Citations

Abstract Background Ceftriaxone-resistant Escherichia coli infections are increasingly common, partially because of the emergence of E. coli sequence type 131 (ST131), including its subclade C2/H30Rx, which produce extended-spectrum β-lactamases. Methods A prospective cohort including 14 US sites, which enrolled monomicrobial ceftriaxone-resistant and susceptible E. coli bloodstream infection (BSI) cases in a 1:1 ratio, was used to compare ST131 versus non-ST131 E. coli BSI, with specific attention to E. coli ST131 C2/H30Rx. Desirability of outcome ranking was determined at 30 days after infection onset. Results This analysis included 282 patients with E. coli BSI; 43% (121/282) were E. coli ST131 and 23% (66/282) belonged to the C2/H30Rx subclade. Resistance to ceftriaxone was present in 79% (96/121) ST131, 86% (57/66) E. coli ST131 C2/H30Rx, and 27% (43/161) E. coli non-ST131. Compared to patients with non-ST131 E. coli BSI, patients with ST131 BSI were older (median 70 years, [Q1 62, Q3 76] years vs 65 years, [51, 74]; P = .005) and more often admitted from long-term care facilities (21/121 [17%] vs 7/161 [4%], P < .001). Overall and empiric carbapenem use was more frequent in the treatment of patients with ST131 BSI compared with non-ST131 BSI (overall 89/121 [74%] vs 50/161 [31%]; empiric: 58/121 [48%] vs 31/161 [19%], P < .001). Desirability of outcome ranking outcomes were similar between groups. Conclusions Most ceftriaxone-resistant E. coli from US patients with E. coli BSI belong to ST131, particularly E. coli ST131 C2/H30Rx, serving as an important driver of carbapenem use.

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.

Abstract The landscape of Alzheimer's disease (AD) and related dementias (ADRD) diagnosis is evolving rapidly, driven by advances in disease understanding, biomarker tools, and disease‐modifying therapies. Modern diagnostic approaches emphasize biological precision, early detection, and dynamic frameworks that adapt to treatment‐induced changes in disease biology. These frameworks enable opportunities for personalized interventions—encompassing pharmacological and non‐pharmacological strategies—and for enhanced clinical trial design. However, implementing these advancements globally is influenced by diverse cultural, infrastructural, and regulatory factors. The 2024 Alzheimer's Association International Conference Advancements: Modernizing Diagnosis, held in Japan, provided a unique platform to explore these global dynamics, particularly from an Asian perspective. This article highlights key discussions from the conference, exploring the role of biomarker‐based diagnostic frameworks in shaping the future of AD/ADRD research, diagnosis, and treatment. We highlight regional challenges and successes and emphasize ethical considerations and practical strategies needed to ensure equitable access to diagnostic and therapeutic innovations. Highlights Advances in biomarkers are reshaping Alzheimer's disease diagnosis and treatment. Modern diagnostic frameworks highlight biological precision, early detection, and dynamic frameworks. The 2024 Alzheimer's Association International Conference Advancements: Modernizing Diagnosis explored challenges and opportunities in global biomarker implementation. The conference explored geographic‐specific impacts, focusing on Asia.

