthcare.”
Within the next decade, McDonagh expects to see the first translational applications of digital twins in the clinic, whether to support diagnosis, patient stratification in clinical trials, or predicting how a patient will respond to a given treatment. “It really does open the door to being able to identify new targets that are causing disease in rare disease patients, but also in more complex, common diseases,” she said. “Ultimately, digital twins will help in the development of new, safer, more effective treatments and more personalized medicine.”
Going forward, Ooka expects medical applications of digital twins to evolve in stages, starting with smaller, disease-specific models, and later becoming large-scale tools that can predict future outcomes and enable patients to alter their disease trajectories through personalized interventions. This evolution will go beyond purely technical improvements, potentially shaking the foundations of healthcare systems as we know them today.
“The field will require new ecosystem models, not only new analytical technologies,” said Ooka. “Medical digital twins cannot be built by academia, industry, hospitals, or technology companies alone. They require long-term participant engagement, trusted data governance, scientific rigor, clinical relevance, and business sustainability.”
Ooka has been actively working on setting up such an ecosystem in Japan through the COI-NEXT initiative, bringing together universities, regional companies, and global partners to return insights derived from their data to local communities.
“Ultimately, I would like to create a system in which individuals can receive personalized health recommendations based on their own longitudinal biological data,” he concluded. “This means moving beyond one-time testing toward a continuous feedback loop: measure, interpret, intervene, and re-measure. At the same time, such a platform could contribute to pharmaceutical research by connecting real-world human biology, lifestyle, and molecular data in a way that supports more precise and efficient drug development.
“My hope is that digital twins will help create a future where healthcare is no longer centered only on diagnosing and treating disease, but on continuously supporting each person’s optimal health throughout life.”
Clara Rodríguez Fernández is a science journalist specializing in biotechnology, medicine, deeptech, and startup innovation. She previously worked as a reporter at Sifted and editor at Labiotech, and she holds an MRes degree in bioengineering from Imperial College London.
The post From Multi-Omics to Digital Twins: A Data-Driven Future for Precision Medicine appeared first on Inside Precision Medicine.
Researchers have uncovered evidence that age-related genetic mutations commonly associated with cancer and blood disorders may also contribute to the chronic brain inflammation characteristic of Alzheimer’s disease.
In a study published in Cell, investigators from the Icahn School of Medicine at Mount Sinai and Boston Children’s Hospital identified somatic mutations in brain immune cells from patients with Alzheimer’s disease. The findings suggest that genetic alterations acquired during aging may reshape the behavior of these cells, driving inflammatory processes linked to neurodegeneration.
The work introduces a potential new biological mechanism connecting aging, immune dysfunction, and Alzheimer’s disease, a condition that affects millions of people worldwide and remains one of the leading causes of dementia.
Inflammation has long been recognized as a central feature of Alzheimer’s disease. Activated immune cells are commonly observed surrounding amyloid plaques and other pathological hallmarks of the disease. However, the factors that initiate and sustain these harmful immune responses have remained incompletely understood.
The new study focused on microglia, the brain’s resident immune cells. These specialized cells play critical roles in maintaining neural health by clearing cellular debris, regulating synaptic connections, responding to injury, and coordinating immune activity within the central nervous system.
Previous research has shown that microglia can adopt disease-associated states during Alzheimer’s progression, but the molecular triggers responsible for these changes have been unclear.
“Our data suggest that some immune cells in Alzheimer’s disease undergo genetic changes over time that alter their behavior and potentially contribute to chronic inflammation in the brain,” said senior author Samuele Marro, PhD, associate professor in the Nash Family Department of Neuroscience and The Friedman Brain Institute at Mount Sinai.
“These findings provide a new framework for understanding how aging, immune dysfunction, and neurodegeneration may intersect in Alzheimer’s disease.”
To investigate whether acquired mutations might contribute to disease pathology, researchers analyzed 311 postmortem brain samples from individuals with Alzheimer’s disease and age-matched controls.
Using ultra-deep sequencing, the team screened 149 genes frequently associated with cancer and clonal hematopoiesis, an age-related condition in which blood stem cells acquire mutations that allow certain cell populations to expand disproportionately over time.
The analysis revealed significantly higher numbers of somatic mutations in Alzheimer’s disease brains compared with controls.
