Citations

Background/Objectives: Locoregional anal squamous cell carcinoma (ASCC) is usually cured with chemoradiotherapy; however, some patients relapse, require salvage abdominoperineal resection or develop metastatic disease. Approximately 90% of cases are driven by high-risk human papillomavirus, most commonly HPV16. Conventional surveillance using clinical examination and imaging may have limited sensitivity and specificity and is not individualized by recurrence risk. Circulating biomarkers (CBs), particularly circulating tumor DNA (ctDNA), have emerged as promising methods for real-time disease monitoring. We systematically reviewed available evidence evaluating CBs across treatment timepoints in localized ASCC. Methods: A PROSPERO-registered review (ID: 1133987) was conducted according to PRISMA guidelines. PubMed, EMBASE, Cochrane CENTRAL, Web of Science, and major conference proceedings (ASCO, ESMO, ASTRO) were searched on 30 November 2025. We included prospective or retrospective studies (≥10 patients) with stage I–III disease treated with curative-intent chemoradiotherapy that reported CBs, assay characteristics, and at least one clinical outcome. Studies with predominantly localized cohorts were included even if small proportions of metastatic patients were present, provided results relevant to curative-intent populations could be interpreted. Data were synthesized narratively by timepoint (baseline, mid-treatment, end of treatment, post-treatment/surveillance) and assay type given methodological heterogeneity. Results: Fifteen studies were included. CB assays comprised four categories: viral HPV ctDNA assays, tumor-informed ctDNA assays, non-specific total cell-free DNA (cfDNA) quantification, and circulating tumor cell (CTC)-based assays. Baseline detection rates varied by assay type. Viral HPV ctDNA assays demonstrated detection rates of 59–100%, while tumor-informed ctDNA assays showed rates of 79–89%. Across studies, higher CB detection rates and levels were generally associated with greater tumor burden, including more advanced T and N stage disease. Mid-treatment ctDNA clearance identified patients with excellent locoregional control and progression-free survival, whereas persistent ctDNA was associated with treatment failure. End-of-treatment and surveillance ctDNA positivity predicted recurrence within individual cohorts, with reported sensitivities of 80–90%, specificities of 95–99%, and molecular lead times preceding clinical or radiographic detection. In contrast, non-tumor-specific cfDNA dynamics showed more variable prognostic associations and were less consistently linked to tumor burden. Conclusions: Across heterogeneous assays, CB dynamics provide clinically meaningful prognostic information in localized ASCC, particularly when measured during treatment and early surveillance. Viral HPV and tumor-informed ctDNA may have the potential to guide follow-up intensity and inform future escalation or de-escalation strategies; however, prospective, standardized trials are needed to define actionable thresholds and test ctDNA-guided management.

OBJECTIVES We aimed to characterize the frequency of major complications of acute COVID-19 and multisystem inflammatory syndrome in children (MIS-C) and adolescents present at 6 months after initial presentation. METHODS The Pediatric Research Immune Network on SARS-CoV-2 and MIS-C (PRISM) study was a prospective cohort at 20 US pediatric academic medical centers. Individuals aged younger than 21 years meeting any of the following were enrolled: SARS-CoV-2 detection within 7 days, Centers for Disease Control and Prevention (CDC) MIS-C criteria, or CDC MIS-C criteria except having involvement of 1 organ system. The study was designed to assess the proportion of children with major complications (cardiovascular, pulmonary, gastrointestinal/hepatic, renal, and/or neurologic) 6 months after presentation. RESULTS Data were analyzed on 229 hospitalized participants: 59 with COVID-19 (mean age 10.4 years; SD, 5.5) and 170 with MIS-C (mean age 10.1 years; SD, 4.8). Overall, 42.8% were female, making up 55.9% and 38.2% of the COVID-19 and MIS-C groups, respectively. Overall, the frequencies of major complications at 6 months after initial presentation were: 16/195 (8.2%) cardiovascular, 11/154 (7.1%) pulmonary, 11/154 (7.1%) gastrointestinal/hepatic, 3/152 (2.0%) renal, and 3/189 (1.6%) neurologic. Organ system complications did not differ significantly between the COVID-19 and MIS-C groups. Six of 14 (42.9%) COVID-19 and 21/26 (80.7%) MIS-C participants with at least 1 six-month complication had no underlying medical condition that may have been associated with their complication(s). CONCLUSIONS Major complications were infrequent but observed in children without underlying medical conditions, underscoring the importance of prospective follow-up to optimize management and elucidate the complications associated with severe SARS-CoV-2 infection in children.

