Citations

Background: Generative AI and synthetic media have enabled realistic human Embodied Conversational Agents (ECAs) or avatars. A subset of this technology replicates faces and voices to create realistic likenesses. When combined with avatars, these methods enable the creation of “digital twins” of physicians, offering patients scalable, 24/7 clinical communication outside the immediate clinical environment. This study evaluated surgical patient perceptions of an AI-generated surgeon avatar for postoperative education. Methods: We conducted a pilot feasibility study with 30 plastic surgery patients at Mayo Clinic, USA (July–August 2025). A bespoke interactive surgeon avatar was developed in Python using the HeyGen IV model to reproduce the surgeon’s likeness. Patients interacted with the avatar through natural voice queries, which were mapped to predetermined, pre-recorded video responses covering ten common postoperative topics. Patient perceptions were assessed using validated scales of usability, engagement, trust, eeriness, and realism, supplemented by qualitative feedback. Results: The avatar system reliably answered 297 of 300 patient queries (99%). Usability was excellent (mean System Usability Scale score = 87.7 ± 11.5) and engagement high (mean 4.27 ± 0.23). Trust was the highest-rated domain, with all participants (100%) finding the avatar trustworthy and its information believable. Eeriness was minimal (mean = 1.57 ± 0.48), and 96.7% found the avatar visually pleasing. Most participants (86.6%) recognized the avatar as their surgeon, although many still identified it as artificial; voice resemblance was less convincing (70%). Interestingly, participants with prior exposure to deepfakes demonstrated consistently higher acceptance, rating usability, trust, and engagement 5–10% higher than those without prior exposure. Qualitative feedback highlighted clarity, efficiency, and convenience, while noting limitations in realism and conversational scope. Conclusions: The AI-generated physician avatar achieved high patient acceptance without triggering uncanny valley effects. Transparency about the synthetic nature of the technology enhanced, rather than diminished, trust. Familiarity with the physician and institutional credibility likely played a key role in the high trust scores observed. When implemented transparently and with appropriate safeguards, synthetic physician avatars may offer a scalable solution for postoperative education while preserving trust in clinical relationships.

Abstract Introduction: Adult T-cell leukemia/lymphoma (ATLL) is a rare blood disorder associated with human T-cell leukemia virus type 1 (HTLV-1) infection. It is generally categorized into four subtypes: smoldering, chronic, lymphoma, and acute. Given the heterogeneity of these subtypes, there is a significant difference in outcomes. Robust and contemporary epidemiological data are lacking for this rare disorder. We present a retrospective cohort study of patients with ATLL, utilizing the National Cancer Database (NCDB) to better characterize the epidemiology and survival trends in the United States. Methods: This is a retrospective cohort study using the NCDB to identify ATLL cases diagnosed from 2004-2020. Descriptive statistics summarized baseline characteristics, while chi-square tests assessed group differences. Overall survival (OS) was estimated using Kaplan-Meier analysis and compared using log-rank tests. Multivariable Cox proportional hazards models identified independent prognostic factors (p < 0.05 considered significant). Results: A total of 635 patients were identified with ATLL, including 344 (54%) males and 291 (46%) females, with a median age at diagnosis of 59 years (range: 19-90). Most patients came from the Middle Atlantic (NY, NJ, PA) region (33%), followed by the South Atlantic (DC, DE, FL, GA, MD, NC, SC, VA, WV) region (20%) and the East North Central (IL, IN, MI, OH, WI) region (8%). Of the 635 patients, 335 (52%) were Black, 247 (39%) White, and 26 (4%) Asian. Sixty-five (10%) patients were of Hispanic origin. Over half of patients with ATLL (54%) had a median income below $46,000. International