BackgroundFear of childbirth is an important perinatal mental health concern, particularly among women of advanced maternal age. However, the specific interrelations between spousal support and fear of childbirth remain unclear.MethodsThis cross-sectional study recruited 279 pregnant women of advanced maternal age from a tertiary hospital in Henan, China, using convenience sampling. Spousal support and fear of childbirth were assessed using the Spouse Support Inventory and the Childbirth Attitude Questionnaire. A regularized partial-correlation network was estimated using EBICglasso, and central and bridge nodes were identified. Network stability was examined using bootstrap procedures.ResultsThe prevalence of any fear of childbirth, defined as a CAQ score ≥28 and including mild, moderate, and severe categories, was 86.4% (n = 241). Negative associations predominated between the spousal support and fear of childbirth communities. The strongest cross-community association was observed between “teaching you how to do things you do not know how to do” and “concern about fetal health.” The most central nodes were “participating in activities together to reduce your stress” and “providing you with helpful information,” whereas the strongest bridge nodes were “helping you understand why things did not go well” and “giving you encouragement.ConclusionSpecific supportive behaviors, especially informational and cognitive-appraisal support, occupied central positions in the network linking spousal support and fear of childbirth among pregnant women of advanced maternal age. Strengthening these forms of spousal support may inform the development of couple-based interventions to reduce childbirth fear and promote perinatal mental health.
Psychotherapy initiation is associated with discontinuation of psychotropic medications without dose escalation: a ten-year real-world cohort study (2014-2024)
BackgroundIncreasing psychotropic prescribing has raised concerns about long-term safety and regimen complexity in mental health care. Although psychotherapy is an established treatment, its role in medication optimization and psychotropic medication reduction in real-world practice across patient subgroups remains insufficiently characterized.ObjectiveTo evaluate whether initiation of psychotherapy is associated with short-term changes in psychotropic medication exposure and regimen complexity, and to examine differences by sex, age, and diagnostic category. Methods: A retrospective cohort study was conducted using anonymized pharmacy dispensing data from the Mental Health Service of Hospital Marina Baixa (Alicante, Spain) between 2014 and 2024. Patients with at least one active prescription for a benzodiazepine or antidepressant within 90 days before psychotherapy initiation were included. Psychotropic exposure was compared in symmetric 90-day pre- and post-therapy windows using number of active agents, total Defined Daily Doses, and prevalence of benzodiazepine and antidepressant use, with stratified analyses by sex, age group, and diagnosis.ResultsThe cohort comprised 86,502 patients and 20.76 million dispensations. The median number of psychotropic medications decreased from 5 to 2 (p < 0.001), while total dose remained stable (median Defined Daily Dose ≈ 21.7; p = 0.999). Benzodiazepine use declined from 87.6% to 67.5% and antidepressant use from 81.8% to 68.8% (both p < 0.001). Men were more likely than women to discontinue benzodiazepines (odds ratio 1.27, 95% confidence interval 1.13–1.43), and simplification increased with age (median reduction −1 in <18 years to −4 in ≥65 years). The largest benzodiazepine reductions occurred in depressive, personality, and episodic mood disorders (−23 to −27 percentage points).ConclusionsIn routine public mental health care, psychotherapy initiation is associated with substantial simplification of psychotropic treatment regimens without increasing overall medication dose, supporting a potential role in facilitating rational medication simplification.
Trump administration targets disability integration mandate in DOJ memo
The Trump administration released a memo last week that seeks to upend landmark disability laws and court rulings that prioritize people with disabilities receiving care while living in their community instead of at institutions like nursing homes.
The memo — written by the Department of Justice Office of Legal Counsel in response to an inquiry from White House officials — breaks with decades of disability law and practice and argues that the “integration mandate” is not actually a mandate, especially for people with “severe mental illness or disabilities.”
STAT+: Closely watched Pfizer lung cancer drug falls short in clinical trial
Pfizer said Monday that an experimental drug it hoped could replace a widely used chemotherapy in one of the most common forms of lung cancer fell short in a clinical trial.