Our group has recently reported how acute binge alcohol consumption increases heart rate (HR) and aortic pulsatile load (HR x aortic pulse pressure) in the evening following simulated drinking and the subsequent morning after. However, the influence of acute binge alcohol on the subendocardial viability ratio (SEVR), an estimate of myocardial oxygen supply and demand, has not been reported. We hypothesized that acute binge alcohol consumption would decrease the SEVR within 30 minutes after consumption and the morning after. Participants (n=22; 50% females; age 25±1 years; BMI 27±1 kg/m 2 ) arrived for baseline blood pressure and arterial tonometry measures at 4 pm just prior to a standardized meal. Participants were enrolled into a binge alcohol or fluid control protocol in a randomized order, with one month between visits and received the opposite condition upon laboratory return. Alcohol or fluid control doses were administered in two equal allocates at 8 and 9 pm, and participants were provided 15 min to consume during each allocate period. The blood pressure and arterial tonometry measures were repeated 30 minutes after completion of each fluid consumption, and within 15 minutes of waking the morning after fluid control or binge alcohol consumption within the polysomnographic sleep laboratory. The binge alcohol protocol consisted of a 1:3 mixture of 190 proof grain ethanol and fruit juice (i.e., orange or cranberry juice) based on body weight and sex (1g/kg men, 0.85g/kg women), while the fluid control was only fruit juice in equal volume. Reported data are mean ± standard error, and means were considered significantly different when p< 0.05. The SEVR was significantly reduced from baseline (185±8%) after the first (163±7%; p< 0.001) and second (157±8%; p< 0.001) dose of the alcohol, but unchanged after the first (p=0.890) or second dose (p=0.789) of the fluid control. In the second part of our study, SEVR was significantly lower the morning after binge alcohol than the morning after fluid control (169±7 vs. 185±7%; p=0.009). Binge alcohol consumption decreased SEVR both acutely and the morning-after drinking. These findings highlight how binge alcohol may reduce myocardial perfusion and contribute to ischemic heart disease and cardiovascular risk. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Cancer-induced muscle wasting affects many people with cancer, reducing treatment tolerance and survival. We identified a natural dietary compound, ursolic acid, that attenuates muscle atrophy across five preclinical cancer models spanning pancreatic, colon, and lung cancer. Ursolic acid inhibits cancer-induced changes in muscle mRNA expression, preserves muscle strength, and remains protective during chemotherapy, without affecting food intake or tumor burden. These results identify ursolic acid as a promising, translatable dietary supplement for supportive cancer care.

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Introduction Asymptomatic individuals with negative Interferon-γ release assay (IGRA) and tuberculin skin test (TST) results are often labeled as tuberculosis-unexposed individuals. We hypothesize that flow cytometric (FC) detection of antigen-stimulated T-cells in peripheral blood can differentiate IGRA(-)/TST(-) individuals with and without prior potential M. tuberculosis (Mtb) exposure. Methods We used FC multiparameter profiling of T-cell activation markers in TST/IGRA-negative individuals at risk for Mtb exposure and in unexposed controls. Results We studied 54 IGRA(-)/TST(-) subjects, including 27 individuals at risk for prior Mtb exposure. While quantitative IGRA results were negative in all patients, FC profiling of CD3/CD4 + IFN-γ + HLA-DR + T-cells after stimulation with a pool of Mtb-specific region of difference-1 (RD1) peptides, mycobacterial (PPD and MTB300), or Candida antigens, statistically differentiated between unexposed and potentially exposed individuals (P&lt;0.05). ROC analysis showed PPD and RD1 peptides responses had AUCs &gt;0.7 (sensitivity of 55.5–74% and specificity of 66.6–85.1%). The presence of a polyfunctional RD1-reactive CD3/CD8 + IFN-γ + TNF-α + T-cell subset also significantly differentiated these groups (AUC 0.66). Other T-cell markers demonstrated no significant differences. Conclusions FC detection of antigen-specific IFN-γ + HLA-DR + CD4+T-cells and IFN-γ + TNF-α + CD8 + T-cells can differentiate IGRA(-)/TST(-) individuals at risk of Mtb exposure from unexposed controls. Further validation is needed to determine whether this immunoprofiling approach can improve detection of Mtb infections and identification of unexposed individuals for future biomarker studies and vaccine trails.