Prognostic Index (IPI) score was only available for 16 patients: three (19%) patients were in the high-risk group, 7 (43%) in the high-intermediate group, and 6 (37.5%) in the low-intermediate group. Treatment groups included 357 (56%) patients treated with chemotherapy and 29 (4.5%) who received radiation therapy. Thirty-three (5%) patients underwent hematopoietic stem cell transplant (HSCT), including 27 autologous and 16 allogeneic transplants. The median survival time in the cohort was 9 months (95% CI 7.3-10.7). One-year OS rate in the cohort was 43%, while 2-year and 5-year OS rates were 31% and 23.5%, respectively. Patients over the age of 65 had a significantly shorter median OS than those under 65 (4 vs. 11 months, p=0.001) and were significantly less likely to undergo HSCT (p< 0.001). Black patients had significantly shorter median OS compared to White patients (7.8 months vs 13.3 months, p=0.014), despite similar treatment rates with chemotherapy and HSCT. Median OS appeared to improve over time, from 8 months in patients diagnosed between 2004-2009 to 10 months for those diagnosed between 2016-2020, although this was not statistically significant (p=0.145). Treatment with allogeneic HSCT was associated with significantly prolonged median survival, compared to those who did not receive transplant (50 months vs. 7 months, p<0.05). Additionally, receiving chemotherapy and/or radiation compared to not receiving treatment was also associated with significantly prolonged median survival (p<0.05). Patients treated in the Middle Atlantic region had a median survival of 8.8 months, compared to 23.2 months in the West South Central (AR, LA, OK, TX) region (p< 0.001). Increased education and income levels were not significantly associated with improved survival. There were no significant differences in survival between males and females. Conclusions: We provide contemporary estimates on the epidemiology of ATLL. Factors associated with improved prognosis included undergoing HSCT, radiation, or chemotherapy. We report that increased age and Black race are negative prognostic factors. Regional differences in incidence and survival persist in the US. Our study findings underscore the need for increasing access to treatment, including HSCT for those who are good candidates. Additionally, there remains work to be done to address racial disparities in the care of patients with ATLL.

Abstract Background The benefit of high-dose chemotherapy and autologous stem cell transplantation (ASCT) as a consolidation strategy after initial chemotherapy in peripheral T-cell lymphoma (PTCL) is currently unclear, and a randomized trial (EA4232/PTCL-STAT) to answer this question is currently underway. Angioimmunoblastic T-cell lymphoma (AITL) is a rare, aggressive subtype of nodal PTCL with poor prognosis and unique features. Here, we report a large-scale, real-world evidence on the role of ASCT in frontline AITL management. Methods We analyzed a national cohort of 5,827 patients diagnosed with AITL between 2004 and 2020 from the National Cancer Database (NCDB). Baseline demographic, clinical, and socioeconomic characteristics were assessed. Treatment modalities included chemotherapy alone, chemotherapy with ASCT, and no treatment. Overall survival (OS) was evaluated using Kaplan-Meier method and multivariable Cox proportional hazards regression. Propensity score matching (PSM) was performed to validate ASCT's treatment effect. Results Among patients with complete treatment information (n = 5,224), the median age at diagnosis was 68 years (range 20–90). Frontline treatments included chemotherapy alone (68.6%, n = 3,584), chemotherapy with ASCT (14.6%, n = 761), and no treatment (16.8%, n = 879). Compared with patients who did not receive ASCT, those who underwent ASCT were significantly younger (median 60.0 vs 68.0 years), more frequently treated at academic centers (81.5% vs 57.9%, p < 0.001), and more likely to reside in areas with higher educational attainment (p < 0.001). They exhibited lower Charlson-Deyo comorbidity scores (CDS), with a higher proportion having a score of 0 (82.8% vs 73.6%, p < 0.001), and were more likely to have