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Lung Screening Incidental Findings May Guide Follow-Up for Other Cancers

An analysis of the US National Lung Screening Trial (NLST) has found that the presence of certain types of abnormalities in regions outside of the lungs on low-dose computed tomography (LDCT) images may be associated with a significantly increased risk for extrapulmonary cancer.

The abnormalities, termed significant incidental findings (SIFs), could help clinicians decide when follow-up care is likely to catch extrapulmonary cancer early and when it may not be necessary.

“In this paper, we provide an evidence base for making decisions on abnormalities outside of the lungs that might be seen at lung screening,” said study author Ilana Gareen, PhD, a professor of epidemiology at Brown University School of Public Health. “The goal is to give physicians and patients better data so that they can make more informed choices about those abnormalities that should be considered for follow-up and those that most likely can be ignored.”

Writing in JAMA Network Open, Gareen and co-authors explain that LDCT lung cancer screening frequently detects SIFs unrelated to lung cancer; in the NLST, 34% of 26,455 patients screened with LDCT had SIFs reported but the nature of the SIFs varied.

And although there are recommendations for reporting and addressing SIFs, there is limited evidence for an association between SIFs detected at LDCT lung cancer screening and extrapulmonary cancer diagnoses.

To address this, Gareen and team analyzed data from 75,104 LDCT screening rounds performed in 26,445 individuals (mean age, 61 years; 59.0% men) who were randomly assigned to receive LDCT during the NSLT. The participants had a history of heavy smoking (≥30 pack–years), meaning they are also at high risk for several extrapulmonary cancers, including pancreatic, bladder, and kidney cancer.

The researchers focused on SIFs that were labelled as potentially indicative of extrapulmonary cancer (cancer SIF), rather than those that possibly indicated emphysema or cardiovascular disease.

They report that cancer SIFs were recorded for 2265 (3.0%) screening rounds in 1807 (6.8%) participants across the three screening rounds they received.

Participants with cancer SIFs were significantly older than those with no cancer SIF (mean 62.1 vs. 61.4 years) and significantly more likely to have a history of a smoking-related disease (68.6 vs. 65.7%).

Within one year of a screening round, 1025 participants were diagnosed with an extrapulmonary cancer. Of these, 67 (6.5%) had a SIF on LDCT. This corresponds to 3.0% of participants with a cancer SIF.

Overall, the risk for extrapulmonary cancer among the people with a cancer SIF was 29.6 per 1000 screening rounds compared with 13.3 per 1000 screening rounds in those without a cancer SIF. After adjustment for potential confounders, the marginal risk difference between the two groups was 13.9 per 1000 participants, suggesting that for every 1000 people screened, the presence of a cancer SIF is associated with 13.9 additional cases of extrapulmonary cancer.

When the researchers looked at specific cancer types, they found that the marginal risk difference was substantially higher for urinary cancers, at 17.0 per 1000 participants. It was 5.0 for digestive cancer, 12.3 for breast cancer, and 13.8 for other cancers including lymphoma and leukemia.

“In general, if an abnormality is found that might indicate cancer, the patient receives additional imaging to evaluate that abnormality,” Gareen told Inside Precision Medicine. “Our paper provides additional information as to those abnormalities that should be considered to increase the risk of a cancer diagnosis.”

Importantly, mortality from extrapulmonary cancer accounted for 22.3% of the certified deaths in the LDCT arm of the NLST. Therefore “early detection of these cancers may facilitate early treatment and potentially reduce associated morbidity and mortality,” the authors write. “Identification of cancer SIFs associated with extrapulmonary cancers in NLST participants could be used to plan appropriate diagnostic evaluations for patients undergoing lung cancer screening.”

Gareen said the next step will be to determine if the findings are replicated in lung screening in the community, or if the rate in community screening is higher or lower.

In accompanying comment, Patrick Senior and Andrew Creamer, both from Gloucestershire Hospitals NHS Foundation Trust, in Gloucester, United Kingdom, point out that the false positive rate for a cancer SIF was 97% but say “it is hard to imagine a scenario in which an incidental finding with even a possibility of representing cancer would be disregarded.”

However, they note that “when considered in the context of the numbers of people eligible for lung cancer screening programs around the world, acting on such findings poses a considerable additional burden on the health systems that must investigate them.”

Senior and Creamer say that the results “underscore the importance of both a robust health economics analysis of how screening programs manage such incidental findings and patient-centered research to understand the impact that such unexpected results may have on the individual. Further research is needed to ensure that screening programs are confident when faced with information they did not ask for.”

The post Lung Screening Incidental Findings May Guide Follow-Up for Other Cancers appeared first on Inside Precision Medicine.

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Base Editing Shows Early Promise for Treating Beta Thalassemia

The Chinese biotech CorrectSequence Therapeutics, also known as Correctseq, reports good results from a Phase I study of its technology involving editing a person’s hematopoietic stem cells to treat beta thalassemia.