Expectations had been high that the drug, sigvotatug vedotin, could replace docetaxel, a chemotherapy initially approved in 1996. Last year, Pfizer’s CEO, Albert Bourla, said on an earnings call the drug “could be a driver of growth later this decade.” In a note to investors in May, Leerink analyst David Risinger called the upcoming data readout a “major oncology catalyst” and said he had spoken to a doctor who was “optimistic” about its potential.
Pfizer acquired sigvotatug vedotin when it bought the biotechnology firm Seagen for $43 billion in 2023.
AI Discovers Potential Antimicrobial “Prionin” Peptides
Prions are best known for their role in rare, fatal neurodegenerative diseases. But a new study by researchers at the University of Pennsylvania suggests that proteins in this family may also conceal molecular fragments that can kill bacteria, including drug-resistant strains.
The Penn scientists used a deep learning platform called APEX 1.1 to scan millions of short protein fragments derived from nearly 3,000 prion and prion-like proteins. The search identified more than a thousand candidate antimicrobial peptides, which they called “prionins.” In tests 59 synthesized prionins inhibited bacterial pathogens, and two reduced Acinetobacter baumannii burden in mice.
The discovery is unexpected because prions are usually discussed in the context of misfolding, aggregation, and brain disease—not immunity or antibiotic discovery. The new findings suggest that useful biological activities may be hidden inside proteins whose known roles have little to do with infection, and that artificial intelligence can help reveal them.
“Prions have long been seen almost entirely through the lens of disease,” said César de la Fuente, PhD, associate professor and director of the Machine Biology Group at the University of Pennsylvania. “Our work shows that when AI looks across biology at scale, even proteins with a dark reputation can contain useful molecular instructions. In this case, those instructions point to possible new antibiotics.”
De la Fuente is senior and corresponding author of the researchers’ published paper in Nature Microbiology, titled “Deep learning reveals antimicrobial peptides within prions.”
Antibiotic resistance is among the most urgent challenges in medicine, and many existing antibiotics were discovered by searching traditional natural sources. The new study takes a different route: instead of asking where antibiotics usually come from, it asks whether biology has hidden antimicrobial molecules in places scientists would not normally look.
Certain amyloid-associated protein sequences may participate in host defense, the authors wrote. “Several amyloid-associated proteins, including amyloid-β and the cellular prion protein, have been reported to display antimicrobial or host-protective activities, raising the possibility that aggregation-prone proteins may encode cryptic antimicrobial fragments within their primary sequence.” But until now, scientists had not systematically searched prion and prion-like proteins at scale to ask whether they broadly encode hidden antimicrobial peptides.
“Whether such encrypted peptides are broadly embedded across prion and prion-like proteins has not been systematically examined,” the researchers continued. The Penn team took on that task, using AI to move from scattered observations to a global search across millions of possible protein fragments. They mined prion-related proteins with APEX1.1, a deep learning platform for antimicrobial peptide (AMP) discovery. “… using deep learning, we screened 19.3 million fragments from 2,897 curated prion-related proteins and identified 1,179 candidate antimicrobial peptides, which we term prionins,” they stated.
To test the predictions, the researchers synthesized 75 prionins and evaluated them against a panel of clinically relevant bacterial pathogens, including multidrug-resistant strains. Fifty-nine inhibited at least one pathogen, and 42 showed potent activity at concentrations of 16 micromolar or lower against at least one pathogen.
The team then examined how the molecules worked. Many active prionins damaged bacterial membranes, a common mechanism used by antimicrobial peptides. Importantly, several candidates also showed early signs of selectivity: hemolysis was rare, and 16 active peptides showed neither measurable hemolysis nor cytotoxicity at the highest concentrations tested.