We report correlative circulating tumor DNA (ctDNA) analyses from TRANSFORM (ClinicalTrials.gov identifier: NCT03575351 ) evaluating lisocabtagene maraleucel (liso-cel) versus standard of care (salvage immunochemotherapy, high-dose chemotherapy, autologous stem cell transplantation [ASCT]) in second-line large B-cell lymphoma (LBCL). ctDNA association with efficacy was investigated at predefined time points (random assignment, day 43, day 64, and day 126 [3 months after liso-cel, approximately 2 months after ASCT]) for 136 patients using ultrasensitive PhasED-Seq. ctDNA clearance (measurable residual disease [MRD] neg ) predicted longer event-free survival (EFS) at all time points in both arms, with significantly more liso-cel–treated patients achieving MRD neg . Liso-cel demonstrated superior outcomes versus ASCT, including longer EFS, progression-free survival (PFS), and duration of response among patients in complete response (CR) and MRD neg . ctDNA re-emergence in patients with CR after ASCT confirmed its potential in predicting relapse. MRD neg remained significantly associated with EFS after adjusting for positron emission tomography (PET) response, while interaction testing revealed a significant interaction between PET status and treatment arm for EFS. Liso-cel achieved deeper, more durable molecular clearance by ctDNA, consistent with superior EFS and PFS versus ASCT for second-line LBCL treatment. ctDNA-MRD provided prognostic value beyond PET, supporting its role as a complementary biomarker for treatment response and relapse prediction.

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF &lt;40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Background Even for experienced operators, endovascular treatment of unruptured intracranial aneurysms involves intraoperative uncertainty that may lead to adjustments in strategy, prolong the procedure, and potentially cause inefficiency and device waste. This study aimed to evaluate whether pre-procedural testing (PPT) of endovascular treatment using patient-specific models was associated with increased operator confidence and perceived clinical utility, including improvements in procedural efficiency and reduced resource waste. Methods We enrolled a cohort of patients who underwent PPT before endovascular treatment for complex unruptured intracranial aneurysms and compared their outcomes with a control group treated without PPT. The primary outcome was the Training Fidelity Score, a composite of three operator-reported Likert items defined a priori. Secondary outcomes included perceived clinical utility, intraoperative strategy changes, procedural time, radiation exposure, device waste and safety. Results A total of 85 patients met the inclusion criteria (PPT=40; control=45). The Training Fidelity Score was high across the PPT group (median, 4.33/5). Perceived clinical utility was high and further increased significantly after the procedure. A significant reduction was observed in intraoperative strategy changes, with no changes recorded in the PPT group, compared to 6/45 in the control group (RR 0.09; p=0.027). Reductions in treatment time, radiation exposure and device waste were also noted. Conclusion PPT using patient-specific models was associated with increased operator confidence, fewer intraoperative strategy changes, improved procedural efficiency, and reduced device waste without compromising safety. These findings support its use in pre-interventional preparation, but require prospective multicenter validation.

PURPOSE Artificial intelligence (AI) has been rapidly evolving in medicine. While there are existing data about the perceptions and concerns of health care workers regarding AI, those of the hematology and oncology (HemOnc) workforce remain unknown. We assessed opinions of physicians (faculty and fellows), advanced practice providers (APPs), and nurses in HemOnc on integrating AI in medical education (MedEd) and clinical practice (CP). METHODS Group-specific electronic surveys were developed. The HemOnc workforce from the three Mayo Clinic sites was invited to participate. Participation was anonymous, with data collection between November 7, 2024, and January 20, 2025. Results were reported as proportions, with 95% binomial CI when appropriate. RESULTS A total of 344 participants responded, 118 physicians (77 faculty, 41 fellows), 49 APPs, and 177 nurses. Most had used AI although less than half felt very or moderately knowledgeable about it. A total of 95% of fellows, 97% of faculty physicians, 96% of APP, and 92% of nurses believe that AI will be integrated in MedEd. A total of 94% of physicians and 88% of nonphysicians believe that this integration would be beneficial. In CP, 98% of fellows, 96% of faculty physicians, 100% of APPs, and 92% of nurses believe in AI's integration. However, 51% of physicians and 68% of nonphysicians had some concerns, including technology time, medical costs, and burnout. Nonetheless, 85% of fellows, 94% of faculty physicians, 98% of APPs, and 86% of nurses would embrace AI. CONCLUSION The majority of this single health system HemOnc's workforce endorsed AI integration into MedEd and CP but highlighted relevant concerns. Proper education, effective partnership among all stakeholders, and well-organized integration are essential for optimizing AI's incorporation into HemOnc's MedEd and CP and ensuring that it fully supports the mission of providing high-quality patient care.