The trial, published in Nature, included five patients with transfusion dependent beta thalassemia who were able to stop red blood cell transfusions, the standard treatment for the condition, after receiving the base-edited treatment CS-101. The participants continued to have good levels of hemoglobin with no serious side effects during follow-up.

Beta thalassemia is a rare inherited condition affecting around one in 100,000 people in the U.S. Mutations in the beta‑globin gene HBB reduce or stop production of the beta chains of hemoglobin, leading to chronic anemia that varies in its severity.

There are already several therapies on the market for beta thalassemia. The most common treatment is still regular blood transfusions to treat the anemia, but recently the genetic therapies Zynteglo, a lentiviral gene therapy developed by Bluebird Bio, and Casgevy, a CRISPR edited therapy developed by Vertex Pharmaceuticals and CRISPR Therapeutics were approved by the FDA.

Casgevy works by boosting fetal hemoglobin levels to treat the anemia seen in thalassemia patients. It uses CRISPR–Cas9 to cut both strands of DNA at the BCL11A enhancer site, which relies on error‑prone repair and can theoretically generate insertions, deletions, and larger rearrangements.

Correctseq is also aiming to raise fetal hemoglobin levels with CS-101, targeting the same site, but is only changing individual bases without making a full cut, which should reduce risks linked to double‑strand breaks, such as large deletions or chromosomal translocations.

In this study, CS-101 was given to five patients with beta thalassemia, previously treated with blood transfusions. The process involves extracting their stem cells, reactivating fetal hemoglobin production using base editing, giving the patients chemotherapy to clear existing stem cells and make way for the newly edited population, and finally injecting the patients with the edited stem cells.

All five patients were able to stop red blood cell transfusions and had maintained good levels of hemoglobin at three months. These levels stayed at a similar level through a median follow up period of 23 months. No deaths or reported cancers due to the chemotherapy treatment were observed and the safety profile so far is acceptable.

Although these results are promising, this trial is just a small initial study and further work is needed to confirm safety and efficacy of CS-101.“The planned Phase II/III trial will be crucial for evaluating a larger and more genetically diverse patient population across multiple centers,” write the authors.

“Extended follow-up will be required to enable comprehensive analyses of chimerism and clonality, which will facilitate more definitive assessment of long-term safety, engraftment dynamics and clinical benefit.”

One Correctseq’s main competitors is U.S.-based Beam Therapeutics, which is developing a similar base edited treatment. Beam is behind Correctseq in developing its edited therapy for beta thalassemia, but ahead with its therapy for sickle cell disease, something Correctseq are also targeting using a similar pathway.

The Chinese biotech industry is currently on an upward trajectory. Correctseq is one of many Chinese biotech companies currently working to produce competitors for gene therapies like Casgevy and Zynteglo at a more affordable price than those seen in the U.S.

The post Base Editing Shows Early Promise for Treating Beta Thalassemia appeared first on Inside Precision Medicine.

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<![CDATA[Stroke recovery lasts years; learn why fatigue, sleep, hormones and cognition demand ongoing rehab beyond 90 days—and how progress can continue.]]>
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Childhood Dementia Explained by Synaptic Dysfunction, Opens New Therapies

In a new study published in Nature Communications titled, “Modelling synaptic dysfunction in childhood dementia using human iPSC-derived cortical networks,” researchers from Flinders University in Adelaide have uncovered how hyperactive and dysregulated synaptic circuits emerge in the brain tissue of children impacted by Sanfilippo syndrome, a common form of childhood dementia. 

In Australia, an estimated 1400 children currently live with childhood dementia, with hundreds of thousands of cases worldwide. Sanfilippo syndrome is a rare genetic condition that causes fatal brain damage. Children typically reach early developmental milestones before rapidly losing cognitive skills, speech, and mobility. Early symptoms often include hyperactivity and sleep disturbance. 

Alterations in synaptic communication play key roles in neurodegenerative disease progression and cognitive decline. Yet few studies have explored how excitation and inhibition synaptic imbalances contribute to pediatric neurodegenerative disorders. 

Cedric Bardy, PhD, professor and head of the Laboratory for Human Neurophysiology and Genetics at the South Australian Health, describes the study findings as “significant progress.” Chronic overactivity in the brain appears to be a fundamental mechanism contributing to cognitive deterioration in children with Sanfilippo syndrome. 

Using human stem cell-derived cortical neurons and electrophysiology, the team demonstrated that excitatory synapses in the neurons of affected children become abnormally active during early brain development. 

While these neurons initially developed and functioned normally, they became increasingly overactive over time. Brain cell networks showed bursts of intense, highly synchronized electrical activity as they matured, mirroring the hyperactivity and neurological symptoms seen in children with the condition. 