Abstract Introduction: Glioblastoma (GBM) tumors contain distinct cell lineages, including treatment-sensitive cells (TSCs) relying on aerobic glycolysis and treatment-resistant persister cells (TRPCs) dependent on lipid metabolism. Our systems biology-based, spatial multi-omics and multi-targeted approach decodes a critical node in GBM progression, lipid acquisition in TRPCs, and validates this through a retrospective analysis of statin-treated patients, offering clinical insights into the potential benefits of targeting lipid metabolism in high TRPC signature GBM patients. Methods: We performed spatial transcriptomics and proteomics, combined with 3D rendering of the tumor microenvironment (TME) and geospatially resolved single-cell neighborhood analysis of IHC-stained GBM tissues using our custom-developed SNAQ algorithm to investigate the immune microenvironment and lipid distribution specific to TSCs and TRPCs. To characterize lipid dynamics, we employed flow cytometry, time-lapse imaging, and holotomography to track lipid transfer from Tumor-Associated Myeloid Cells (TAMCs) to TRPCs. A critical node for GBM progression was identified through in vivo survival and tumor progression studies that assessed effects of genetic targeting of CCR2 and pharmacological targeting of CSF1R and FABP3. Retrospective analysis was conducted on 4,085 GBM patients to evaluate the effect of statin treatment on GBM outcomes based on their TRPC signature. Results: Our study establishes the skewed accumulation of TAMCs within the TRPC microenvironment through upregulation of the CCL2/7-CCR2 and CSF1-CSF1R pathways. TAMCs actively transfer lipids to TRPCs to fulfill their metabolic demands. Targeting elements responsible for lipid incorporation in TRPCs, such as CCR2, CSF1R, and FABP3 in vivo, resulted in tumor immune microenvironment remodeling, significant tumor growth reduction, and improved survival. This reinforces the critical role of metabolic communications via lipid trafficking in TRPCs as an essential node in GBM progression. Our retrospective study showed that GBM patients with elevated TRPC signatures, typically associated with shorter survival, had no significant survival differences when treated with lipid-lowering statins. This suggests statins may disrupt lipid metabolism in TRPCs, reducing TAMC lipid support and improving patient outcomes. Conclusion: Our study highlights lipid trafficking in TRPCs as a key mechanism in GBM progression, suggesting that targeting TAMC recruitment and lipid acquisition in TRPCs may offer a promising therapy. Additionally, our retrospective analysis highlights the potential of transcriptomic profiling for patient stratification based on TRPC signature expression levels, enabling the identification of patients to benefit from lipid metabolism-targeted therapies. Citation Format: Avirup Chakraborty, Changlin Yang, Diana Feier, Aryeh Silver, Nyla T. Searl, Miruna Anica, Avinash Pittu, Illena West, Olusegun O. Sobanjo, Ethan D. Hodge, Mia K. Engelbart, Srabasti Sengupta, Nagheme Thomas, Christina Von Roemeling, Maryam Rahman, Matthew Sarkisian, Jianping Huang, Jeffrey Harrison, Duane A. Mitchell, Loic P. Deleyrolle. Disrupting lipid metabolism in GBM: A systems biology approach to targeting treatment-resistant persister cells and tumor associated myeloid cell-mediated support [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB082.

Background: Electronic Health Records (EHRs) encompass valuable data essential for disease prediction. The application of artificial intelligence (AI), particularly deep learning, significantly enhances disease prediction by analyzing extensive EHR datasets to identify hidden patterns, facilitating early detection. Recently, numerous foundation models pretrained on extensive data have demonstrated efficacy in disease prediction using EHRs. However, there remains some unanswered questions on how to best utilize such models, especially with very small fine-tuning cohorts. Methods: We utilized Med-BERT, an EHR-specific foundation model, and reformulated the disease binary prediction task into a token prediction task and a next visit mask token prediction task to align with Med-BERT’s pretraining task format in order to improve the accuracy of pancreatic cancer (PaCa) prediction in both few-shot and fully supervised settings. Results: The reformulation of the task into a token prediction task, referred to as Med-BERT-Sum, demonstrated slightly superior performance in both few-shot scenarios and larger data samples. Furthermore, reformulating the prediction task as a Next Visit Mask Token Prediction task (Med-BERT-Mask) significantly outperformed the conventional Binary Classification (BC) prediction task (Med-BERT-BC) by 3% to 7% in few-shot scenarios with data sizes ranging from 10 to 500 samples. These findings highlight that aligning the downstream task with Med-BERT’s pretraining objectives substantially enhances the model’s predictive capabilities, thereby improving its effectiveness in predicting both rare and common diseases. Conclusions: Reformatting disease prediction tasks to align with the pretraining of foundation models enhances prediction accuracy, leading to earlier detection and timely intervention. This approach improves treatment effectiveness, survival rates, and overall patient outcomes for PaCa and potentially other cancers.