Several of the most frequently mutated genes, including TET2, DNMT3A, and ASXL1, are well-known drivers of clonal hematopoiesis and have previously been implicated in age-related blood disorders and cancer development.
Many of these mutations were highly enriched in microglia-like immune cells while being largely absent from neurons, suggesting that immune cells may be a primary target of these age-related genetic alterations.
The researchers also examined matched blood samples from some patients and found that many of the same mutations detected in the brain were present in circulating blood cells.
This observation suggests a possible route by which mutated immune cells originating from the blood may enter the brain and adopt microglia-like functions.
Growing evidence has indicated that peripheral immune cells can contribute to neuroinflammation under certain conditions. The new findings raise the possibility that age-related expansion of mutated blood cell clones could influence inflammatory processes within the brain.
Such a mechanism would provide a direct biological link between clonal hematopoiesis—an increasingly recognized consequence of aging—and neurodegenerative disease.
To determine whether these mutations actively alter immune cell behavior, the researchers combined single-cell analyses with stem cell-based experimental models.
Using CRISPR gene editing, they engineered induced pluripotent stem cell-derived microglia-like cells carrying mutations identified in Alzheimer’s disease samples.
The resulting cells displayed profound changes in gene expression, adopting inflammatory programs and disease-associated microglial states that have previously been linked to neurodegeneration.
“Our study provides functional evidence that mutations commonly associated with aging blood cells and cancer biology can directly alter the behavior of brain immune cells,” said co-corresponding author Eirini Papapetrou, MD, PhD, professor of oncological sciences at Mount Sinai and director of the Center for Advancement of Blood Cancer Therapies.
“These mutated cells showed inflammatory signatures strongly associated with neurodegeneration.”
The findings suggest that these mutations are not merely bystanders but may actively influence cellular pathways involved in disease progression.
The study expands the growing view of Alzheimer’s disease as a disorder involving complex interactions between the immune system and the nervous system.
Historically, Alzheimer’s research has focused heavily on amyloid-beta plaques and tau tangles. More recently, attention has shifted toward the role of neuroinflammation and immune dysfunction as key drivers of disease progression.
The identification of somatic mutations in microglia-like cells adds another layer to this emerging picture. Rather than being solely inherited or environmentally driven, some aspects of Alzheimer’s pathology may arise from genetic alterations that accumulate naturally with age.
Because clonal hematopoiesis becomes increasingly common in older adults, the findings may have implications beyond Alzheimer’s disease and could influence understanding of other neurodegenerative disorders characterized by chronic inflammation.
Although the study does not establish that these mutations directly cause Alzheimer’s disease, it identifies a plausible mechanism through which age-related genetic changes could exacerbate neurodegeneration.
Future studies will be needed to determine how early these mutations emerge, whether they predict disease risk, and whether interventions targeting mutated immune cell populations could slow disease progression.
“This work highlights a potentially important connection between aging blood biology and neurodegenerative disease,” said Marro.
“If confirmed in future studies, these findings could open new avenues for therapies that target harmful inflammatory immune cell populations in the brain.”
The researchers are now planning follow-up studies in animal models to further investigate the role of mutated immune cells in Alzheimer’s disease and evaluate whether reducing their inflammatory activity can modify the course of neurodegeneration.
The post Age-Related Mutations in Brain Immune Cells Linked to Alzheimer’s Inflammation appeared first on Inside Precision Medicine.
Scientific publishing is facing a growing challenge from fabricated research produced by industrial-scale paper mills. But researchers and publishers are fighting back through technology and collaboration to protect the integrity of the scientific record.
Scientific publishing is based on the trust that the data are real and that peer review ensures quality. But that trust is being eroded by commercial enterprises known as paper mills—coordinated commercial operations that sell authorship slots in fraudulent or manipulated manuscripts, then submit those manuscripts to journals.
Unlike traditional misconduct, these are not lone researchers cutting corners but businesses producing research at scale, often tailored to meet the demands of specific fields, journals, and career incentives.
A recent analysis of almost 19,000 online adverts for paper mills revealed authorship slots being sold for between $36 to $5,600 depending on the position of the slot, highlighting how commercialized the market is. The average for a first author position was $1,030 and, although the study did not examine which adverts resulted in published papers, another investigation traced approximately 1,000 authorship adverts to more than 400 published papers.