“This hyperactivity offers a clear biological explanation for early behavioral changes, and it brings us closer to understanding the complex mechanisms contributing to childhood dementia,” said Bardy.

Results also demonstrated that these neurons are vulnerable to stress. When exposed to mild nutrient deprivation, excitatory synaptic abnormalities increased, suggesting that common illnesses or physiological stressors may accelerate neurological decline. 

“Our research shows that disrupted synaptic communication is not simply a byproduct of degeneration. It is an early driver of the disease,” Bardy says. 

Childhood Dementia Initiative CEO and founder, Megan Maack, is a co-author of the study and has been involved in guiding the project since its inception. 

“This research is significant not just for Sanfilippo syndrome, but for the field of childhood dementia as a whole,” said Maack. “By identifying the precise cellular mechanisms driving the disease, we are moving towards a personalized medicine approach—the kind of targeted treatment strategy that has transformed outcomes for children with cancer.”

Researchers are now evaluating whether drugs that are already on the market for use in other conditions could be repurposed for childhood dementia. Bardy says the team has already demonstrated that these synaptic imbalances can be corrected with certain medications in the laboratory, indicating that they represent a genuine therapeutic target. 

The post Childhood Dementia Explained by Synaptic Dysfunction, Opens New Therapies appeared first on GEN – Genetic Engineering and Biotechnology News.

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<![CDATA[Learn more about the history of pharmacogenomics. ]]>
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Mustafa Suleyman: AI development won’t hit a wall anytime soon—here’s why

We evolved for a linear world. If you walk for an hour, you cover a certain distance. Walk for two hours and you cover double that distance. This intuition served us well on the savannah. But it catastrophically fails when confronting AI and the core exponential trends at its heart.

From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops (floating-point operations‚ the core unit of computation) for early systems to over 10²⁶ flops for today’s largest models. This is an explosion. Everything else in AI follows from this fact.

The skeptics keep predicting walls. And they keep being wrong in the face of this epic generational compute ramp. Often, they point out that Moore’s Law is slowing. They also mention a lack of data, or they cite limitations on energy.

But when you look at the combined forces driving this revolution, the exponential trend seems quite predictable. To understand why, it’s worth looking at the complex and fast-moving reality beneath the headlines.

Think of AI training as a room full of people working calculators. For years, adding computational power meant adding more people with calculators to that room. Much of the time those workers sat idle, drumming their fingers on desks, waiting for the numbers to come through for their next calculation. Every pause was wasted potential. Today’s revolution goes beyond more and better calculators (although it delivers those); it is actually about ensuring that all those calculators never stop, and that they work together as one.

Three advances are now converging to enable this. First, the basic calculators got faster. Nvidia’s chips have delivered an over sevenfold increase in raw performance in just six years, from 312 teraflops in 2020 to 2,250 teraflops today. Our own Maia 200 chip, launched this January, delivers 30% better performance per dollar than any other hardware in our fleet. Second, the numbers arrive faster thanks to a technology called HBM, or high bandwidth memory, which stacks chips vertically like tiny skyscrapers; the latest generation, HBM3, triples the bandwidth of its predecessor, feeding data to processors fast enough to keep them busy all the time. Third, the room of people with calculators became an office and then a whole campus or city. Technologies like NVLink and InfiniBand connect hundreds of thousands of GPUs into warehouse-size supercomputers that function as single cognitive entities. A few years ago this was impossible.

These gains all come together to deliver dramatically more compute. Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore’s Law would predict only about a 5x improvement over this period. We saw 50x. We’ve gone from two GPUs training AlexNet, the image recognition model that kicked off the modern boom in deep learning in 2012, to over 100,000 GPUs in today’s largest clusters, each one individually far more powerful than its predecessors.

Then there’s the revolution in software. Research from Epoch AI suggests that the compute required to reach a fixed performance level halves approximately every eight months, much faster than the traditional 18-to-24-month doubling of Moore’s Law. The costs of serving some recent models have collapsed by a factor of up to 900 on an annualized basis. AI is becoming radically cheaper to deploy.

The numbers for the near future are just as staggering. Consider that leading labs are growing capacity at nearly 4x annually. Since 2020, the compute used to train frontier models has grown 5x every year. Global AI-relevant compute is forecast to hit 100 million H100-equivalents by 2027, a tenfold increase in three years. Put all this together and we’re looking at something like another 1,000x in effective compute by the end of 2028. It’s plausible that by 2030 we’ll bring an additional 200 gigawatts of compute online every year—akin to the peak energy use of the UK, France, Germany, and Italy put together.

What does all this get us? I believe it will drive the transition from chatbots to nearly human-level agents—semiautonomous systems capable of writing code for days, carrying out weeks- and months-long projects, making calls, negotiating contracts, managing logistics. Forget basic assistants that answer questions. Think teams of AI workers that deliberate, collaborate, and execute. Right now we’re only in the foothills of this transition, and the implications stretch far beyond tech. Every industry built on cognitive work will be transformed.

