Citations

Abstract The anticipated clinical demand for carbon ion radiotherapy (CIRT) in the United States is expected to include a substantial proportion of clinically complex disease sites that require accurate, highly conformal, and adaptive treatment. These clinical needs, together with the unique physical and biological characteristics of carbon ion beams, form the foundation of the design philosophy for the Mayo Clinic Florida (MCF) CIRT program. The program remains under active development, with clinical deployment planned for 2028. Building upon the clinical rationale and anticipated U.S. patient population discussed in Part I of this two‐part review, this Vision Article describes how MCF intends to develop an integrated CIRT treatment system. The program is guided by two overarching principles: fast, accurate, and safe beam delivery and accurate treatment planning and treatment delivery, supported by an integrated patient‐specific quality assurance framework. Key elements include high‐dose‐rate dose‐driven continuous scanning with advanced beam monitoring and irradiation control, delivery‐aware treatment planning, the Monte Carlo‐based BioPhys computational platform integrating physical and biological dose modeling, a hierarchical adaptive workflow, and patient‐specific quality assurance spanning pretreatment, on‐treatment, and post‐treatment verification. This framework provides a technical roadmap for the integrated clinical implementation of CIRT at MCF.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited disorder leading to kidney failure. Tolvaptan, the only FDA-approved therapy, modestly slows disease progression but is limited by significant adverse effects, highlighting the need for more effective and better-tolerated treatments. Ciliary polycystins are central to ADPKD pathogenesis. We previously demonstrated that a cilia base-localized pool of CDK6 restricts ciliary polycystin abundance by suppressing axonemal polyglutamylation. However, whether CDK6 inhibition can restore polycystin function and suppress ADPKD progression in vivo remains unknown. Methods: Abemaciclib was tested in in vitro 3D cyst assays and ex vivo metanephric organ cultures. In vivo efficacy of early- and late-stage intervention were assessed in F1(129/B6) Pkd1 R3277C/R3277C mice receiving daily abemaciclib. Tolvaptan served as a therapeutic control. Cyst burden, fibrosis in the kidneys, kidney function, and primary cilia of renal epithelial cells were analyzed at study endpoints. Results: CDK6 was prominently upregulated in cyst-lining cells in human and murine PKD kidneys. At doses substantially lower than those used in oncology, abemaciclib robustly suppressed cyst growth in vitro and ex vivo . In vivo , long-term early treatment arrested cyst progression more effectively than tolvaptan without detectable toxicity. Late intervention largely halted cyst growth in females and reversed cyst progression in males. Mechanistically, abemaciclib restored axonemal polyglutamylation and increased ciliary polycystin levels. Conclusions: Abemaciclib has greater therapeutic efficacy and tolerability than tolvaptan in this preclinical model, largely driven by CDK6 inhibition–mediated restoration of axonemal polyglutamylation and increased ciliary polycystin abundance.

Abstract Global incidence and mortality rates for endometrial and cervical cancers have been steadily rising, while these rates for ovarian cancer have decreased. Although these cancers represent unique diseases and challenges, overlapping biology and responses to targeted and immunotherapies create opportunities to advance research through shared knowledge and resources. In 2025, investigators funded by National Cancer Institute (NCI) Specialized Programs of Research Excellence (SPORE) and other NCI-supported grants convened a hybrid meeting to identify challenges and knowledge gaps in translational research, highlight advances, define shareable resources and technologies, and foster collaboration among the Gynecologic Cancer SPORE sites, patient advocates, and NCI staff. Special sessions were arranged for presentations by Patient Advocates, Early-stage Investigators, Investigators Transitioning to Gynecologic Cancer Research. Meeting-wide workshops on Sharing Resources and Reducing Time to Activation of Clinical Trials and networking events were included. Collectively, the presentations converged on three overarching themes: (i) interception and early disease biology, (ii) development of innovative targeted and immune-based therapies, (iii) critical role of shared resources and collaborative infrastructure in accelerating translational progress. The unifying theme across disease sites was molecular advances to improve early detection/prevention and to drive therapeutic advancements. Novel findings discussed included: candidate precursor cells and mechanisms of high grade serous ovarian cancer development; therapeutic vaccines and their delivery methods for cervical dysplasia; identification of immune-targetable ovarian cancer antigens; various novel molecularly targeted drugs and immune-based agents for endometrial and ovarian cancers; and approaches to bypass drug resistance. Workshops delineated actionable strategies to further advance gynecologic cancer translational research.

