Citations

Background Large vessel occlusion (LVO) stroke is associated with a high risk of permanent disability. There are no reliable tools providing periprocedural information on clot characteristics, such as composition and length, that may impact first pass effect, which has been shown to improve functional outcomes. In the first study of a novel technology, we evaluated whether the Clotild Smart Guidewire System (Clotild), an impedance-based device, can provide in situ information on clot characteristics during a mechanical thrombectomy procedure. Methods This is the first-in-human, international, multicentric, single-arm trial involving LVO stroke patients. Of 45 enrolled subjects, 41 constituted the safety population; 39 was the per protocol population, of which 26 subjects with complete impedance data comprised the performance population. The latter was divided into development (11 subjects) and validation (15 subjects) populations for prediction model development and assessment. The safety endpoint assessed Clotild-related intracranial vessel perforation/dissection. Performance endpoints evaluated Clotild’s ability to perform classification of impedance measurements distinguishing substantial versus negligible local content in red blood cells (RBCs) and platelets, to detect the arterial wall and the distal end of the clot. Results The safety endpoint was met in all subjects. Performance analysis comparing the agreement between Clotild’s output and impedance experts’ readings showed an area under the receiver operating characteristic curve (AUC) of 0.97 (95% CI 0.93 to 0.99) for RBC, and 0.94 (95% CI 0.89 to 0.98) for platelet clot composition. Regarding detection, Clotild’s performance showed an AUC of 0.69 (95% CI 0.59 to 0.77) for the arterial wall, and 0.83 (95% CI 0.59 to 1.00) for the distal end of the clot. Conclusions Clotild was proven safe to acquire impedance data and to provide information on in situ clot characteristics in large vessel occlusions.

Abstract Spatial transcriptomics links gene expression to tissue architecture, providing a mechanistic view of cellular organization. Yet existing datasets cover few donors and miss the complexity of human disease. Experimental costs remain prohibitive, and large-scale profiling is impractically slow for population-level studies. Accurate computational methods are urgently needed. Predicting gene expression from standard histology, however, remains an open problem, as current approaches transfer poorly to unseen cohorts and diseases. Here, we present Phoenix, a (latent) flow matching generative model that infers pan-cancer spatially resolved single-cell gene expression with high accuracy. Phoenix analyzes treatment response in silico: Applied to 763 head and neck cancer patients, it identified three new spatial biomarkers that we validated across two cancers (breast cancer, n = 84; ovarian cancer, n = 157) and treatment regimens (platinum, trastuzumab). Phoenix generalizes beyond carcinomas: In a large sarcoma cohort (802 tissue microarray cores), it accurately predicted cell-type-specific signatures in held-out samples and captured chemotherapy-induced immune remodeling. Phoenix also extends across species: In a mouse model, it accurately predicted the expression of pancreatic cancer lineage markers and the mutant mKras^G12D allele in silico. In total, we evaluated Phoenix on over 10,000 patients. Our results establish virtual spatial transcriptomics as a scalable framework for studying tissue organization, therapeutic response, and disease mechanisms.

ABSTRACT The epidemiology of myelodysplastic syndromes/neoplasms (MDS) is challenging to define due to inconsistent reporting, complex diagnostic procedures, and evolving diagnostic criteria. Using the National MDS Natural History Study—a prospective cohort with centrally adjudicated histopathology and genetic variant review—we characterized the landscape of MDS across the United States and identified differences across demographics. Among 2115 participants, 64% (1346) had an MDS spectrum condition, including MDS (24%), MDS/myeloproliferative neoplasm (5%) and precursor conditions—clonal cytopenia of undetermined significance (22%) and idiopathic cytopenia/dysplasia of undetermined significance (13%). The median age was 74 years, and participants were predominantly male (66%), White (91%), and Non‐Hispanic (92%). Myeloid‐associated variants were detected in 68% of participants, most commonly in TET2, DNMT3A, ASXL1, SF3B1, and SRSF2 . Black, compared to White, participants were younger at diagnosis (69 vs. 74 years, p = 0.01), had equal or increased prevalence of higher‐risk MDS, lower hemoglobin, and higher peripheral blood blasts, yet were less likely to receive MDS‐directed therapy (14% vs. 42%, p = 0.008). Black and Hispanic participants had fewer detectable gene mutations than White participants. Females had lower variant allele frequencies and fewer RNA splicing gene mutations than males. After multivariable adjustment, TP53 mutations, MDS diagnosis, and higher‐risk disease were associated with worse progression‐free and overall survival; age was also associated with overall survival. Black race trended toward improved progression‐free survival. These findings highlight the need for enhanced understanding of MDS pathogenesis across patient groups and refined prognostic tools to improve personalized management of MDS spectrum conditions. Trial Registration: ClinicalTrials.gov identifier: NCT02775383

