Citations

Abstract Background Glioblastoma (GBM) represents the most common and aggressive central nervous system malignancy where overall survival is a dismal 14.6 months despite an aggressive standard of care treatment regimen that includes maximally safe surgical resection, radiotherapy, and temozolomide. It has recently been established that GBM tumors have increased transferrin receptor expression and iron content which corresponds with worse patient prognosis. This has led to the hypothesis that iron metabolism is a viable therapeutic target. An emergent therapeutic strategy to target GBM iron metabolism is gallium maltolate (GaM), which is an orally bioavailable form of Ga3+ that functions as a redox inactive iron mimic to disrupt iron metabolism. As mitochondrial iron metabolism serves as a central connection point between iron and DNA metabolism, the goal of this study was to determine if GaM could disrupt this system. Methods The effects of GaM on GBM cells were interrogated using conventional techniques including colony formation assays to assess clonogenicity, alkaline comet assays to evaluate DNA damage induction, and MitoFerro Green analysis to evaluate mitochondrial iron content. The effects of GaM on tumor growth in vivo were evaluated using a subcutaneous flank xenograft model system to determine the induction of DNA damage and an orthtopic model system to evaluate the combinatorial effects of GaM, radiation, and temozolomide on murine survival. Results This study uncovered that maintenance of high mitochondrial iron content corresponds with increased proliferative capacity in GBM cells. This study also shows that GaM can deplete mitochondrial iron to induce DNA damage both in vitro and in a subcutaneous xenograft model. Furthermore, GaM enhances the efficacy of radiation and temozolomide when delivered concomitant and adjuvantly in an orthotopic GBM model system. Conclusion These results suggest that GaM warrants further consideration for use in combination with standard of care GBM therapy.

Abstract Biallelic colony‐stimulating factor 1 receptor ( CSF1R ) variants are a major cause of early‐onset CSF1R ‐related disorder (CSF1R‐RD), although fewer than 30 cases have been reported and genotype–phenotype correlations remain unclear. The aim of this study was to characterize the clinical, radiological, and genetic features of patients with brain abnormalities, neurodegeneration, and dysosteosclerosis (BANDDOS). Six patients with biallelic CSF1R variants underwent detailed clinical, neurological, and magnetic resonance imaging evaluation. Three siblings with a homozygous p.Gly585Val variant showed marked phenotypic variability, ranging from severe childhood‐onset neurodegeneration to an asymptomatic presentation, all with white matter abnormalities. Three unrelated patients carried different biallelic CSF1R variants and presented with congenital skeletal abnormalities or early‐onset neurological disease of variable severity. These cases broaden the phenotypic spectrum of biallelic CSF1R‐RD and demonstrate that disease severity cannot be predicted solely by genotype, suggesting an important role for genetic or environmental modifiers.

Abstract Therapeutic resistance in ovarian cancer is frequently driven by persistent replication stress, yet the molecular mechanisms that convert replication stress into a therapeutically exploitable vulnerability remain incompletely understood. Here, we identify drug-induced nuclear cathepsin L (nCTSL) as a previously unrecognized regulator of replication stress and DNA repair. Clofarabine (CLF) combined with the ATR inhibitor AZD6738, or the CHK1 inhibitor prexasertib promoted nuclear accumulation of CTSL, where it remodeled the replication machinery through degradation of CCNE1, MCM3, MCM6, and geminin, accompanied by loss of RAD51 and 53BP1 and increased γH2AX and phospho-RPA2. DNA fiber analysis demonstrated marked inhibition of replication fork progression following CLF-based combinations, whereas CTSL depletion accelerated fork progression and abolished therapy-induced replication stress. Reconstitution with the nuclear M1F CTSL isoform restored replication restraint, confirming a direct role for nuclear CTSL in regulating replication dynamics. GFP-based DNA repair reporter assays further revealed that CLF-based combinations suppress DNA repair competence in a CTSL-dependent manner, indicating that nuclear CTSL couples replication stress amplification with functional inhibition of repair pathways. Functionally, CLF-based combinations selectively targeted transformed fallopian tube secretory epithelial cells while sparing non-transformed counterparts, demonstrated broad activity in patient-derived ovarian cancer ascites spheroids, and significantly inhibited tumor growth and prolonged survival in vivo . Collectively, our findings identify nuclear CTSL as a mechanistic driver of replication stress that remodels the replication machinery, impairs DNA repair, and creates a therapeutically exploitable vulnerability in ovarian cancer. We propose that nuclear CTSL promotes a transition from replication competence to replication catastrophe, thereby establishing a conceptual framework for biomarker-guided therapeutic strategies targeting CTSL-dependent replication stress.

Background & Aims: Multi-target stool DNA (mt-sDNA) is an established stool-based colorectal cancer screening test, yet long-term performance is not well-described. We evaluated follow-up colonoscopy rates & quality, neoplasia yield, and repeat-testing adherence over 10 years with an integrated screening program. Methods: We assembled a retrospective cohort of mt-sDNA tests (September 2014–July 2025) within a multi-site health system. A validated large-language-model pipeline extracted colonoscopy, pathology, and quality endpoints from free-text reports. Detection outcomes (adenoma detection rate, advanced neoplasia, modified advanced adenoma, sessile serrated lesion (SSL), and negative exams) were calculated among high-quality exams; adenocarcinoma, bowel preparation adequacy, and cecal intubation rate (CIR) used all screening exams. We assessed colonoscopy completion ≤1 year after positive mt-sDNA, follow-up colonoscopy findings, findings by time-to-colonoscopy, and adherence to repeat mt-sDNA after negative result. Results: Among 148,624 tests in 110,410 individuals, positivity was 13.6%. Of 19,506 positives, 65.1% completed colonoscopy within 12 months (median 82 days). Colonoscopy after a positive mt-sDNA had high yield: advanced adenoma diagnosis was 30.7%; adenocarcinoma was 1.3%. Of patients who had colonoscopy within 3 years of a negative mt-sDNA test, 45.2% had adenoma/serrated lesion, 9.3% had advanced adenoma and 0.5% had colon cancer. Adenoma detection rate after positive mt-sDNA (>50%) exceeded specialty society quality benchmarks and increased over time; Longer intervals (>6 months) to colonoscopy after a positive test were associated with higher advanced adenoma (trend P=0.001).After an initial negative mt-sDNA, only 35.3% were screened again by mt-sDNA testing within 3 years. Conclusions: Over a decade,mt-sDNA screening achieved high colonoscopy qualitybut revealed critical gaps in the care cascade. One-third of positive tests lacked timely follow-up and nearly two-thirds of negative tests were not repeated on schedule, suggesting opportunities for system-level interventions.

Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.