Two of the strongest candidates were tested in a mouse skin-infection model caused by Acinetobacter baumannii, a difficult-to-treat pathogen. A single topical dose of each peptide significantly lowered the bacterial burden, with effects comparable to the antibiotic polymyxin B in the model tested. The researchers observed no treatment-associated weight loss. In summary, they wrote, “What makes this exciting is that the predictions held up experimentally,” said Marcelo D T Torres, PhD, co-first author of the study. “We went from millions of hidden protein fragments to synthesized molecules that killed bacteria in the lab, and then to candidates that worked in an animal infection model. That is the difference between an AI screen and a true discovery platform.”
The findings build on the de la Fuente lab’s broader effort to mine the biological world for “encrypted peptides”—short, hidden sequences within larger proteins that can have biological functions when isolated. Previous work from the group has searched human proteins, extinct organisms, archaea, microbiomes, and venoms. The prion study expands that concept into one of biology’s most unexpected protein classes.
The study also raises a provocative possibility at the intersection of neurodegeneration and innate immunity. It does not establish that these peptides are naturally released during infection or function physiologically in host defense, they stated. But it suggests that prion and prion-like proteins may contain cryptic antimicrobial sequences, opening a new way to think about prion biology and its possible links to immunity. “… it establishes prion-related proteins as a productive source space for antibiotic discovery and provides a framework for testing whether cryptic peptides contribute to defense in specific biological contexts.”
The researchers emphasize that this is an early discovery, not a new treatment ready for patients. The study does not change the established role of misfolded prions in devastating neurodegenerative disease. Instead, it suggests prion and prion-like proteins as a rich and previously overlooked source space for antibiotic discovery. “Our findings identify prion and prion-like proteins as an unexpectedly rich reservoir of encrypted AMPs,” the authors concluded. “This expands a growing view that antimicrobial activity can be hidden within proteins not canonically annotated as immune effectors and extends that concept to prion biology … These results connect prion-related sequence space to antimicrobial function and highlight unconventional protein classes as sources of antibiotic leads.”
“For a long time, drug discovery has been limited not only by what we can test, but by where we choose to look,” de la Fuente said. “AI is changing that. It gives us a way to search the hidden layers of biology and ask whether molecules associated with one story—in this case, disease—may also carry another story with therapeutic potential.”
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Experiences and Acceptance of Community-Based Mobile Health Services Among People in Underserved Rural Areas of Korea: Mixed Methods Study
Background: Community-based mobile health (mHealth) services are increasingly used to support chronic disease management in underserved rural populations facing workforce shortages, geographic isolation, and rapid aging. South Korea entered a super-aged society in December 2024, intensifying pressures in rural regions where multiple mHealth programs are embedded within primary care and public health systems. However, evidence on sustained use in real-world settings remains limited. Objective: This study aimed to explore user experiences and acceptance of community-based mHealth services in an underserved rural area of South Korea and identify facilitators and barriers to sustained engagement, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Methods: A convergent mixed methods design was used, with qualitative and quantitative data collected in parallel, analyzed separately, and integrated at the interpretation stage. Overall, 24 participants with ≥6 months of experience using 1 of 4 publicly funded mHealth services in Pyeongchang County, Gangwon State, were purposively recruited. Semistructured interviews guided by the UTAUT2 were analyzed using directed content analysis, combining deductive and inductive coding. Structured questionnaires assessing usability and behavioral intention were analyzed using descriptive statistics. Findings were integrated through joint interpretation. Results: Participants had a mean age of 71.3 (SD 9.2) years, and 70.8% (17/24) were female; hypertension (18/24, 75%) and hyperlipidemia (15/24, 58.3%) were the most common. Perceived difficulty was low (mean 2.54, SD 2.06, on a 0‐10 scale), intention for continued use was high (23/24, 95.8%), and recommendation intention was unanimous (24/24, 100%). Willingness to pay was reported by 79.2% (19/24), most commonly KRW 1000‐5000 (US $1-3) per month. Qualitative findings identified performance expectancy, social influence, facilitating conditions, and habit as the most salient determinants of sustained use. Real-time monitoring enhanced health awareness, motivated dietary modification, and increased physical activity. Public health center nurses served as human-in-the-loop facilitators, providing continuous training, troubleshooting, and emotional support, while family and peers reinforced engagement. Habit formation emerged as a central mechanism, with 91.7% (22/24) integrating mHealth use into routines anchored to waking, exercise, and bedtime. Effort expectancy barriers among older participants were mitigated through nurse-led training, and hedonic motivation was driven by intrinsic satisfaction and peer interaction. Integrated analysis showed convergence for ease of use and behavioral intention, and partial divergence for willingness to pay. Conclusions: Community-based mHealth services were successfully integrated into daily life and supported chronic disease self-management among older adults in an underserved rural setting. Sustained engagement was driven by perceived health benefits, continuous human support, and habit formation rather than technology features alone, underscoring the importance of relationship-centered, human-in-the-loop implementation models. Strengthening intuitive design, hands-on onboarding, multidisciplinary primary care teams, and stable financing will be essential for equitable digital health adoption in rural and aging communities.