PURPOSE For patients with advanced biliary tract cancer (BTC), cancer-related time spent in health care systems, time toxicity, represents a critical but understudied burden affecting quality of life. This study aimed to quantify time toxicity in patients receiving first-line systemic therapy and evaluate whether the addition of durvalumab to gemcitabine plus cisplatin (GC) increases health care contact time. METHODS This international, multicenter, retrospective cohort study included patients with advanced BTC seen between January 2019 and April 2024 at Mayo Clinic Enterprise (US), and between January 2021 and April 2024 at Kyorin University Hospital (Japan), and Hospital Israelita Albert Einstein (Brazil). Patients received GC plus durvalumab (GCD) or GC. Time toxicity was recorded from treatment initiation until discontinuation or last follow-up during first-line systemic therapy. Multiple regression analysis identified factors associated with high time toxicity. RESULTS Among 193 patients (102 GCD, 91 GC; median age 65 years; 50% male; 49% Eastern Cooperative Oncology Group performance status [PS] 0), median time on treatment (TOT) was 156 days (95% CI, 122 to 190). The median proportion of total time toxicity was 14.4% (IQR, 10.9-20.6). Planned visits accounted for 11.9%, while unplanned visits were infrequent (1.8%). Time toxicity strongly correlated with TOT ( r = 0.824) and progression-free survival ( r = 0.827). Despite longer treatment in the GCD group than the GC group (212 v 134 days; P = .027), total time toxicity was similar (27 v 18 days; P = .19). Younger age and poor PS were independent factors of higher time toxicity. CONCLUSION Time toxicity represents a meaningful metric that allows for more comprehensive integration of the net benefit in patients with advanced BTC with a limited lifespan.

Abstract Patients at risk for progression from myeloid precursor states (idiopathic cytopenia of undetermined significance [ICUS] and clonal cytopenia of undetermined significance [CCUS]) to myelodysplastic syndrome (MDS) are not well defined. Epidemiologic risk factors, clonal changes for MDS development, and evolution from one MDS state to another were available in the MDS Natural History Study (NHS), a prospective cohort enrolled at diverse US sites. Extensive clinical data and biospecimens were collected at baseline and follow-up. A total of 1177 (56%) individuals with MDS or MDS-related precursor conditions (ICUS/idiopathic dysplasia of undetermined significance [IDUS], CCUS, and MDS/myeloproliferative neoplasm overlap) were enrolled and diagnosed in the MDS NHS. The median age was 74 years, with 89% aged ≥60 years, and most were male patients. Median follow-up time was 1.6 years and was similar among ICUS/IDUS, CCUS, and MDS. During follow-up period, 68% of participants terminated the study, 35% died, and 38% had disease progression. Using a refined clinical definition of progression, the greatest proportion of progression was observed among higher-risk-MDS (116 [73%]), followed by lower-risk-MDS (170 [52%]); fewer progression events were observed among participants with CCUS (65 [22%]) and ICUS/IDUS (46 [17%]). Predictors of progression are delineated, including female sex, higher quantity of mutations, the presence of TP53 mutation, and poor/very poor cytogenetic score. Based on these prospective data, guidelines for clinical management including monitoring and surveillance are outlined. The MDS NHS provides real-world data to illustrate how clinical and genotypic differences inform the classification, disease course, and approach to therapy, with informed monitoring guidelines for patients. This trial was registered at www.ClinicalTrials.gov as NCT02775383.