Abstract Background Drug-resistant E. coli is a leading cause of antimicrobial resistance-associated deaths globally. Specifically, resistance to ceftriaxone (CRO-R) is increasing in E. coli. High-risk clonal group ST131 and its pandemic H30 subclone are of high concern yet studies characterizing these infections are limited. We evaluated baseline characteristics and clinical outcomes associated with H30 ST131, non-H30 ST131 and non-ST131 E. coli bloodstream infections (BSI). Methods Patients with monomicrobial carbapenem-susceptible E. coli BSI that were matched 1:1 by study site (CRO-R and CRO-susceptible community-acquired and hospital onset cases) were prospectively enrolled from 14 United States hospitals between November 12, 2020 to April 28, 2021 in the multicenter Study of Highly Resistant E. coli (SHREC). Isolates underwent whole genome sequencing. The primary outcome was a 30-day Desirability of Outcome Ranking (DOOR) after index culture including clinical response to treatment and all-cause mortality. Results There were 92 (33%) H30 ST131, 29 (10%) non-H30 ST131, and 161 (57%) non-ST131 isolates in 282 E. coli BSI (Table 1). Most ceftriaxone resistance was conferred by CTX-M-15 produced by H30 ST131 isolates (Figure 1, Table 1). H30 ST131 BSI patients were older (median age [IQR] 70.5 [63,76] vs. 67 [56,77] vs 65 [51,74] years, p = 0.017), had higher Charlson comorbidity indices (3 [2,5] vs. 2 [1,4] vs. 2 [1,4], p=0.009), and were more often admitted from long-term care facilities (18/92 [20%] vs. 3/29 [10%] vs. 7/161 [4%], p = 0.003) compared to non-H30 ST131 and non-ST131 BSI patients. Among H30 ST131 isolates, high rates of antibiotic resistance were observed to cephalosporins and fluoroquinolones, resulting in significantly more carbapenem use compared with non-H30 ST131 and non-ST131 isolates (75/92 [82%] vs. 14/29 [48%] vs. 50/161 [31%], p < 0.001) (Figure 2). 30-day DOOR and hospital length of stay did not differ between groups (Table 2). Conclusion Compared with non-H30 ST131 and non-ST131 E. coli BSI, H30 ST131 E. coli BSI have a unique epidemiology with more healthcare exposures, comorbidities and antibiotic resistance and are more likely to be treated with carbapenems, though no significant difference in clinical outcomes was observed. Disclosures Yohei Doi, MD, PHD, AbbVie: Honoraria|Entasis: Grant/Research Support|Gilead: Advisor/Consultant|GSK: Advisor/Consultant|Meiji Seika: Advisor/Consultant|Moderna: Advisor/Consultant|Pfizer: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi: Honoraria Elie Saade, MD, MPH, FIDSA, Janssen Global Services: Advisor/Consultant|Janssen Global Services: Advisor/Consultant|Janssen Research and Development: Advisor/Consultant|Janssen Research and Development: Advisor/Consultant Loren G. Miller, MD MPH, Armata: Grant/Research Support|Contrafect: Grant/Research Support|GSK: Grant/Research Support|Merck: Grant/Research Support|Paratek: Grant/Research Support Michael J. Satlin, MD, AbbVie: DSMB participant|bioMerieux: Grant/Research Support|Merck: Grant/Research Support|Selux Diagnostics: Grant/Research Support|SNIPRBiome: Grant/Research Support W. Charles Huskins, MD, MSc, ADMA Biologics: Advisor/Consultant|Bristol Myers Squibb: Stocks/Bonds (Public Company)|Pfizer: Advisor/Consultant|Pfizer: Stocks/Bonds (Public Company)|Zimmer Biomet: Stocks/Bonds (Public Company) Carol Hill, PhD, Glaxo SmithKline: Retirement Health, Cash Balance Plan|Glaxo SmithKline: Stocks/Bonds (Public Company) Robin Patel, MD, a patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, a: See above|MicuRx Pharmaceuticals and BIOFIRE: Grant/Research Support|PhAST, Day Zero Diagnostics, Abbott Laboratories, Sysmex, DEEPULL DIAGNOSTICS, S.L., Netflix, Oxford Nanopore Technologies and CARB-X: Advisor/Consultant|Up-to-Date and the Infectious Diseases Board Review Course.: Honoraria Vance G. Fowler, MD, MHS, Affinergy: Advisor/Consultant|ArcBio: Stocks/Bonds (Private Company)|Armata: Advisor/Consultant|Astra Zeneca: Advisor/Consultant|Astra Zeneca: Grant/Research Support|Basilea: Advisor/Consultant|Basilea: Grant/Research Support|ContraFect: Advisor/Consultant|ContraFect: Grant/Research Support|Debiopharm: Advisor/Consultant|Destiny: Advisor/Consultant|EDE: Grant/Research Support|Genentech: Advisor/Consultant|Genentech: Grant/Research Support|GSK: Advisor/Consultant|Janssen: Advisor/Consultant|Karius: Grant/Research Support|MedImmune: Grant/Research Support|Merck: Grant/Research Support|sepsis diagnostics: Patent pending|UptoDate: Royalties|Valanbuio: Stocks/Bonds (Private Company)|Valanbuio: Stocks/Bonds (Private Company) David van Duin, MD, PhD, Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Qpex: Advisor/Consultant|Roche: Advisor/Consultant|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support