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Functional Outcome Prediction in Young Adults With Mental Health Symptoms Using Machine Learning and Large Language Models: Longitudinal Observational Study
Background: Functional impairments associated with mental health conditions are on the rise. Predicting functional outcomes may improve the targeting of preventive interventions. While prognostic models have primarily focused on psychosis, early recognition services require a transdiagnostic approach. Objective: This study aimed to predict global functioning within a 2-year follow-up using baseline clinical and structural magnetic resonance imaging (MRI) data in a population-based sample of young, help-seeking individuals presenting with affective and anxiety symptoms as well as attention-deficit hyperactivity disorder. Methods: We classified 357 help-seeking individuals aged 18‐35 years recruited from 9 sites as “impaired” (Global Assessment of Functioning [GAF] ≤60; n=228) or “nonimpaired” (GAF>60; n=129) at year 1 and/or year 2 follow-up. GAF classification group status at follow-up was predicted using linear support vector machine (SVM), decision tree, and large language model (LLM) Llama-3 using clinical assessments and/or structural MRI. Leave-one-site-out (SVM) or external sample (LLM) was used for validation. Results: SVM achieved balanced accuracy of 69.2% using clinical features only. Items related to baseline occupational functioning, interpersonal relationships, cognitive functioning, psychotic and affective symptoms, as well as the presence of anxiety disorder, were most predictive. The decision tree further reduced the feature set to 5 predictive items, achieving balanced accuracy of 76.6%. Although amygdala and hippocampal subregions achieved balanced accuracy of 57.1%, structural MRI did not improve the overall prediction. Llama-3 performed comparably well to SVM (balanced accuracy of 72.6%). Conclusions: Machine learning demonstrated good performance in predicting global functioning. Interestingly, the out-of-the-box LLM performed comparably well without being trained or fine-tuned, highlighting the potential of leveraging free-text data for mental health prognosis.
Gene Therapy Restores Brain Function and Behavior in Fragile X Syndrome
A University of California, Riverside-led research team has developed a gene therapy that restored production of a missing brain protein, corrected abnormalities in brain circuitry, and improved behavior in a mouse model of Fragile X syndrome (FXS). The study, published in the journal Molecular Therapy Nucleic Acids, tested an adeno-associated virus (AAV)-based therapy carrying a normal human version of the FMR1 gene to produce the Fragile X messenger ribonucleoprotein (FMRP) and found that early treatment normalized several measures of brain activity while improving social behavior, exploratory behavior, and cognitive flexibility.
“In a typical brain, FMRP acts like a brake or a volume control,” said senior author Iryna Ethell, PhD, a professor of biomedical sciences at the UC Riverside School of Medicine. “Without it, neural circuits become overactive and less efficient, which contributes to many of the developmental and behavioral challenges associated with FXS.”