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Hodgkin lymphoma provide expert, multidisciplinary recommendations for the diagnosis and treatment of Hodgkin lymphoma. The panel convenes annually to update recommendations based on a review of recently published clinical trials. This selection from the NCCN Guidelines for Hodgkin Lymphoma focuses on the management of newly diagnosed and relapsed/refractory classic Hodgkin lymphoma in adults aged 18 to 60 years.

Abstract Background Measurement of cardiac troponin (cTn) using high-sensitivity assays is recommended for the diagnosis of myocardial infarction. We determined sex-specific and uniform 99th percentile upper reference limits (URLs) using the new Elecsys® Troponin T high-sensitivity Gen 6 assay in a global, healthy reference range cohort. Methods Lithium-heparin plasma and serum samples were prospectively collected from apparently healthy individuals aged ≥20 years across 34 global sites in the United States, Europe, China, and Japan. cTnT was measured using the Troponin T high-sensitivity Gen 6 assay on the Cobas® e 801 analyzer. Exclusion criteria were defined according to the 2022 IFCC guidance. Uniform and sex-specific 99th percentile URLs and nonparametric 95% CIs were determined in plasma and serum separately and combined. Results The final study population comprised 4147 participants (52.5% female) with a median (25th–75th percentiles) age of 48.0 (33.0–59.0) years; 45.8%, 47.9%, 5.2%, and 1.1% were White, Asian, Black, and other/unknown, respectively. For sample matrices combined (n = 8294), 81.0% and 99.2% of cTnT values were above the limit of detection in females and males, respectively. Sex-specific 99th percentile URLs (95% CI) were 18 (16–23) ng/L for females and 32 (28–35) ng/L for males; the uniform 99th percentile URL was 27 (24–31) ng/L. URLs were comparable in plasma and serum samples. Conclusions This study determined sex-specific and uniform 99th percentile URLs for the Troponin T high-sensitivity Gen 6 assay that were comparable irrespective of the matrix used in a large, global, healthy reference population.

ABSTRACT Artificial intelligence (AI), encompassing machine learning, deep learning and generative AI, is poised to redefine nephrology by enabling earlier detection, more precise risk stratification and workflow-integrated clinical decision support across the spectrum of kidney disease. This state-of-the-art review synthesizes emerging applications of AI in acute kidney injury (AKI), chronic kidney disease (CKD), dialysis and kidney transplantation, with attention to clinical integration, real-world validation, workflow implementation and translational challenges. In AKI, predictive models trained on high-frequency electronic health record data and intensive care unit telemetry have demonstrated strong performance in forecasting critical events, yet translation into routine clinical workflows remains limited. In CKD, machine learning tools support progression risk stratification and phenotype clustering, with the potential to inform individualized surveillance and therapy. AI-enabled dialysis management systems optimize ultrafiltration, anemia control and vascular access surveillance, while generative AI and large language models offer novel capabilities for clinical documentation, triage and patient education. In transplantation, AI applications span organ allocation, dynamic graft monitoring and digital pathology-assisted rejection classification, with validated tools such as the iBox system gaining regulatory recognition. Implementation challenges include data heterogeneity, bias, interpretability, regulatory uncertainty and workflow integration. Looking ahead, multimodal integration of imaging, pathology and multi-omics data may support biologically informed precision nephrology. Reinforcement learning, digital twins and ambient intelligence are emerging as adaptive decision-support paradigms rather than autonomous care systems. Regulatory frameworks are evolving to accommodate adaptive algorithms, underscoring the need for clinician engagement in model development, validation and deployment. As AI matures from pilot to practice, nephrologists who embrace and help shape these tools will lead the transition toward a more personalized, efficient and equitable future for kidney care.