Introduction: Among patients with cardiogenic shock (CS), high right atrial pressure (RAP) and low mean arterial pressure (MAP) are associated with in-hospital mortality. Pressure-adjusted heart rate (PAHR), defined as heart rate * RAP-to-MAP ratio (HR * [RAP/MAP]), integrates these parameters and has been used to identify cardiovascular dysfunction in critical illness. Higher PAHR values associate with higher surgical ICU mortality, but the prognostic significance of PAHR has not been assessed in cardiac intensive care unit (CICU) patients with CS. Hypothesis: We hypothesized that higher PAHR values are associated with higher risk of in-hospital mortality in patients with CS. Methods: The Critical Care Cardiology Trials Network (CCCTN) is a multinational registry of CICUs coordinated by the TIMI Study Group. Among CS admissions (2018-2023) undergoing invasive hemodynamic assessment within 24h of CICU admission, we calculated PAHR (in bpm) and assessed its relationship with in-hospital mortality. Patients on temporary mechanical circulatory support were excluded. Odds ratios were adjusted for age, sex, vasoactive-inotropic score (VIS), SCAI stage, and preceding cardiac arrest. Subgroup analyses stratifying by CS subtype were performed. Results: Among the 1411 CS admissions in the analysis cohort (mean age 61 years, 31% women), 18% had AMI-CS, 75% had HF-CS, and 7% had secondary CS. At the time of assessment (median 1.4h after CICU admission), 75% were on vasoactive support (median VIS 3.8). Median HR was 92 (Q1-Q3 77-109) bpm, RAP 15 (Q1-Q3 10-19) mmHg, MAP 75 (Q1-Q3 67-87) mmHg, and PAHR 17 (Q1-Q3 11-24) bpm. A stepwise gradient of higher in-hospital mortality with increasing PAHR values (p-trend <0.001; Fig ) was observed. In adjusted models, a higher PAHR was associated with higher in-hospital mortality (aOR per 10-bpm, 1.35 [95% CI, 1.15-1.58]), and had stronger prognostic associations with mortality than its individual hemodynamic components (Fig) . Conclusion: Higher PAHR is strongly associated with in-hospital mortality in CS patients. PAHR, a simple parameter which is readily calculable without needing a pulmonary artery catheter, may aid in identifying CS patients at higher risk of death.

Abstract Objectives Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes—real-time search and pretrained model utilization—it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. Materials and Methods RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics (“cancer immunotherapy and target therapy” and “LLMs in medicine”) were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. Results The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions—relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values &lt;.05). Discussion RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. Conclusion By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.