FXS is the most common single-gene cause of autism spectrum disorder. According to the researchers, the disorder typically manifests from expansion of CGG repeats in the 5′ untranslated region of FMR1. The mutation causes methylation and silencing of the gene, leading to a major reduction or complete loss of FMRP, an RNA-binding protein that regulates numerous messenger RNAs involved in synapse formation, maturation, and function. Loss of the protein can lead to abnormal synaptic activity and increased cortical hyperexcitability.
FXS can produce sensory hypersensitivity, seizures, anxiety, intellectual disability, developmental delays, repetitive behaviors, and social communication difficulty. Current treatments for this syndrome don’t seek to cure it, rather they are aimed at managing the associated symptoms of anxiety, hyperactivity, irritability, aggression, depression, and seizures.
The therapy developed by the research team was designed to replace missing FMRP rather than repair the original mutation. To do this, the researchers used an AAV9 viral vector to deliver human FMR1 isoform 7, one of the most abundant forms of the protein found in the brain. The therapy was tested in newborn mice lacking FMRP via intracerebroventricular injections at either a low or high doses.
The work built on earlier research that explored the potential of AAV-mediated restoration of FMRP in rodent models. These prior studies used a range of viral serotypes, promoters, delivery routes, and FMRP isoforms and showed they could partially or completely correct specific biochemical, physiological, and behavioral abnormalities. The researchers noted that studies involving mouse and rat FMRP homologs had shown that restoring the protein could improve a range of Fragile X-related deficits.
The current study showed that high-dose treatment produced the strongest positive effects in the mouse models. Electroencephalography showed normalization of baseline gamma power, improvements in responses to sound, reduced background neural activity, and improved habituation to repeated auditory stimuli. The therapy also restored abnormal patterns of brain-wave coupling that have been associated with Fragile X-related dysfunction.
Behavioral testing showed that these improvements persisted into adulthood. Mice receiving the higher dose displayed normalized exploratory behavior, improved social preference, and better performance in probabilistic reversal learning, a measure of cognitive flexibility that requires adapting when previously rewarded behaviors stop producing rewards.
“Fragile X mice tend to persist with an old solution even after the rules change,” Ethell said. “After treatment, they became much better at adapting, performing similarly to mice with normal FMR1 function.”
The researchers noted that their work showed the importance of delivering at therapy for FXS early in its development. They said that widespread distribution of the potential new gene therapy throughout the brain was necessary to achieve a therapeutic benefit. There was a clear relationship between the proportion of neurons expressing the therapeutic gene and the degree of functional recovery, which indicated that restoring FMRP in a sufficient number of cortical cells is critical for correcting any behavioral deficits.
While a promising step, the investigators said that the work was a preclinical study and that future research will now focus on developing delivery methods that can of have broad distribution across the human brain. The team also believes their approach could have broader applications.
“Beyond FXS, the findings may provide a roadmap for treating other genetic neurodevelopmental disorders caused by the loss of a single critical protein,” Ethell said. “Our study shows it may be possible to restore function across complex brain networks by replacing a missing gene. That gives us reason to be optimistic about the future of genetic medicine.”
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Sleep Habits Can Influence Effects of Alzheimer’s Disease Risk Genes
Research led by Edith Cowan University in Australia suggests the impact of genetic mutations that impact Alzheimer’s disease risk are influenced by a person’s sleep habits.
As reported in the journal Alzheimer’s & Dementia, the researchers confirmed links with aquaporin-4 gene (AQP4) variants and changes in brain volume, atrophy and cognition linked to Alzheimer’s disease.
The investigators also showed how long people sleep, how long it takes them to fall asleep, how often their sleep is disturbed, and how good or poor their sleep is overall contributed to the effect of these mutations.
“Our study shows that individuals carrying certain AQP4 variants showed faster grey matter loss when they reported shorter sleep,” said study co-author Ayeisha Milligan Armstrong, PhD, a researcher at Edith Cowan University, in a press statement.
“It’s not just which genes you carry—it’s how those genes interact with the world around you. The same variant can look protective or detrimental depending on how someone is sleeping. That’s important, because sleep is one of the few modifiable factors people can actually act on.”