advanced-stage disease (93.0% vs 86.9%, p < 0.001). Furthermore, a higher proportion of patients who received ASCT were diagnosed in more recent years (p < 0.001). Sex, race, and IPI score distribution showed no significant baseline differences. Median follow-up time was 81.1 months (95% CI: 77.4–84.9), and the median OS was 23.3 months (95% CI: 21.3–25.2) for the entire cohort. For patients receiving chemotherapy with ASCT, the median OS was 139.2 months (95% CI: 104.2–174.2). In comparison, chemotherapy alone yielded a median OS of 23.6 months (95% CI: 21.4–25.8), while patients who received no treatment had a median OS of 4.0 months (95% CI: 2.7–5.2); overall p < 0.001. Median OS also significantly improved with more recent diagnosis eras: 20.1 months for patients diagnosed in 2004–2009 (95% CI: 16.8–23.4), 23.9 months for those diagnosed in 2010–2015 (95% CI: 21.0–26.7), and 26.7 months for patients diagnosed in 2016–2020 (95% CI: 23.1–30.4) (p < 0.001). Analyses consistently demonstrated longer median OS in female patients (27.5 vs. 20.7 months in males, p <0.001), and in patients treated at academic centers (27.5 vs. 16.4 months at non-academic centers, p < 0.001). Median OS varied by race (White: 24.0 months vs. Black: 15.2 months vs. Others: 30.3 months; p = 0.017). Patients with higher household income and higher educational attainment experienced significantly improved survival (p = 0.005 and p = 0.017, respectively). Patients with early-stage disease, lower IPI scores, and lower CDS also showed significantly longer survival (p < 0.001 for all comparisons). Multivariate Cox regression showed that chemotherapy with ASCT significantly reduced mortality risk compared to chemotherapy alone (HR = 0.438, 95% CI: 0.364–0.527, p < 0.001). No treatment significantly increased mortality risk (HR = 1.941, 95% CI: 1.634–2.305, p < 0.001). PSM analysis further validated that chemotherapy with ASCT significantly lowered mortality risk compared to chemotherapy alone (HR = 0.608, 95% CI: 0.503–0.736, p < 0.001), after matching for age at diagnosis, sex, race, income, education level, academic center, stage, CDS, and diagnosis era. Conclusion This large-scale, real-world analysis suggests that chemotherapy followed by ASCT is associated with significantly improved overall survival in patients with AITL, supported by unadjusted Kaplan-Meier analysis, adjusted multivariate Cox regression, as well as PSM analysis. These data support ASCT as an important consolidation strategy for AITL in real-world practice.

Abstract Background Primary central nervous system lymphoma (PCNSL) is a rare but aggressive non-Hodgkin lymphoma with limited prospective data to guide optimal therapy. This study evaluates real-world treatment patterns and survival outcomes using a large national cohort. Methods We analyzed data from the National Cancer Database (NCDB) for patients diagnosed with PCNSL between 2004 and 2020. Baseline demographics and treatment characteristics were analyzed using descriptive statistics. Survival outcomes were estimated using the Kaplan-Meier method and compared using log-rank tests. Multivariable Cox proportional hazards models were constructed to identify independent predictors of overall survival (OS). Results A total of 14,450 patients with PCNSL were included; 14,439 had first-line treatment data available, and 13,390 had survival data. Median age at diagnosis was 67 years (range 18-90), 49.2% were female, and 6.6% were African American. Chemotherapy alone was the modality of treatment for 81.6% (n=11,787), 12.2% (n=1,769) received chemoradiation, 3.8% (n=545) received chemotherapy and underwent autologous stem cell transplant (ASCT) but no radiation, and 2.4% (n=348) received radiation only. The median follow-up time was 73.5 months (95% CI 71.4-75.6), and the median OS for the group was 17.4 months (95% CI 16.2-18.6). Survival differed significantly by age group (median OS: <65 yrs, 46.1 months [95% CI 41.7 -50.6]; 65-79 yrs, 12.5 months [95% CI 11.2-13.7]; ≥80 yrs, 3.2 months [95% CI 2.9-3.5]; p<0.001), by year of diagnosis (median OS: 2004-2009, 11.0 months [95% CI 9.9-12.1]; 2010-2015, 20.0 months [95% CI 17.4-22.6], 