Abstract Purpose Validate the semiquantitative (SQ)-MRI score for chronic pancreatitis (CP) diagnosis in an independent, multicenter cohort. Methods We analyzed MRIs performed at baseline in 337 participants with acute (AP) or recurrent AP (RAP) and 67 with non-calcific chronic pancreatitis (NCCP) enrolled in the PROCEED study from 6/2017 to 3/2023. MRIs were performed at multiple clinical centers using site-specific clinical protocols and a variety of MRI vendors to evaluate the SQ-MRI score in a real-world setting. The SQ-MRI score was calculated using the T1 signal intensity ratio of the pancreas to the spleen (T1 score), arteriovenous enhancement ratio (AVR), and pancreatic body diameter (PBD). We adjusted for confounding by participant characteristics via propensity score weighting. Results Median AVR was higher in AP/RAP (1.18, IQR: 0.97–1.44) than in the NCCP group (1.03, IQR: 0.78–1.22, p = 0.007). Pancreatic body diameters were larger in AP/RAP than in the NCCP group − 7–14 mm (10% vs 25%), 14–20 mm (40% vs 54%), and > 20 mm (50% vs 21%), respectively ( p < 0.01). Median T1 score was higher in AP/RAP (1.18, IQR: 0.96–1.40) than in the NCCP group (1.04, IQR: 0.86–1.32, p = 0.10). Median SQ-MRI score in the AP/RAP group was significantly lower, 1.91 (IQR: 0.97, 2.95), than in the NCCP group, 2.74 (IQR: 2.04–4.03) ( p < 0.001). The propensity score-adjusted AUC for SQ-MRI to distinguish AP/RAP from NCCP was 0.64, which was higher than for individual parameters. Conclusion We have validated the SQ-MRI score in a multicenter cohort using an independent, real-world MRI dataset.

Craniosynostosis is a congenital disorder characterized by premature fusion of cranial sutures. Lambdoid synostosis is a rare and severe form of the disease, but its developmental etiology remains poorly understood. Although ectopic cartilage has been associated with lambdoid suture fusion, whether abnormal chondrocyte development is causative has not been directly tested. Here, we investigated the role of platelet-derived growth factor receptor alpha (PDGFRα) signaling in chondrocyte development and lambdoid suture morphogenesis. Mesoderm-specific expression of an autoactivated Pdgfra allele (Pdgfra+/K) caused premature fusion of the lambdoid and occipitointerparietal sutures, preceded by excessive cartilage expansion during embryogenesis. Spatial transcriptomic analysis identified a PDGFRα-dependent transcriptional program characterized by increased Col2a1, Sox9, and Sfrp2 expression, indicating maintenance of chondrocytes in a proliferative progenitor state. Consistently, PDGFRα-activated chondrocytes exhibited increased proliferation. Moreover, expression of Pdgfra+/K in the Col2a1Cre lineage selectively induced occipitointerparietal suture fusion. Together, these findings demonstrate that PDGFRα signaling promotes proliferative cartilage and impairs chondrocyte maturation, identifying dysregulated chondrogenesis as a developmental mechanism underlying lambdoid craniosynostosis.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

ABSTRACT Background Implementation of remote robotic‐assisted surgery (rRAS) has the potential to significantly improve access to high‐quality surgical care for patients worldwide. Advances in robotic systems and telecommunications now enable highly reliable, low latency, and cybersecure connectivity. These technological developments, together with the broader adoption of telemedicine, have driven an increasing interest in the implementation of rRAS. Methods The Clinical Robotic Surgery Association (CRSA) convened a conference to develop international clinical recommendations for rRAS using the Danish Model of Consensus. The society identified five key issue categories and invited a panel of experts to address specific questions for each. Panel members and a jury of 10 unbiased, non‐expert in rRAS professionals participated in a day‐long conference on November 19, 2025 (Los Angeles, USA). During the conference, participants presented, discussed, and reached consensus on specific guidelines relevant to each category. Immediately after the conference, the jury reviewed and approved the recommended guidelines. Results Multiple recommendations were approved for each of the following categories: Operating Models, Roles and Responsibilities (2 recommendations), Clinical Pathway (2 recommendations), Emergency Management (8 recommendations), Technical Considerations (5 recommendations), and Medico‐legal and Ethical Considerations (4 recommendations). Conclusions There was consensus that with recent advances in telecommunication and robotic technologies, rRAS can be offered safely and effectively if the identified safeguards are realized. Adoption of rRAS is expected to continue advancing rapidly.

Abstract How glial cells sense and regulate neuronal hypoactivity remains poorly understood. Using in vivo imaging in anesthetized, sleeping, and behaving mice, we identified a glial purinergic signaling mechanism that converts neuronal hypoactivity into local circuit regulation. Cortical hypoactivity increased spatially confined ATP release through astrocytic pannexin-1 hemichannels, which were structurally enriched near perisomatic parvalbumin-positive boutons. Local ATP release directed microglial process movement and stabilized bulbous endings through microglial P2Y12 signaling, accompanied by localized Ca²⁺ activity in microglial processes. Disruption of astrocytic pannexin-1 or microglial P2Y12 impaired bulbous-ending stabilization and abolished rebound increases in neuronal activity during emergence from anesthesia. Similar ATP release and bulbous-ending formation were observed during chemogenetic neuronal silencing or natural sleep in freely behaving mice. These findings identify astrocyte-to-microglia purinergic signaling as a mechanism that converts neuronal hypoactivity into synapse-selective microglial regulation of circuit activity.