Researchers now think the brain gets rid of amyloid‑beta using a kind of plumbing system that washes waste away along the outside of blood vessels. In this system, fluid moves through the spaces around blood vessels, helped by tiny water channels called aquaporin‑4, encoded by AQP4, which sit on the parts of astrocyte cells that wrap tightly around those vessels.
“Given that AQP4 has been identified as an important mediator of brain amyloid beta clearance, variation within the AQP4 gene has been investigated in relation to neurodegenerative diseases and their associated phenotypes,” write the authors.
“A bi-directional relationship has been observed between suboptimal sleep and increasing brain amyloid beta accumulation…Importantly, a previous study utilizing data from the Australian Imaging, Biomarker and Lifestyle cohort reported that the relationship between sleep and cross-sectional brain amyloid beta burden was moderated by genetic variants in AQP4.”
To investigate this link further, the researchers studied 351 cognitively normal people already showing ongoing build‑up of brain amyloid‑beta on positron emission tomography (PET) imaging. They genotyped the group for 13 mutations in the AQP4 gene and also assessed sleep duration and quality, brain volume, amyloid burden and cognition scores.
Several AQP4 variants interacted with sleep measures to predict gray‑matter atrophy, brain ventricular volume, white‑matter volume, and cognitive decline. For example, people carrying certain variants who also had shorter sleep duration were more likely to have faster grey‑matter loss, and other variants magnified the impact of poorer global sleep quality on ventricular enlargement in the brain.
One variant showed a direct association with better global cognitive performance and two other variants seemed to be linked to less cognitive decline as sleep disturbances increased.
“We’ve known for a while that poor sleep and Alzheimer’s risk are linked,” said first author Tenielle Porter, PhD, also a researcher at Edith Cowan University.
“What this shows is that rather than assuming everyone at risk follows the same pathway, a more targeted and personalized approach to Alzheimer’s prevention may be needed. But we’re not at the point of recommending genetic testing; our findings need replication in larger and more diverse cohorts.”
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Magnetic Algae Microrobots Boost Chemotherapy Penetration in Bladder Tumors
Researchers from the University of Edinburgh and Xiamen University have developed microscopic algae-based robots capable of delivering chemotherapy directly into bladder tumors, significantly improving drug penetration and therapeutic efficacy in preclinical models.
The study, published in Nature Nanotechnology, describes a machine-guided drug delivery platform that combines biodegradable microalgae, magnetic control, real-time ultrasound imaging, and artificial intelligence-assisted navigation. In mouse models of bladder cancer, the system achieved more than a tenfold increase in drug penetration and reduced tumor burden to less than 3% of that observed with conventional chemotherapy delivery.
“Our microrobots are engineered from tablet-like microalgae, can be remotely guided to the tumor using real-time imaging feedback, and release drugs exactly where they are needed to drive rapid tissue penetration in a minimally invasive way,” said study co-lead Qi Zhou, PhD, of the University of Edinburgh.
Addressing a major challenge in bladder cancer therapy
Bladder cancer is one of the most common malignancies worldwide, with approximately 75% of cases diagnosed as non-muscle-invasive disease. Standard treatment typically involves surgical removal of visible tumors followed by intravesical chemotherapy, in which drugs are delivered directly into the bladder through a catheter.
While this approach limits systemic toxicity, its effectiveness is often constrained by poor penetration of drugs through the bladder’s protective barriers and into tumor tissue. Much of the chemotherapy remains near the surface, requiring prolonged exposure times and higher drug doses to achieve therapeutic benefit.
To overcome these limitations, the research team developed what they call a “drug-loaded magnetic Coscinodiscus granii” (DMCG) microrobot. The platform uses naturally occurring diatom algae, whose porous silica shells provide an ideal structure for carrying therapeutic cargo. The algae are coated with magnetic nanoparticles, loaded with the chemotherapy drug doxorubicin, and sealed with a protective polymer layer that enables controlled drug release.