2016-2020, 29.7 months [95% CI 26.0-33.4]; p<0.001), and by frontline treatment strategy (p<0.001). Patients treated with chemotherapy followed by ASCT had the longest median OS (not reached), while those receiving radiation alone had the poorest outcomes with a median OS of 3.9 months (95% CI 3.2-4.7); chemoradiation patients had a median OS of 31.8 months (95% CI 27.5-36.1), and chemotherapy only patients had a median OS of 13.9 months (95% CI 12.7-15.1)(p<0.001). African American patients had an inferior outcome with a median OS of 10.2 months [95% CI 7.2-13.1], compared to “other” races with a median OS of 36.9 months [95% CI 27.6-46.1] (p<0.001), although this is more similar to the median outcome for white patients (17.0 months [95% CI 15.8-18.3]). Treatment at an academic center (25.7 vs 11.0 months) and a high case-volume center (defined as >median number of cases per facility) (18.6 vs 10.5 months), and lower Charlson-Deyo comorbidity scores (CDS) (defined as CDS 0-1) (21.7 vs 6.8 months) were also associated with superior OS (p<0.001). In multivariable analysis, increasing age ( 65-79 y.o HR 1.60 [1.52-1.1.68], ≥80 y.o HR 2.95 [2.75-3.17]), CDS≥2 (HR 1.36 [1.28-1.44]), and African American race (HR 1.12 [1.02-1.24]) were adverse predictors of OS (all p<0.001), while treatment strategy (ASCT [HR 0.17], chemoradiation [HR 0.53], chemotherapy only [HR 0.68] compared to radiation alone), more recent year of diagnosis (2010-2015 [HR 0.81], 2016-2020 [0.70]), and treatment at an academic center [HR 0.81] remained predictors of superior OS (all p<0.001). Conclusion In this large national cohort of patients with PCNSL, treatment with chemotherapy followed by ASCT was associated with the most favorable survival outcomes. Patients treated with chemoradiation or multi-agent chemotherapy also demonstrated improved survival compared to those receiving radiation monotherapy or single-agent chemotherapy alone, respectively. Age, comorbidities, race, and treatment at academic centers were also significant independent predictors of OS. These findings highlight substantial disparities in outcomes and underscore the importance of optimizing treatment strategies, improving access to high-intensity therapies, and supporting timely referral to academic or high case-volume centers, particularly for younger and medically fit patients.

Abstract Description Evidence consistent with immunoediting in human cancer has been identified through associative studies. To provide direct evidence that differences in the neoantigen profile are immune-mediated, we compared the neoantigen profiles of cutaneous squamous cell carcinoma (cSCC) from immunocompetent and immunosuppressed patients. Despite consistency in the overall mutational burden and signature, immunocompetent patients have a lower clonal mutational burden compared to immunosuppressed patients. Furthermore, clonal mutations in cSCC from immunocompetent patients have a lower proportion of neoantigens predicted to bind MHC class I, supporting the role of the immune system in shaping the neoantigen profile. In single-cell RNA sequencing of intratumoral CD8+ T cells from a separate cohort of cSCC from immunocompetent and immunosuppressed patients, we observe an increase in exhausted CD8+ T cells in tumors from immunocompetent patients, suggesting an explanation for the persistence of binding neoantigens subclonally. Finally, we compare clonal and subclonal neoantigens as an unbiased screen for characteristics associated with neoantigen immunogenicity. Neoantigens with a large increase in predicted MHC class I binding relative to the normal peptide are enriched in subclonal neoantigens in immunocompetent patients. Overall, this work supports the immune system’s role in sculpting the neoantigen profile of cSCC. Funding Sources Supported by American Cancer Society - Simone Charitable Foundation - Discovery Boost Grant, AZ-DBG-23-1151311-01-IBCD; NIH F30CA281056; R35GM149235 Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Abstract INTRODUCTION Glioblastoma (GBM) is a brain cancer known for its complex tumor microenvironment and ability to reprogram glucose metabolism and evade