The magnitude of the problem is difficult to estimate. A 2022 report by the Committee on Publication Ethics (COPE) and the International Association of Scientific, Technical & Medical Publishers (STM) found that the percentage of suspect papers submitted to journals was around two percent overall but increased sharply to as high as 46% in journals targeted by paper mills.
For honest researchers, it might be difficult to understand why paper mills even exist. At the heart of the issue is what Adrian Barnett, PhD, a professor in the Australian Centre for Health Services and Innovation at Queensland University of Technology, described as the “publish or perish” phenomenon.
“If I could do one simple thing tomorrow, I would ban all the university league tables,” said Barnett. “They’re just encouraging corruption.” Ranking systems that prioritize publication volume can push researchers toward quantity over quality, making paper mills an easy way to meet expectations.
Furthermore, publication is often not just a measure of success but a requirement for career progression. “For the clients, it’s believed that they need publications that they can’t achieve through their own efforts, either because they don’t have the time, the facilities, the training, or the money to do research and yet, for whatever reason, their employers expect them to,” explained Jennifer Byrne, PhD, a professor of molecular oncology and lead of the Publication and Research Integrity in Medical Research group at the University of Sydney.
Byrne has published extensively about paper mills and publication integrity; she got into the field accidentally when she came across some papers about a gene that her team discovered many years earlier. “In 2014–2015, we realized that five or six different groups suddenly published very similar papers about this gene in different journals,” she said. “And I just thought, that doesn’t really make a lot of sense.”
Upon investigation, Byrne found that the papers, and a further 48 similar publications, showed features consistent with mass production. She has since proposed that human gene research in general is highly vulnerable to paper mills. “You can hide fake research quite effectively in experimental fields, because it’s very difficult and time-consuming to reproduce experimental studies,” she said.
Aside from the obvious fraud, paper mills are problematic for several reasons. Byrne describes them as “a billion-dollar problem” with few resources devoted to tackling it. And although she and others have advocated for scaled investments, progress so far has been slow.
The publishing system can also reinforce the problem. Paper mills are profit-driven, but journals also benefit through article processing charges and citations, creating what Byrne describes as a “circle” in which “everyone gets what they want.”
The consequences of paper mill papers being published can influence real research. The papers are cited, reused, and built upon, wasting both time and money for all involved.
More broadly, the erosion of trust can drive researchers away from entire fields. In Byrne’s case, she stopped doing preclinical cancer research. “I left because there were a lot of papers that I couldn’t trust. When you get to the point where you can’t trust most of the recent literature, it’s very difficult to continue,” she said.
There are also more sinister risks. Barnett recalled reports of paper mills exploiting their clients, including instances of potential blackmail. “If you’ve been a regular customer and then you suddenly stop, they might try and squeeze more money,” he said. “They’ve got absolutely no scruples.”
Despite these impacts, deterrents are limited. “There are almost none,” said Byrne.
Retractions are often slow, meaning damage is done before action is taken, and retraction rates are far below where they should be.
In an April 2026 report to a U.S. Congress hearing on the state of scientific publishing, Kate Travis, managing editor of Retraction Watch, showed that the retraction rate was around 0.2% in 2025, up from 0.02% in 2016. Yet, the report states that Retraction Watch “are confident that the rate […] should be about two percent—10 times what it is today.”
Concerns about problematic papers are often raised by individual researchers or so-called science sleuths on platforms such as PubPeer. Although they have become skilled at spotting telltale signs of a paper mill, like manipulated images, distinct layouts, author affiliations that might not match the topic of the paper, unusual patterns of coauthors, and fake peer reviews, it is difficult for the untrained eye to detect problems from a single paper.
This is why there have been calls for increased awareness. “Awareness is always the first step,” said Byrne, who is working with The Lancet–World Conferences on Research Integrity Foundation commission to address critical issues related to research integrity.
Efforts to extend awareness are also being coordinated through initiatives such as United2Act, which brings together stakeholders from research institutions, publishers, sleuths, and universities to develop shared guidance and educational resources.
But even with greater coordination, human detection has limits. As paper mills scale, automated tools are becoming essential.
Earlier this year, Barnett, Byrne, and colleagues published a paper in the BMJ showing that their large language model (LLM) could flag papers suspected of being from paper mills by analyzing sentence-level patterns. The model identified 9.9% of more than 2.6 million cancer research papers for further review. Many of the papers were linked to regions with strong publication incentives, including China.