Magnetic navigation and intelligent control
Unlike conventional drug carriers that rely on passive diffusion, the algae microrobots can actively move through the bladder under the influence of externally applied magnetic fields.
Researchers developed multiple modes of movement, including rolling, tumbling, spinning, and swirling. Rolling modes allow the robots to travel efficiently through the bladder while minimizing premature drug leakage. Once they reach a tumor, the robots switch to rotational modes that generate localized fluid flows around the porous algae structure, accelerating drug release and enhancing penetration into surrounding tissue.
The system incorporates real-time ultrasound imaging and deep learning-based tracking algorithms that identify both the tumor and the microrobot swarm. Using this feedback, robotic magnetic controllers can autonomously guide the swarm to target regions and trigger localized drug delivery.
The researchers liken the collective behavior of the microrobots to schools of fish or flocks of birds moving in coordinated swarms through complex environments.
Enhanced drug penetration
A key innovation of the platform is its ability to generate convective fluid flow around the tumor.
The rotating microrobots create microscopic currents that transport drug molecules more efficiently than diffusion alone. Laboratory experiments demonstrated that this mechanism substantially increased release of doxorubicin from the algae carriers and improved penetration through both hydrogel barriers and three-dimensional tumor spheroids.
In tumor spheroid models, the rotating microrobots increased drug penetration depth by approximately 150 micrometers and boosted overall fluorescence intensity, a measure of drug accumulation, by nearly 370% compared with non-actuated controls.
The approach also allowed researchers to separate transport and release functions. Swarms could travel rapidly in locomotion mode before switching to localized swirling behavior that increased drug release by more than threefold compared with transport mode alone.
Strong anti-tumor effects in mice
The team then evaluated the technology in an orthotopic mouse model of bladder cancer.
Using ultrasound guidance, the researchers navigated the microrobot swarms directly to bladder tumors, where they generated localized flow fields and released chemotherapy. Histological analysis revealed that tumor-specific accumulation of doxorubicin increased dramatically compared with free drug administration. Mean fluorescence intensity within tumors increased by more than 1,000%, while the tumor-to-normal tissue ratio rose from 0.56 to 3.6.
The researchers subsequently conducted a one-week treatment study consisting of four intravesical chemotherapy sessions delivered on alternating days.
The results were striking. Bioluminescence imaging showed that tumor burden in mice receiving microrobot-assisted therapy fell to just 2.36% of that observed in animals treated with free doxorubicin and 0.59% of that seen in untreated controls. The authors estimate this corresponds to more than a 40-fold improvement in therapeutic efficacy.
According to the researchers, the treatment did not produce detectable systemic toxicity. Body weight remained stable throughout the study, and analyses of major organs and blood chemistry revealed no significant adverse effects. Tumors treated with the microrobots also exhibited increased apoptosis and reduced cellular proliferation compared with controls.
Toward minimally invasive cancer therapy
The investigators believe the platform could eventually support more effective and less invasive treatment strategies for bladder cancer.
Current intravesical chemotherapy often requires patients to retain therapeutic agents in the bladder for extended periods. In contrast, the algae microrobot system achieved its therapeutic effects after approximately 30 minutes of active treatment while maintaining bladder tissue integrity and avoiding mechanical damage to the urothelium.
The authors suggest the technology may be particularly valuable for patients who are poor candidates for surgery or as an adjunctive therapy following tumor resection to reduce recurrence risk.
Future work will focus on refining the automated imaging-feedback system, studying long-term outcomes and pharmacokinetics, and evaluating the platform in larger animal models before potential clinical translation. Researchers also envision adapting the technology for drug delivery in other body cavities, including abdominal and gynecological applications.
“This study highlights a non-invasive approach to overcoming the biological barriers that limit drug penetration in bladder tumors,” said Professor Xiaohui Yan, PhD, of Xiamen University. “We are now discussing translational follow-up studies with hospitals, with the long-term aim of clinical trials after further preclinical validation and regulatory review.”
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