host immune responses. CAR-T cell therapy has attracted considerable attention due to its potential for high specificity and efficacy across various cancer types. However, its success in GBM has been limited due to the unique and hostile metabolic landscape of the tumor microenvironment. GBM tumor cells exhibit enhanced aerobic glycolysis, resulting in elevated glucose consumption. This restricts glucose availability for T cells, impairing activation and anti-tumor response. In our study, we enhanced the tumoricidal activity of CD70 CAR T-cells by overexpressing the glucose transporters GLUT1 and GLUT3, thereby improving their metabolic fitness and enabling them to overcome tumor-induced glucose deprivation. METHODS The CD70 CAR T-cell glucose metabolic pathway was modulated via glucose transporter overexpression. The efficacy and safety of metabolically modified CD70 CAR T-cells was investigated in in vitro and in vivo platforms. RESULTS GLUTs overexpression does not negatively affect T-cell differentiation and function but improves glucose consumption and intra-tumoral T-cell trafficking. We then used CD70 CAR-T platform and overexpress GLUT1 and GLUT3 to engineer T-cells. GLUT overexpression provided metabolic advantage to CD70CAR T-cells, which showed enhanced activation and cell avidity when co-cultured with GBM cells in both glucose enriched and restricted conditions. The metabolic modifications regulate CAR T-cell efficacy by enhancing intra-tumoral trafficking and persistence, followed by improved anti-tumor response and survival in mice. The lack of significant alterations in body weight and organ morphology in mice receiving GLUT-overexpressing CD70 CAR T-cells suggests that the adoptive transfer of these metabolically enhanced T cells does not elicit toxicity, thereby demonstrating a favorable safety profile during treatment. CONCLUSION Our study demonstrates development of immunotherapeutic strategies by incorporating core principles of tumor and immune cell metabolism, thus providing evidence that targeting immune-metabolic pathways is a potential approach to treat brain tumor.

Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating “hallucinations”. Objective: To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual Assistant (AIVA) Platform. This system utilizes a retrieval-augmented generation (RAG) architecture, integrating Gemini 2.0 Flash with a medically verified knowledge base via Google Vertex AI, to safely deliver dynamic, patient-facing postoperative guidance grounded in validated clinical content. Methods: The AIVA Platform was evaluated through 750 simulated patient interactions derived from 250 unique postoperative queries across 20 high-frequency recovery domains. Three blinded physician reviewers assessed formal system performance, evaluating classification metrics (accuracy, precision, recall, F1-score), relevance (SSI Index), completeness, and consistency (5-point Likert scale). Safety guardrails were tested with 120 out-of-scope queries and 30 emergency escalation scenarios. Additionally, groundedness, fluency, and readability were assessed using automated LLM metrics. Results: The system achieved 98.4% classification accuracy (precision 1.0, recall 0.98, F1-score 0.9899). Physician reviews showed high completeness (4.83/5), consistency (4.49/5), and relevance (SSI Index 2.68/3). Safety guardrails successfully identified 100% of out-of-scope and escalation scenarios. Groundedness evaluations demonstrated strong context precision (0.951), recall (0.910), and faithfulness (0.956), with 95.6% verification agreement. While fluency and semantic alignment were high (BERTScore F1 0.9013, ROUGE-1 0.8377), readability was 11th-grade level (Flesch–Kincaid 46.34). Conclusion: The simulated testing demonstrated strong technical accuracy, safety, and clinical relevance in simulated postoperative care. Its architecture effectively balances flexibility and safety, addressing key limitations of standalone NLP and LLMs. While readability remains a challenge, these findings establish a solid foundation, demonstrating readiness for clinical trials and real-world testing within surgical care pathways.