However, Barnett emphasized that the model “is not a 100% proof, it’s a quick and simple flag that should encourage reviewers to look at those papers and look for other signs of paper mill activity.”
Other paper mill detection technologies are also available. Platforms such as Clear Skies, which is used by the STM Integrity Hub, use machine learning to detect patterns across large bodies of literature, while image-forensics tools and cross-publisher data sharing help identify duplicated figures and submissions.
Alongside these tools, Barnett suggested that researchers may increasingly need to provide a “breadcrumb trail,” through preregistration of hypotheses and transparent workflows to demonstrate the authenticity of their work.
Platforms such as PubPeer and Retraction Watch also play a role, enabling researchers to flag concerns and share evidence about suspect papers after publication. These flags then prompt journal retractions and investigations, making it a critical component in the fight against paper mill activity.
Aside from technology, Byrne would like to see tighter regulation for the commercial publishing industry, akin to something like the ISO 9001 quality management standards that have been widely adopted across industries like manufacturing, engineering, and healthcare.
“We need a regulatory framework that rewards journals that do the right thing and that care about publishing quality,” she said. “And we need to disincentivize the current commercial drive towards publishing anything for money.”
Byrne believes that funders and researchers should be demanding these standards. “They pay for the research, the journal subscriptions, the article processing charges, and give their research for free,” she said. “They don’t ask anything in return, in terms of quality standards, and that’s unacceptable.”
Marie Soulière, PhD, an elected trustee of COPE and chair of the COPE Paper mill Working Group, acknowledged that “a standard such as ISO 9001 could help with process consistency, documentation, and accountability.” But she said, “it would not be a direct solution to publication fraud or paper mills” and “would need to sit alongside integrity-specific controls, not replace them.”
Publishers are increasingly shifting from isolated responses to coordinated action. Initiatives like the STM Integrity Hub and United2Act are driving cross-industry collaboration and shared detection approaches.
Soulière said that several recommendations from the COPE/STM 2022 “have been put into practice, particularly around cross-publisher collaboration, shared screening approaches, and investment in integrity infrastructure.”
A central strategy, highlighted in a publication from the United2Act working groups, uses the “Swiss Cheese Model,” a move toward layered screening that combines tools such as plagiarism screening, image forensics, citation analysis, and author verification. “Each safeguard has limitations, but multiple checks together make it harder for fraudulent papers to pass through,” said Soulière.
Publishers are also strengthening internal processes. As Adya Misra, PhD, associate director of research integrity at Sage, described: “Our research integrity team acts centrally to support editors and internal journal teams with both prevention of suspicious or problematic research and the correction of the scholarly record … in line with COPE guidance.”
A spokesperson for Taylor & Francis highlighted their work on external collaborations designed to address the root causes of integrity issues. They are partnering with the National Science Library at the Chinese Academy of Sciences to develop research integrity and publishing ethics training programs, designed to ensure that students and researchers at all levels receive adequate support and to help them avoid exploitation by unethical third-party services such as paper mills.
Even as safeguards improve, artificial intelligence (AI) is moving the goalposts. Many current detection strategies were developed to target structured forms of fraud; template-driven papers, recycled images, and repeated patterns across manuscripts. But these signals are beginning to disappear. “Our system worked because the paper mills would have a template, but now with AI, there is no template,” said Barnett. “It’s going to absolutely change everything.”
Barnett and his colleague Matt Spick, PhD, a lecturer in health and biomedical data analytics at the University of Surrey, recently demonstrated this by generating a complete scientific paper in just under 30 minutes using publicly available data and the OpenAI platform PRISM.
“All we did was give it the dataset and said write a paper for an Elsevier journal,” Barnett explained. “If an honors student had given me this paper, I would have been pretty pleased.”
Paradoxically, AI could also be bad news for paper mills as people realize they can create the papers themselves at little to no cost.
With AI adding to the challenges that publishers and researchers already face, the future could appear bleak. Barnett recalled an analogy describing the AI problem as an oil spill in a digital ocean, “We don’t know how deep it is, can’t get to the bottom of it, and it’s very difficult to clean up.”
Even removing a single problematic paper can require significant time and effort, while thousands more remain undetected. But Byrne remains positive that the work being done can have an impact.