The obvious constraint here is energy. A single refrigerator-size AI rack consumes 120 kilowatts, equivalent to 100 homes. But this hunger collides with another exponential: Solar costs have fallen by a factor of nearly 100 over 50 years; battery prices have dropped 97% over three decades. There is a pathway to clean scaling coming into view.

The capital is deployed. The engineering is delivering. The $100 billion clusters, the 10-gigawatt power draws, the warehouse-scale supercomputers … these are no longer science fiction. Ground is being broken for these projects now across the US and the world. As a result, we are heading toward true cognitive abundance. At Microsoft AI, this is the world our superintelligence lab is planning for and building.

Skeptics accustomed to a linear world will continue predicting diminishing returns. They will continue being surprised. The compute explosion is the technological story of our time, full stop. And it is still only just beginning.

Mustafa Suleyman is CEO of Microsoft AI.

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<![CDATA[Experts share how roluperidone provides a new opportunity in the schizophrenia treatment landscape.]]>
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Continuous attractor dynamics in spatial navigation: from population geometry to flexible computation

A central computational problem in spatial navigation is how spatial representations remain stable under noise and uncertainty, and update reliable estimations of continuous variables such as head-direction and position, which respectively rely on the head-direction system and the grid-cells system in the entorhinal cortex. The two systems demonstrate strong population-level dynamics, suggesting a potential framework to explain the critical problem of spatial representations. Currently, the framework involves continuous attractor networks and the neural field theories as an unified perspective, from which the population activity can be described as evolving of continuous variables on a low-dimensional attractor manifold, together with the selective instantiation of these dynamics across symmetry-related or context-dependent subspaces. From this viewpoint, a key question is how different sources of information, such as self-motion, sensory cues and environmental structure, interact with attractor dynamics to regulate the evolution and stability of population states. Specifically, external inputs can stabilize attractor states by anchoring them to landmarks; intrinsic network connectivity, symmetry, and multi-timescale dynamics determine whether an attractor is stable and whether it supports continuous motion; environmental boundaries and geometric constraints can systematically shape the local geometry of spatial activity patterns; direction- or context-dependent signals may selectively recruit neuronal subpopulations with specific tuning preferences; and cross-level organization of attractor dynamics, enabling a unified representational and control framework from individual decision-making to collective behavioral organization. Through the joint action of these mechanistic dimensions, continuous attractor representations are able to support the core computations required for navigation. More broadly, this perspective provides a theoretical foundation for understanding how continuous spatial representations are computed, read out, and flexibly manipulated to support planning and behavioral control.
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Prefrontal and hippocampal microstructural gray matter following cognitive training under moderate hypoxia in mood disorders: a randomized controlled trial

BackgroundCognitive impairment persists during partial or full remission in 50–70% of individuals with mood disorders and impacts daily functioning and clinical prognosis. Preclinical evidence suggests that extended exposure to moderate hypoxia, combined with motor-cognitive learning, may elevate neuroplasticity and improve cognition. In these individuals with remitted mood disorders, we found that cognitive training under repeated moderate normobaric hypoxia improved executive function, and here investigate neurobiological mechanisms.MethodsParticipants with major depressive disorder (MDD) or bipolar disorder (BD) in partial or full remission were randomized to 3 weeks of 3.5-h daily normobaric hypoxia (12% O2) combined with cognitive training five to 6 days per week or treatment-as-usual (TAU). Participants were assessed with cognitive tests and diffusion-weighted MRI at baseline and 1 month after treatment completion (week 8) as part of the ALTIBRAIN trial (ClinicalTrials.gov: NCT06121206). Prefrontal and hippocampal gray matter microstructure were modelled with Neurite Orientation Dispersion and Density Imaging (NODDI).ResultsFifty-seven participants (mean age 39 years, SD: 13, 70% female) with baseline MRI data were included. No significant effects of hypoxia-cognition training vs. TAU on neurite density index (NDI) or orientation dispersion index (ODI) were observed in either the prefrontal cortex or hippocampus (all p-FDR ≥ 0.832). No significant associations were observed between microstructural changes and changes in cognitive function in either region (all p-FDR ≥ 0.721). At baseline, microstructure in both regions was not associated with executive function or global cognition (all p > 0.40).ConclusionThe absence of detectable microstructural changes, despite selective improvements in executive function, indicates that NODDI-derived metrics did not capture structural correlates of the cognitive response to hypoxia-cognition training. Whether this reflects functional neural mechanisms, measurement insensitivity, or the timing of the single follow-up assessment remains to be determined. Future studies should incorporate multiple imaging time points to capture the dynamic trajectories of putative microstructural brain changes.