Abstract Alzheimer’s Disease (AD) 1 is a progressive neurodegenerative disorder with limited therapeutic options, driving interest in drug repurposing to accelerate treatment discovery. Drug repurposing has emerged as a promising strategy to accelerate therapeutic discovery by repositioning existing drugs for new clinical indications. Recent computational repurposing approaches, including knowledge graph reasoning, transcriptomic signature analysis, and integrative literature mining, have demonstrated strong predictive capabilities 2 . However, these methods often yield divergent drug rankings, which makes it difficult to decide which candidates to advance for experimental follow-up and results in substantial gaps between computational predictions and feasible in vivo validation 2 .To bridge this computational-experimental gap, we proposed an advanced prioritization framework leveraging large language models (LLMs). Our method systematically evaluated three state-of-the-art (SOTA) and representative computational methods (TxGNN 3 , Composition-based Graph Convolutional Network (CompGCN) 4 , and a regularized logistic regression (RLR) 5 , to analyze both their predictive performance and pharmaceutical class distributions. By integrating the strengths and divergences of these models, we generated a unified, streamlined list of 90 candidate drugs for further prioritization. We then utilized an LLM-based agent to perform evidence synthesis from biomedical literature abstracts for each candidate. This process mimics expert manual curation but significantly reduces human effort and time by efficiently distilling vast textual data into actionable insights. Applying consistent and transparent selection criteria, we obtained a refined and prioritized list of drug candidates suitable for subsequent in vivo experimental validation. The robustness and clinical relevance of our framework were validated using real-world data from Alzheimer’s patient cohorts, clinical trial registries, and expert pharmacological reviews. This comprehensive validation confirmed that our LLM-driven approach enhances efficiency, consistency, scalability, and generalizability. By integrating computational predictions with scalable evidence synthesis and multifaceted validation, our framework facilitated rapid and informed prioritization of repurposed drugs. Our framework can potentially accelerate the translational pathway toward viable AD therapeutics. Moreover, the versatility of our framework can also be applied to drug repurposing efforts for other diseases beyond AD.

Background Challenges in predicting which neoantigens mediate tumor rejection limit the efficacy of neoantigen vaccines to treat cancers, especially for cancers with a high mutational burden like cutaneous squamous cell carcinoma (cSCC). Only a small portion of neoantigens prioritized by current methods elicit effective T cell responses, demonstrating the critical need for improved criteria for the prediction of tumor-rejecting neoantigens. Methods Publicly available human cSCC datasets were used to assess the frequency of shared mutations between patients. A transplantable ultraviolet light-induced mouse model of cSCC was generated. The mutational signature and driver mutations in the mouse model were compared with human tumors. Neoantigens were prioritized in the mouse model, and tumor-rejecting neoantigens were identified through (enzyme-linked immunosorbent spot (ELISpot) and in vivo prophylactic vaccination. Binding of the neoantigens and corresponding wild-type peptides to major histocompatibility complex (MHC) class I was determined. Structural modeling of peptide:MHC complexes was performed to assess for changes in structural characteristics of the neoantigens relative to the wild-type peptides. Results A minority of human cSCC tumors shared neoantigens. The mouse cSCC model recapitulated the mutational signature and driver mutations found in human disease and was constrained by CD8 T cells. Two MHC class I neoantigens were identified in the mouse model that constrained cSCC growth. One tumor-rejecting neoantigen exhibited improved MHC binding, and the other had increased solvent accessibility of the mutated residue, compared with wild-type. Across known neoantigens that do not impact MHC binding, increased exposure of the mutated residue distinguished tumor-rejecting from non-immunogenic neoantigens. Conclusions Given the paucity of shared mutations, this work supports the need for personalized neoantigen vaccines in cSCC. To facilitate further discovery, we provide a clinically relevant mouse cSCC model with two defined neoantigens that mediate tumor rejection. Structural changes in the exposure of features that promote T cell receptor recognition defined tumor-rejecting neoantigens. Incorporation of structural modeling to predict changes in T cell receptor accessibility is anticipated to improve the selection of neoantigens for inclusion in personalized cancer vaccines.