“I’m actually really positive, because I think the biggest thing is awareness,” she said, noting that when she gives talks, she asks if the audience has heard of paper mills. “In 2023, that might have been five percent of people, and yet by 2025 it had increased to 30%–50%,” she said.
Soulière added that increased collaboration and transparency within scholarly publishing is another positive takeaway.
“Publishers, editors, institutions, and other stakeholders are no longer treating these issues as isolated problems,” she said. “They are investing in stronger screening systems, clearer policies, and better cross-sector coordination. In that sense, this moment is also driving progress and innovation.
“While the risks are serious, the response from the sector shows that trust can be reinforced, and that the system is becoming better equipped to detect problems earlier and protect the scholarly record more effectively,” Soulière concluded.
Laura Cowen is a freelance medical journalist who has been covering healthcare news for over 10 years. Her main specialties are oncology and diabetes, but she has written about subjects ranging from cardiology to ophthalmology and is particularly interested in infectious diseases and public health.
The post Paper Mills and the Fight Against Scientific Fraud appeared first on Inside Precision Medicine.
Researchers at Nagoya University, Japan, have developed a nanowire-based microfluidic device they say can improve the detection and analysis of cancer-associated extracellular vesicles (EVs) in blood samples. The new device uses zinc oxide nanowires modified with antibodies to selectively capture EVs linked to cancer while preserving both their surface proteins and internal microRNAs, which can then be analyzed. The findings, published in the journal Device, indicate that new device could potentially improve liquid biopsies for ovarian cancer as well as other cancers.
“In this study, we developed a nanowire microfluidic device capable of selectively capturing cancer-associated EVs with high efficiency, while suppressing nonspecific adsorption through simple chemical modification,” said senior author Takao Yasui, PhD, a professor in the graduate school of engineering at Nagoya University. “We also demonstrated that this approach maintains both EV membrane proteins and internal microRNAs intact, showing strong potential for highly sensitive analysis of cancer states.”
EVs are an emerging class of analytes because they carry molecular cargo such as messenger RNAs, microRNAs, and membrane proteins that reflect the cells from which they originate. To date, however, there have been challenges finding way to isolate EVs from complex biological fluids.
According to the researchers, techniques to isolate EVs such as ultracentrifugation, size-exclusion chromatography, and polymer-based precipitation can be time-consuming, require large sample volumes, and provide limited specificity. These methods also don’t reliably distinguish between EV subtypes.
Previously, investigators in the Yasui lab had developed zinc oxide nanowire devices capable of efficiently enriching EVs through charge interactions and hydrogen bonding. Further, the researchers noted that polyketone-coated zinc oxide nanowires improved the efficiency and purity of EV isolation within a microfluidic system designed for cancer diagnostics. Those findings led the team to explore whether polyketone chemistry could provide a more controlled method of attaching antibodies to the nanowires.
To do this, the team developed a platform based on N-hydroxysuccinimide ester-functionalized polyketones, known as pKNHS. They synthesized six pKNHS variants with different chain lengths and found that pKNHS 4.2 provided the most effective combination of nanowire stability and antibody immobilization.
The technology was first evaluated using cultured breast cancer cells. Antibody-free nanowires captured approximately 65% of CD9-positive EVs, while nanowires conjugated with CD9 antibodies captured efficiency of about 90%.
Next, the platform was testing using antibodies directed against ovarian cancer-associated markers CLDN3, FOLR1, and TROP2. These modified nanowires selectively recovered EVs from ovarian cancer cells and were subsequently used to isolate EVs from blood serum samples obtained from six patients with high-grade serous ovarian carcinoma and six individuals without cancer.
Analysis of the captured EVs showed distinct microRNA patterns of the EVs collected from cancer patients compared with those from the people without cancer. In total, the team identified 126 microRNAs shared among EVs captured using all three ovarian cancer markers. They also found unique microRNA populations linked to each marker, including 40 associated with CLDN3, 37 with FOLR1, and 45 with TROP2.
The system also facilitated the analysis of both EV membrane proteins and encapsulated microRNAs, which could scientists to better understand the relationships between surface markers and the associated molecular cargo.
Future work will focus on comparing the technology with existing clinical diagnostic methods and expanding its ability to capture additional EV subpopulations. The long-term goal is to apply the approach to noninvasive liquid biopsies and early diagnosis across a range of cancer types.
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